From 69e972079974174df49c4f1af27ba6fbd00a0f22 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 22 Nov 2022 10:37:05 -0500 Subject: [PATCH] release --- CHANGELOG.md | 9 +- demo/faceid/index.js | 357 +- demo/faceid/index.js.map | 4 +- demo/segmentation/index.js | 1 - demo/typescript/index.js | 98 +- demo/typescript/index.js.map | 4 +- dist/human.esm-nobundle.js | 14363 +-------------- dist/human.esm.js | 486 +- dist/human.esm.js.map | 6 +- dist/human.js | 2 +- dist/human.node-gpu.js | 14467 +-------------- dist/human.node-wasm.js | 14469 +--------------- dist/human.node.js | 14467 +-------------- dist/tfjs.esm.js | 22 +- dist/tfjs.version.js | 33 +- test/build.log | 1543 +- test/test.log | 2001 +-- typedoc/assets/search.js | 2 +- typedoc/classes/Env.html | 108 +- typedoc/classes/GraphModel.html | 8 +- typedoc/classes/Human.html | 8 +- typedoc/classes/Tensor-1.html | 8 +- typedoc/classes/WebCam.html | 8 +- typedoc/classes/models.Models.html | 8 +- typedoc/enums/Rank.html | 8 +- typedoc/functions/draw.all.html | 8 +- typedoc/functions/draw.body.html | 8 +- typedoc/functions/draw.canvas.html | 8 +- typedoc/functions/draw.face.html | 8 +- typedoc/functions/draw.gesture.html | 8 +- typedoc/functions/draw.hand.html | 8 +- typedoc/functions/draw.init.html | 8 +- typedoc/functions/draw.object.html | 8 +- typedoc/functions/draw.person.html | 8 +- typedoc/functions/empty.html | 8 +- typedoc/functions/match.distance.html | 8 +- typedoc/functions/match.find.html | 8 +- typedoc/functions/match.similarity.html | 8 +- typedoc/functions/models.validateModel.html | 8 +- typedoc/index.html | 8 +- typedoc/interfaces/BodyConfig.html | 8 +- typedoc/interfaces/BodyKeypoint.html | 8 +- typedoc/interfaces/BodyResult.html | 8 +- typedoc/interfaces/Config.html | 8 +- typedoc/interfaces/DrawOptions.html | 8 +- typedoc/interfaces/FaceAntiSpoofConfig.html | 8 +- typedoc/interfaces/FaceAttentionConfig.html | 8 +- typedoc/interfaces/FaceConfig.html | 8 +- typedoc/interfaces/FaceDescriptionConfig.html | 8 +- typedoc/interfaces/FaceDetectorConfig.html | 8 +- typedoc/interfaces/FaceEmotionConfig.html | 8 +- typedoc/interfaces/FaceGearConfig.html | 8 +- typedoc/interfaces/FaceIrisConfig.html | 8 +- typedoc/interfaces/FaceLivenessConfig.html | 8 +- typedoc/interfaces/FaceMeshConfig.html | 8 +- typedoc/interfaces/FaceResult.html | 8 +- typedoc/interfaces/FilterConfig.html | 8 +- typedoc/interfaces/GenericConfig.html | 8 +- typedoc/interfaces/GestureConfig.html | 8 +- typedoc/interfaces/HandConfig.html | 8 +- typedoc/interfaces/HandResult.html | 8 +- typedoc/interfaces/ModelInfo.html | 8 +- typedoc/interfaces/ObjectConfig.html | 8 +- typedoc/interfaces/ObjectResult.html | 8 +- typedoc/interfaces/PersonResult.html | 8 +- typedoc/interfaces/Result.html | 8 +- typedoc/interfaces/SegmentationConfig.html | 8 +- typedoc/interfaces/WebCamConfig.html | 8 +- typedoc/interfaces/models.KernelOps.html | 8 +- typedoc/interfaces/models.ModelStats.html | 8 +- typedoc/modules/Tensor.html | 8 +- typedoc/modules/draw.html | 8 +- typedoc/modules/match.html | 8 +- typedoc/modules/models.html | 8 +- typedoc/types/AnyCanvas.html | 8 +- typedoc/types/AnyImage.html | 8 +- typedoc/types/AnyVideo.html | 8 +- typedoc/types/BackendEnum.html | 8 +- typedoc/types/BodyAnnotation.html | 8 +- typedoc/types/BodyAnnotationBlazePose.html | 8 +- .../types/BodyAnnotationEfficientPose.html | 8 +- typedoc/types/BodyGesture.html | 8 +- typedoc/types/BodyLandmark.html | 8 +- typedoc/types/BodyLandmarkBlazePose.html | 8 +- typedoc/types/BodyLandmarkEfficientNet.html | 8 +- typedoc/types/BodyLandmarkMoveNet.html | 8 +- typedoc/types/BodyLandmarkPoseNet.html | 8 +- typedoc/types/Box.html | 8 +- typedoc/types/Emotion.html | 8 +- typedoc/types/Events.html | 8 +- typedoc/types/ExternalCanvas.html | 10 +- typedoc/types/FaceGesture.html | 8 +- typedoc/types/FaceLandmark.html | 8 +- typedoc/types/Finger.html | 8 +- typedoc/types/FingerCurl.html | 8 +- typedoc/types/FingerDirection.html | 8 +- typedoc/types/Gender.html | 8 +- typedoc/types/GestureResult.html | 8 +- typedoc/types/HandGesture.html | 8 +- typedoc/types/HandType.html | 8 +- typedoc/types/ImageObjects.html | 8 +- typedoc/types/Input.html | 8 +- typedoc/types/IrisGesture.html | 8 +- typedoc/types/ObjectType.html | 8 +- typedoc/types/Point.html | 8 +- typedoc/types/Race.html | 8 +- typedoc/types/SegmentationEnum.html | 8 +- typedoc/types/Tensor4D.html | 8 +- typedoc/types/WarmupEnum.html | 8 +- typedoc/types/match.Descriptor.html | 8 +- typedoc/types/match.MatchOptions.html | 8 +- typedoc/variables/defaults.html | 8 +- typedoc/variables/draw.options.html | 8 +- typedoc/variables/env-1.html | 10 +- types/human.d.ts | 13 +- 115 files changed, 1872 insertions(+), 61347 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c3e15193..7cde4251 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/human - Version: **3.0.0** + Version: **3.0.1** Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition** Author: **Vladimir Mandic ** @@ -9,8 +9,13 @@ ## Changelog -### **HEAD -> main** 2022/11/21 mandic00@live.com +### **3.0.1** 2022/11/22 mandic00@live.com + +### **origin/main** 2022/11/22 mandic00@live.com + +- polish demos +- add facedetect demo and fix model async load - enforce markdown linting - cleanup git history - default empty result diff --git a/demo/faceid/index.js b/demo/faceid/index.js index 67a9f377..635d01de 100644 --- a/demo/faceid/index.js +++ b/demo/faceid/index.js @@ -4,359 +4,6 @@ author: ' */ - -// demo/faceid/index.ts -import * as H from "../../dist/human.esm.js"; - -// demo/faceid/indexdb.ts -var db; -var database = "human"; -var table = "person"; -var log = (...msg) => console.log("indexdb", ...msg); -async function open() { - if (db) - return true; - return new Promise((resolve) => { - const request = indexedDB.open(database, 1); - request.onerror = (evt) => log("error:", evt); - request.onupgradeneeded = (evt) => { - log("create:", evt.target); - db = evt.target.result; - db.createObjectStore(table, { keyPath: "id", autoIncrement: true }); - }; - request.onsuccess = (evt) => { - db = evt.target.result; - log("open:", db); - resolve(true); - }; - }); -} -async function load() { - const faceDB = []; - if (!db) - await open(); - return new Promise((resolve) => { - const cursor = db.transaction([table], "readwrite").objectStore(table).openCursor(null, "next"); - cursor.onerror = (evt) => log("load error:", evt); - cursor.onsuccess = (evt) => { - if (evt.target.result) { - faceDB.push(evt.target.result.value); - evt.target.result.continue(); - } else { - resolve(faceDB); - } - }; - }); -} -async function count() { - if (!db) - await open(); - return new Promise((resolve) => { - const store = db.transaction([table], "readwrite").objectStore(table).count(); - store.onerror = (evt) => log("count error:", evt); - store.onsuccess = () => resolve(store.result); - }); -} -async function save(faceRecord) { - if (!db) - await open(); - const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; - db.transaction([table], "readwrite").objectStore(table).put(newRecord); - log("save:", newRecord); -} -async function remove(faceRecord) { - if (!db) - await open(); - db.transaction([table], "readwrite").objectStore(table).delete(faceRecord.id); - log("delete:", faceRecord); -} - -// demo/faceid/index.ts -var humanConfig = { - cacheSensitivity: 0, - modelBasePath: "../../models", - filter: { enabled: true, equalization: true }, - debug: true, - face: { - enabled: true, - detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, - description: { enabled: true }, - iris: { enabled: true }, - emotion: { enabled: false }, - antispoof: { enabled: true }, - liveness: { enabled: true } - }, - body: { enabled: false }, - hand: { enabled: false }, - object: { enabled: false }, - gesture: { enabled: true } -}; -var matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; -var options = { - minConfidence: 0.6, - minSize: 224, - maxTime: 3e4, - blinkMin: 10, - blinkMax: 800, - threshold: 0.5, - distanceMin: 0.4, - distanceMax: 1, - mask: humanConfig.face.detector.mask, - rotation: humanConfig.face.detector.rotation, - cropFactor: humanConfig.face.detector.cropFactor, - ...matchOptions -}; -var ok = { - faceCount: { status: false, val: 0 }, - faceConfidence: { status: false, val: 0 }, - facingCenter: { status: false, val: 0 }, - lookingCenter: { status: false, val: 0 }, - blinkDetected: { status: false, val: 0 }, - faceSize: { status: false, val: 0 }, - antispoofCheck: { status: false, val: 0 }, - livenessCheck: { status: false, val: 0 }, - distance: { status: false, val: 0 }, - age: { status: false, val: 0 }, - gender: { status: false, val: 0 }, - timeout: { status: true, val: 0 }, - descriptor: { status: false, val: 0 }, - elapsedMs: { status: void 0, val: 0 }, - detectFPS: { status: void 0, val: 0 }, - drawFPS: { status: void 0, val: 0 } -}; -var allOk = () => ok.faceCount.status && ok.faceSize.status && ok.blinkDetected.status && ok.facingCenter.status && ok.lookingCenter.status && ok.faceConfidence.status && ok.antispoofCheck.status && ok.livenessCheck.status && ok.distance.status && ok.descriptor.status && ok.age.status && ok.gender.status; -var current = { face: null, record: null }; -var blink = { - start: 0, - end: 0, - time: 0 -}; -var human = new H.Human(humanConfig); -human.env.perfadd = false; -human.draw.options.font = 'small-caps 18px "Lato"'; -human.draw.options.lineHeight = 20; -var dom = { - video: document.getElementById("video"), - canvas: document.getElementById("canvas"), - log: document.getElementById("log"), - fps: document.getElementById("fps"), - match: document.getElementById("match"), - name: document.getElementById("name"), - save: document.getElementById("save"), - delete: document.getElementById("delete"), - retry: document.getElementById("retry"), - source: document.getElementById("source"), - ok: document.getElementById("ok") -}; -var timestamp = { detect: 0, draw: 0 }; -var startTime = 0; -var log2 = (...msg) => { - dom.log.innerText += msg.join(" ") + "\n"; - console.log(...msg); -}; -async function webCam() { - const cameraOptions = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } }; - const stream = await navigator.mediaDevices.getUserMedia(cameraOptions); - const ready = new Promise((resolve) => { - dom.video.onloadeddata = () => resolve(true); - }); - dom.video.srcObject = stream; - void dom.video.play(); - await ready; - dom.canvas.width = dom.video.videoWidth; - dom.canvas.height = dom.video.videoHeight; - dom.canvas.style.width = "50%"; - dom.canvas.style.height = "50%"; - if (human.env.initial) - log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label); - dom.canvas.onclick = () => { - if (dom.video.paused) - void dom.video.play(); - else - dom.video.pause(); - }; -} -async function detectionLoop() { - var _a; - if (!dom.video.paused) { - if ((_a = current.face) == null ? void 0 : _a.tensor) - human.tf.dispose(current.face.tensor); - await human.detect(dom.video); - const now = human.now(); - ok.detectFPS.val = Math.round(1e4 / (now - timestamp.detect)) / 10; - timestamp.detect = now; - requestAnimationFrame(detectionLoop); - } -} -function drawValidationTests() { - let y = 32; - for (const [key, val] of Object.entries(ok)) { - let el = document.getElementById(`ok-${key}`); - if (!el) { - el = document.createElement("div"); - el.id = `ok-${key}`; - el.innerText = key; - el.className = "ok"; - el.style.top = `${y}px`; - dom.ok.appendChild(el); - } - if (typeof val.status === "boolean") - el.style.backgroundColor = val.status ? "lightgreen" : "lightcoral"; - const status = val.status ? "ok" : "fail"; - el.innerText = `${key}: ${val.val === 0 ? status : val.val}`; - y += 28; - } -} -async function validationLoop() { - var _a; - const interpolated = human.next(human.result); - human.draw.canvas(dom.video, dom.canvas); - await human.draw.all(dom.canvas, interpolated); - const now = human.now(); - ok.drawFPS.val = Math.round(1e4 / (now - timestamp.draw)) / 10; - timestamp.draw = now; - ok.faceCount.val = human.result.face.length; - ok.faceCount.status = ok.faceCount.val === 1; - if (ok.faceCount.status) { - const gestures = Object.values(human.result.gesture).map((gesture) => gesture.gesture); - if (gestures.includes("blink left eye") || gestures.includes("blink right eye")) - blink.start = human.now(); - if (blink.start > 0 && !gestures.includes("blink left eye") && !gestures.includes("blink right eye")) - blink.end = human.now(); - ok.blinkDetected.status = ok.blinkDetected.status || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax; - if (ok.blinkDetected.status && blink.time === 0) - blink.time = Math.trunc(blink.end - blink.start); - ok.facingCenter.status = gestures.includes("facing center"); - ok.lookingCenter.status = gestures.includes("looking center"); - ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0; - ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence; - ok.antispoofCheck.val = human.result.face[0].real || 0; - ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence; - ok.livenessCheck.val = human.result.face[0].live || 0; - ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence; - ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]); - ok.faceSize.status = ok.faceSize.val >= options.minSize; - ok.distance.val = human.result.face[0].distance || 0; - ok.distance.status = ok.distance.val >= options.distanceMin && ok.distance.val <= options.distanceMax; - ok.descriptor.val = ((_a = human.result.face[0].embedding) == null ? void 0 : _a.length) || 0; - ok.descriptor.status = ok.descriptor.val > 0; - ok.age.val = human.result.face[0].age || 0; - ok.age.status = ok.age.val > 0; - ok.gender.val = human.result.face[0].genderScore || 0; - ok.gender.status = ok.gender.val >= options.minConfidence; - } - ok.timeout.status = ok.elapsedMs.val <= options.maxTime; - drawValidationTests(); - if (allOk() || !ok.timeout.status) { - dom.video.pause(); - return human.result.face[0]; - } - ok.elapsedMs.val = Math.trunc(human.now() - startTime); - return new Promise((resolve) => { - setTimeout(async () => { - await validationLoop(); - resolve(human.result.face[0]); - }, 30); - }); -} -async function saveRecords() { - var _a, _b, _c, _d; - if (dom.name.value.length > 0) { - const image = (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, dom.canvas.width, dom.canvas.height); - const rec = { id: 0, name: dom.name.value, descriptor: (_b = current.face) == null ? void 0 : _b.embedding, image }; - await save(rec); - log2("saved face record:", rec.name, "descriptor length:", (_d = (_c = current.face) == null ? void 0 : _c.embedding) == null ? void 0 : _d.length); - log2("known face records:", await count()); - } else { - log2("invalid name"); - } -} -async function deleteRecord() { - if (current.record && current.record.id > 0) { - await remove(current.record); - } -} -async function detectFace() { - var _a, _b, _c, _d; - dom.canvas.style.height = ""; - (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize); - if (!((_b = current == null ? void 0 : current.face) == null ? void 0 : _b.tensor) || !((_c = current == null ? void 0 : current.face) == null ? void 0 : _c.embedding)) - return false; - console.log("face record:", current.face); - log2(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`); - await human.tf.browser.toPixels(current.face.tensor, dom.canvas); - if (await count() === 0) { - log2("face database is empty: nothing to compare face with"); - document.body.style.background = "black"; - dom.delete.style.display = "none"; - return false; - } - const db2 = await load(); - const descriptors = db2.map((rec) => rec.descriptor).filter((desc) => desc.length > 0); - const res = human.match.find(current.face.embedding, descriptors, matchOptions); - current.record = db2[res.index] || null; - if (current.record) { - log2(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1e3 * res.similarity) / 10}%`); - dom.name.value = current.record.name; - dom.source.style.display = ""; - (_d = dom.source.getContext("2d")) == null ? void 0 : _d.putImageData(current.record.image, 0, 0); - } - document.body.style.background = res.similarity > options.threshold ? "darkgreen" : "maroon"; - return res.similarity > options.threshold; -} -async function main() { - var _a, _b, _c, _d; - ok.faceCount.status = false; - ok.faceConfidence.status = false; - ok.facingCenter.status = false; - ok.blinkDetected.status = false; - ok.faceSize.status = false; - ok.antispoofCheck.status = false; - ok.livenessCheck.status = false; - ok.age.status = false; - ok.gender.status = false; - ok.elapsedMs.val = 0; - dom.match.style.display = "none"; - dom.retry.style.display = "none"; - dom.source.style.display = "none"; - dom.canvas.style.height = "50%"; - document.body.style.background = "black"; - await webCam(); - await detectionLoop(); - startTime = human.now(); - current.face = await validationLoop(); - dom.canvas.width = ((_b = (_a = current.face) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape[1]) || options.minSize; - dom.canvas.height = ((_d = (_c = current.face) == null ? void 0 : _c.tensor) == null ? void 0 : _d.shape[0]) || options.minSize; - dom.source.width = dom.canvas.width; - dom.source.height = dom.canvas.height; - dom.canvas.style.width = ""; - dom.match.style.display = "flex"; - dom.save.style.display = "flex"; - dom.delete.style.display = "flex"; - dom.retry.style.display = "block"; - if (!allOk()) { - log2("did not find valid face"); - return false; - } - return detectFace(); -} -async function init() { - var _a, _b; - log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); - log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " ")); - log2("initializing webcam..."); - await webCam(); - log2("loading human models..."); - await human.load(); - log2("initializing human..."); - log2("face embedding model:", humanConfig.face.description.enabled ? "faceres" : "", ((_a = humanConfig.face["mobilefacenet"]) == null ? void 0 : _a.enabled) ? "mobilefacenet" : "", ((_b = humanConfig.face["insightface"]) == null ? void 0 : _b.enabled) ? "insightface" : ""); - log2("loading face database..."); - log2("known face records:", await count()); - dom.retry.addEventListener("click", main); - dom.save.addEventListener("click", saveRecords); - dom.delete.addEventListener("click", deleteRecord); - await human.warmup(); - await main(); -} -window.onload = init; +import*as S from"../../dist/human.esm.js";var l,F="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(F,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:0,modelBasePath:"../../models",filter:{enabled:!0,equalization:!0},debug:!0,face:{enabled:!0,detector:{rotation:!0,return:!0,cropFactor:1.6,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},r={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,distanceMin:.4,distanceMax:1,mask:g.face.detector.mask,rotation:g.face.detector.rotation,cropFactor:g.face.detector.cropFactor,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},distance:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.distance.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},y={detect:0,draw:0},I=0,d=(...a)=>{t.log.innerText+=a.join(" ")+` +`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&d("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function P(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>r.blinkMin&&Math.abs(u.end-u.start)=r.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=r.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=r.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=r.minSize,e.distance.val=s.result.face[0].distance||0,e.distance.status=e.distance.val>=r.distanceMin&&e.distance.val<=r.distanceMax,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=r.minConfidence}return e.timeout.status=e.elapsedMs.val<=r.maxTime,P(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function z(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),d("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),d("known face records:",await b())}else d("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,r.minSize,r.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),d(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${100*(c.face.distance||0)}cm/${Math.round(100*(c.face.distance||0)/2.54)}in`),await s.tf.browser.toPixels(c.face.tensor,t.canvas),await b()===0)return d("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match.find(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(d(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>r.threshold?"darkgreen":"maroon",o.similarity>r.threshold}async function M(){var a,n,o,i;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((n=(a=c.face)==null?void 0:a.tensor)==null?void 0:n.shape[1])||r.minSize,t.canvas.height=((i=(o=c.face)==null?void 0:o.tensor)==null?void 0:i.shape[0])||r.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(d("did not find valid face"),!1)}async function q(){var a,n;d("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),d("options:",JSON.stringify(r).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),d("initializing webcam..."),await H(),d("loading human models..."),await s.load(),d("initializing human..."),d("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),d("loading face database..."),d("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",z),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q; //# sourceMappingURL=index.js.map diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map index 3f5d4efb..e15df82b 100644 --- a/demo/faceid/index.js.map +++ b/demo/faceid/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "indexdb.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n cacheSensitivity: 0,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: true }, // lets run with histogram equilizer\n debug: true,\n face: {\n enabled: true,\n detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 30000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm\n distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n cropFactor: humanConfig.face.detector.cropFactor,\n ...matchOptions,\n};\n\nconst ok: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n distance: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.distance.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) void dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.distance.val = human.result.face[0].distance || 0;\n ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);\n await human.tf.browser.toPixels(current.face.tensor, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n const res = human.match.find(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n }\n return detectFace();\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], - "mappings": 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+ "names": ["H", "db", "database", "table", "log", "msg", "open", "resolve", "request", "evt", "load", "faceDB", "cursor", "count", "store", "save", "faceRecord", "newRecord", "remove", "humanConfig", "matchOptions", "options", "ok", "allOk", "current", "blink", "human", "dom", "timestamp", "startTime", "log", "msg", "webCam", "cameraOptions", "stream", "ready", "resolve", "detectionLoop", "_a", "now", "drawValidationTests", "y", "key", "val", "el", "status", "validationLoop", "interpolated", "gestures", "gesture", "saveRecords", "_b", "_c", "_d", "image", "rec", "save", "count", "deleteRecord", "remove", "detectFace", "db", "load", "descriptors", "desc", "res", "main", "init"] } diff --git a/demo/segmentation/index.js b/demo/segmentation/index.js index e557dc2e..1040e70e 100644 --- a/demo/segmentation/index.js +++ b/demo/segmentation/index.js @@ -71,7 +71,6 @@ async function main() { await human.webcam.start({ element: dom.webcam, crop: true, width: window.innerWidth / 2, height: window.innerHeight / 2 }); // use human webcam helper methods and associate webcam stream with a dom element if (!human.webcam.track) dom.fps.innerText = 'webcam error'; - console.log(human.webcam); // processing loop async function loop() { diff --git a/demo/typescript/index.js b/demo/typescript/index.js index 14dfc71c..2bf3f49d 100644 --- a/demo/typescript/index.js +++ b/demo/typescript/index.js @@ -4,100 +4,6 @@ author: ' */ - -// demo/typescript/index.ts -import * as H from "../../dist/human.esm.js"; -var width = 1920; -var humanConfig = { - modelBasePath: "../../models", - filter: { enabled: true, equalization: false, flip: false }, - face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } }, - body: { enabled: true }, - hand: { enabled: false }, - object: { enabled: false }, - segmentation: { enabled: false }, - gesture: { enabled: true } -}; -var human = new H.Human(humanConfig); -human.env.perfadd = false; -human.draw.options.font = 'small-caps 18px "Lato"'; -human.draw.options.lineHeight = 20; -var dom = { - video: document.getElementById("video"), - canvas: document.getElementById("canvas"), - log: document.getElementById("log"), - fps: document.getElementById("status"), - perf: document.getElementById("performance") -}; -var timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; -var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; -var log = (...msg) => { - dom.log.innerText += msg.join(" ") + "\n"; - console.log(...msg); -}; -var status = (msg) => dom.fps.innerText = msg; -var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors.toString() + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | "); -async function detectionLoop() { - if (!dom.video.paused) { - if (timestamp.start === 0) - timestamp.start = human.now(); - await human.detect(dom.video); - const tensors = human.tf.memory().numTensors; - if (tensors - timestamp.tensors !== 0) - log("allocated tensors:", tensors - timestamp.tensors); - timestamp.tensors = tensors; - fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3; - fps.frames++; - fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3; - if (fps.frames % 100 === 0 && !dom.video.paused) - log("performance", { ...fps, tensors: timestamp.tensors }); - } - timestamp.detect = human.now(); - requestAnimationFrame(detectionLoop); -} -async function drawLoop() { - var _a, _b, _c; - if (!dom.video.paused) { - const interpolated = human.next(human.result); - const processed = await human.image(dom.video); - human.draw.canvas(processed.canvas, dom.canvas); - const opt = { bodyLabels: `person confidence [score] and ${(_c = (_b = (_a = human.result) == null ? void 0 : _a.body) == null ? void 0 : _b[0]) == null ? void 0 : _c.keypoints.length} keypoints` }; - await human.draw.all(dom.canvas, interpolated, opt); - perf(interpolated.performance); - } - const now = human.now(); - fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3; - timestamp.draw = now; - status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`); - setTimeout(drawLoop, 30); -} -async function webCam() { - const devices = await human.webcam.enumerate(); - const id = devices[0].deviceId; - await human.webcam.start({ element: dom.video, crop: false, width, id }); - dom.canvas.width = human.webcam.width; - dom.canvas.height = human.webcam.height; - dom.canvas.onclick = async () => { - if (human.webcam.paused) - await human.webcam.play(); - else - human.webcam.pause(); - }; -} -async function main() { - log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); - log("platform:", human.env.platform, "| agent:", human.env.agent); - status("loading..."); - await human.load(); - log("backend:", human.tf.getBackend(), "| available:", human.env.backends); - log("models stats:", human.models.stats()); - log("models loaded:", human.models.loaded()); - log("environment", human.env); - status("initializing..."); - await human.warmup(); - await webCam(); - await detectionLoop(); - await drawLoop(); -} -window.onload = main; +import*as m from"../../dist/human.esm.js";var v=1920,b={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!0},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+` +`,console.log(...t)},r=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function p(){var d,i,c;if(!a.video.paused){let l=e.next(e.result),u=await e.image(a.video);e.draw.canvas(u.canvas,a.canvas);let w={bodyLabels:`person confidence [score] and ${(c=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,w),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(p,30)}async function y(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!1,width:v,id:d}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function h(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),r("initializing..."),await e.warmup(),await y(),await f(),await p()}window.onload=h; //# sourceMappingURL=index.js.map diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index 62861400..622fbf92 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n // backend: 'webgpu',\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: true },\n // hand: { enabled: true },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n const devices = await human.webcam.enumerate();\n const id = devices[0].deviceId; // use first available video source\n await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models stats:', human.models.stats());\n log('models loaded:', human.models.loaded());\n log('environment', human.env);\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await webCam(); // start webcam\n await detectionLoop(); // start detection loop\n await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"], - 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for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __reExport = (target, mod3, secondTarget) => (__copyProps(target, mod3, "default"), secondTarget && __copyProps(secondTarget, mod3, "default")); -var __publicField = (obj, key, value) => { - __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); - return value; -}; -var __accessCheck = (obj, member, msg) => { - if (!member.has(obj)) - throw TypeError("Cannot " + msg); -}; -var __privateGet = (obj, member, getter) => { - __accessCheck(obj, member, "read from private field"); - return getter ? getter.call(obj) : member.get(obj); -}; -var __privateAdd = (obj, member, value) => { - if (member.has(obj)) - throw TypeError("Cannot add the same private member more than once"); - member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -}; -var __privateSet = (obj, member, value, setter) => { - __accessCheck(obj, member, "write to private field"); - setter ? setter.call(obj, value) : member.set(obj, value); - return value; -}; - -// dist/tfjs.esm.js -var tfjs_esm_exports = {}; -__export(tfjs_esm_exports, { - version: () => version7 -}); -__reExport(tfjs_esm_exports, dist_star); -__reExport(tfjs_esm_exports, dist_star2); -__reExport(tfjs_esm_exports, dist_star3); -__reExport(tfjs_esm_exports, dist_star4); -__reExport(tfjs_esm_exports, dist_star5); -__reExport(tfjs_esm_exports, dist_star6); -import * as dist_star from "@tensorflow/tfjs-core/dist/index.js"; -import * as dist_star2 from "@tensorflow/tfjs-converter/dist/index.js"; -import * as dist_star3 from "@tensorflow/tfjs-backend-cpu/dist/index.js"; -import * as dist_star4 from "@tensorflow/tfjs-backend-webgl/dist/index.js"; -import * as dist_star5 from "@tensorflow/tfjs-backend-wasm/dist/index.js"; -import * as dist_star6 from "@tensorflow/tfjs-backend-webgpu/dist/index.js"; -var version = "4.1.0"; -var version2 = "4.1.0"; -var version3 = "4.1.0"; -var version4 = "4.1.0"; -var version5 = "4.1.0"; -var version6 = "0.0.1-alpha.16"; -var version7 = { - tfjs: version, - "tfjs-core": version, - "tfjs-converter": version2, - "tfjs-backend-cpu": version3, - "tfjs-backend-webgl": version4, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 -}; - -// src/util/util.ts -function log(...msg) { - const dt = new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) - console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) - throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") - return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) - msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) - msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) - log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) - prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) - prev[key] = mergeDeep(pVal, oVal); - else - prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: true, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - iouThreshold: 0.1, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +var st=Object.defineProperty;var Zn=Object.getOwnPropertyDescriptor;var Xn=Object.getOwnPropertyNames;var qn=Object.prototype.hasOwnProperty;var Un=(e,t,n)=>t in e?st(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var oe=(e,t)=>{for(var n in t)st(e,n,{get:t[n],enumerable:!0})},F5=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of Xn(t))!qn.call(e,s)&&s!==n&&st(e,s,{get:()=>t[s],enumerable:!(o=Zn(t,s))||o.enumerable});return e},w=(e,t,n)=>(F5(e,t,"default"),n&&F5(n,t,"default"));var E=(e,t,n)=>(Un(e,typeof t!="symbol"?t+"":t,n),n),B5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var v0=(e,t,n)=>(B5(e,t,"read from private field"),n?n.call(e):t.get(e)),Z0=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Y0=(e,t,n,o)=>(B5(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var r={};oe(r,{version:()=>Ke});w(r,fA);w(r,mA);w(r,pA);w(r,uA);w(r,hA);w(r,bA);import*as fA from"@tensorflow/tfjs-core/dist/index.js";import*as mA from"@tensorflow/tfjs-converter/dist/index.js";import*as pA from"@tensorflow/tfjs-backend-cpu/dist/index.js";import*as uA from"@tensorflow/tfjs-backend-webgl/dist/index.js";import*as hA from"@tensorflow/tfjs-backend-wasm/dist/index.js";import*as bA from"@tensorflow/tfjs-backend-webgpu/dist/index.js";var H5="4.1.0",Yn="4.1.0",Kn="4.1.0",Jn="4.1.0",Qn="4.1.0",_n="0.0.1-alpha.16",Ke={tfjs:H5,"tfjs-core":H5,"tfjs-converter":Yn,"tfjs-backend-cpu":Kn,"tfjs-backend-webgl":Jn,"tfjs-backend-wasm":Qn,"tfjs-backend-webgpu":_n};function h(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function G5(e,t){let n=e.endsWith("/")?"":"/",s=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!s.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${s}`);return s}var T=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function At(e,t,n="config",o=[]){for(let s of Object.keys(t))if(typeof t[s]=="object")At(e[s],t[s],s,o);else{let A=e&&typeof e[s]!="undefined";A||o.push({reason:"unknown property",where:`${n}.${s} = ${t[s]}`});let a=e&&typeof e[s]==typeof t[s];A&&!a&&o.push({reason:"property type mismatch",where:`${n}.${s} = ${t[s]}`,expected:typeof e[s]})}return t.debug&&n==="config"&&o.length>0&&h("invalid configuration",o),o}function Q(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(s=>{let A=n[s],a=o[s];Array.isArray(A)&&Array.isArray(a)?n[s]=A.concat(...a):t(A)&&t(a)?n[s]=Q(A,a):n[s]=a}),n),{})}var Le={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var V5=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -278,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var Z5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -291,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,X5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -304,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,q5=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -318,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,U5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -342,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,Y5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -365,6046 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) - return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) - this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) - this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) - return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) - return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) - source = sourceTexture; - else - source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) - return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) - return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) - sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage; - const rgb2 = tfjs_esm_exports.split(squeeze14, 3, 2); - const min2 = [tfjs_esm_exports.min(rgb2[0]), tfjs_esm_exports.min(rgb2[1]), tfjs_esm_exports.min(rgb2[2])]; - const max5 = [tfjs_esm_exports.max(rgb2[0]), tfjs_esm_exports.max(rgb2[1]), tfjs_esm_exports.max(rgb2[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tfjs_esm_exports.sub(rgb2[0], min2[0]), tfjs_esm_exports.sub(rgb2[1], min2[1]), tfjs_esm_exports.sub(rgb2[2], min2[2])]; - const range = [tfjs_esm_exports.sub(max5[0], min2[0]), tfjs_esm_exports.sub(max5[1], min2[1]), tfjs_esm_exports.sub(max5[2], min2[2])]; - const enh = [tfjs_esm_exports.mul(sub11[0], factor), tfjs_esm_exports.mul(sub11[1], factor), tfjs_esm_exports.mul(sub11[2], factor)]; - const stack5 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2); - final = tfjs_esm_exports.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tfjs_esm_exports.dispose([...sub11, ...range, ...enh]); - } else { - final = tfjs_esm_exports.expandDims(squeeze14, 0); - } - tfjs_esm_exports.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") - throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document === "undefined") - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } - } else { - if (typeof env.Canvas !== "undefined") - c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") - c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) - log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tfjs_esm_exports.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tfjs_esm_exports.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) - throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) - throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tfjs_esm_exports.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb2 = tfjs_esm_exports.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tfjs_esm_exports.expandDims(rgb2, 0); - tfjs_esm_exports.dispose(rgb2); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tfjs_esm_exports.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tfjs_esm_exports.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) - throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tfjs_esm_exports.cast(tensor7, "float32"); - tfjs_esm_exports.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) - log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) - log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) - targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) - targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) - targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) - targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) - throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) - inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) - outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) - fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) - log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) - fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) - fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) - fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) - fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) - fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) - fx.add("hue", config3.filter.hue); - if (config3.filter.negative) - fx.add("negative"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.vintage) - fx.add("brownie"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.kodachrome) - fx.add("kodachrome"); - if (config3.filter.technicolor) - fx.add("technicolor"); - if (config3.filter.polaroid) - fx.add("polaroid"); - if (config3.filter.pixelate !== 0) - fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) - outCanvas = fx.apply(inCanvas); - else - outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) - fx = null; - env.filter = !!fx; - } - if (!getTensor) - return { tensor: null, canvas: outCanvas }; - if (!outCanvas) - throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tfjs_esm_exports.browser) { - pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tfjs_esm_exports.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) - tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tfjs_esm_exports.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tfjs_esm_exports.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tfjs_esm_exports.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tfjs_esm_exports.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb2 = tfjs_esm_exports.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tfjs_esm_exports.dispose(pixels); - pixels = rgb2; - } - if (!pixels) - throw new Error("input error: cannot create tensor"); - const casted = tfjs_esm_exports.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0); - tfjs_esm_exports.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tfjs_esm_exports.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tfjs_esm_exports.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) - return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tfjs_esm_exports.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tfjs_esm_exports.dispose(last.inputTensor); - last.inputTensor = tfjs_esm_exports.clone(input); - } else { - const t2 = {}; - t2.diff = tfjs_esm_exports.sub(input, last.inputTensor); - t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); - t2.sum = tfjs_esm_exports.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tfjs_esm_exports.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tfjs_esm_exports.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) - log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) - log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tfjs_esm_exports.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2); - t2.diff = tfjs_esm_exports.sub(t2.input1, t2.input2); - t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); - t2.sum = tfjs_esm_exports.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tfjs_esm_exports.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - __publicField(this, "browser"); - __publicField(this, "node"); - __publicField(this, "worker"); - __publicField(this, "platform", ""); - __publicField(this, "agent", ""); - __publicField(this, "backends", []); - __publicField(this, "initial"); - __publicField(this, "filter"); - __publicField(this, "tfjs"); - __publicField(this, "offscreen"); - __publicField(this, "perfadd", false); - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - __publicField(this, "kernels", []); - __privateAdd(this, _canvas, void 0); - __privateAdd(this, _image, void 0); - __privateAdd(this, _imageData, void 0); - this.browser = typeof navigator !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: version7["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined") { - const raw = navigator.userAgent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = navigator.userAgent.replace(raw[0], ""); - if (this.platform[1]) - this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - async updateBackend() { - this.backends = Object.keys(tfjs_esm_exports.engine().registryFactory); - try { - this.tensorflow = { - version: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].TF_Version : void 0, - gpu: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) - Object.defineProperty(this, "cpu", { value: cpu }); - else - this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - __publicField(this, "config"); - __publicField(this, "element"); - __publicField(this, "stream"); - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) - this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) - this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) - this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) - this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) - this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) - this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) - log("webcam", "cannot get dom element", webcamConfig.element); - return; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) - log("webcam", "unknown dom element", webcamConfig.element); - return; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) - requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) - requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) - requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) - log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) - log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) - return; - if (this.element.paused) - await this.element.play(); - else - this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) - log("webcam", "no devices"); - return; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return; - } - if (!this.stream) { - if (this.config.debug) - log("webcam", "no stream"); - return; - } - this.element.srcObject = this.stream; - const ready3 = new Promise((resolve) => { - if (!this.element) - resolve(false); - else - this.element.onloadeddata = () => resolve(true); - }); - await ready3; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - }); - __publicField(this, "pause", () => { - if (this.element) - this.element.pause(); - }); - __publicField(this, "play", async () => { - if (this.element) - await this.element.play(); - }); - __publicField(this, "stop", () => { - if (this.config.debug) - log("webcam", "stop"); - if (this.track) - this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - get track() { - if (!this.stream) - return void 0; - return this.stream.getVideoTracks()[0]; - } - get capabilities() { - if (!this.track) - return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - get constraints() { - if (!this.track) - return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - get settings() { - if (!this.stream) - return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - get label() { - if (!this.track) - return ""; - return this.track.label; - } - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// models/models.json -var models_exports = {}; -__export(models_exports, { - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) - log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) - modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tfjs_esm_exports.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tfjs_esm_exports.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - let loaded = false; - try { - model23.findIOHandler(); - if (options.debug) - log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) - model23.loadSync(artifacts); - else - model23 = await tfjs_esm_exports.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; - if (options.verbose) - log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) - log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version8 = "3.0.0"; - -// src/tfjs/humangl.ts -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - desynchronized: true - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) - return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") - return; - if (config2.name in tfjs_esm_exports.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tfjs_esm_exports.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tfjs_esm_exports.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl); - tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tfjs_esm_exports.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tfjs_esm_exports.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tfjs_esm_exports.env().flagRegistry.WEBGL_VERSION) - tfjs_esm_exports.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tfjs_esm_exports.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) - log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tfjs_esm_exports.scalar(255, "float32"); - constants.tf1 = tfjs_esm_exports.scalar(1, "float32"); - constants.tf2 = tfjs_esm_exports.scalar(2, "float32"); - constants.tf05 = tfjs_esm_exports.scalar(0.5, "float32"); - constants.tf127 = tfjs_esm_exports.scalar(127.5, "float32"); - constants.rgb = tfjs_esm_exports.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) - return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) - return "webgpu"; - if (env.webgl.supported && env.webgl.backend) - return "webgl"; - if (env.wasm.supported && env.wasm.backend) - return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tfjs_esm_exports.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tfjs_esm_exports.mod(op.inputs.a, op.inputs.b))) - }; - tfjs_esm_exports.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => { - const backend4 = tfjs_esm_exports.getBackend(); - tfjs_esm_exports.setBackend("cpu"); - const t2 = tfjs_esm_exports.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tfjs_esm_exports.setBackend(backend4); - return t2; - }) - }; - tfjs_esm_exports.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) - log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) - instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) - log("running inside web worker"); - } - if (env.browser && instance.config.backend === "tensorflow") { - if (instance.config.debug) - log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { - if (instance.config.debug) - log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) - log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - let available = Object.keys(tfjs_esm_exports.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tfjs_esm_exports.engine().registryFactory); - } - if (instance.config.debug) - log("available backends:", available); - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) - log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) - log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tfjs_esm_exports.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) - tfjs_esm_exports.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) - log("wasm path:", instance.config.wasmPath); - if (typeof tfjs_esm_exports.setWasmPaths !== "undefined") - tfjs_esm_exports.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else - throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) - log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tfjs_esm_exports.setBackend(instance.config.backend); - await tfjs_esm_exports.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) - defaultFlags = JSON.parse(JSON.stringify(tfjs_esm_exports.env().flags)); - } - if (tfjs_esm_exports.getBackend() === "humangl" || tfjs_esm_exports.getBackend() === "webgl") { - if (tfjs_esm_exports.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - tfjs_esm_exports.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tfjs_esm_exports.env().flagRegistry.WEBGL_EXP_CONV) - tfjs_esm_exports.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tfjs_esm_exports.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tfjs_esm_exports.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tfjs_esm_exports.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) - continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", tfjs_esm_exports.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) - log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tfjs_esm_exports.env().set(key, val); - } - } - tfjs_esm_exports.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tfjs_esm_exports.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - env.initial = false; - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) - log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - }; - tfjs_esm_exports.registerKernel(kernelConfig); - } - env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) - log("draw error: invalid canvas"); - else if (!input.getContext) - log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d"); - if (!ctx) - log("draw error: cannot get canvas context"); - else - return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") - return opt.color; - const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - labelColor: "rgba(173, 216, 230, 1)", - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - rightEyeIris: [473, 474, 475, 476, 477], - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } -]; -var UV468 = [ - [0.499976992607117, 0.652534008026123], - [0.500025987625122, 0.547487020492554], - [0.499974012374878, 0.602371990680695], - [0.482113003730774, 0.471979022026062], - [0.500150978565216, 0.527155995368958], - [0.499909996986389, 0.498252987861633], - [0.499523013830185, 0.40106201171875], - [0.289712011814117, 0.380764007568359], - [0.499954998493195, 0.312398016452789], - [0.499987006187439, 0.269918978214264], - [0.500023007392883, 0.107050001621246], - [0.500023007392883, 0.666234016418457], - [0.5000159740448, 0.679224014282227], - 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168, - 6, - 195, - 4, - 98, - 97, - 2, - 326, - 327, - 33, - 160, - 158, - 133, - 153, - 144, - 362, - 385, - 387, - 263, - 373, - 380, - 57, - 40, - 37, - 0, - 267, - 270, - 287, - 321, - 314, - 17, - 84, - 91, - 78, - 81, - 13, - 311, - 308, - 402, - 14, - 178 -]; -var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; -var VTX7 = [33, 133, 362, 263, 1, 78, 308]; -var UV68 = VTX68.map((x) => UV468[x]); -var UV33 = VTX33.map((x) => UV468[x]); -var UV7 = VTX7.map((x) => UV468[x]); -function connectionsToIndices(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - 246, - 161, - 160, - 159, - 158, - 157, - 173, - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - 247, - 30, - 29, - 27, - 28, - 56, - 190, - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - 113, - 225, - 224, - 223, - 222, - 221, - 189, - 35, - 124, - 46, - 53, - 52, - 65, - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - 466, - 388, - 387, - 386, - 385, - 384, - 398, - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - 467, - 260, - 259, - 257, - 258, - 286, - 414, - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - 342, - 445, - 444, - 443, - 442, - 441, - 413, - 265, - 353, - 276, - 283, - 282, - 295, - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) - return; - let l = localOptions.faceLabels.slice(); - if (f.score) - l = replace(l, "[score]", 100 * f.score); - if (f.gender) - l = replace(l, "[gender]", f.gender); - if (f.genderScore) - l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) - l = replace(l, "[age]", f.age); - if (f.distance) - l = replace(l, "[distance]", 100 * f.distance); - if (f.real) - l = replace(l, "[real]", 100 * f.real); - if (f.live) - l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) - emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - } - if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) - l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); - if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) - l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); - if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) - l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); - if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) - l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); - labels(ctx, l, f.box[0], f.box[1], localOptions); -} -function drawIrisElipse(f, ctx) { - var _a, _b, _c, _d; - if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { - ctx.strokeStyle = localOptions.useDepth ? 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p2=class{constructor(){E(this,"config");E(this,"element");E(this,"stream");E(this,"devices",[]);E(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});E(this,"start",async t=>{var s,A;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let a=document.getElementById(t.element);if(a&&a instanceof HTMLVideoElement)this.element=a;else{this.config.debug&&h("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof 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Mr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Pr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],kr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],wr=[[474,475],[475,476],[476,477],[477,474]],Er=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],zr=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Sr=[[469,470],[470,471],[471,472],[472,469]],jr=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function se(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Ir={lips:se(Mr),leftEye:se(Pr),leftEyebrow:se(kr),leftIris:se(wr),rightEye:se(Er),rightEyebrow:se(zr),rightIris:se(Sr),faceOval:se(jr)},Nr=Object.entries(Ir).map(([e,t])=>t.map(n=>[n,e])).flat(),ZA=new Map(Nr),t2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Re=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Me=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];var G;function Lr(e,t){var o,s,A,a,i,c,x,y,l;if(!G.drawLabels||((o=G.faceLabels)==null?void 0:o.length)===0)return;let n=G.faceLabels.slice();if(e.score&&(n=V(n,"[score]",100*e.score)),e.gender&&(n=V(n,"[gender]",e.gender)),e.genderScore&&(n=V(n,"[genderScore]",100*e.genderScore)),e.age&&(n=V(n,"[age]",e.age)),e.distance&&(n=V(n,"[distance]",100*e.distance)),e.real&&(n=V(n,"[real]",100*e.real)),e.live&&(n=V(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let f=e.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);f.length>3&&(f.length=3),n=V(n,"[emotions]",f.join(" "))}(A=(s=e.rotation)==null?void 0:s.angle)!=null&&A.roll&&(n=V(n,"[roll]",be(e.rotation.angle.roll))),(i=(a=e.rotation)==null?void 0:a.angle)!=null&&i.yaw&&(n=V(n,"[yaw]",be(e.rotation.angle.yaw))),(x=(c=e.rotation)==null?void 0:c.angle)!=null&&x.pitch&&(n=V(n,"[pitch]",be(e.rotation.angle.pitch))),(l=(y=e.rotation)==null?void 0:y.gaze)!=null&&l.bearing&&(n=V(n,"[gaze]",be(e.rotation.gaze.bearing))),O0(t,n,e.box[0],e.box[1],G)}function Or(e,t){var n,o,s,A;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=G.useDepth?"rgba(255, 200, 255, 0.3)":G.color,t.beginPath();let a=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,i=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),G.fillPolygons&&(t.fillStyle=G.useDepth?"rgba(255, 255, 200, 0.3)":G.color,t.fill())}if(((s=e.annotations)==null?void 0:s.rightEyeIris)&&((A=e.annotations)==null?void 0:A.rightEyeIris[0])){t.strokeStyle=G.useDepth?"rgba(255, 200, 255, 0.3)":G.color,t.beginPath();let a=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),G.fillPolygons&&(t.fillStyle=G.useDepth?"rgba(255, 255, 200, 0.3)":G.color,t.fill())}}function Cr(e,t){var n;if(G.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*be(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*be(e.rotation.angle.pitch)/90,A=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),a=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints && f.mesh.length >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) - continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) - continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) - curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) - return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${s}, + ${e.box[0]+e.box[2]} ${s}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(a),t.stroke(A)}}function Wr(e,t){var n;if(G.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];yt(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let s=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];yt(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[s[0],s[1]],4)}}function Dr(e,t){if(G.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;ne.mesh[s]);dt(t,o,G)}Or(e,t)}}function Fr(e,t){if(G.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Fr(s,o),Dr(s,o),Cr(s,o),Wr(s,o))}}function b2(e,t,n){var A,a;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round";for(let i=0;i0)){let c=o.bodyLabels.slice();c=V(c,"[score]",100*t[i].score),O0(s,c,t[i].box[0],t[i].box[1],o)}if(o.drawPoints&&t[i].keypoints)for(let c=0;c0&&t[i].keypoints){s.font=o.font;for(let c of t[i].keypoints){if(!c.score||c.score===0)continue;let x=o.bodyPartLabels.slice();x=V(x,"[label]",c.part),x=V(x,"[score]",100*c.score),O0(s,x,c.position[0],c.position[1],o)}}if(o.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let c of Object.values(t[i].annotations))for(let x of c)o1(s,x,o)}}}function g2(e,t,n){var A,a;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let i of t){if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,X0(s,i.box[0],i.box[1],i.box[2],i.box[3],o),o.drawLabels&&((A=o.handLabels)==null?void 0:A.length)>0){let c=o.handLabels.slice();c=V(c,"[label]",i.label),c=V(c,"[score]",100*i.score),O0(s,c,i.box[0],i.box[1],o)}s.stroke()}if(o.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let c of i.keypoints)s.fillStyle=ge(c[2],o),K0(s,c[0],c[1],0,o);if(o.drawLabels&&i.annotations&&((a=o.fingerLabels)==null?void 0:a.length)>0)for(let[c,x]of Object.entries(i.annotations)){let y=o.fingerLabels.slice();y=V(y,"[label]",c),O0(s,y,x[x.length-1][0],x[x.length-1][1],o)}if(o.drawPolygons&&i.annotations){let c=x=>{if(!(!x||x.length===0||!x[0]))for(let y=0;y0?y-1:0][0],x[y>0?y-1:0][1]),s.lineTo(x[y][0],x[y][1]),s.stroke()}};s.lineWidth=o.lineWidth,c(i.annotations.index),c(i.annotations.middle),c(i.annotations.ring),c(i.annotations.pinky),c(i.annotations.thumb)}}}}function T2(e,t,n){var A;let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let a of t)if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,X0(s,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((A=o.objectLabels)==null?void 0:A.length)>0){let i=o.objectLabels.slice();i=V(i,"[label]",a.label),i=V(i,"[score]",100*a.score),O0(s,i,a.box[0],a.box[1],o)}s.stroke()}}}function v2(e,t,n){var s;let o=Q(e0,n);if(!(!t||!e)&&o.drawGestures&&((s=o.gestureLabels)==null?void 0:s.length)>0){let A=S0(e);if(!A)return;A.font=o.font,A.fillStyle=o.color;let a=1;for(let i=0;i1&&x[1].length>0){let y=c[1]>0?`#${c[1]}`:"",l=o.gestureLabels.slice();l=V(l,"[where]",c[0]),l=V(l,"[who]",y),l=V(l,"[what]",x[1]),O0(A,l,8,2+a*o.lineHeight,o),a+=1}}}}var Ae={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6413,7473 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) - return; - const ctx = getCanvasContext(output); - if (!ctx) - return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) - return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - "leftEyeInside", - "leftEye", - "leftEyeOutside", - "rightEyeInside", - "rightEye", - "rightEyeOutside", - "leftEar", - "rightEar", - "leftMouth", - "rightMouth", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftPinky", - "rightPinky", - "leftIndex", - "rightIndex", - "leftThumb", - "rightThumb", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle", - "leftHeel", - "rightHeel", - "leftFoot", - "rightFoot", - "bodyCenter", - "bodyTop", - "leftPalm", - "leftHand", - "rightPalm", - "rightHand" -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.x)), y: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) - model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) - log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tfjs_esm_exports.tidy(() => { - const split6 = tfjs_esm_exports.split(boxesTensor, 12, 1); - let xCenter = tfjs_esm_exports.squeeze(split6[0]); - let yCenter = tfjs_esm_exports.squeeze(split6[1]); - let width = tfjs_esm_exports.squeeze(split6[2]); - let height = tfjs_esm_exports.squeeze(split6[3]); - xCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(xCenter, inputSize), anchor.x); - yCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(yCenter, inputSize), anchor.y); - width = tfjs_esm_exports.mul(tfjs_esm_exports.div(width, inputSize), cropFactor[0]); - height = tfjs_esm_exports.mul(tfjs_esm_exports.div(height, inputSize), cropFactor[1]); - const xMin = tfjs_esm_exports.sub(xCenter, tfjs_esm_exports.div(width, 2)); - const yMin = tfjs_esm_exports.sub(yCenter, tfjs_esm_exports.div(height, 2)); - const xMax = tfjs_esm_exports.add(xMin, width); - const yMax = tfjs_esm_exports.add(yMin, height); - const boxes = tfjs_esm_exports.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tfjs_esm_exports.sigmoid(logitsTensor); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tfjs_esm_exports.squeeze(t2.logitsRaw); - t2.boxes = tfjs_esm_exports.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) - model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) - log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - let final; - if (cropBox) { - t2.cropped = tfjs_esm_exports.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - height, - width, - [0, 0] - ]; - t2.pad = tfjs_esm_exports.pad(t2.cropped || input, padding); - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [size2, size2]); - final = tfjs_esm_exports.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tfjs_esm_exports.div(t2.resize, constants.tf255); - } else { - final = tfjs_esm_exports.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - kpt4.positionRaw[1] / width + cropBox[0], - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) - return null; - const t2 = {}; - [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) - return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tfjs_esm_exports.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tfjs_esm_exports.dispose(preparedBox); - if (!bodyResult) - continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) - model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) - return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tfjs_esm_exports.squeeze(res); - const arr = tfjs_esm_exports.split(t2.squeeze, 6, 1); - t2.stack = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tfjs_esm_exports.squeeze(t2.stack); - t2.scores = tfjs_esm_exports.squeeze(arr[4]); - t2.classes = tfjs_esm_exports.squeeze(arr[5]); - tfjs_esm_exports.dispose([res, ...arr]); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) - continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tfjs_esm_exports.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) - model4 = null; - if (!model4) - model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]); - const max5 = tfjs_esm_exports.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tfjs_esm_exports.argMax(reshaped, 0); - const mod3 = tfjs_esm_exports.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tfjs_esm_exports.div(coordinates, width); - const y = (await div15.data())[0]; - tfjs_esm_exports.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tfjs_esm_exports.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) - return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tfjs_esm_exports.tidy(() => { - var _a, _b; - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tfjs_esm_exports.mul(resize, constants.tf2); - const norm = tfjs_esm_exports.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) - resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tfjs_esm_exports.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tfjs_esm_exports.squeeze(resT); - tfjs_esm_exports.dispose(resT); - const stack5 = tfjs_esm_exports.unstack(squeeze14, 2); - tfjs_esm_exports.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tfjs_esm_exports.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/facemeshutil.ts -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tfjs_esm_exports.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tfjs_esm_exports.div(crop, constants.tf255); - tfjs_esm_exports.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) - product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) - column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) - product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) - anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tfjs_esm_exports.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var faceBoxScaleFactor = 1.4; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) - model5 = null; - if (!model5) - model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tfjs_esm_exports.scalar(inputSize4, "int32"); - anchors = tfjs_esm_exports.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) - return tfjs_esm_exports.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tfjs_esm_exports.add(t2.boxStarts, anchors); - t2.boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tfjs_esm_exports.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tfjs_esm_exports.div(t2.centers, inputSizeT); - t2.halfBoxSize = tfjs_esm_exports.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tfjs_esm_exports.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tfjs_esm_exports.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tfjs_esm_exports.mul(t2.starts, inputSizeT); - t2.endNormalized = tfjs_esm_exports.mul(t2.ends, inputSizeT); - const boxes = tfjs_esm_exports.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) - return []; - const t2 = {}; - t2.resized = tfjs_esm_exports.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t2.div = tfjs_esm_exports.div(t2.resized, constants.tf127); - t2.normalized = tfjs_esm_exports.sub(t2.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2); - t2.concat = tfjs_esm_exports.concat([t2.concat512, t2.concat384], 1); - t2.batch = tfjs_esm_exports.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tfjs_esm_exports.squeeze(res[0]); - } else { - t2.batch = tfjs_esm_exports.squeeze(res); - } - tfjs_esm_exports.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tfjs_esm_exports.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tfjs_esm_exports.sigmoid(t2.logits); - t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { - const b = {}; - b.bbox = tfjs_esm_exports.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tfjs_esm_exports.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tfjs_esm_exports.squeeze(b.slice); - b.landmarks = tfjs_esm_exports.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const rawBox = { - startPoint: [points[0], points[1]], - endPoint: [points[2], points[3]], - landmarks: await b.landmarks.array(), - confidence - }; - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); - const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); - const squaredBox = squarifyBox(enlargedBox); - boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tfjs_esm_exports.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var model6; -var inputSize5 = 0; -var irisEnlarge = 2.3; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) - model6 = null; - if (!model6) - model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) - inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); - const boxSize = getBoxSize(box); - let crop = tfjs_esm_exports.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tfjs_esm_exports.image.flipLeftRight(crop); - tfjs_esm_exports.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize) { - if (!(model6 == null ? void 0 : model6["executor"])) - return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]); - tfjs_esm_exports.dispose(leftEyeCrop); - tfjs_esm_exports.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tfjs_esm_exports.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tfjs_esm_exports.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - }; - for (const val of Object.values(t2)) { - if (!val) - return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) - rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) - rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - if (!(model7 == null ? void 0 : model7["executor"])) - return []; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tfjs_esm_exports.dispose(face4.tensor); - if (equilized) - face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } else if (!model7) { - if (config3.debug) - log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tfjs_esm_exports.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh.keepInvalid) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tfjs_esm_exports.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tfjs_esm_exports.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) - face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - newCache.push(calculatedBox); - } - tfjs_esm_exports.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) - faces.push(face4); - else - tfjs_esm_exports.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) - model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) - model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) - model7 = await loadModel(config3.face.attention.modelPath); - else - model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -async function load6(config3) { - var _a; - if (env.initial) - model8 = null; - if (!model8) - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model8["modelUrl"]); - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) - return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - t2.channels = tfjs_esm_exports.mul(t2.resize, constants.rgb); - t2.grayscale = tfjs_esm_exports.sum(t2.channels, 3, true); - t2.grayscaleSub = tfjs_esm_exports.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tfjs_esm_exports.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) - obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) - model9 = null; - if (!model9) - model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input) { - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) - return tensor6; - const crop = tfjs_esm_exports.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - const norm = tfjs_esm_exports.mul(crop, constants.tf255); - tfjs_esm_exports.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) - return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tfjs_esm_exports.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tfjs_esm_exports.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tfjs_esm_exports.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) - log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tfjs_esm_exports.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) - inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) - return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) - return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) - silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) - silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) - model10 = null; - if (!model10) - model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) - return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tfjs_esm_exports.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) - model11 = null; - if (!model11) - model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) - return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tfjs_esm_exports.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) - model12 = null; - if (!model12) - model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) - return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) - return; - const t2 = {}; - const box = [[0, 0.1, 0.9, 0.9]]; - t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) - [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) - obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) - age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) - model13 = null; - if (!model13) - model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) - log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) - return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) - return; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - t2.enhance = tfjs_esm_exports.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) - model14 = null; - if (!model14) - model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) - log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) - return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) - return; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - t2.enhance = tfjs_esm_exports.tidy(() => { - const [red, green, blue] = tfjs_esm_exports.split(t2.resize, 3, 3); - const redNorm = tfjs_esm_exports.mul(red, rgb[0]); - const greenNorm = tfjs_esm_exports.mul(green, rgb[1]); - const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]); - const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]); - const normalize2 = tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, constants.tf05), 2); - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) - model15 = null; - if (!model15) - model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) - return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) - model16 = null; - if (!model16) - model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) - log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) - return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) - return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - thetaZ = Math.asin(r10); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) - thetaX = 0; - if (Number.isNaN(thetaY)) - thetaY = 0; - if (Number.isNaN(thetaZ)) - thetaZ = 0; - return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) - return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!f) - return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) - return []; - if (!faces) - return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tfjs_esm_exports.dispose(faces[i].tensor); - if (masked) - faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tfjs_esm_exports.squeeze(faces[i].tensor) : null; - tfjs_esm_exports.dispose(faces[i].tensor); - if (faces[i].tensor) - delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) - res.age = descRes.age; - if (descRes.gender) - res.gender = descRes.gender; - if (descRes.genderScore) - res.genderScore = descRes.genderScore; - if (descRes.descriptor) - res.embedding = descRes.descriptor; - if (descRes.race) - res.race = descRes.race; - if (emotionRes) - res.emotion = emotionRes; - if (antispoofRes) - res.real = antispoofRes; - if (livenessRes) - res.live = livenessRes; - if (irisSize > 0) - res.distance = irisSize; - if (rotation) - res.rotation = rotation; - if (tensor6) - res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) - delete instance.performance.face; - if (instance.performance.age) - delete instance.performance.age; - if (instance.performance.gender) - delete instance.performance.gender; - if (instance.performance.emotion) - delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") - this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) - this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) - slope = -slope; - else if (slope > 0) - slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) - return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) - return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) - isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) - isDiagonal = 1 * weightageAt; - else - isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) - cos_in = 1; - else if (cos_in < -1) - cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) - fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) - fingerCurl = FingerCurl.half; - else - fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalUpLeft; - else - estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalDownLeft; - else - estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) - voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) - voteDiagonal += options3.DISTANCE_VOTE_POWER; - else - voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) - return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) - return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) - return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) - poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) - gestures.push({ face: i, gesture: "facing center" }); - else - gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) - gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) - gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) - gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) - gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) - continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) - center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (leftIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (rightIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) - gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - gestures.push({ iris: i, gesture: "looking up" }); - if (center) - gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) - fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) - gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposeutil.ts -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tfjs_esm_exports.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - { x: 0.890625, y: 0.109375 }, - { x: 0.890625, y: 0.109375 }, - { x: 0.921875, y: 0.109375 }, - { x: 0.921875, y: 0.109375 }, - { x: 0.953125, y: 0.109375 }, - { x: 0.953125, y: 0.109375 }, - { x: 0.984375, y: 0.109375 }, - { x: 0.984375, y: 0.109375 }, - { x: 0.015625, y: 0.140625 }, - { x: 0.015625, y: 0.140625 }, - { x: 0.046875, y: 0.140625 }, - { x: 0.046875, y: 0.140625 }, - { x: 0.078125, y: 0.140625 }, - { x: 0.078125, y: 0.140625 }, - { x: 0.109375, y: 0.140625 }, - { x: 0.109375, y: 0.140625 }, - { x: 0.140625, y: 0.140625 }, - { x: 0.140625, y: 0.140625 }, - { x: 0.171875, y: 0.140625 }, - { x: 0.171875, y: 0.140625 }, - { x: 0.203125, y: 0.140625 }, - { x: 0.203125, y: 0.140625 }, - { x: 0.234375, y: 0.140625 }, - { x: 0.234375, y: 0.140625 }, - { x: 0.265625, y: 0.140625 }, - { x: 0.265625, y: 0.140625 }, - { x: 0.296875, y: 0.140625 }, - { x: 0.296875, y: 0.140625 }, - { x: 0.328125, y: 0.140625 }, - { x: 0.328125, y: 0.140625 }, - { x: 0.359375, y: 0.140625 }, - { x: 0.359375, y: 0.140625 }, - { x: 0.390625, y: 0.140625 }, - { x: 0.390625, y: 0.140625 }, - { x: 0.421875, y: 0.140625 }, - { x: 0.421875, y: 0.140625 }, - { x: 0.453125, y: 0.140625 }, - { x: 0.453125, y: 0.140625 }, - { x: 0.484375, y: 0.140625 }, - { x: 0.484375, y: 0.140625 }, - { x: 0.515625, y: 0.140625 }, - { x: 0.515625, y: 0.140625 }, - { x: 0.546875, y: 0.140625 }, - { x: 0.546875, y: 0.140625 }, - { x: 0.578125, y: 0.140625 }, - { x: 0.578125, y: 0.140625 }, - { x: 0.609375, y: 0.140625 }, - { x: 0.609375, y: 0.140625 }, - { x: 0.640625, y: 0.140625 }, - { x: 0.640625, y: 0.140625 }, - { x: 0.671875, y: 0.140625 }, - { x: 0.671875, y: 0.140625 }, - { x: 0.703125, y: 0.140625 }, - { x: 0.703125, y: 0.140625 }, - { x: 0.734375, y: 0.140625 }, - { x: 0.734375, y: 0.140625 }, - { x: 0.765625, y: 0.140625 }, - { x: 0.765625, y: 0.140625 }, - { x: 0.796875, y: 0.140625 }, - { x: 0.796875, y: 0.140625 }, - { x: 0.828125, y: 0.140625 }, - { x: 0.828125, y: 0.140625 }, - { x: 0.859375, y: 0.140625 }, - { x: 0.859375, y: 0.140625 }, - { x: 0.890625, y: 0.140625 }, - { x: 0.890625, y: 0.140625 }, - { x: 0.921875, y: 0.140625 }, - { x: 0.921875, y: 0.140625 }, - { x: 0.953125, y: 0.140625 }, - { x: 0.953125, y: 0.140625 }, - { x: 0.984375, y: 0.140625 }, - { x: 0.984375, y: 0.140625 }, - { x: 0.015625, y: 0.171875 }, - { x: 0.015625, y: 0.171875 }, - { x: 0.046875, y: 0.171875 }, - { x: 0.046875, y: 0.171875 }, - { x: 0.078125, y: 0.171875 }, - { x: 0.078125, y: 0.171875 }, - { x: 0.109375, y: 0.171875 }, - { x: 0.109375, y: 0.171875 }, - { x: 0.140625, y: 0.171875 }, - { x: 0.140625, y: 0.171875 }, - { x: 0.171875, y: 0.171875 }, - { x: 0.171875, y: 0.171875 }, - { x: 0.203125, y: 0.171875 }, - { x: 0.203125, y: 0.171875 }, - { x: 0.234375, y: 0.171875 }, - { x: 0.234375, y: 0.171875 }, - { x: 0.265625, y: 0.171875 }, - { x: 0.265625, y: 0.171875 }, - { x: 0.296875, y: 0.171875 }, - { x: 0.296875, y: 0.171875 }, - { x: 0.328125, y: 0.171875 }, - { x: 0.328125, y: 0.171875 }, - { x: 0.359375, y: 0.171875 }, - { x: 0.359375, y: 0.171875 }, - { x: 0.390625, y: 0.171875 }, - { x: 0.390625, y: 0.171875 }, - { x: 0.421875, y: 0.171875 }, - { x: 0.421875, y: 0.171875 }, - { x: 0.453125, y: 0.171875 }, - { x: 0.453125, y: 0.171875 }, - { x: 0.484375, y: 0.171875 }, - { x: 0.484375, y: 0.171875 }, - { x: 0.515625, y: 0.171875 }, - { x: 0.515625, y: 0.171875 }, - { x: 0.546875, y: 0.171875 }, - { x: 0.546875, y: 0.171875 }, - { x: 0.578125, y: 0.171875 }, - { x: 0.578125, y: 0.171875 }, - { x: 0.609375, y: 0.171875 }, - { x: 0.609375, y: 0.171875 }, - { x: 0.640625, y: 0.171875 }, - { x: 0.640625, y: 0.171875 }, - { x: 0.671875, y: 0.171875 }, - { x: 0.671875, y: 0.171875 }, - { x: 0.703125, y: 0.171875 }, - { x: 0.703125, y: 0.171875 }, - { x: 0.734375, y: 0.171875 }, - { x: 0.734375, y: 0.171875 }, - { x: 0.765625, y: 0.171875 }, - { x: 0.765625, y: 0.171875 }, - { x: 0.796875, y: 0.171875 }, - { x: 0.796875, y: 0.171875 }, - { x: 0.828125, y: 0.171875 }, - { x: 0.828125, y: 0.171875 }, - { x: 0.859375, y: 0.171875 }, - { x: 0.859375, y: 0.171875 }, - { x: 0.890625, y: 0.171875 }, - { x: 0.890625, y: 0.171875 }, - 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{ x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 } -]; - -// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]); - t2.div = tfjs_esm_exports.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tfjs_esm_exports.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tfjs_esm_exports.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tfjs_esm_exports.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tfjs_esm_exports.mul(t2.sub, this.inputSizeTensor); - t2.add = tfjs_esm_exports.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tfjs_esm_exports.mul(t2.add, this.inputSizeTensor); - const res = tfjs_esm_exports.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tfjs_esm_exports.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tfjs_esm_exports.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tfjs_esm_exports.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tfjs_esm_exports.div(t2.resize, constants.tf127); - t2.image = tfjs_esm_exports.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tfjs_esm_exports.squeeze(t2.batched); - t2.slice = tfjs_esm_exports.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tfjs_esm_exports.sigmoid(t2.slice); - t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tfjs_esm_exports.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tfjs_esm_exports.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tfjs_esm_exports.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tfjs_esm_exports.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tfjs_esm_exports.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (config3.skipAllowed) - this.skipped++; - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) - useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) - continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tfjs_esm_exports.div(croppedInput, constants.tf255); - tfjs_esm_exports.dispose(croppedInput); - tfjs_esm_exports.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tfjs_esm_exports.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tfjs_esm_exports.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tfjs_esm_exports.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tfjs_esm_exports.dispose(keypoints); - tfjs_esm_exports.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tfjs_esm_exports.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) - hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -async function predict14(input, config3) { - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) - return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) - box[0] = pt[0]; - if (pt[1] < box[1]) - box[1] = pt[1]; - if (pt[0] > box[2]) - box[2] = pt[0]; - if (pt[1] > box[3]) - box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function load15(config3) { - var _a, _b; - if (env.initial) { - handDetectorModel = null; - handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, - config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null - ]); - } else { - if (config3.debug) - log("cached model:", handDetectorModel["modelUrl"]); - if (config3.debug) - log("cached model:", handPoseModel["modelUrl"]); - } - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) - handPipeline = new HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; -} - -// src/hand/handtrack.ts -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect2(config3) { - var _a; - if (env.initial) - models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) - models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) - return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [height, width]); - t2.cast = tfjs_esm_exports.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tfjs_esm_exports.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tfjs_esm_exports.squeeze(t2.rawScores, [0]); - const classScores = tfjs_esm_exports.unstack(t2.scores, 1); - tfjs_esm_exports.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tfjs_esm_exports.stack(classScores, 1); - tfjs_esm_exports.dispose(classScores); - t2.max = tfjs_esm_exports.max(t2.filtered, 1); - t2.argmax = tfjs_esm_exports.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tfjs_esm_exports.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tfjs_esm_exports.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) - hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tfjs_esm_exports.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tfjs_esm_exports.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tfjs_esm_exports.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) - return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - const t0 = now(); - if (!newResult) - return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) - bufferedResult.canvas = newResult.canvas; - if (newResult.error) - bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) - coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) - coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) - bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) - bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) - bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var model17; -async function load16(config3) { - if (!model17 || env.initial) - model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) - model17 = await load16(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tfjs_esm_exports.unstack(t2.squeeze, 2); - t2.fg = tfjs_esm_exports.softmax(t2.fgRaw); - t2.mul = tfjs_esm_exports.mul(t2.fg, constants.tf255); - t2.expand = tfjs_esm_exports.expandDims(t2.mul, 2); - t2.output = tfjs_esm_exports.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tfjs_esm_exports.squeeze(input); - t2.concat = tfjs_esm_exports.concat([t2.input, t2.output], -1); - rgba = tfjs_esm_exports.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tfjs_esm_exports.cast(t2.output, "int32"); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) - return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - return (options4.multiplier || 20) * sum3; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) - return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.max(Math.min(norm, 1), 0); - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) - break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenetfix.ts -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) - continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - cache5.padding = [ - [0, 0], - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - [0, 0] - ]; - t2.pad = tfjs_esm_exports.pad(input, cache5.padding); - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tfjs_esm_exports.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load17(config3) { - var _a; - if (env.initial) - model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) - log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) - inputSize8 = 256; - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (totalScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) - bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) - return []; - if (!config3.skipAllowed) - cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load18(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) - log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tfjs_esm_exports.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tfjs_esm_exports.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tfjs_esm_exports.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tfjs_esm_exports.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) - return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tfjs_esm_exports.div(resizeT, constants.tf255); - const transposeT = tfjs_esm_exports.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) - objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tfjs_esm_exports.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) - j++; - if (!this.less(k, j)) - break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) - return min2; - if (a > max5) - return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) - break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) - continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) - queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) - return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) - result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) - continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) - poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) - return []; - const res = tfjs_esm_exports.tidy(() => { - if (!model20.inputs[0].shape) - return []; - const resized = tfjs_esm_exports.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0])); - results3d[1] = tfjs_esm_exports.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) - tfjs_esm_exports.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) - return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load19(config3) { - if (!model20 || env.initial) - model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tfjs_esm_exports.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tfjs_esm_exports.tensor(0); - t.r2i = tfjs_esm_exports.tensor(0); - t.r3i = tfjs_esm_exports.tensor(0); - t.r4i = tfjs_esm_exports.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tfjs_esm_exports.tensor(ratio); -} -async function load20(config3) { - if (!model21 || env.initial) - model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tfjs_esm_exports.tidy(() => { - const squeeze14 = tfjs_esm_exports.squeeze(r, [0]); - const mul15 = tfjs_esm_exports.mul(squeeze14, constants.tf255); - const cast8 = tfjs_esm_exports.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb2 = fgr ? normalize(fgr) : tfjs_esm_exports.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tfjs_esm_exports.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tfjs_esm_exports.concat([rgb2, a], -1); - tfjs_esm_exports.dispose([rgb2, a]); - return rgba; -} -function getState(state) { - return tfjs_esm_exports.tidy(() => { - const r = {}; - r.unstack = tfjs_esm_exports.unstack(state, -1); - r.concat = tfjs_esm_exports.concat(r.unstack, 1); - r.split = tfjs_esm_exports.split(r.concat, 4, 1); - r.stack = tfjs_esm_exports.concat(r.split, 2); - r.squeeze = tfjs_esm_exports.squeeze(r.stack, [0]); - r.expand = tfjs_esm_exports.expandDims(r.squeeze, -1); - r.add = tfjs_esm_exports.add(r.expand, 1); - r.mul = tfjs_esm_exports.mul(r.add, 127.5); - r.cast = tfjs_esm_exports.cast(r.mul, "int32"); - r.tile = tfjs_esm_exports.tile(r.cast, [1, 1, 3]); - r.alpha = tfjs_esm_exports.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tfjs_esm_exports.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) - model21 = await load20(config3); - if (!(model21 == null ? void 0 : model21["executor"])) - return null; - t.src = tfjs_esm_exports.div(input, 255); - if (ratio !== config3.segmentation.ratio) - init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - tfjs_esm_exports.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var model22; -async function load21(config3) { - if (!model22 || env.initial) - model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) - model22 = await load21(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); - t2.alpha = tfjs_esm_exports.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tfjs_esm_exports.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tfjs_esm_exports.squeeze(input); - t2.concat = tfjs_esm_exports.concat([t2.input, t2.mul], -1); - rgba = tfjs_esm_exports.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tfjs_esm_exports.cast(t2.mul, "int32"); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) - return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) - return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) - ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) - log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += m.sizeFromManifest; - totalSizeWeights += m.sizeLoadedWeights; - totalSizeLoading += m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) - this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) - this.reset(); - if (instance) - this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; - if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) - [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) - promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) - continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) - continue; - const res = validateModel(this.instance, model23, defined); - if (res) - missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) - person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) - person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var ut=0;function Hr(e,t,n){let o=Q(e0,n);if(!t||!e)return;let s=S0(e);if(!!s){s.lineJoin="round",s.font=o.font;for(let A=0;ATt,kpt:()=>gt});var gt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Tt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var j0,Pe=224,A1,Zr=5,M2=[8,16,32,32,32];function Xr(){let e=[],t=0;for(;tn.x)),y:r.tensor1d(e.map(n=>n.y))}}async function a1(e){if(M.initial&&(j0=null),!j0&&e.body.detector&&e.body.detector.modelPath){j0=await L(e.body.detector.modelPath);let t=j0!=null&&j0.executor?Object.values(j0.modelSignature.inputs):void 0;Pe=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&j0&&h("cached model:",j0.modelUrl);return Xr(),j0}var s1=[5,5];function qr(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,Pe),t.x),s=r.add(r.div(s,Pe),t.y),A=r.mul(r.div(A,Pe),s1[0]),a=r.mul(r.div(a,Pe),s1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),x=r.add(i,A),y=r.add(c,a);return r.stack([i,c,x,y],1)})}async function Ur(e,t,n,o){var x,y;let s=[],A={};A.boxes=qr(e,A1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((x=n.body.detector)==null?void 0:x.minConfidence)||.1,((y=n.body.detector)==null?void 0:y.iouThreshold)||.1);let a=await A.nms.data(),i=await A.scores.data(),c=await A.boxes.array();for(let l of Array.from(a)){let f=i[l],d=c[l],u=[Math.round(d[0]*o[0]),Math.round(d[1]*o[1]),Math.round(d[2]*o[0]),Math.round(d[3]*o[1])],m={score:f,boxRaw:d,box:u};s.push(m)}return Object.keys(A).forEach(l=>r.dispose(A[l])),s}async function i1(e,t,n){let o={};o.res=j0==null?void 0:j0.execute(e,["Identity"]),o.logitsRaw=r.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=r.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=r.squeeze(o.logitsRaw),o.boxes=r.squeeze(o.boxesRaw);let s=await Ur(o.boxes,o.logits,t,n);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}function J0(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],s[0]-o[0],s[1]-o[1]],a=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:a}}function l1(e,t=[1,1]){let n=[e.map(x=>x[0]),e.map(x=>x[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+s[0])/2,(o[1]+s[1])/2],a=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+s[0],-A[1]+s[1]),i=[Math.trunc(A[0]-a),Math.trunc(A[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function P2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var g0,Rt=256,vt=Number.MAX_SAFE_INTEGER,Yr={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},w2=[],ae=[[0,0],[0,0],[0,0],[0,0]],c1=0,x1=e=>1-1/(1+Math.exp(e)),y1=e=>a1(e);async function f1(e){if(M.initial&&(g0=null),g0)e.debug&&h("cached model:",g0.modelUrl);else{g0=await L(e.body.modelPath);let t=g0!=null&&g0.executor?Object.values(g0.modelSignature.inputs):void 0;Rt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return g0}function d1(e,t,n){var A,a;let o={};if(!((A=e==null?void 0:e.shape)!=null&&A[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;let s;if(n&&(o.cropped=r.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let i=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],c=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ae=[[0,0],i,c,[0,0]],o.pad=r.pad(o.cropped||e,ae),o.resize=r.image.resizeBilinear(o.pad,[t,t]),s=r.div(o.resize,O.tf255)}else e.shape[1]!==t?(o.resize=r.image.resizeBilinear(o.cropped||e,[t,t]),s=r.div(o.resize,O.tf255)):s=r.div(o.cropped||e,O.tf255);return Object.keys(o).forEach(i=>r.dispose(o[i])),s}function Kr(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ae[2][0]+ae[2][1])/t[0]-ae[2][0]),Math.trunc(o.position[1]*(t[1]+ae[1][0]+ae[1][1])/t[1]-ae[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/o+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function Jr(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function Qr(e,t,n){if(!(g0!=null&&g0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=g0==null?void 0:g0.execute(e,Yr.landmarks);let s=(await o.poseflag.data())[0],A=await o.ld.data(),a=await o.world.data();Object.keys(o).forEach(u=>r.dispose(o[u]));let i=[],c=5;for(let u=0;uu.position),l=J0(y,[n[0],n[1]]),f={};for(let[u,m]of Object.entries(Tt)){let g=[];for(let P=0;Pb.part===m[P]),p=x.find(b=>b.part===m[P+1]);v&&p&&g.push([v.position,p.position])}f[u]=g}return{id:0,score:Math.trunc(100*s)/100,box:l.box,boxRaw:l.boxRaw,keypoints:x,annotations:f}}async function Mt(e,t){var A,a,i;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>T()-c1,s=vt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&s&&w2!==null)vt++;else{let c=[];if((a=(A=t.body)==null?void 0:A.detector)!=null&&a.enabled){let x=d1(e,224);c=await i1(x,t,n),r.dispose(x)}else c=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let x=0;xr.dispose(o[x])),s}async function wt(e,t){if(!(T0!=null&&T0.executor))return[];let n=(t.object.skipTime||0)>T()-p1,o=kt<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Pt.length>0?(kt++,Pt):(kt=0,new Promise(async s=>{let 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t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,Ot),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,De),t.centersNormalized=r.div(t.centers,De),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,De),t.endNormalized=r.mul(t.ends,De);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function N1(e,t){var i,c,x,y;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=r.image.resizeBilinear(e,[ie,ie]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf05);let o=B0==null?void 0:B0.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let l=o.sort((f,d)=>f.size-d.size);n.concat384=r.concat([l[0],l[2]],2),n.concat512=r.concat([l[1],l[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(o)?n.batch=r.squeeze(o[0]):n.batch=r.squeeze(o);r.dispose(o),n.boxes=as(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((c=t.face.detector)==null?void 0:c.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let s=await n.nms.array(),A=[],a=await n.scores.data();for(let l=0;l(((y=t.face.detector)==null?void 0:y.minConfidence)||0)){let d={};d.bbox=r.slice(n.boxes,[s[l],0],[1,-1]),d.slice=r.slice(n.batch,[s[l],j1-1],[1,-1]),d.squeeze=r.squeeze(d.slice),d.landmarks=r.reshape(d.squeeze,[j1,-1]);let u=await d.bbox.data(),m={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await 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a=N2(I2(P1([e[n],e[o]]),is)),i=We(a),c=r.image.cropAndResize(t,[[a.startPoint[1]/s,a.startPoint[0]/s,a.endPoint[1]/s,a.endPoint[0]/s]],[0],[le,le]);if(A&&M.kernels.includes("flipleftright")){let x=r.image.flipLeftRight(c);r.dispose(c),c=x}return{box:a,boxSize:i,crop:c}},C1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[C0[`${n}EyeUpper0`][He.upperCenter]][2],s=e[C0[`${n}EyeLower0`][He.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function F1(e,t,n){if(!(R0!=null&&R0.executor))return e;let{box:o,boxSize:s,crop:A}=O1(e,t,Be.leftBounds[0],Be.leftBounds[1],n,!0),{box:a,boxSize:i,crop:c}=O1(e,t,Be.rightBounds[0],Be.rightBounds[1],n,!0),x=r.concat([A,c]);r.dispose(A),r.dispose(c);let y=R0.execute(x);r.dispose(x);let l=await y.data();r.dispose(y);let f=l.slice(0,He.numCoordinates*3),{rawCoords:d,iris:u}=C1(f,o,s,!0),m=l.slice(He.numCoordinates*3),{rawCoords:g,iris:P}=C1(m,a,i,!1),v=ls(e);Math.abs(v)<30?(L2(e,d,"left",null),L2(e,g,"right",null)):v<1?L2(e,d,"left",["EyeUpper0","EyeLower0"]):L2(e,g,"right",["EyeUpper0","EyeLower0"]);let p=W1(e,u,"left"),b=W1(e,P,"right");return e.concat(p).concat(b)}async function H1(e,t){var A,a,i,c,x,y,l,f,d,u;let n={lips:await((a=(A=t.filter(m=>m.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(m=>m.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(x=t.filter(m=>m.size===142))==null?void 0:x[0])==null?void 0:y.data()),irisR:await((f=(l=t.filter(m=>m.size===10))==null?void 0:l[1])==null?void 0:f.data()),eyeR:await((u=(d=t.filter(m=>m.size===142))==null?void 0:d[1])==null?void 0:u.data())};for(let m of Object.values(n))if(!m)return e;let o=Re.reduce((m,g)=>m+=e[g][2],0)/Re.length;for(let m=0;mm+=e[g][2],0)/Me.length;for(let m=0;mT()-q0.timestamp,o=q0.skipped<(((x=t.face.detector)==null?void 0:x.skipFrames)||0);!t.skipAllowed||!n||!o||q0.boxes.length===0?(q0.boxes=await N1(e,t),q0.timestamp=T(),q0.skipped=0):q0.skipped++;let s=[],A=[],a=0,i=n2;for(let v=0;vW.shape[W.shape.length-1]===1).data();if(k.faceScore=Math.round(100*q[0])/100,k.faceScore<(((u=t.face.detector)==null?void 0:u.minConfidence)||1)){if(p.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=S2(p,e),k.boxRaw=j2(p,e),k.score=k.boxScore,k.mesh=p.landmarks.map(W=>[(p.startPoint[0]+p.endPoint[0])/2+(p.endPoint[0]+p.startPoint[0])*W[0]/Fe(),(p.startPoint[1]+p.endPoint[1])/2+(p.endPoint[1]+p.startPoint[1])*W[1]/Fe()]),k.meshRaw=k.mesh.map(W=>[W[0]/(e.shape[2]||1),W[1]/(e.shape[1]||1),(W[2]||0)/i]);for(let W of Object.keys(Te))k.annotations[W]=[k.mesh[Te[W]]]}}else{let W=N.find(D=>D.shape[D.shape.length-1]===1404),Z=r.reshape(W,[-1,3]),J=await Z.array();r.dispose(Z),(m=t.face.attention)!=null&&m.enabled?J=await 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0:t.enabled)&&(K==null?void 0:K.signature)&&Object.keys(((n=K==null?void 0:K.signature)==null?void 0:n.outputs)||{}).length<6&&(K=null),K?e.debug&&h("cached model:",K.modelUrl):(o=e.face.attention)!=null&&o.enabled?K=await L(e.face.attention.modelPath):K=await L((s=e.face.mesh)==null?void 0:s.modelPath),n2=K.executor&&((A=K==null?void 0:K.inputs)==null?void 0:A[0].shape)?(a=K==null?void 0:K.inputs)==null?void 0:a[0].shape[2]:256,K}var Z1=ve,X1=e2;var xs=["angry","disgust","fear","happy","sad","surprise","neutral"],I0,O2=[],q1=0,U1=0,Ft=Number.MAX_SAFE_INTEGER;async function Y1(e){var t;return M.initial&&(I0=null),I0?e.debug&&h("cached model:",I0.modelUrl):I0=await L((t=e.face.emotion)==null?void 0:t.modelPath),I0}async function Bt(e,t,n,o){var a,i;if(!I0)return[];let s=Ft<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>T()-U1;return t.skipAllowed&&A&&s&&q1===o&&O2[n]&&O2[n].length>0?(Ft++,O2[n]):(Ft=0,new Promise(async c=>{var y;let x=[];if((y=t.face.emotion)!=null&&y.enabled){let l={},f=I0!=null&&I0.inputs[0].shape?I0.inputs[0].shape[2]:0;l.resize=r.image.resizeBilinear(e,[f,f],!1),l.channels=r.mul(l.resize,O.rgb),l.grayscale=r.sum(l.channels,3,!0),l.grayscaleSub=r.sub(l.grayscale,O.tf05),l.grayscaleMul=r.mul(l.grayscaleSub,O.tf2),l.emotion=I0==null?void 0:I0.execute(l.grayscaleMul),U1=T();let d=await l.emotion.data();for(let u=0;u(t.face.emotion.minConfidence||0)&&x.push({score:Math.min(.99,Math.trunc(100*d[u])/100),emotion:xs[u]});x.sort((u,m)=>m.score-u.score),Object.keys(l).forEach(u=>r.dispose(l[u]))}O2[n]=x,q1=o,c(x)}))}var f0,ce=[],J1=0,Q1=0,Ht=Number.MAX_SAFE_INTEGER;async function _1(e){var t;return M.initial&&(f0=null),f0?e.debug&&h("cached model:",f0.modelUrl):f0=await L((t=e.face.description)==null?void 0:t.modelPath),f0}function ds(e){let t=e.image||e.tensor||e;if(!(f0!=null&&f0.inputs[0].shape))return t;let n=r.image.resizeBilinear(t,[f0.inputs[0].shape[2],f0.inputs[0].shape[1]],!1),o=r.mul(n,O.tf255);return r.dispose(n),o}async function Gt(e,t,n,o){var i,c,x,y;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(f0!=null&&f0.executor))return s;let A=Ht<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>T()-J1;return t.skipAllowed&&A&&a&&Q1===o&&((x=ce==null?void 0:ce[n])==null?void 0:x.age)>0&&((y=ce==null?void 0:ce[n])==null?void 0:y.genderScore)>0?(Ht++,ce[n]):(Ht=0,new Promise(async l=>{var f;if((f=t.face.description)!=null&&f.enabled){let d=ds(e),u=f0==null?void 0:f0.execute(d);J1=T(),r.dispose(d);let g=await u.find(B=>B.shape[1]===1).data(),P=Math.trunc(200*Math.abs(g[0]-.5))/100;P>(t.face.description.minConfidence||0)&&(s.gender=g[0]<=.5?"female":"male",s.genderScore=Math.min(.99,P));let v=r.argMax(u.find(B=>B.shape[1]===100),1),p=(await v.data())[0];r.dispose(v);let j=await 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x=r.image.resizeBilinear(e,[x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[2]:0,x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[1]:0],!1),y=x0==null?void 0:x0.execute(x),l=(await y.data())[0];C2[n]=Math.round(100*l)/100,t3=o,n3=T(),r.dispose([x,y]),c(C2[n])}))}var d0,W2=[],qt=Number.MAX_SAFE_INTEGER,s3=0,A3=0;async function a3(e){var t;return M.initial&&(d0=null),d0?e.debug&&h("cached model:",d0.modelUrl):d0=await L((t=e.face.liveness)==null?void 0:t.modelPath),d0}async function Ut(e,t,n,o){var a,i;if(!(d0!=null&&d0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>T()-A3,A=qt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&s3===o&&W2[n]?(qt++,W2[n]):(qt=0,new Promise(async c=>{let x=r.image.resizeBilinear(e,[d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[2]:0,d0!=null&&d0.inputs[0].shape?d0.inputs[0].shape[1]:0],!1),y=d0==null?void 0:d0.execute(x),l=(await y.data())[0];W2[n]=Math.round(100*l)/100,s3=o,A3=T(),r.dispose([x,y]),c(W2[n])}))}var W0,Yt=[],ms=["white","black","asian","indian","other"],ps=[15,23,28,35.5,45.5,55.5,65],l3=0,c3=0,Kt=Number.MAX_SAFE_INTEGER;async function x3(e){var t;return M.initial&&(W0=null),W0?e.debug&&h("cached model:",W0.modelUrl):W0=await L((t=e.face.gear)==null?void 0:t.modelPath),W0}async function Jt(e,t,n,o){var a,i;if(!W0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=Kt<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>T()-c3;return t.skipAllowed&&A&&s&&l3===o&&Yt[n]?(Kt++,Yt[n]):(Kt=0,new Promise(async c=>{var P,v;if(!(W0!=null&&W0.inputs[0].shape))return;let x={},y=[[0,.1,.9,.9]];x.resize=r.image.cropAndResize(e,y,[0],[W0.inputs[0].shape[2],W0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(P=t.face.gear)!=null&&P.enabled&&([x.age,x.gender,x.race]=W0.execute(x.resize,["age_output","gender_output","race_output"]));let f=await x.gender.data();l.gender=f[0]>f[1]?"male":"female",l.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let d=await x.race.data();for(let p=0;p(((v=t.face.gear)==null?void 0:v.minConfidence)||.2)&&l.race.push({score:Math.round(100*d[p])/100,race:ms[p]});l.race.sort((p,b)=>b.score-p.score);let m=Array.from(await x.age.data()).map((p,b)=>[ps[b],p]).sort((p,b)=>b[1]-p[1]),g=m[0][0];for(let p=1;pr.dispose(x[p])),Yt[n]=l,l3=o,c3=T(),c(l)}))}var M0,D2=[],y3=0,f3=0,Qt=Number.MAX_SAFE_INTEGER;async function m3(e){return M.initial&&(M0=null),M0?e.debug&&h("cached model:",M0.modelUrl):M0=await L(e.face.ssrnet.modelPathAge),M0}async function _t(e,t,n,o){var a,i,c,x;if(!M0)return{age:0};let s=Qt<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>T()-f3;return t.skipAllowed&&s&&A&&y3===o&&((c=D2[n])==null?void 0:c.age)&&((x=D2[n])==null?void 0:x.age)>0?(Qt++,D2[n]):(Qt=0,new Promise(async y=>{var d;if(!(M0!=null&&M0.inputs)||!M0.inputs[0]||!M0.inputs[0].shape)return;let l={};l.resize=r.image.resizeBilinear(e,[M0.inputs[0].shape[2],M0.inputs[0].shape[1]],!1),l.enhance=r.mul(l.resize,O.tf255);let f={age:0};if((d=t.face.ssrnet)!=null&&d.enabled&&(l.age=M0.execute(l.enhance)),l.age){let u=await l.age.data();f.age=Math.trunc(10*u[0])/10}Object.keys(l).forEach(u=>r.dispose(l[u])),D2[n]=f,y3=o,f3=T(),y(f)}))}var D0,F2=[],u3=0,h3=0,$t=Number.MAX_SAFE_INTEGER,e5=[.2989,.587,.114];async function b3(e){var t;return M.initial&&(D0=null),D0?e.debug&&h("cached model:",D0.modelUrl):D0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),D0}async function t5(e,t,n,o){var a,i,c,x;if(!D0)return{gender:"unknown",genderScore:0};let s=$t<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>T()-h3;return t.skipAllowed&&s&&A&&u3===o&&((c=F2[n])==null?void 0:c.gender)&&((x=F2[n])==null?void 0:x.genderScore)>0?($t++,F2[n]):($t=0,new Promise(async y=>{var u;if(!(D0!=null&&D0.inputs[0].shape))return;let l={};l.resize=r.image.resizeBilinear(e,[D0.inputs[0].shape[2],D0.inputs[0].shape[1]],!1),l.enhance=r.tidy(()=>{let[m,g,P]=r.split(l.resize,3,3),v=r.mul(m,e5[0]),p=r.mul(g,e5[1]),b=r.mul(P,e5[2]),j=r.addN([v,p,b]);return r.mul(r.sub(j,O.tf05),2)});let f={gender:"unknown",genderScore:0};(u=t.face.ssrnet)!=null&&u.enabled&&(l.gender=D0.execute(l.enhance));let d=await l.gender.data();f.gender=d[0]>d[1]?"female":"male",f.genderScore=d[0]>d[1]?Math.trunc(100*d[0])/100:Math.trunc(100*d[1])/100,Object.keys(l).forEach(m=>r.dispose(l[m])),F2[n]=f,u3=o,h3=T(),y(f)}))}var P0,n5=[],T3=0,v3=0,R3=Number.MAX_SAFE_INTEGER;async function M3(e){var t;return M.initial&&(P0=null),P0?e.debug&&h("cached model:",P0.modelUrl):P0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),P0}async function o5(e,t,n,o){var a,i;if(!(P0!=null&&P0.executor))return[];let s=R3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>T()-v3;return t.skipAllowed&&A&&s&&T3===o&&n5[n]?(R3++,n5[n]):new Promise(async c=>{var y;let x=[];if(((y=t.face.mobilefacenet)==null?void 0:y.enabled)&&(P0==null?void 0:P0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[P0.inputs[0].shape[2],P0.inputs[0].shape[1]],!1),l.data=P0.execute(l.crop);let f=await l.data.data();x=Array.from(f),Object.keys(l).forEach(d=>r.dispose(l[d]))}n5[n]=x,T3=o,v3=T(),c(x)})}var k0,r5=[],k3=0,w3=0,E3=Number.MAX_SAFE_INTEGER;async function z3(e){return M.initial&&(k0=null),k0?e.debug&&h("cached model:",k0.modelUrl):k0=await L(e.face.insightface.modelPath),k0}async function s5(e,t,n,o){var a,i;if(!(k0!=null&&k0.executor))return[];let s=E3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>T()-w3;return t.skipAllowed&&A&&s&&k3===o&&r5[n]?(E3++,r5[n]):new Promise(async c=>{var y;let x=[];if(((y=t.face.insightface)==null?void 0:y.enabled)&&(k0==null?void 0:k0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[k0.inputs[0].shape[2],k0.inputs[0].shape[1]],!1),l.data=k0.execute(l.crop);let f=await l.data.data();x=Array.from(f),Object.keys(l).forEach(d=>r.dispose(l[d]))}r5[n]=x,k3=o,w3=T(),c(x)})}var us=e=>{let t=(l,f)=>Math.atan2(l[1]-f[1],l[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,s=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=s?e.mesh[473]:e.mesh[468],a=s?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=s?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],c=[(a[0]-A[0])/i[0]-n[0],o*(A[1]-a[1])/i[1]-n[1]],x=Math.sqrt(c[0]*c[0]+c[1]*c[1]);return x=Math.min(x,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],c)+Math.PI/2)%Math.PI,strength:x}},j3=(e,t)=>{let n=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},o=(m,g)=>{let P=m[0]-g[0],v=m[1]-g[1],p=m[2]-g[2];return[P,v,p]},s=(m,g)=>{let P=m[1]*g[2]-m[2]*g[1],v=m[2]*g[0]-m[0]*g[2],p=m[0]*g[1]-m[1]*g[0];return[P,v,p]},A=m=>{let[g,P,v,p,b,j,k,N,B]=m,q,W,Z;return p<1?p>-1?(Z=Math.asin(p),W=Math.atan2(-k,g),q=Math.atan2(-j,b)):(Z=-Math.PI/2,W=-Math.atan2(N,B),q=0):(Z=Math.PI/2,W=Math.atan2(N,B),q=0),Number.isNaN(q)&&(q=0),Number.isNaN(W)&&(W=0),Number.isNaN(Z)&&(Z=0),{pitch:2*-q,yaw:2*-W,roll:2*-Z}},a=e.meshRaw;if(!a||a.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[a[10],a[152],a[234],a[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),x=n(o(c[1],c[0])),y=n(o(c[3],c[2])),l=n(s(y,x));y=s(x,l);let f=[y[0],y[1],y[2],x[0],x[1],x[2],l[0],l[1],l[2]],d=A(f),u=a.length===478?us(e):{bearing:0,strength:0};return{angle:d,matrix:f,gaze:u}};function I3(e,t){let n=e==null?void 0:e.annotations;if(!n)return 0;let o=Math.max(Math.abs(n.leftEyeIris[3][0]-n.leftEyeIris[1][0]),Math.abs(n.rightEyeIris[3][0]-n.rightEyeIris[1][0]))/t;return Math.round(1.17/o)/100}var A5=async(e,t)=>{var u,m,g,P,v,p,b,j,k,N,B,q,W,Z,J,R,D,U,H,a0,o0,C,F;let n=T(),o,s,A,a,i,c,x,y,l,f=[];e.state="run:face";let d=await G1(t,e.config);if(e.performance.face=M.perfadd?(e.performance.face||0)+Math.trunc(T()-n):Math.trunc(T()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let S=0;S200?j3(d[S],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(m=e.config.face.emotion)!=null&&m.enabled?Bt(d[S].tensor||r.tensor([]),e.config,S,d.length):[]:(e.state="run:emotion",n=T(),a=(g=e.config.face.emotion)!=null&&g.enabled?await Bt(d[S].tensor||r.tensor([]),e.config,S,d.length):[],e.performance.emotion=M.perfadd?(e.performance.emotion||0)+Math.trunc(T()-n):Math.trunc(T()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?x=(P=e.config.face.antispoof)!=null&&P.enabled?Xt(d[S].tensor||r.tensor([]),e.config,S,d.length):0:(e.state="run:antispoof",n=T(),x=(v=e.config.face.antispoof)!=null&&v.enabled?await Xt(d[S].tensor||r.tensor([]),e.config,S,d.length):0,e.performance.antispoof=M.perfadd?(e.performance.antispoof||0)+Math.trunc(T()-n):Math.trunc(T()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?y=(p=e.config.face.liveness)!=null&&p.enabled?Ut(d[S].tensor||r.tensor([]),e.config,S,d.length):0:(e.state="run:liveness",n=T(),y=(b=e.config.face.liveness)!=null&&b.enabled?await Ut(d[S].tensor||r.tensor([]),e.config,S,d.length):0,e.performance.liveness=M.perfadd?(e.performance.antispoof||0)+Math.trunc(T()-n):Math.trunc(T()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?s=(j=e.config.face.gear)!=null&&j.enabled?Jt(d[S].tensor||r.tensor([]),e.config,S,d.length):null:(e.state="run:gear",n=T(),s=(k=e.config.face.gear)!=null&&k.enabled?await Jt(d[S].tensor||r.tensor([]),e.config,S,d.length):null,e.performance.gear=Math.trunc(T()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(o=(N=e.config.face.ssrnet)!=null&&N.enabled?_t(d[S].tensor||r.tensor([]),e.config,S,d.length):null,A=(B=e.config.face.ssrnet)!=null&&B.enabled?t5(d[S].tensor||r.tensor([]),e.config,S,d.length):null):(e.state="run:ssrnet",n=T(),o=(q=e.config.face.ssrnet)!=null&&q.enabled?await _t(d[S].tensor||r.tensor([]),e.config,S,d.length):null,A=(W=e.config.face.ssrnet)!=null&&W.enabled?await t5(d[S].tensor||r.tensor([]),e.config,S,d.length):null,e.performance.ssrnet=Math.trunc(T()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(Z=e.config.face.mobilefacenet)!=null&&Z.enabled?o5(d[S].tensor||r.tensor([]),e.config,S,d.length):null:(e.state="run:mobilefacenet",n=T(),i=(J=e.config.face.mobilefacenet)!=null&&J.enabled?await o5(d[S].tensor||r.tensor([]),e.config,S,d.length):null,e.performance.mobilefacenet=Math.trunc(T()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start InsightFace:"),e.config.async?c=(R=e.config.face.insightface)!=null&&R.enabled?s5(d[S].tensor||r.tensor([]),e.config,S,d.length):null:(e.state="run:mobilefacenet",n=T(),c=(D=e.config.face.insightface)!=null&&D.enabled?await s5(d[S].tensor||r.tensor([]),e.config,S,d.length):null,e.performance.mobilefacenet=Math.trunc(T()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?l=Gt(d[S].tensor||r.tensor([]),e.config,S,d.length):(e.state="run:description",n=T(),l=await Gt(d[S].tensor||r.tensor([]),e.config,S,d.length),e.performance.description=M.perfadd?(e.performance.description||0)+Math.trunc(T()-n):Math.trunc(T()-n)),e.analyze("End Description:"),e.config.async&&([o,A,a,i,c,l,s,x,y]=await Promise.all([o,A,a,i,c,l,s,x,y])),e.analyze("Finish Face:"),((U=e.config.face.ssrnet)==null?void 0:U.enabled)&&o&&A&&(l={...l,age:o.age,gender:A.gender,genderScore:A.genderScore}),((H=e.config.face.gear)==null?void 0:H.enabled)&&s&&(l={...l,age:s.age,gender:s.gender,genderScore:s.genderScore,race:s.race}),((a0=e.config.face.mobilefacenet)==null?void 0:a0.enabled)&&i&&(l.descriptor=i),((o0=e.config.face.insightface)==null?void 0:o0.enabled)&&c&&(l.descriptor=c);let te=(C=e.config.face.iris)!=null&&C.enabled?I3(d[S],t.shape[2]):0,ne=(F=e.config.face.detector)!=null&&F.return?r.squeeze(d[S].tensor):null;r.dispose(d[S].tensor),d[S].tensor&&delete d[S].tensor;let i0={...d[S],id:S};l.age&&(i0.age=l.age),l.gender&&(i0.gender=l.gender),l.genderScore&&(i0.genderScore=l.genderScore),l.descriptor&&(i0.embedding=l.descriptor),l.race&&(i0.race=l.race),a&&(i0.emotion=a),x&&(i0.real=x),y&&(i0.live=y),te>0&&(i0.distance=te),N0&&(i0.rotation=N0),ne&&(i0.tensor=ne),f.push(i0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),f};var 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b0.all)n[b0.getName(o)]={curl:de.getName(t.curls[o]),direction:$.getName(t.directions[o])};return n}function B3(e){let t=[];if(!e||e.length===0)return t;let n=F3(e);for(let o of N3){let s=o.matchAgainst(n.curls,n.directions);s>=vs&&t.push({name:o.name,confidence:s})}return t}var H3=e=>{if(!e)return[];let t=[];for(let n=0;nc.part==="leftWrist"),s=e[n].keypoints.find(c=>c.part==="rightWrist"),A=e[n].keypoints.find(c=>c.part==="nose");A&&o&&s&&o.position[1]c.part==="leftShoulder"),i=e[n].keypoints.find(c=>c.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},G3=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),s=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/s)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing 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P=Math.abs(e[a].mesh[145][1]-e[a].annotations.rightEyeIris[0][1])/e[a].box[3],v=Math.abs(e[a].mesh[374][1]-e[a].annotations.leftEyeIris[0][1])/e[a].box[3];(v<.01||P<.01||v>.022||P>.022)&&(d=!1),(v<.01||P<.01)&&t.push({iris:a,gesture:"looking down"}),(v>.022||P>.022)&&t.push({iris:a,gesture:"looking up"}),d&&t.push({iris:a,gesture:"looking center"})}return t},Z3=e=>{if(!e)return[];let t=[];for(let n=0;n0){let s=o.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:n,gesture:`${s.name} forward`});let A=o.reduce((a,i)=>a.position[1][A[0]*t[0],A[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:s,confidence:e.confidence}}function G2(e,t=1.5){let n=o2(e),o=H2(e),s=[t*o[0]/2,t*o[1]/2],A=[n[0]-s[0],n[1]-s[1]],a=[n[0]+s[0],n[1]+s[1]];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function V2(e){let t=o2(e),n=H2(e),s=Math.max(...n)/2,A=[t[0]-s,t[1]-s],a=[t[0]+s,t[1]+s];return{startPoint:A,endPoint:a,palmLandmarks:e.palmLandmarks}}function ws(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function K3(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ws(n)}var X3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function pe(e,t){let n=0;for(let o=0;o[a.x,a.y]),this.anchorsTensor=r.tensor2d(this.anchors),this.inputSize=((A=(s=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:s.shape)==null?void 0:A[2])||0,this.inputSizeTensor=r.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=r.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=r.slice(t,[0,0],[-1,2]),n.boxSizes=r.slice(t,[0,2],[-1,2]),n.div=r.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=r.add(n.div,this.anchorsTensor),n.halfBoxSizes=r.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=r.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=r.mul(n.sub,this.inputSizeTensor),n.add=r.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=r.mul(n.add,this.inputSizeTensor);let o=r.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(s=>r.dispose(n[s])),o}normalizeLandmarks(t,n){let o={};o.reshape=r.reshape(t,[-1,7,2]),o.div=r.div(o.reshape,this.inputSizeTensor),o.landmarks=r.add(o.div,this.anchors[n]?this.anchors[n]:0);let s=r.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}async predict(t,n){var i;let o={};o.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=r.div(o.resize,O.tf127),o.image=r.sub(o.div,O.tf1),o.batched=this.model.execute(o.image),o.predictions=r.squeeze(o.batched),o.slice=r.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=r.sigmoid(o.slice),o.scores=r.squeeze(o.sigmoid);let s=await o.scores.data();o.boxes=r.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await r.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),a=[];for(let c of A){let x={};x.box=r.slice(o.norm,[c,0],[1,-1]),x.slice=r.slice(o.predictions,[c,5],[1,14]),x.norm=this.normalizeLandmarks(x.slice,c),x.palmLandmarks=r.reshape(x.norm,[-1,2]);let y=await x.box.data(),l=y.slice(0,2),f=y.slice(2,4),d=await x.palmLandmarks.array(),u={startPoint:l,endPoint:f,palmLandmarks:d,confidence:s[c]},m=Y3(u,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(m),Object.keys(x).forEach(g=>r.dispose(x[g]))}return Object.keys(o).forEach(c=>r.dispose(o[c])),a}};var js=5,$3=1.65,en=[0,5,9,13,17,1,2],Is=0,Ns=2,tn=0,X2=class{constructor(t,n){E(this,"handDetector");E(this,"handPoseModel");E(this,"inputSize");E(this,"storedBoxes");E(this,"skipped");E(this,"detectedHands");var o,s,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(s=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:s[0].shape)==null?void 0:A[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(a=>a[0]),o=t.map(a=>a[1]),s=[Math.min(...n),Math.min(...o)],A=[Math.max(...n),Math.max(...o)];return{startPoint:s,endPoint:A}}getBoxForPalmLandmarks(t,n){let o=t.map(A=>c5([...A,1],n)),s=this.calculateLandmarksBoundingBox(o);return G2(V2(s),js)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=G2(V2(n),$3);o.palmLandmarks=[];for(let s=0;s[a[0]*(d[0]-this.inputSize/2),a[1]*(d[1]-this.inputSize/2),a[2]*d[2]]),c=l5(o,[0,0]),x=i.map(d=>[...c5(d,c),d[2]]),y=J3(s),l=[...o2(n),1],f=[pe(l,y[0]),pe(l,y[1])];return x.map(d=>[Math.trunc(d[0]+f[0]),Math.trunc(d[1]+f[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,s,A=(n.hand.skipTime||0)>T()-tn,a=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&A&&a&&(s=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,s&&s.length>0&&(s.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...s],this.storedBoxes.length>0&&(o=!0));let i=[];for(let c=0;c=n.hand.minConfidence/4){let j=r.reshape(p,[-1,3]),k=await j.array();r.dispose(p),r.dispose(j);let N=this.transformRawCoords(k,m,y,u),B=this.getBoxForHandLandmarks(N);this.storedBoxes[c]={...B,confidence:b};let q={landmarks:N,confidence:b,boxConfidence:x.confidence,fingerConfidence:b,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};i.push(q)}else this.storedBoxes[c]=null;r.dispose(p)}else{let y=G2(V2(x),$3),l={confidence:x.confidence,boxConfidence:x.confidence,fingerConfidence:0,box:{topLeft:y.startPoint,bottomRight:y.endPoint},landmarks:[]};i.push(l)}}return this.storedBoxes=this.storedBoxes.filter(c=>c!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var nn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},je,Ie,on;async function x5(e,t){let n=await on.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let y of a)y[0]i[2]&&(i[2]=y[0]),y[1]>i[3]&&(i[3]=y[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let x=B2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:x})}return o}async function rn(e){var n,o;M.initial&&(je=null,Ie=null),!je||!Ie?[je,Ie]=await Promise.all([e.hand.enabled?L((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?L((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&h("cached model:",je.modelUrl),e.debug&&h("cached model:",Ie.modelUrl));let t=je?new Z2(je):void 0;return t&&Ie&&(on=new X2(t,Ie)),[je,Ie]}var n0=[null,null],Os=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ue=[[0,0],[0,0]],Cs=["hand","fist","pinch","point","face","tip","pinchtip"],An=4,an=1.6,Ws=512,Ds=1.4,q2=Number.MAX_SAFE_INTEGER,d5=0,$0=[0,0],t0={boxes:[],hands:[]},ln={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function cn(e){var t;if(M.initial&&(n0[0]=null),n0[0])e.debug&&h("cached model:",n0[0].modelUrl);else{u2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),n0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=n0[0].executor?Object.values(n0[0].modelSignature.inputs):void 0;ue[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,ue[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return n0[0]}async function xn(e){var t;if(M.initial&&(n0[1]=null),n0[1])e.debug&&h("cached model:",n0[1].modelUrl);else{n0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=n0[1].executor?Object.values(n0[1].modelSignature.inputs):void 0;ue[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,ue[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return n0[1]}async function Fs(e,t){let n=[];if(!e||!n0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,Ws),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await n0[0].executeAsync(o.cast,Os),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[An]),i.splice(An,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await o.nms.data(),y=await o.max.data(),l=await o.argmax.data();for(let f of Array.from(x)){let d=r.slice(o.boxes,f,1),u=await d.data();r.dispose(d);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],g=P2(m,Ds),P=[Math.trunc(m[0]*$0[0]),Math.trunc(m[1]*$0[1]),Math.trunc(m[2]*$0[0]),Math.trunc(m[3]*$0[1])],v=y[f],p=Cs[l[f]],b={id:c++,score:v,box:P,boxRaw:g,label:p};n.push(b)}return Object.keys(o).forEach(f=>r.dispose(o[f])),n.sort((f,d)=>d.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function y5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&n0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[ue[1][0],ue[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=n0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let y=(await s.reshaped.array()).map(l=>[l[0]/ue[1][1],l[1]/ue[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=y.map(l=>[$0[0]*(l[0]+t.boxRaw[0]),$0[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=B2(o.keypoints);for(let l of Object.keys(ln))o.annotations[l]=ln[l].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function f5(e,t){var s,A;if(!((s=n0[0])!=null&&s.executor)||!((A=n0[1])!=null&&A.executor)||!n0[0].inputs[0].shape||!n0[1].inputs[0].shape)return[];$0=[e.shape[2]||0,e.shape[1]||0],q2++;let n=(t.hand.skipTime||0)>T()-d5,o=q2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?t0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>T()-d5,c=q2<3*(t.hand.skipFrames||0);t.skipAllowed&&t0.hands.length===t.hand.maxDetected?t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))):t.skipAllowed&&i&&c&&t0.hands.length>0?t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))):(t0.boxes=await Fs(e,t),d5=T(),t0.hands=await Promise.all(t0.boxes.map(y=>y5(e,y,t))),q2=0);let x=[...t0.boxes];if(t0.boxes.length=0,t.cacheSensitivity>0)for(let y=0;y.05&&l.box[3]/(e.shape[1]||1)>.05&&t0.hands[y].fingerScore&&t0.hands[y].fingerScore>(t.hand.minConfidence||0)){let f=P2(l.box,an),d=P2(l.boxRaw,an);t0.boxes.push({...x[y],box:f,boxRaw:d})}}for(let y=0;y({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var r2={};oe(r2,{connected:()=>Y2,horizontal:()=>m5,kpt:()=>U2,relative:()=>u5,vertical:()=>p5});var U2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],m5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],p5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],u5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Y2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ee(),h5=0;function yn(e,t){var a,i,c,x,y,l,f,d,u,m,g,P,v,p,b,j,k,N,B,q,W,Z,J;let n=T();if(!e)return ee();let o=Date.now()-e.timestamp,s=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R((s-1)*z.body[R].box[F]+C)/s),U=e.body[R].boxRaw.map((C,F)=>((s-1)*z.body[R].boxRaw[F]+C)/s),H=e.body[R].keypoints.map((C,F)=>{var S,N0,te,ne,i0,Ye,C5,W5,D5;return{score:C.score,part:C.part,position:[z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].position[0]||0)+(C.position[0]||0))/s:C.position[0],z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].position[1]||0)+(C.position[1]||0))/s:C.position[1],z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].position[2]||0)+(C.position[2]||0))/s:C.position[2]],positionRaw:[z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].positionRaw[0]||0)+(C.positionRaw[0]||0))/s:C.positionRaw[0],z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].positionRaw[1]||0)+(C.positionRaw[1]||0))/s:C.positionRaw[1],z.body[R].keypoints[F]?((s-1)*(z.body[R].keypoints[F].positionRaw[2]||0)+(C.positionRaw[2]||0))/s:C.positionRaw[2]],distance:[z.body[R].keypoints[F]?((s-1)*(((S=z.body[R].keypoints[F].distance)==null?void 0:S[0])||0)+(((N0=C.distance)==null?void 0:N0[0])||0))/s:(te=C.distance)==null?void 0:te[0],z.body[R].keypoints[F]?((s-1)*(((ne=z.body[R].keypoints[F].distance)==null?void 0:ne[1])||0)+(((i0=C.distance)==null?void 0:i0[1])||0))/s:(Ye=C.distance)==null?void 0:Ye[1],z.body[R].keypoints[F]?((s-1)*(((C5=z.body[R].keypoints[F].distance)==null?void 0:C5[2])||0)+(((W5=C.distance)==null?void 0:W5[2])||0))/s:(D5=C.distance)==null?void 0:D5[2]]}}),a0={},o0={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?o0=E2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?o0=R2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(o0=r2);for(let[C,F]of Object.entries(o0.connected)){let S=[];for(let N0=0;N0i0.part===F[N0]),ne=H.find(i0=>i0.part===F[N0+1]);te&&ne&&S.push([te.position,ne.position])}a0[C]=S}z.body[R]={...e.body[R],box:D,boxRaw:U,keypoints:H,annotations:a0}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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R=0;R((s-1)*z.face[R].box[a0]+H)/s),U=e.face[R].boxRaw.map((H,a0)=>((s-1)*z.face[R].boxRaw[a0]+H)/s);if(e.face[R].rotation){let H={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};H.matrix=(f=e.face[R].rotation)==null?void 0:f.matrix,H.angle={roll:((s-1)*(((u=(d=z.face[R].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((g=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/s,yaw:((s-1)*(((v=(P=z.face[R].rotation)==null?void 0:P.angle)==null?void 0:v.yaw)||0)+(((b=(p=e.face[R].rotation)==null?void 0:p.angle)==null?void 0:b.yaw)||0))/s,pitch:((s-1)*(((k=(j=z.face[R].rotation)==null?void 0:j.angle)==null?void 0:k.pitch)||0)+(((B=(N=e.face[R].rotation)==null?void 0:N.angle)==null?void 0:B.pitch)||0))/s},H.gaze={bearing:((s-1)*(((q=z.face[R].rotation)==null?void 0:q.gaze.bearing)||0)+(((W=e.face[R].rotation)==null?void 0:W.gaze.bearing)||0))/s,strength:((s-1)*(((Z=z.face[R].rotation)==null?void 0:Z.gaze.strength)||0)+(((J=e.face[R].rotation)==null?void 0:J.gaze.strength)||0))/s},z.face[R]={...e.face[R],rotation:H,box:D,boxRaw:U}}else z.face[R]={...e.face[R],box:D,boxRaw:U}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R((s-1)*z.object[R].box[a0]+H)/s),U=e.object[R].boxRaw.map((H,a0)=>((s-1)*z.object[R].boxRaw[a0]+H)/s);z.object[R]={...e.object[R],box:D,boxRaw:U}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let D=0;D((s-1)*z.persons[D].box[H]+U)/s)}e.gesture&&(z.gesture=e.gesture),z.width=e.width,z.height=e.height;let A=T();return h5=M.perfadd?h5+Math.round(A-n):Math.round(A-n),e.performance&&(z.performance={...e.performance,interpolate:h5}),z}var m0;async function b5(e){return!m0||M.initial?m0=await L(e.segmentation.modelPath):e.debug&&h("cached model:",m0.modelUrl),m0}async function fn(e,t){var s;if(m0||(m0=await b5(t)),!(m0!=null&&m0.executor)||!((s=m0==null?void 0:m0.inputs)!=null&&s[0].shape))return null;let n={};n.resize=r.image.resizeBilinear(e,[m0.inputs[0].shape?m0.inputs[0].shape[1]:0,m0.inputs[0].shape?m0.inputs[0].shape[2]:0],!1),n.norm=r.div(n.resize,O.tf255),n.res=m0.execute(n.norm),n.squeeze=r.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=r.unstack(n.squeeze,2),n.fg=r.softmax(n.fgRaw),n.mul=r.mul(n.fg,O.tf255),n.expand=r.expandDims(n.mul,2),n.output=r.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=r.squeeze(e),n.concat=r.concat([n.input,n.output],-1),o=r.cast(n.concat,"int32");break;case"alpha":o=r.cast(n.output,"int32");break;default:o=r.tensor(0)}return Object.keys(n).forEach(A=>r.dispose(n[A])),o}var T5={};oe(T5,{distance:()=>g5,find:()=>Gs,similarity:()=>Hs});function g5(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let s=0;s{if(e===0)return 1;let A=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function Hs(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=g5(e,t,n);return pn(o,n.order||2,n.min||0,n.max||1)}function Gs(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,s=-1;for(let a=0;aa2,validateModel:()=>tt});var un=.005,w0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function v5(e){for(let t of m5){let 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0:x.enabled)&&!this.models.facemesh?V1(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((y=this.instance.config.face.gear)==null?void 0:y.enabled)&&!this.models.gear?x3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((l=this.instance.config.face.ssrnet)==null?void 0:l.enabled)&&!this.models.ssrnetage?m3(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)==null?void 0:f.enabled)&&!this.models.ssrnetgender?b3(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)==null?void 0:d.enabled)&&!this.models.mobilefacenet?M3(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((u=this.instance.config.face.insightface)==null?void 0:u.enabled)&&!this.models.insightface?z3(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((m=this.instance.config.body.modelPath)==null?void 0:m.includes("blazepose"))?f1(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?y1(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((g=this.instance.config.body.modelPath)==null?void 0:g.includes("efficientpose"))?g1(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((P=this.instance.config.body.modelPath)==null?void 0:P.includes("movenet"))?Tn(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((v=this.instance.config.body.modelPath)==null?void 0:v.includes("posenet"))?In(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((b=(p=this.instance.config.hand.detector)==null?void 0:p.modelPath)==null?void 0:b.includes("handtrack"))?cn(this.instance.config):null,n.handskeleton=this.instance.config.hand.enabled&&this.instance.config.hand.landmarks&&!this.models.handskeleton&&((k=(j=this.instance.config.hand.detector)==null?void 0:j.modelPath)==null?void 0:k.includes("handtrack"))?xn(this.instance.config):null,(B=(N=this.instance.config.hand.detector)==null?void 0:N.modelPath)!=null&&B.includes("handdetect")&&([n.handpose,n.handskeleton]=this.models.handpose?[null,null]:await rn(this.instance.config)),n.centernet=this.instance.config.object.enabled&&!this.models.centernet&&((q=this.instance.config.object.modelPath)==null?void 0:q.includes("centernet"))?u1(this.instance.config):null,n.nanodet=this.instance.config.object.enabled&&!this.models.nanodet&&((W=this.instance.config.object.modelPath)==null?void 0:W.includes("nanodet"))?Mn(this.instance.config):null,n.selfie=this.instance.config.segmentation.enabled&&!this.models.selfie&&((Z=this.instance.config.segmentation.modelPath)==null?void 0:Z.includes("selfie"))?O5(this.instance.config):null,n.meet=this.instance.config.segmentation.enabled&&!this.models.meet&&((J=this.instance.config.segmentation.modelPath)==null?void 0:J.includes("meet"))?b5(this.instance.config):null,n.rvm=this.instance.config.segmentation.enabled&&!this.models.rvm&&((R=this.instance.config.segmentation.modelPath)==null?void 0:R.includes("rvm"))?L5(this.instance.config):null;for(let[D,U]of Object.entries(n))U!=null&&U.then&&U.then(H=>this.models[D]=H);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(y0).find(s=>s.startsWith(n.name));!o||(n.size=y0[o].sizeLoadedWeights,n.url=y0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let s=tt(this.instance,o,n);s&&t.push(s)}return t}};function Hn(e,t,n,o,s){var i,c,x,y,l,f;let A=0,a=[];for(let d of e){let u={id:A++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]b.box[1]&&d.box[1]+d.box[3]u.body.box[0]&&b.box[0]+b.box[2]u.body.box[1]&&b.box[1]+b.box[3]u.body.box[0]&&b.box[1]+b.box[3]>u.body.box[1]&&b.box[1]+b.box[3]{b&&b.length===4&&(m.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};P(u.face.box),P((y=u.body)==null?void 0:y.box),P((l=u.hands.left)==null?void 0:l.box),P((f=u.hands.right)==null?void 0:f.box);let v=Math.min(...m),p=Math.min(...g);u.box=[v,p,Math.max(...m)-v,Math.max(...g)-p],(s==null?void 0:s[1])&&(s==null?void 0:s[2])&&(u.boxRaw=[u.box[0]/s[2],u.box[1]/s[1],u.box[2]/s[2],u.box[3]/s[1]]),a.push(u)}return a}var nt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14030,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; -var body3 = ` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,ot=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -14599,563 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`; - -// src/warmup.ts -async function warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") - img = new Image(); - else if (env.Image) - img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) - ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) - img.src = src; - else - resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") - img = atob(face3); - else - img = atob(body3); - let res; - if ("node" in tfjs_esm_exports && tfjs_esm_exports.getBackend() === "tensorflow") { - const data = tfjs_esm_exports["node"].decodeJpeg(img); - const expanded = tfjs_esm_exports.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) - log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") - res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) - res = await warmupCanvas(instance); - else - res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tfjs_esm_exports.env().flagRegistry.ENGINE_COMPILE_ONLY) - return; - const backendType = tfjs_esm_exports.getBackend(); - const webGLBackend = tfjs_esm_exports.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tfjs_esm_exports.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) - continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) - shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tfjs_esm_exports.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) - res.forEach((t2) => tfjs_esm_exports.dispose(t2)); - else - tfjs_esm_exports.dispose(res); - } catch (e) { - if (instance.config.debug) - log("compile fail model:", modelName); - } - tfjs_esm_exports.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) - log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tfjs_esm_exports.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) - log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) - instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - constructor(userConfig) { - __publicField(this, "version"); - __publicField(this, "config"); - __publicField(this, "result"); - __publicField(this, "state"); - __publicField(this, "process"); - __publicField(this, "tf"); - __publicField(this, "env", env); - __publicField(this, "draw", draw_exports); - __publicField(this, "match", match_exports); - __publicField(this, "models"); - __publicField(this, "events"); - __publicField(this, "faceTriangulation"); - __publicField(this, "faceUVMap"); - __publicField(this, "performance"); - __privateAdd(this, _numTensors, void 0); - __privateAdd(this, _analyzeMemoryLeaks, void 0); - __privateAdd(this, _checkSanity, void 0); - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) - return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) - log(...msg, leaked); - }); - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) - return null; - if (!input) - return "input is not defined"; - if (this.env.node && !(input instanceof tfjs_esm_exports.Tensor)) - return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - __publicField(this, "webcam", new WebCam()); - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) - this.events.dispatchEvent(new Event(event)); - }); - __privateAdd(this, _loops, {}); - const tfVersion = (version7.tfjs || tfjs_esm_exports.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version8; - Object.defineProperty(this, "version", { value: version8 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tfjs_esm_exports; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) - log(`version: ${this.version}`); - if (this.config.debug) - log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) - log("environment:", envTemp); - } - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) - this.config = mergeDeep(this.config, userConfig); - return msgs; - } - now() { - return now(); - } - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) - return null; - const processed = await process2(input, this.config); - if (!processed.tensor) - return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) - tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) - tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) - tensor6 = await predict21(processed.tensor, this.config); - tfjs_esm_exports.dispose(processed.tensor); - return tensor6; - } - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) - log("error: backend check failed"); - await tfjs_esm_exports.ready(); - if (this.env.browser) { - if (this.config.debug) - log("configuration:", this.config); - if (this.config.debug) - log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) - log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) - this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - next(result = this.result) { - return calc2(result, this.config); - } - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) - kernels[kernel.name] += ms; - else - kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) - log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) - this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) - this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) - this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) - delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) - faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) - bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) - bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) - bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) - delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) - bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) - bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) - bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) - bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) - handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) - handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) - delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) - handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) - handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) - objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) - objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) - delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) - objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) - objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) - [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) - this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) - delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tfjs_esm_exports.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) - log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) - await this.detect(element); - if (delay > 0) - await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) - requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) - log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -export { - Env, - Human, - Human as default, - config as defaults, - draw_exports as draw, - empty, - env, - match_exports as match, - models_exports2 as models -}; +2Q==`;async function aA(e){let t=(s,A="application/octet-stream")=>fetch(`data:${A};base64,${s}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(nt);break;case"body":case"full":n=await t(ot);break;default:n=null}if(n){let s=await createImageBitmap(n);o=await e.detect(s,e.config),s.close()}return o}async function iA(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+nt;break;case"full":case"body":n="data:image/jpeg;base64,"+ot;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(M.Image)o=new M.Image;else{t(void 0);return}o.onload=async()=>{let s=L0(o.naturalWidth,o.naturalHeight);if(!s)h("Warmup: Canvas not found"),t(void 0);else{let A=s.getContext("2d");A&&A.drawImage(o,0,0);let a=await e.image(s,!0),i=a.tensor?await e.detect(a.tensor,e.config):void 0;t(i)}},n?o.src=n:t(void 0)})}async function lA(e){let t=s=>Buffer.from(s,"base64"),n;e.config.warmup==="face"?n=t(nt):n=t(ot);let o;if("node"in r&&r.getBackend()==="tensorflow"){let s=r.node.decodeJpeg(n),A=r.expandDims(s,0);e.tf.dispose(s),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function cA(e){let t;return typeof createImageBitmap=="function"?t=await aA(e):typeof Image!="undefined"||M.Canvas!==void 0?t=await iA(e):t=await lA(e),t}async function xA(e){var i,c,x,y;if(!r.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=r.getBackend(),n=r.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;r.env().set("ENGINE_COMPILE_ONLY",!0);let o=r.engine().state.numTensors,s=[];for(let[l,f]of Object.entries(e.models.models)){if(!f)continue;let d=(f==null?void 0:f.modelSignature)&&((c=(i=f==null?void 0:f.inputs)==null?void 0:i[0])==null?void 0:c.shape)?[...f.inputs[0].shape]:[1,64,64,3],u=(f==null?void 0:f.modelSignature)&&((y=(x=f==null?void 0:f.inputs)==null?void 0:x[0])==null?void 0:y.dtype)?f.inputs[0].dtype:"float32";for(let g=0;gr.dispose(P)):r.dispose(g)}catch(g){e.config.debug&&h("compile fail model:",l)}r.dispose(m)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&h("tensor leak:",a-o)}async function Gn(e,t){await $e(e,!1);let n=T();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ee():new Promise(async o=>{await e.models.load(),await xA(e);let s=await cA(e),A=T();e.config.debug&&h("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ue,i2,l2,rt,he,Vn=class{constructor(t){E(this,"version");E(this,"config");E(this,"result");E(this,"state");E(this,"process");E(this,"tf");E(this,"env",M);E(this,"draw",bt);E(this,"match",T5);E(this,"models");E(this,"events");E(this,"faceTriangulation");E(this,"faceUVMap");E(this,"performance");Z0(this,Ue,void 0);Z0(this,i2,void 0);Z0(this,l2,void 0);E(this,"analyze",(...t)=>{if(!v0(this,i2))return;let n=this.tf.engine().state.numTensors,o=v0(this,Ue);Y0(this,Ue,n);let s=n-o;s!==0&&h(...t,s)});Z0(this,rt,t=>{if(!v0(this,l2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});E(this,"webcam",new p2);E(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Z0(this,he,{});let n=(Ke.tfjs||r.version_core).replace(/-(.*)/,"");Le.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Le.modelBasePath=M.browser?"../models/":"file://models/",this.version=xt,Object.defineProperty(this,"version",{value:xt}),this.config=JSON.parse(JSON.stringify(Le)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),_5(this.config),this.tf=r,this.state="idle",Y0(this,Ue,0),Y0(this,i2,!1),Y0(this,l2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new a2(this),ht(),this.result=ee(),this.process={tensor:null,canvas:null},this.faceTriangulation=Z1,this.faceUVMap=X1,tt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Le)),this.config.backend=t,lt(),M.initial=!0}validate(t){let n=At(Le,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return T()}image(t,n=!1){return y2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await y2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Cn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await fn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await Dn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return Q5(this.config,t,n)}async init(){await $e(this,!0),await this.tf.ready(),lt()}async load(t){this.state="load";let n=T(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await $e(this,!1)||h("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(T()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return yn(t,this.config)}async warmup(t){let n=T(),o=await Gn(this,t),s=T();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,P,v,p,b,j,k,N,B,q,W,Z,J,R,D,U,H,a0,o0,C,F;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=v0(this,rt).call(this,t);A&&(h(A,t),this.emit("error"),o(ee(A)));let a=T();await this.load(),s=T(),this.state="image";let i=await y2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o(ee("could not convert input to tensor"));return}this.emit("image"),s=T(),this.config.skipAllowed=await J5(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(T()-s):Math.trunc(T()-s),this.analyze("Check Changed:");let c=[],x=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?A5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=T(),c=this.config.face.enabled?await A5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?x=this.config.body.enabled?j5(i.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?x=this.config.body.enabled?Mt(i.tensor,f):[]:(v=this.config.body.modelPath)!=null&&v.includes("efficientpose")?x=this.config.body.enabled?jt(i.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(x=this.config.body.enabled?M5(i.tensor,f):[]),this.performance.body&&delete this.performance.body):(s=T(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?x=this.config.body.enabled?await j5(i.tensor,f):[]:(j=this.config.body.modelPath)!=null&&j.includes("blazepose")?x=this.config.body.enabled?await Mt(i.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?x=this.config.body.enabled?await jt(i.tensor,f):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(x=this.config.body.enabled?await M5(i.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((q=(B=this.config.hand.detector)==null?void 0:B.modelPath)!=null&&q.includes("handdetect")?y=this.config.hand.enabled?x5(i.tensor,d):[]:(Z=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&Z.includes("handtrack")&&(y=this.config.hand.enabled?f5(i.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(s=T(),(R=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&R.includes("handdetect")?y=this.config.hand.enabled?await x5(i.tensor,d):[]:(U=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&U.includes("handtrack")&&(y=this.config.hand.enabled?await f5(i.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((H=this.config.object.modelPath)!=null&&H.includes("nanodet")?l=this.config.object.enabled?k5(i.tensor,this.config):[]:(a0=this.config.object.modelPath)!=null&&a0.includes("centernet")&&(l=this.config.object.enabled?wt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=T(),(o0=this.config.object.modelPath)!=null&&o0.includes("nanodet")?l=this.config.object.enabled?await k5(i.tensor,this.config):[]:(C=this.config.object.modelPath)!=null&&C.includes("centernet")&&(l=this.config.object.enabled?await wt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,x,y,l]=await Promise.all([c,x,y,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(s=T(),u=[...G3(c),...H3(x),...Z3(y),...V3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(T()-s):Math.trunc(T()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(T()-a):Math.trunc(T()-a);let m=((F=this.process.tensor)==null?void 0:F.shape)||[0,0,0,0];this.result={face:c,body:x,hand:y,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Hn(c,x,y,u,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(v0(this,he)[t.id]||(this.config.debug&&h("video start",t.id),v0(this,he)[t.id]=!0),!t.paused&&v0(this,he)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),v0(this,he)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),v0(this,he)[t.id]=!1)}};Ue=new WeakMap,i2=new WeakMap,l2=new WeakMap,rt=new WeakMap,he=new WeakMap;export{m2 as Env,Vn as Human,Vn as default,Le as defaults,bt as draw,ee as empty,M as env,T5 as match,Bn as models}; diff --git a/dist/human.esm.js b/dist/human.esm.js index dbe54d9a..c466e76a 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -244,66 +244,66 @@ __export(tfjs_esm_exports, { ZerosLike: () => Fs, _FusedMatMul: () => fo, abs: () => Yt, - acos: () => f0, - acosh: () => h0, + acos: () => fv, + acosh: () => hv, add: () => xe, - addN: () => g0, - all: () => x0, - any: () => y0, - argMax: () => b0, - argMin: () => C0, - asin: () => S0, - asinh: () => w0, - atan: () => I0, - atan2: () => v0, - atanh: () => k0, + addN: () => gv, + all: () => xv, + any: () => yv, + argMax: () => bv, + argMin: () => Cv, + asin: () => Sv, + asinh: () => wv, + atan: () => Iv, + atan2: () => vv, + atanh: () => kv, avgPool: () => td, - avgPool3d: () => _0, + avgPool3d: () => _v, backend: () => Oie, backend_util: () => S, - basicLSTMCell: () => E0, + basicLSTMCell: () => Ev, batchNorm: () => wi, - batchNorm2d: () => A0, - batchNorm3d: () => R0, - batchNorm4d: () => F0, + batchNorm2d: () => Av, + batchNorm3d: () => Rv, + batchNorm4d: () => Fv, batchToSpaceND: () => rd, bincount: () => od, booleanMaskAsync: () => XG, - broadcastArgs: () => D0, + broadcastArgs: () => Dv, broadcastTo: () => Ii, broadcast_util: () => br, - browser: () => Qv, + browser: () => Q0, buffer: () => le, cast: () => Ke, - ceil: () => O0, - clipByValue: () => P0, + ceil: () => Ov, + clipByValue: () => Pv, clone: () => Br, complex: () => Tr, concat: () => gt, - concat1d: () => M0, - concat2d: () => L0, - concat3d: () => B0, - concat4d: () => V0, - conv1d: () => z0, + concat1d: () => Mv, + concat2d: () => Lv, + concat3d: () => Bv, + concat4d: () => Vv, + conv1d: () => zv, conv2d: () => vi, - conv2dTranspose: () => W0, - conv3d: () => U0, - conv3dTranspose: () => H0, + conv2dTranspose: () => Wv, + conv3d: () => Uv, + conv3dTranspose: () => Hv, copyRegisteredKernels: () => Dne, - cos: () => q0, - cosh: () => K0, + cos: () => qv, + cosh: () => Kv, cosineWindow: () => il, - cumprod: () => j0, - cumsum: () => X0, + cumprod: () => jv, + cumsum: () => Xv, customGrad: () => Cr, - denseBincount: () => Y0, + denseBincount: () => Yv, deprecationWarn: () => eC, - depthToSpace: () => Q0, + depthToSpace: () => Qv, depthwiseConv2d: () => Bp, deregisterOp: () => xK, device_util: () => yi, - diag: () => Z0, - dilation2d: () => J0, + diag: () => Zv, + dilation2d: () => Jv, disableDeprecationWarnings: () => vie, dispose: () => Dt, disposeVariables: () => kie, @@ -377,7 +377,7 @@ __export(tfjs_esm_exports, { losses: () => cq, lowerBound: () => xk, matMul: () => Xe, - math: () => jv, + math: () => j0, max: () => Us, maxPool: () => bd, maxPool3d: () => yk, @@ -449,7 +449,7 @@ __export(tfjs_esm_exports, { searchSorted: () => al, selu: () => u1, separableConv2d: () => p1, - serialization: () => p0, + serialization: () => pv, setBackend: () => Eie, setPlatform: () => Pie, setThreadsCount: () => kee, @@ -496,8 +496,8 @@ __export(tfjs_esm_exports, { tensor4d: () => S1, tensor5d: () => w1, tensor6d: () => I1, - tensor_util: () => hv, - test_util: () => d0, + tensor_util: () => h0, + test_util: () => dv, tidy: () => Ee, tile: () => ki, time: () => _ie, @@ -554,8 +554,8 @@ var IV = (r, e, t6, o) => { return r; }; var rp = (r, e, t6) => (t6 = r != null ? yV(SV(r)) : {}, IV(e || !r || !r.__esModule ? ub(t6, "default", { value: r, enumerable: true }) : t6, r)); -var rv = qt((Pne, tv) => { - tv.exports = It; +var r0 = qt((Pne, t0) => { + t0.exports = It; var xo = null; try { xo = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports; @@ -583,7 +583,7 @@ var rv = qt((Pne, tv) => { if (r < 0) return Ji; if (r >= QI) - return ev; + return e0; } else { if (r <= -XI) return Mr; @@ -637,8 +637,8 @@ var rv = qt((Pne, tv) => { It.NEG_ONE = wb; var JI = vt(-1, 2147483647, false); It.MAX_VALUE = JI; - var ev = vt(-1, -1, true); - It.MAX_UNSIGNED_VALUE = ev; + var e0 = vt(-1, -1, true); + It.MAX_UNSIGNED_VALUE = e0; var Mr = vt(0, -2147483648, false); It.MIN_VALUE = Mr; var me = It.prototype; @@ -884,9 +884,9 @@ var rv = qt((Pne, tv) => { return new It(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t6); }; }); -var Mv = qt(() => { +var M0 = qt(() => { }); -var Lv = qt(() => { +var L0 = qt(() => { }); var Bk = qt((Lk, cC) => { (function(r, e, t6) { @@ -3558,26 +3558,26 @@ function Sb(r, e) { } var y = {}; Ue(y, { arraysEqual: () => Pr, assert: () => E, assertNonNegativeIntegerDimensions: () => yt, assertNonNull: () => Jr, assertShapesMatch: () => ht, bytesFromStringArray: () => fb, bytesPerElement: () => Sm, checkConversionForErrors: () => mb, clamp: () => op, computeStrides: () => hs, createScalarValue: () => QV, createShuffledIndices: () => RV, decodeString: () => Ap, distSquared: () => _V, encodeString: () => gi, fetch: () => JV, fingerPrint64: () => YV, flatten: () => Oo, getArrayFromDType: () => lb, getTypedArrayFromDType: () => cb, hasEncodingLoss: () => OV, hexToLong: () => Kc, indexToLoc: () => LV, inferDtype: () => np, inferFromImplicitShape: () => DV, isBoolean: () => BI, isFunction: () => fs, isInt: () => na, isNumber: () => VI, isPromise: () => Wc, isScalarShape: () => EV, isString: () => Po, isTypedArray: () => Wt, isValidDtype: () => db, locToIndex: () => MV, makeOnesTypedArray: () => zc, makeZerosNestedTypedArray: () => PV, makeZerosTypedArray: () => ap, nearestDivisor: () => sp, nearestLargerEven: () => kV, now: () => ou, parseAxisParam: () => Qa, randUniform: () => TV, repeatedTry: () => FV, rightPad: () => ji, shuffle: () => LI, shuffleCombo: () => vV, sizeFromShape: () => ze, sizeToSquarishShape: () => AV, squeezeShape: () => pb, sum: () => NV, swap: () => Cm, tanh: () => $V, toNestedArray: () => Ki, toTypedArray: () => $p }); -var kb = rp(rv()); +var kb = rp(r0()); var ru = kb.default || kb; function Kc(r) { return ru.fromString(r, true, 16); } -var nv = Kc("c3a5c85c97cb3127"); +var n0 = Kc("c3a5c85c97cb3127"); var tu = Kc("b492b66fbe98f273"); var gr = Kc("9ae16a3b2f90404f"); function vb(r) { return r.xor(r.shru(47)); } -function sv(r, e, t6) { +function s0(r, e, t6) { let o = r.slice(e, e + t6); return ru.fromBytes(Array.from(o), true, true); } function bt(r, e) { - return sv(r, e, 8); + return s0(r, e, 8); } -function ov(r, e) { - return sv(r, e, 4); +function o0(r, e) { + return s0(r, e, 4); } function jt(r, e) { return e === 0 ? r : r.shru(e).or(r.shl(64 - e)); @@ -3602,12 +3602,12 @@ function KV(r, e = r.length) { return hi(s, a, t6); } if (e >= 4) { - let t6 = gr.add(e * 2), o = ov(r, 0); - return hi(o.shl(3).add(e), ov(r, e - 4), t6); + let t6 = gr.add(e * 2), o = o0(r, 0); + return hi(o.shl(3).add(e), o0(r, e - 4), t6); } if (e > 0) { let t6 = r[0], o = r[e >> 1], n = r[e - 1], s = t6 + (o << 8), a = e + (n << 2); - return vb(gr.mul(s).xor(nv.mul(a))).mul(gr); + return vb(gr.mul(s).xor(n0.mul(a))).mul(gr); } return gr; } @@ -3632,7 +3632,7 @@ function YV(r, e = r.length) { o = jt(o.add(n).add(a[0]).add(bt(r, p + 8)), 37).mul(tu), n = jt(n.add(a[1]).add(bt(r, p + 48)), 42).mul(tu), o = o.xor(i[1]), n = n.add(a[0]).add(bt(r, p + 40)), s = jt(s.add(i[0]), 33).mul(tu), a = Fm(r, p, a[1].mul(tu), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(bt(r, p + 16))), [s, o] = [o, s], p += 64; while (p !== u); let l = tu.add(s.and(255).shl(1)); - return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = jt(o.add(n).add(a[0]).add(bt(r, p + 8)), 37).mul(l), n = jt(n.add(a[1]).add(bt(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(bt(r, p + 40))), s = jt(s.add(i[0]), 33).mul(l), a = Fm(r, p, a[1].mul(l), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(bt(r, p + 16))), [s, o] = [o, s], hi(hi(a[0], i[0], l).add(vb(n).mul(nv)).add(s), hi(a[1], i[1], l).add(o), l); + return p = c, i[0] = i[0].add(e - 1 & 63), a[0] = a[0].add(i[0]), i[0] = i[0].add(a[0]), o = jt(o.add(n).add(a[0]).add(bt(r, p + 8)), 37).mul(l), n = jt(n.add(a[1]).add(bt(r, p + 48)), 42).mul(l), o = o.xor(i[1].mul(9)), n = n.add(a[0].mul(9).add(bt(r, p + 40))), s = jt(s.add(i[0]), 33).mul(l), a = Fm(r, p, a[1].mul(l), o.add(i[0])), i = Fm(r, p + 32, s.add(i[1]), n.add(bt(r, p + 16))), [s, o] = [o, s], hi(hi(a[0], i[0], l).add(vb(n).mul(n0)).add(s), hi(a[1], i[1], l).add(o), l); } function QV(r, e) { return e === "string" ? gi(r) : $p([r], e); @@ -3726,7 +3726,7 @@ var Nb = class { console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); } }; -function av(r, e, t6) { +function a0(r, e, t6) { let o = {}, n = {}; for (let p = 0; p < e.length; p++) o[e[p].id] = true; @@ -3770,7 +3770,7 @@ function av(r, e, t6) { } return i; } -function iv(r, e, t6, o) { +function i0(r, e, t6, o) { for (let n = e.length - 1; n >= 0; n--) { let s = e[n], a = []; if (s.outputs.forEach((p) => { @@ -3797,10 +3797,10 @@ function iv(r, e, t6, o) { } } } -var uv = 20; +var u0 = 20; var jc = 3; var Tb = 7; -function pv(r, e, t6, o) { +function p0(r, e, t6, o) { let n = hs(e), s = tz(r, e, t6, n), a = e.length, i = Om(r, e, t6, n, s), p = ["Tensor"]; return o && (p.push(` dtype: ${t6}`), p.push(` rank: ${a}`), p.push(` shape: [${e}]`), p.push(" values:")), p.push(i.map((u) => " " + u).join(` `)), p.join(` @@ -3818,9 +3818,9 @@ function tz(r, e, t6, o) { } function Xc(r, e, t6) { let o; - return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Tb))} + ${parseFloat(r[1].toFixed(Tb))}j` : Po(r) ? o = `'${r}'` : t6 === "bool" ? o = cv(r) : o = parseFloat(r.toFixed(Tb)).toString(), ji(o, e); + return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Tb))} + ${parseFloat(r[1].toFixed(Tb))}j` : Po(r) ? o = `'${r}'` : t6 === "bool" ? o = c0(r) : o = parseFloat(r.toFixed(Tb)).toString(), ji(o, e); } -function cv(r) { +function c0(r) { return r === 0 ? "false" : "true"; } function Om(r, e, t6, o, n, s = true) { @@ -3830,17 +3830,17 @@ function Om(r, e, t6, o, n, s = true) { let h = Yc(r); return [Xc(h[0], 0, t6)]; } - return t6 === "bool" ? [cv(r[0])] : [r[0].toString()]; + return t6 === "bool" ? [c0(r[0])] : [r[0].toString()]; } if (p === 1) { - if (i > uv) { + if (i > u0) { let g = jc * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - jc) * a, i * a)); return t6 === "complex64" && (x = Yc(x), b = Yc(b)), ["[" + x.map((C, w) => Xc(C, n[w], t6)).join(", ") + ", ..., " + b.map((C, w) => Xc(C, n[i - jc + w], t6)).join(", ") + "]"]; } return ["[" + (t6 === "complex64" ? Yc(r) : Array.from(r)).map((g, x) => Xc(g, n[x], t6)).join(", ") + "]"]; } let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = []; - if (i > uv) { + if (i > u0) { for (let h = 0; h < jc; h++) { let g = h * l, x = g + l; m.push(...Om(r.slice(g, x), u, t6, c, n, false)); @@ -3932,13 +3932,13 @@ var st = class { var rs = null; var Rp = null; var rz = null; -function lv(r) { +function l0(r) { rs = r; } -function mv(r) { +function m0(r) { Rp = r; } -function dv(r) { +function d0(r) { rz = r; } var it = class { @@ -4012,7 +4012,7 @@ var it = class { } toString(e = false) { let t6 = this.dataSync(); - return pv(t6, this.shape, this.dtype, e); + return p0(t6, this.shape, this.dtype, e); } cast(e) { return this.throwIfDisposed(), Rp.cast(this, e); @@ -4042,8 +4042,8 @@ var va = class extends it { } }; Object.defineProperty(va, Symbol.hasInstance, { value: (r) => r instanceof it && r.assign != null && r.assign instanceof Function }); -var hv = {}; -Ue(hv, { assertTypesMatch: () => Fb, getTensorsInContainer: () => Qc, isTensorInList: () => sz, makeTypesMatch: () => Re }); +var h0 = {}; +Ue(h0, { assertTypesMatch: () => Fb, getTensorsInContainer: () => Qc, isTensorInList: () => sz, makeTypesMatch: () => Re }); var _b; (function(r) { r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6"; @@ -4090,9 +4090,9 @@ function sz(r, e) { } function Qc(r) { let e = []; - return fv(r, e, /* @__PURE__ */ new Set()), e; + return f0(r, e, /* @__PURE__ */ new Set()), e; } -function fv(r, e, t6) { +function f0(r, e, t6) { if (r == null) return; if (r instanceof it) { @@ -4104,7 +4104,7 @@ function fv(r, e, t6) { let o = r; for (let n in o) { let s = o[n]; - t6.has(s) || (t6.add(s), fv(s, e, t6)); + t6.has(s) || (t6.add(s), f0(s, e, t6)); } } function az(r) { @@ -4452,12 +4452,12 @@ var xi = class { throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`); let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e)); E(s instanceof it, () => "The result y returned by f() must be a tensor."); - let a = av(this.state.activeTape, t6, s); + let a = a0(this.state.activeTape, t6, s); if (!n && a.length === 0 && t6.length > 0) throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); return this.tidy("backward", () => { let i = {}; - i[s.id] = o == null ? iz(s.shape) : o, iv(i, a, (u) => this.tidy(u), uz); + i[s.id] = o == null ? iz(s.shape) : o, i0(i, a, (u) => this.tidy(u), uz); let p = t6.map((u) => i[u.id]); return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => { for (let c of u.saved) @@ -4521,7 +4521,7 @@ function Ob() { let e = new Uc(r); r._tfengine = new xi(e); } - return UI(r._tfengine.ENV), lv(() => r._tfengine), r._tfengine; + return UI(r._tfengine.ENV), l0(() => r._tfengine), r._tfengine; } var T = Ob(); function uz(r, e) { @@ -4583,9 +4583,9 @@ function or(r, e) { let o = []; for (; Array.isArray(t6) || Wt(t6) && e !== "string"; ) o.push(t6.length), t6 = t6[0]; - return Array.isArray(r) && O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && xv(r, o, []), o; + return Array.isArray(r) && O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && x0(r, o, []), o; } -function xv(r, e, t6) { +function x0(r, e, t6) { if (t6 = t6 || [], !Array.isArray(r) && !Wt(r)) { E(e.length === 0, () => `Element arr[${t6.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`); return; @@ -4593,9 +4593,9 @@ function xv(r, e, t6) { E(e.length > 0, () => `Element arr[${t6.join("][")}] should be a primitive, but is an array of ${r.length} elements`), E(r.length === e[0], () => `Element arr[${t6.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`); let o = e.slice(1); for (let n = 0; n < r.length; ++n) - xv(r[n], o, t6.concat(n)); + x0(r[n], o, t6.concat(n)); } -function gv(r, e, t6, o) { +function g0(r, e, t6, o) { if (r !== "string_or_numeric") { if (r == null) throw new Error("Expected dtype cannot be null."); @@ -4605,9 +4605,9 @@ function gv(r, e, t6, o) { } function v(r, e, t6, o = "numeric") { if (r instanceof it) - return gv(o, r.dtype, e, t6), r; + return g0(o, r.dtype, e, t6), r; let n = np(r); - if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), gv(o, n, e, t6), r == null || !Wt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { + if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), g0(o, n, e, t6), r == null || !Wt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") { let p = r == null ? "null" : r.constructor.name; throw new Error(`Argument '${e}' passed to '${t6}' must be a Tensor or TensorLike, but got '${p}'`); } @@ -4673,7 +4673,7 @@ function nr(r, e, t6) { } var Zc = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; var Mm = 4; -async function bv(r, e) { +async function b0(r, e) { let t6 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r); for (let a = 0; a < n.length; ++a) { let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i]; @@ -4780,10 +4780,10 @@ function dz(r) { }), o.buffer; } var Bb = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); -function yv(r) { +function y0(r) { return Bb ? Buffer.byteLength(r) : new Blob([r]).size; } -function Cv(r) { +function C0(r) { if (Bb) return Buffer.from(r).toString("base64"); let e = new Uint8Array(r), t6 = ""; @@ -4791,7 +4791,7 @@ function Cv(r) { t6 += String.fromCharCode(e[o]); return btoa(t6); } -function Sv(r) { +function S0(r) { if (Bb) { let o = Buffer.from(r, "base64"); return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength); @@ -4842,7 +4842,7 @@ async function Dp(r, e) { function Ps(r) { if (r.modelTopology instanceof ArrayBuffer) throw new Error("Expected JSON model topology, received ArrayBuffer."); - return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : yv(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : yv(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength }; + return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : y0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : y0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength }; } function Vm(r) { let e = []; @@ -4917,15 +4917,15 @@ var lt = class { }), n; } }; -var wv = (r) => lt.registerSaveRouter(r); -var Iv = (r) => lt.registerLoadRouter(r); -var vv = (r) => lt.getSaveHandlers(r); -var kv = (r, e) => lt.getLoadHandlers(r, e); +var w0 = (r) => lt.registerSaveRouter(r); +var I0 = (r) => lt.registerLoadRouter(r); +var v0 = (r) => lt.getSaveHandlers(r); +var k0 = (r, e) => lt.getLoadHandlers(r, e); var Wb = "tensorflowjs"; var Ub = 1; var nu = "models_store"; var bi = "model_info_store"; -function Nv() { +function N0() { if (!O().getBool("IS_BROWSER")) throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); let r = typeof window == "undefined" ? self : window, e = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB; @@ -4939,7 +4939,7 @@ function Gb(r) { } var Ms = class { constructor(e) { - if (this.indexedDB = Nv(), e == null || !e) + if (this.indexedDB = N0(), e == null || !e) throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); this.modelPath = e; } @@ -4982,9 +4982,9 @@ var Ms = class { } }; Ms.URL_SCHEME = "indexeddb://"; -var Tv = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ms.URL_SCHEME) ? yz(r.slice(Ms.URL_SCHEME.length)) : null; -lt.registerSaveRouter(Tv); -lt.registerLoadRouter(Tv); +var T0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ms.URL_SCHEME) ? yz(r.slice(Ms.URL_SCHEME.length)) : null; +lt.registerSaveRouter(T0); +lt.registerLoadRouter(T0); function yz(r) { return new Ms(r); } @@ -4993,7 +4993,7 @@ function bz(r) { } var zm = class { constructor() { - this.indexedDB = Nv(); + this.indexedDB = N0(); } async listModels() { return new Promise((e, t6) => { @@ -5034,15 +5034,15 @@ var zm = class { }; var Ta = "/"; var Op = "tensorflowjs_models"; -var _v = "info"; +var _0 = "info"; var Cz = "model_topology"; var Sz = "weight_specs"; var wz = "weight_data"; var Iz = "model_metadata"; -function Ev(r) { - return { info: [Op, r, _v].join(Ta), topology: [Op, r, Cz].join(Ta), weightSpecs: [Op, r, Sz].join(Ta), weightData: [Op, r, wz].join(Ta), modelMetadata: [Op, r, Iz].join(Ta) }; +function E0(r) { + return { info: [Op, r, _0].join(Ta), topology: [Op, r, Cz].join(Ta), weightSpecs: [Op, r, Sz].join(Ta), weightData: [Op, r, wz].join(Ta), modelMetadata: [Op, r, Iz].join(Ta) }; } -function $v(r) { +function $0(r) { for (let e of Object.values(r)) window.localStorage.removeItem(e); } @@ -5061,7 +5061,7 @@ var Ls = class { throw new Error("The current environment does not support local storage."); if (this.LS = window.localStorage, e == null || !e) throw new Error("For local storage, modelPath must not be null, undefined or empty."); - this.modelPath = e, this.keys = Ev(this.modelPath); + this.modelPath = e, this.keys = E0(this.modelPath); } async save(e) { if (e.modelTopology instanceof ArrayBuffer) @@ -5069,11 +5069,11 @@ var Ls = class { { let t6 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = Ps(e); try { - this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t6), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, Cv(e.weightData)); + this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t6), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, C0(e.weightData)); let s = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 }; return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n }; } catch (s) { - throw $v(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); + throw $0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`); } } } @@ -5099,13 +5099,13 @@ var Ls = class { let a = this.LS.getItem(this.keys.weightData); if (a == null) throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); - return t6.weightData = Sv(a), t6; + return t6.weightData = S0(a), t6; } }; Ls.URL_SCHEME = "localstorage://"; -var Av = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ls.URL_SCHEME) ? Nz(r.slice(Ls.URL_SCHEME.length)) : null; -lt.registerSaveRouter(Av); -lt.registerLoadRouter(Av); +var A0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ls.URL_SCHEME) ? Nz(r.slice(Ls.URL_SCHEME.length)) : null; +lt.registerSaveRouter(A0); +lt.registerLoadRouter(A0); function Nz(r) { return new Ls(r); } @@ -5114,7 +5114,7 @@ var Wm = class { E(O().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), E(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; } async listModels() { - let e = {}, t6 = Op + Ta, o = Ta + _v; + let e = {}, t6 = Op + Ta, o = Ta + _0; for (let n = 0; n < this.LS.length; ++n) { let s = this.LS.key(n); if (s.startsWith(t6) && s.endsWith(o)) { @@ -5126,11 +5126,11 @@ var Wm = class { } async removeModel(e) { e = kz(e); - let t6 = Ev(e); + let t6 = E0(e); if (this.LS.getItem(t6.info) == null) throw new Error(`Cannot find model at path '${e}'`); let o = JSON.parse(this.LS.getItem(t6.info)); - return $v(t6), o; + return $0(t6), o; } }; var Pp = "://"; @@ -5161,7 +5161,7 @@ function Um(r) { throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Xt.getSchemes().join(",")}`); return { scheme: r.split(Pp)[0], path: r.split(Pp)[1] }; } -async function Rv(r, e, t6 = false) { +async function R0(r, e, t6 = false) { E(r !== e, () => `Old path and new path are the same: '${r}'`); let o = lt.getLoadHandlers(r); E(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), E(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`); @@ -5172,7 +5172,7 @@ async function Rv(r, e, t6 = false) { let l = await a.save(c); return t6 && !u && await Xt.getManager(i).removeModel(p), l.modelArtifactsInfo; } -async function Fv() { +async function F0() { let r = Xt.getSchemes(), e = {}; for (let t6 of r) { let o = await Xt.getManager(t6).listModels(); @@ -5183,15 +5183,15 @@ async function Fv() { } return e; } -async function Dv(r) { +async function D0(r) { let e = Um(r); return Xt.getManager(e.scheme).removeModel(e.path); } -async function Ov(r, e) { - return Rv(r, e, false); +async function O0(r, e) { + return R0(r, e, false); } -async function Pv(r, e) { - return Rv(r, e, true); +async function P0(r, e) { + return R0(r, e, true); } var Hb = class { constructor() { @@ -5238,11 +5238,11 @@ if (O().get("IS_BROWSER")) { } catch (r) { } } -var Tz = { importFetch: () => Mv() }; +var Tz = { importFetch: () => M0() }; var qb; var Kb = class { constructor() { - this.util = Lv(), this.textEncoder = new this.util.TextEncoder(); + this.util = L0(), this.textEncoder = new this.util.TextEncoder(); } fetch(e, t6) { return O().global.fetch != null ? O().global.fetch(e, t6) : (qb == null && (qb = Tz.importFetch()), qb(e, t6)); @@ -5284,13 +5284,13 @@ function Gm(r, e = false) { } Ob(); var $z = { buffer: le, cast: Ke, clone: Br, print: Gm }; -mv($z); +m0($z); var Ea = {}; -Ue(Ea, { browserFiles: () => Vv, browserHTTPRequest: () => Uv, concatenateArrayBuffers: () => Fp, copyModel: () => Ov, decodeWeights: () => Lm, encodeWeights: () => bv, fromMemory: () => Gv, fromMemorySync: () => Jb, getLoadHandlers: () => kv, getModelArtifactsForJSON: () => Dp, getModelArtifactsForJSONSync: () => zb, getModelArtifactsInfoForJSON: () => Ps, getSaveHandlers: () => vv, getWeightSpecs: () => Vm, http: () => qm, isHTTPScheme: () => Hm, listModels: () => Fv, loadWeights: () => zv, moveModel: () => Pv, registerLoadRouter: () => Iv, registerSaveRouter: () => wv, removeModel: () => Dv, weightsLoaderFactory: () => Qb, withSaveHandler: () => Hv, withSaveHandlerSync: () => qv }); +Ue(Ea, { browserFiles: () => V0, browserHTTPRequest: () => U0, concatenateArrayBuffers: () => Fp, copyModel: () => O0, decodeWeights: () => Lm, encodeWeights: () => b0, fromMemory: () => G0, fromMemorySync: () => Jb, getLoadHandlers: () => k0, getModelArtifactsForJSON: () => Dp, getModelArtifactsForJSONSync: () => zb, getModelArtifactsInfoForJSON: () => Ps, getSaveHandlers: () => v0, getWeightSpecs: () => Vm, http: () => qm, isHTTPScheme: () => Hm, listModels: () => F0, loadWeights: () => z0, moveModel: () => P0, registerLoadRouter: () => I0, registerSaveRouter: () => w0, removeModel: () => D0, weightsLoaderFactory: () => Qb, withSaveHandler: () => H0, withSaveHandlerSync: () => q0 }); var Az = "model"; var Rz = ".json"; var Fz = ".weights.bin"; -function Bv(r) { +function B0(r) { return new Promise((e) => setTimeout(e)).then(r); } var _a = class { @@ -5307,9 +5307,9 @@ var _a = class { throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); { let o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = Bm(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; - if (a.download = this.modelJsonFileName, a.href = s, await Bv(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { + if (a.download = this.modelJsonFileName, a.href = s, await B0(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; - i.download = this.weightDataFileName, i.href = t6, await Bv(() => i.dispatchEvent(new MouseEvent("click"))); + i.download = this.weightDataFileName, i.href = t6, await B0(() => i.dispatchEvent(new MouseEvent("click"))); } return { modelArtifactsInfo: Ps(e) }; } @@ -5381,7 +5381,7 @@ lt.registerSaveRouter(Dz); function Oz(r = "model") { return new _a(r); } -function Vv(r) { +function V0(r) { return new jb(r); } function Xb(r, e, t6, o) { @@ -5403,7 +5403,7 @@ async function Yb(r, e) { let t6 = e.fetchFunc == null ? O().platform.fetch : e.fetchFunc, o = r.map((l) => t6(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await Xb(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1; return e.onProgress == null ? await Promise.all(i) : await Xb(i, e.onProgress, p, u); } -async function zv(r, e = "", t6, o) { +async function z0(r, e = "", t6, o) { return Qb((a) => Yb(a, { requestInit: o }))(r, e, t6); } function Qb(r) { @@ -5503,7 +5503,7 @@ function Lz(r) { function Hm(r) { return r.match(Jc.URL_SCHEME_REGEX) != null; } -var Wv = (r, e) => { +var W0 = (r, e) => { if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null)) return null; { @@ -5513,12 +5513,12 @@ var Wv = (r, e) => { } return null; }; -lt.registerSaveRouter(Wv); -lt.registerLoadRouter(Wv); +lt.registerSaveRouter(W0); +lt.registerLoadRouter(W0); function qm(r, e) { return new Jc(r, e); } -function Uv(r, e) { +function U0(r, e) { return qm(r, e); } var el = class { @@ -5542,21 +5542,21 @@ var Zb = class { e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t6) => Promise.resolve(e.save(t6))); } }; -function Gv(r, e, t6, o) { +function G0(r, e, t6, o) { let n = arguments; return new Zb(Jb(...n)); } function Jb(r, e, t6, o) { return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new el(r) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new el({ modelTopology: r })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new el({ modelTopology: r, weightSpecs: e, weightData: t6, trainingConfig: o })); } -function Hv(r) { +function H0(r) { return new Km(r); } -function qv(r) { +function q0(r) { return new Km(r); } -var jv = {}; -Ue(jv, { confusionMatrix: () => Kv }); +var j0 = {}; +Ue(j0, { confusionMatrix: () => K0 }); function Bz(r, e, t6 = false, o = false) { let n = v(r, "a", "matMul"), s = v(e, "b", "matMul"); [n, s] = Re(n, s); @@ -5583,7 +5583,7 @@ function vie() { function eC(r) { O().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings()."); } -dv(eC); +d0(eC); function kie() { T.disposeVariables(); } @@ -5669,10 +5669,10 @@ function Hz(r, e, t6) { let s = tl(Ke(o, "int32"), t6), a = tl(Ke(n, "int32"), t6), i = Mp(s), p = Xe(i, a); return Ke(p, "int32"); } -var Kv = N({ confusionMatrix_: Hz }); +var K0 = N({ confusionMatrix_: Hz }); var br = {}; -Ue(br, { assertAndGetBroadcastShape: () => Je, getBroadcastDims: () => Xv, getReductionAxes: () => jm }); -function Xv(r, e) { +Ue(br, { assertAndGetBroadcastShape: () => Je, getBroadcastDims: () => X0, getReductionAxes: () => jm }); +function X0(r, e) { let t6 = r.length, o = []; for (let n = 0; n < t6; n++) { let s = t6 - 1 - n, a = r[s] || 1; @@ -5706,8 +5706,8 @@ function Je(r, e) { } return t6; } -var Qv = {}; -Ue(Qv, { fromPixels: () => Zz, fromPixelsAsync: () => Yz, toPixels: () => Qz }); +var Q0 = {}; +Ue(Q0, { fromPixels: () => Zz, fromPixelsAsync: () => Yz, toPixels: () => Qz }); function Xm(r, e, t6) { if (Jr(r), e != null && e.length !== 3) throw new Error("tensor3d() requires shape to have three numbers"); @@ -5719,7 +5719,7 @@ function Xm(r, e, t6) { return xr(r, e, o, t6); } var su; -function Yv(r, e = 3) { +function Y0(r, e = 3) { if (e > 4) throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); if (r == null) @@ -5795,7 +5795,7 @@ async function Yz(r, e = 3) { o != null && o.width === r.width && o.height === r.height ? t6 = o : t6 = r; } else t6 = r; - return Yv(t6, e); + return Y0(t6, e); } async function Qz(r, e) { let t6 = v(r, "img", "toPixels"); @@ -5832,10 +5832,10 @@ async function Qz(r, e) { } return t6 !== r && t6.dispose(), p; } -var Zz = N({ fromPixels_: Yv }); +var Zz = N({ fromPixels_: Y0 }); var Ym = {}; -Ue(Ym, { prepareAndValidate: () => Zv }); -function Zv(r, e) { +Ue(Ym, { prepareAndValidate: () => Z0 }); +function Z0(r, e) { let t6 = r.shape.length, o = e.shape.length; if (t6 < 1) throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t6}.`); @@ -5859,7 +5859,7 @@ function Zv(r, e) { return [p, a, u, c]; } var rl = {}; -Ue(rl, { calculateShapes: () => Jv, validateInput: () => Qm, validateUpdateShape: () => tC }); +Ue(rl, { calculateShapes: () => J0, validateInput: () => Qm, validateUpdateShape: () => tC }); function tC(r, e, t6) { let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t6.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`; if (t6.rank < n) @@ -5892,7 +5892,7 @@ function Qm(r, e, t6) { } tC(t6, e, r); } -function Jv(r, e, t6) { +function J0(r, e, t6) { let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t6.length, a = 1; for (let l = n; l < s; ++l) a *= t6[l]; @@ -5900,7 +5900,7 @@ function Jv(r, e, t6) { return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c }; } var ut = {}; -Ue(ut, { assertParamsValid: () => eW, computeFlatOffset: () => sW, computeOutShape: () => rW, getNormalizedAxes: () => oW, isSliceContinous: () => nW, maskToAxes: () => tW, parseSliceParams: () => aW, sliceInfo: () => iW, startForAxis: () => i0, startIndicesWithElidedDims: () => n0, stopForAxis: () => u0, stopIndicesWithElidedDims: () => s0, stridesForAxis: () => a0, stridesWithElidedDims: () => t0 }); +Ue(ut, { assertParamsValid: () => eW, computeFlatOffset: () => sW, computeOutShape: () => rW, getNormalizedAxes: () => oW, isSliceContinous: () => nW, maskToAxes: () => tW, parseSliceParams: () => aW, sliceInfo: () => iW, startForAxis: () => iv, startIndicesWithElidedDims: () => nv, stopForAxis: () => uv, stopIndicesWithElidedDims: () => sv, stridesForAxis: () => av, stridesWithElidedDims: () => tv }); var rC = -2; var Jz = -1; function eW(r, e, t6) { @@ -5921,7 +5921,7 @@ function rW(r, e, t6) { o[n] = Math.ceil((e[n] - r[n]) / t6[n]); return o; } -function t0(r, e, t6, o) { +function tv(r, e, t6, o) { let n = [...r]; for (let s = n.length; s < o.length; s++) n.push(1); @@ -5929,10 +5929,10 @@ function t0(r, e, t6, o) { s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop()); return n; } -function r0(r, e, t6) { +function rv(r, e, t6) { return t6 <= r ? t6 : t6 - (e - 1); } -function o0(r, e) { +function ov(r, e) { let t6 = []; for (let o = 0; o < r; o++) t6.push(e + o); @@ -5942,30 +5942,30 @@ function oW(r, e, t6, o, n, s, a, i, p) { let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u); if (e.length && t6 > 0) { let d = e[0], f = t6 + 1; - c = n0(a, d, f, o, r), l = s0(i, d, f, n, r), m = t0(s, d, f, r); + c = nv(a, d, f, o, r), l = sv(i, d, f, n, r), m = tv(s, d, f, r); } else for (let d = 0; d < u; d++) - c[d] = i0(a, o, s, r, d, p), l[d] = u0(i, n, s, r, d, p), m[d] = a0(s, d, p); + c[d] = iv(a, o, s, r, d, p), l[d] = uv(i, n, s, r, d, p), m[d] = av(s, d, p); return { begin: c, end: l, strides: m }; } -function n0(r, e, t6, o, n) { - let s = [...n], a = o0(t6, e); +function nv(r, e, t6, o, n) { + let s = [...n], a = ov(t6, e); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = 0; else { - let p = r0(e, t6, i), u = o[p]; + let p = rv(e, t6, i), u = o[p]; r & 1 << p && (u = 0), s[i] = u; } return s; } -function s0(r, e, t6, o, n) { - let s = [...n], a = o0(t6, e); +function sv(r, e, t6, o, n) { + let s = [...n], a = ov(t6, e); for (let i = 0; i < s.length; i++) if (a.indexOf(i) > -1) s[i] = Number.MAX_SAFE_INTEGER; else { - let p = r0(e, t6, i), u = o[p]; + let p = rv(e, t6, i), u = o[p]; r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u; } for (let i = 0; i < s.length; i++) { @@ -5974,17 +5974,17 @@ function s0(r, e, t6, o, n) { } return s; } -function a0(r, e, t6) { +function av(r, e, t6) { let o = r[e]; return (t6 & 1 << e || o == null) && (o = 1), o; } -function i0(r, e, t6, o, n, s) { +function iv(r, e, t6, o, n, s) { let a = e[n], i = t6[n] || 1; (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER); let p = o[n]; return a < 0 && (a += p), a = op(0, a, p - 1), a; } -function u0(r, e, t6, o, n, s) { +function uv(r, e, t6, o, n, s) { let a = e[n], i = t6[n] || 1; (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER); let p = o[n]; @@ -6046,7 +6046,7 @@ function iW(r, e, t6, o, n, s, a, i, p) { if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k) throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`); } else - m.begin[C] = e0(m.begin[C], 0, m.strides[C], k, _, $), m.end[C] = e0(m.end[C], 1, m.strides[C], k, _, $); + m.begin[C] = ev(m.begin[C], 0, m.strides[C], k, _, $), m.end[C] = ev(m.end[C], 1, m.strides[C], k, _, $); let P = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k; d = d && P, f = f && (C === 0 && m.strides[C] === 1 || P); } else @@ -6081,7 +6081,7 @@ function uW(r, e) { r.begin != null && (e.begin[t6] = r.begin[o]), r.end != null && (e.end[t6] = r.end[o]), e.strides[t6] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t6), r.endMask & 1 << o && (e.endMask |= 1 << t6), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(Jz), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t6) : (e.finalShapeGatherIndices.push(t6), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t6] = o, t6++; } } -function e0(r, e, t6, o, n, s) { +function ev(r, e, t6, o, n, s) { if (n[e]) return t6 > 0 ? s[e] : s[e + 1 & 1]; { @@ -6089,8 +6089,8 @@ function e0(r, e, t6, o, n, s) { return a < s[0] ? s[0] : a > s[1] ? s[1] : a; } } -var p0 = {}; -Ue(p0, { Serializable: () => ol, SerializationMap: () => Bs, registerClass: () => Er }); +var pv = {}; +Ue(pv, { Serializable: () => ol, SerializationMap: () => Bs, registerClass: () => Er }); var ol = class { getClassName() { return this.constructor.className; @@ -6113,15 +6113,15 @@ var Bs = class { function Er(r) { E(r.className != null, () => "Class being registered does not have the static className property defined."), E(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), E(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Bs.register(r); } -var d0 = {}; -Ue(d0, { TEST_EPSILON_FLOAT16: () => c0, createVideoElement: () => hW, encodeStrings: () => m0, expectArrayBuffersEqual: () => fW, expectArraysClose: () => cW, expectArraysEqual: () => mW, expectNumbersClose: () => l0, expectPromiseToFail: () => lW, expectValuesInRange: () => dW, play: () => gW, testEpsilon: () => Zm }); +var dv = {}; +Ue(dv, { TEST_EPSILON_FLOAT16: () => cv, createVideoElement: () => hW, encodeStrings: () => mv, expectArrayBuffersEqual: () => fW, expectArraysClose: () => cW, expectArraysEqual: () => mW, expectNumbersClose: () => lv, expectPromiseToFail: () => lW, expectValuesInRange: () => dW, play: () => gW, testEpsilon: () => Zm }); var pW = 1e-3; -var c0 = 0.1; +var cv = 0.1; function cW(r, e, t6) { return t6 == null && (t6 = Zm()), oC(r, e, (o, n) => nC(o, n, t6)); } function Zm() { - return T.backend.floatPrecision() === 32 ? pW : c0; + return T.backend.floatPrecision() === 32 ? pW : cv; } function oC(r, e, t6) { let o = true; @@ -6156,7 +6156,7 @@ function mW(r, e) { let t6 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e; return Po(r) || Po(r[0]) || Po(e) || Po(e[0]) ? oC(r, t6, (o, n) => o == n) : oC(r, e, (o, n) => nC(o, n, 0)); } -function l0(r, e, t6) { +function lv(r, e, t6) { if (t6 == null && (t6 = Zm()), !nC(r, e, t6)) throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`); typeof expect != "undefined" && expect().nothing(); @@ -6177,10 +6177,10 @@ function fW(r, e) { if (t6[n] !== o[n]) throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t6[n]} instead`); } -function m0(r) { +function mv(r) { for (let e = 0; e < r.length; e++) { let t6 = r[e]; - Array.isArray(t6) ? m0(t6) : r[e] = gi(t6); + Array.isArray(t6) ? mv(t6) : r[e] = gi(t6); } return r; } @@ -6240,12 +6240,12 @@ function IW(r) { let t6 = { x: v(r, "x", "acos") }; return T.runKernel(sa, t6); } -var f0 = N({ acos_: IW }); +var fv = N({ acos_: IW }); function vW(r) { let t6 = { x: v(r, "x", "acosh") }; return T.runKernel(aa, t6); } -var h0 = N({ acosh_: vW }); +var hv = N({ acosh_: vW }); function kW(r) { E(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), E(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`); let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t6 = e[0]; @@ -6259,56 +6259,56 @@ function kW(r) { let o = e; return T.runKernel(Mo, o); } -var g0 = N({ addN_: kW }); +var gv = N({ addN_: kW }); function NW(r, e = null, t6 = false) { let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t6 }; return T.runKernel(Lo, n, s); } -var x0 = N({ all_: NW }); +var xv = N({ all_: NW }); function TW(r, e = null, t6 = false) { let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t6 }; return T.runKernel(Bo, n, s); } -var y0 = N({ any_: TW }); +var yv = N({ any_: TW }); function _W(r, e = 0) { let o = { x: v(r, "x", "argMax") }, n = { axis: e }; return T.runKernel(Vo, o, n); } -var b0 = N({ argMax_: _W }); +var bv = N({ argMax_: _W }); function EW(r, e = 0) { let o = { x: v(r, "x", "argMin") }, n = { axis: e }; return T.runKernel(Za, o, n); } -var C0 = N({ argMin_: EW }); +var Cv = N({ argMin_: EW }); function $W(r) { let t6 = { x: v(r, "x", "asin") }; return T.runKernel(ia, t6); } -var S0 = N({ asin_: $W }); +var Sv = N({ asin_: $W }); function AW(r) { let t6 = { x: v(r, "x", "asinh") }; return T.runKernel(ua, t6); } -var w0 = N({ asinh_: AW }); +var wv = N({ asinh_: AW }); function RW(r) { let t6 = { x: v(r, "x", "atan") }; return T.runKernel(pa, t6); } -var I0 = N({ atan_: RW }); +var Iv = N({ atan_: RW }); function FW(r, e) { let t6 = v(r, "a", "atan2"), o = v(e, "b", "atan2"); [t6, o] = Re(t6, o); let n = { a: t6, b: o }; return T.runKernel(la, n); } -var v0 = N({ atan2_: FW }); +var vv = N({ atan2_: FW }); function DW(r) { let t6 = { x: v(r, "x", "atanh") }; return T.runKernel(ca, t6); } -var k0 = N({ atanh_: DW }); +var kv = N({ atanh_: DW }); function OW(r, e, t6, o, n = "NHWC", s) { - let a = r[3], i = [...e, a], p = T0(n); + let a = r[3], i = [...e, a], p = Tv(n); return uu(r, i, t6, s, o, null, null, p); } function aC(r, e, t6, o, n, s, a = "channelsLast") { @@ -6329,7 +6329,7 @@ function PW(r, e, t6, o, n, s, a = "NDHWC") { l = "channelsFirst", c = [i, p, u, r[1], r[1]]; else throw new Error(`Unknown dataFormat ${a}`); - return N0(r, c, t6, o, n, false, l, s); + return Nv(r, c, t6, o, n, false, l, s); } function uu(r, e, t6, o, n, s, a = false, i = "channelsLast") { let [p, u, c, l] = [-1, -1, -1, -1]; @@ -6342,7 +6342,7 @@ function uu(r, e, t6, o, n, s, a = false, i = "channelsLast") { let [m, d, , f] = e, [h, g] = ed(t6), [x, b] = ed(o), C = Lp(m, x), w = Lp(d, b), { padInfo: k, outHeight: _, outWidth: $ } = BW(n, u, c, h, g, C, w, s, i), A = a ? f * l : f, R; return i === "channelsFirst" ? R = [p, A, _, $] : i === "channelsLast" && (R = [p, _, $, A]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: $, outChannels: A, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: w, dilationHeight: x, dilationWidth: b, inShape: r, outShape: R, filterShape: e }; } -function N0(r, e, t6, o, n, s = false, a = "channelsLast", i) { +function Nv(r, e, t6, o, n, s = false, a = "channelsLast", i) { let [p, u, c, l, m] = [-1, -1, -1, -1, -1]; if (a === "channelsLast") [p, u, c, l, m] = r; @@ -6432,7 +6432,7 @@ function iu(r) { function lr(r, e) { return iu(r) || iu(e); } -function T0(r) { +function Tv(r) { if (r === "NHWC") return "channelsLast"; if (r === "NCHW") @@ -6475,7 +6475,7 @@ function UW(r, e, t6, o, n, s = "NDHWC") { let u = { x: i }, c = { filterSize: e, strides: t6, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(ip, u, c); return l = Ke(l, i.dtype), p ? z(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l; } -var _0 = N({ avgPool3d_: UW }); +var _v = N({ avgPool3d_: UW }); function GW(r, e = 0) { E(r.length >= 1, () => "Pass at least one tensor to concat"); let t6 = Na(r, "tensors", "concat", "string_or_numeric"); @@ -6511,7 +6511,7 @@ function jW(r, e, t6, o, n, s) { let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t6, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = gt([u, l], 1), d = Xe(m, i), f = xe(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = He(f, [0, 0], x), C = He(f, [0, g], x), w = He(f, [0, g * 2], x), k = He(f, [0, g * 3], x), _ = xe(ae(zs(b), nl(C)), ae(c, zs(xe(a, w)))), $ = ae(nl(_), zs(k)); return [_, $]; } -var E0 = N({ basicLSTMCell_: jW }); +var Ev = N({ basicLSTMCell_: jW }); function XW(r, e, t6) { let o = v(r, "x", "batchToSpaceND"), n = e.reduce((i, p) => i * p); E(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), E(t6.length === e.length, () => `crops.length is ${t6.length} but should be equal to blockShape.length ${e.length}`), E(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`); @@ -6519,7 +6519,7 @@ function XW(r, e, t6) { return T.runKernel(xs, s, a); } var rd = N({ batchToSpaceND_: XW }); -function $0(r) { +function $v(r) { let e; return r.rank === 0 || r.rank === 1 ? e = z(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = z(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = z(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e; } @@ -6529,7 +6529,7 @@ function YW(r, e, t6, o, n, s) { n != null && (u = v(n, "scale", "batchNorm")); let c; o != null && (c = v(o, "offset", "batchNorm")), E(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), E(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), E(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - let m = { x: $0(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(an, m, d); + let m = { x: $v(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(an, m, d); return z(f, a.shape); } var wi = N({ batchNorm_: YW }); @@ -6539,21 +6539,21 @@ function QW(r, e, t6, o, n, s) { let c; return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), E(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), E(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); } -var A0 = N({ batchNorm2d_: QW }); +var Av = N({ batchNorm2d_: QW }); function ZW(r, e, t6, o, n, s) { let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), E(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), E(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); } -var R0 = N({ batchNorm3d_: ZW }); +var Rv = N({ batchNorm3d_: ZW }); function JW(r, e, t6, o, n, s) { let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t6, "variance", "batchNorm"), u; n != null && (u = v(n, "scale", "batchNorm")); let c; return o != null && (c = v(o, "offset", "batchNorm")), E(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), E(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), E(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && E(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && E(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), wi(a, i, p, c, u, s); } -var F0 = N({ batchNorm4d_: JW }); +var Fv = N({ batchNorm4d_: JW }); function eU(r, e, t6) { let o = v(r, "x", "bincount"), n = v(e, "weights", "bincount"); E(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), E(t6 >= 0, () => `size must be non-negative, but got ${t6}.`), E(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`); @@ -6570,7 +6570,7 @@ function tU(r, e) { let n = { s0: t6, s1: o }; return T.runKernel(up, n); } -var D0 = N({ broadcastArgs_: tU }); +var Dv = N({ broadcastArgs_: tU }); function rU(r, e) { let t6 = v(r, "broadcastTo", "x"), o = t6.shape; if (yt(e), e.length < t6.rank) @@ -6597,7 +6597,7 @@ function oU(r) { let t6 = { x: v(r, "x", "ceil", "float32") }; return T.runKernel(Uo, t6); } -var O0 = N({ ceil_: oU }); +var Ov = N({ ceil_: oU }); function Ws(r, e, t6) { yt(r); let o = { shape: r, value: e, dtype: t6 }; @@ -6610,23 +6610,23 @@ function nU(r, e, t6) { let n = { x: o }, s = { clipValueMin: e, clipValueMax: t6 }; return T.runKernel(lo, n, s); } -var P0 = N({ clipByValue_: nU }); +var Pv = N({ clipByValue_: nU }); function sU(r) { return gt(r, 0); } -var M0 = N({ concat1d_: sU }); +var Mv = N({ concat1d_: sU }); function aU(r, e) { return gt(r, e); } -var L0 = N({ concat2d_: aU }); +var Lv = N({ concat2d_: aU }); function iU(r, e) { return gt(r, e); } -var B0 = N({ concat3d_: iU }); +var Bv = N({ concat3d_: iU }); function uU(r, e) { return gt(r, e); } -var V0 = N({ concat4d_: uU }); +var Vv = N({ concat4d_: uU }); function pU(r, e, t6, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "conv2d", "float32"), p = v(e, "filter", "conv2d", "float32"), u = i, c = false; i.rank === 3 && (c = true, u = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), E(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), E(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Pt("conv2d", o, a); @@ -6642,7 +6642,7 @@ function cU(r, e, t6, o, n = "NWC", s = 1, a) { let l = z(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = z(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = vi(m, l, [1, t6], o, "NHWC", [1, s], a); return c ? z(g, [g.shape[2], g.shape[3]]) : z(g, [g.shape[0], g.shape[2], g.shape[3]]); } -var z0 = N({ conv1d_: cU }); +var zv = N({ conv1d_: cU }); function lU(r, e, t6, o, n, s = "NHWC", a) { E(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let i = r, p = e, u = false; @@ -6657,14 +6657,14 @@ function mU(r, e, t6, o, n, s) { let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose"); return nd(t6, a, i, o, n, "NHWC", s); } -var W0 = N({ conv2dTranspose_: mU }); +var Wv = N({ conv2dTranspose_: mU }); function dU(r, e, t6, o, n = "NDHWC", s = [1, 1, 1]) { let a = v(r, "x", "conv3d"), i = v(e, "filter", "conv3d"), p = a, u = false; a.rank === 4 && (u = true, p = z(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), E(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), E(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), E(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), E(lr(t6, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t6} and dilations '${s}'`), E(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`); let c = { x: p, filter: i }, l = { strides: t6, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(lp, c, l); return u ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var U0 = N({ conv3d_: dU }); +var Uv = N({ conv3d_: dU }); function fU(r, e, t6, o, n) { E(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`); let s = r, a = e, i = false; @@ -6674,39 +6674,39 @@ function fU(r, e, t6, o, n) { let c = { dy: a, filter: t6 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(mp, c, l); return i ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m; } -var G0 = N({ conv3DBackpropInput_: fU }); +var Gv = N({ conv3DBackpropInput_: fU }); function hU(r, e, t6, o, n) { let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose"); - return G0(t6, s, a, o, n); + return Gv(t6, s, a, o, n); } -var H0 = N({ conv3dTranspose_: hU }); +var Hv = N({ conv3dTranspose_: hU }); function gU(r) { let t6 = { x: v(r, "x", "cos", "float32") }; return T.runKernel(qo, t6); } -var q0 = N({ cos_: gU }); +var qv = N({ cos_: gU }); function xU(r) { let t6 = { x: v(r, "x", "cosh", "float32") }; return T.runKernel(Ko, t6); } -var K0 = N({ cosh_: xU }); +var Kv = N({ cosh_: xU }); function yU(r, e = 0, t6 = false, o = false) { let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t6, reverse: o }; return T.runKernel(jo, s, a); } -var j0 = N({ cumprod_: yU }); +var jv = N({ cumprod_: yU }); function bU(r, e = 0, t6 = false, o = false) { let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t6, reverse: o }; return T.runKernel(Xo, s, a); } -var X0 = N({ cumsum_: bU }); +var Xv = N({ cumsum_: bU }); function CU(r, e, t6, o = false) { let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount"); E(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), E(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), E(t6 >= 0, () => `size must be non-negative, but got ${t6}.`), E(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`); let a = { x: n, weights: s }, i = { size: t6, binaryOutput: o }; return T.runKernel(ti, a, i); } -var Y0 = N({ denseBincount_: CU }); +var Yv = N({ denseBincount_: CU }); function SU(r, e, t6 = "NHWC") { let o = v(r, "x", "depthToSpace", "float32"), n = t6 === "NHWC" ? o.shape[1] : o.shape[2], s = t6 === "NHWC" ? o.shape[2] : o.shape[3], a = t6 === "NHWC" ? o.shape[3] : o.shape[1]; E(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), E(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying @@ -6717,7 +6717,7 @@ function SU(r, e, t6 = "NHWC") { let i = { x: o }, p = { blockSize: e, dataFormat: t6 }; return T.runKernel(Qo, i, p); } -var Q0 = N({ depthToSpace_: SU }); +var Qv = N({ depthToSpace_: SU }); function wU(r, e, t6, o, n = "NHWC", s = [1, 1], a) { let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(e, "filter", "depthwiseConv2d", "float32"), u = i, c = false; i.rank === 3 && (c = true, u = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), E(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), E(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`); @@ -6731,7 +6731,7 @@ function IU(r) { let t6 = { x: v(r, "x", "diag") }; return T.runKernel(hp, t6); } -var Z0 = N({ diag_: IU }); +var Zv = N({ diag_: IU }); function vU(r, e, t6, o, n = [1, 1], s = "NHWC") { let a = v(r, "x", "dilation2d"), i = v(e, "filter", "dilation2d"); E(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), E(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), E(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`); @@ -6740,7 +6740,7 @@ function vU(r, e, t6, o, n = [1, 1], s = "NHWC") { let c = { x: p, filter: i }, l = { strides: t6, pad: o, dilations: n }, m = T.runKernel(gp, c, l); return u ? z(m, [m.shape[1], m.shape[2], m.shape[3]]) : m; } -var J0 = N({ dilation2d_: vU }); +var Jv = N({ dilation2d_: vU }); function kU(r, e) { let t6 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric"); [t6, o] = Re(t6, o), Je(t6.shape, o.shape); @@ -8978,7 +8978,7 @@ function CC() { return new Promise((r) => dq(() => r())); } var S = {}; -Ue(S, { ERF_A1: () => $q, ERF_A2: () => Aq, ERF_A3: () => Rq, ERF_A4: () => Fq, ERF_A5: () => Dq, ERF_P: () => Eq, PARALLELIZE_THRESHOLD: () => Ud, RowPartitionType: () => Ks, SELU_SCALE: () => _q, SELU_SCALEALPHA: () => Tq, applyActivation: () => yu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => DU, assertParamsConsistent: () => fq, assignToTypedArray: () => Vq, axesAreInnerMostDims: () => uC, calculateShapes: () => Jv, checkEinsumDimSizes: () => qq, checkPadOnDimRoundingMode: () => Pt, combineLocations: () => nk, combineRaggedTensorToTensorShapes: () => gq, complexWithEvenIndex: () => Mq, complexWithOddIndex: () => Lq, computeConv2DInfo: () => uu, computeConv3DInfo: () => N0, computeDefaultPad: () => iC, computeDilation2DInfo: () => OW, computeOptimalWindowSize: () => Cq, computeOutAndReduceShapes: () => FU, computeOutShape: () => hq, computePool2DInfo: () => aC, computePool3DInfo: () => PW, convertConv2DDataFormat: () => T0, decodeEinsumEquation: () => Gq, eitherStridesOrDilationsAreOne: () => lr, expandShapeToKeepDim: () => Aa, exponent: () => Wq, exponents: () => zq, fromStringArrayToUint8: () => dK, fromUint8ToStringArray: () => mK, getAxesPermutation: () => OU, getBroadcastDims: () => Xv, getComplexWithIndex: () => Bq, getEinsumComputePath: () => Kq, getEinsumPermutation: () => Hq, getFusedBiasGradient: () => xu, getFusedDyActivation: () => gu, getImageCenter: () => Sq, getInnerMostAxes: () => MU, getPermuted: () => Iq, getRaggedRank: () => yq, getReductionAxes: () => jm, getReshaped: () => wq, getReshapedPermuted: () => vq, getRowPartitionTypesHelper: () => xq, getSliceBeginCoords: () => kq, getSliceSize: () => Nq, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => Qq, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => Zq, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => Jq, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => rK, getSparseReshapeInputOutputMismatchErrorMessage: () => nK, getSparseReshapeInputOutputMultipleErrorMessage: () => oK, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => eK, getSparseReshapeNegativeOutputDimErrorMessage: () => tK, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => uK, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => sK, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => aK, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => iK, getUndoAxesPermutation: () => PU, isIdentityPermutation: () => jq, log: () => GV, mergeRealAndImagArrays: () => Oq, prepareAndValidate: () => Zv, prepareSplitSize: () => Yq, segment_util: () => wC, shouldFuse: () => bu, slice_util: () => ut, splitRealAndImagArrays: () => Pq, tupleValuesAreOne: () => iu, upcastType: () => dt, validateDefaultValueShape: () => bq, validateInput: () => Qm, validateUpdateShape: () => tC, warn: () => Os }); +Ue(S, { ERF_A1: () => $q, ERF_A2: () => Aq, ERF_A3: () => Rq, ERF_A4: () => Fq, ERF_A5: () => Dq, ERF_P: () => Eq, PARALLELIZE_THRESHOLD: () => Ud, RowPartitionType: () => Ks, SELU_SCALE: () => _q, SELU_SCALEALPHA: () => Tq, applyActivation: () => yu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => DU, assertParamsConsistent: () => fq, assignToTypedArray: () => Vq, axesAreInnerMostDims: () => uC, calculateShapes: () => J0, checkEinsumDimSizes: () => qq, checkPadOnDimRoundingMode: () => Pt, combineLocations: () => nk, combineRaggedTensorToTensorShapes: () => gq, complexWithEvenIndex: () => Mq, complexWithOddIndex: () => Lq, computeConv2DInfo: () => uu, computeConv3DInfo: () => Nv, computeDefaultPad: () => iC, computeDilation2DInfo: () => OW, computeOptimalWindowSize: () => Cq, computeOutAndReduceShapes: () => FU, computeOutShape: () => hq, computePool2DInfo: () => aC, computePool3DInfo: () => PW, convertConv2DDataFormat: () => Tv, decodeEinsumEquation: () => Gq, eitherStridesOrDilationsAreOne: () => lr, expandShapeToKeepDim: () => Aa, exponent: () => Wq, exponents: () => zq, fromStringArrayToUint8: () => dK, fromUint8ToStringArray: () => mK, getAxesPermutation: () => OU, getBroadcastDims: () => X0, getComplexWithIndex: () => Bq, getEinsumComputePath: () => Kq, getEinsumPermutation: () => Hq, getFusedBiasGradient: () => xu, getFusedDyActivation: () => gu, getImageCenter: () => Sq, getInnerMostAxes: () => MU, getPermuted: () => Iq, getRaggedRank: () => yq, getReductionAxes: () => jm, getReshaped: () => wq, getReshapedPermuted: () => vq, getRowPartitionTypesHelper: () => xq, getSliceBeginCoords: () => kq, getSliceSize: () => Nq, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => Qq, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => Zq, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => Jq, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => rK, getSparseReshapeInputOutputMismatchErrorMessage: () => nK, getSparseReshapeInputOutputMultipleErrorMessage: () => oK, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => eK, getSparseReshapeNegativeOutputDimErrorMessage: () => tK, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => uK, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => sK, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => aK, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => iK, getUndoAxesPermutation: () => PU, isIdentityPermutation: () => jq, log: () => GV, mergeRealAndImagArrays: () => Oq, prepareAndValidate: () => Z0, prepareSplitSize: () => Yq, segment_util: () => wC, shouldFuse: () => bu, slice_util: () => ut, splitRealAndImagArrays: () => Pq, tupleValuesAreOne: () => iu, upcastType: () => dt, validateDefaultValueShape: () => bq, validateInput: () => Qm, validateUpdateShape: () => tC, warn: () => Os }); function fq(r, e) { let t6 = r[0].length; r.forEach((n, s) => { @@ -9726,7 +9726,7 @@ var rf = class { } }; var Ye = {}; -Ue(Ye, { OP_SCOPE_SUFFIX: () => Lb, abs: () => Yt, acos: () => f0, acosh: () => h0, add: () => xe, addN: () => g0, all: () => x0, any: () => y0, argMax: () => b0, argMin: () => C0, asin: () => S0, asinh: () => w0, atan: () => I0, atan2: () => v0, atanh: () => k0, avgPool: () => td, avgPool3d: () => _0, basicLSTMCell: () => E0, batchNorm: () => wi, batchNorm2d: () => A0, batchNorm3d: () => R0, batchNorm4d: () => F0, batchToSpaceND: () => rd, bincount: () => od, booleanMaskAsync: () => XG, broadcastArgs: () => D0, broadcastTo: () => Ii, buffer: () => le, cast: () => Ke, ceil: () => O0, clipByValue: () => P0, clone: () => Br, complex: () => Tr, concat: () => gt, concat1d: () => M0, concat2d: () => L0, concat3d: () => B0, concat4d: () => V0, conv1d: () => z0, conv2d: () => vi, conv2dTranspose: () => W0, conv3d: () => U0, conv3dTranspose: () => H0, cos: () => q0, cosh: () => K0, cosineWindow: () => il, cumprod: () => j0, cumsum: () => X0, denseBincount: () => Y0, depthToSpace: () => Q0, depthwiseConv2d: () => Bp, diag: () => Z0, dilation2d: () => J0, div: () => Ge, divNoNan: () => ek, dot: () => tk, dropout: () => aH, einsum: () => rk, elu: () => ad, enclosingPowerOfTwo: () => xC, equal: () => sd, erf: () => ok, euclideanNorm: () => ak, exp: () => Co, expandDims: () => Fa, expm1: () => ik, eye: () => id, fft: () => zp, fill: () => Ws, floor: () => ud, floorDiv: () => Jm, fused: () => yC, gather: () => pd, gatherND: () => nH, greater: () => cu, greaterEqual: () => cd, ifft: () => hu, imag: () => Si, image: () => uq, inTopKAsync: () => uH, irfft: () => Fd, isFinite: () => uk, isInf: () => pk, isNaN: () => ck, leakyRelu: () => ld, less: () => lk, lessEqual: () => Vp, linalg: () => pq, linspace: () => mk, localResponseNormalization: () => dk, log: () => Da, log1p: () => md, logSigmoid: () => fk, logSoftmax: () => hk, logSumExp: () => hd, logicalAnd: () => lu, logicalNot: () => gd, logicalOr: () => xd, logicalXor: () => gk, losses: () => cq, lowerBound: () => xk, matMul: () => Xe, max: () => Us, maxPool: () => bd, maxPool3d: () => yk, maxPoolWithArgmax: () => bk, maximum: () => Cd, mean: () => mu, meshgrid: () => Ck, min: () => sl, minimum: () => Sd, mirrorPad: () => Sk, mod: () => wk, moments: () => Ik, movingAverage: () => QG, mul: () => ae, multiRNNCell: () => vk, multinomial: () => kk, neg: () => yr, norm: () => pu, notEqual: () => wd, oneHot: () => tl, ones: () => Gs, onesLike: () => Nk, op: () => N, outerProduct: () => Tk, pad: () => Hs, pad1d: () => _k, pad2d: () => Ek, pad3d: () => $k, pad4d: () => Ak, pool: () => Rk, pow: () => Ra, prelu: () => vd, print: () => Gm, prod: () => Fk, raggedGather: () => Dk, raggedRange: () => Ok, raggedTensorToTensor: () => Pk, rand: () => Mk, randomGamma: () => e1, randomNormal: () => Ed, randomStandardNormal: () => t1, randomUniform: () => $d, range: () => Ni, real: () => $a, reciprocal: () => r1, relu: () => Ti, relu6: () => Ad, reshape: () => z, reverse: () => no, reverse1d: () => o1, reverse2d: () => n1, reverse3d: () => s1, reverse4d: () => a1, rfft: () => Wp, round: () => Rd, rsqrt: () => i1, scalar: () => be, scatterND: () => JG, searchSorted: () => al, selu: () => u1, separableConv2d: () => p1, setdiff1dAsync: () => c1, sigmoid: () => zs, sign: () => l1, signal: () => iq, sin: () => m1, sinh: () => d1, slice: () => He, slice1d: () => f1, slice2d: () => h1, slice3d: () => g1, slice4d: () => x1, softmax: () => y1, softplus: () => fd, spaceToBatchND: () => Id, sparse: () => lq, sparseToDense: () => rH, spectral: () => aq, split: () => Oa, sqrt: () => $r, square: () => Qt, squaredDifference: () => Dd, squeeze: () => Up, stack: () => Sr, step: () => Od, stridedSlice: () => b1, string: () => mq, sub: () => Ne, sum: () => et, tan: () => C1, tanh: () => nl, tensor: () => nr, tensor1d: () => mr, tensor2d: () => _i, tensor3d: () => Xm, tensor4d: () => S1, tensor5d: () => w1, tensor6d: () => I1, tile: () => ki, topk: () => v1, transpose: () => Mp, truncatedNormal: () => k1, unique: () => N1, unsortedSegmentSum: () => T1, unstack: () => so, upperBound: () => _1, variable: () => E1, where: () => os, whereAsync: () => Md, zeros: () => Vr, zerosLike: () => Ut }); +Ue(Ye, { OP_SCOPE_SUFFIX: () => Lb, abs: () => Yt, acos: () => fv, acosh: () => hv, add: () => xe, addN: () => gv, all: () => xv, any: () => yv, argMax: () => bv, argMin: () => Cv, asin: () => Sv, asinh: () => wv, atan: () => Iv, atan2: () => vv, atanh: () => kv, avgPool: () => td, avgPool3d: () => _v, basicLSTMCell: () => Ev, batchNorm: () => wi, batchNorm2d: () => Av, batchNorm3d: () => Rv, batchNorm4d: () => Fv, batchToSpaceND: () => rd, bincount: () => od, booleanMaskAsync: () => XG, broadcastArgs: () => Dv, broadcastTo: () => Ii, buffer: () => le, cast: () => Ke, ceil: () => Ov, clipByValue: () => Pv, clone: () => Br, complex: () => Tr, concat: () => gt, concat1d: () => Mv, concat2d: () => Lv, concat3d: () => Bv, concat4d: () => Vv, conv1d: () => zv, conv2d: () => vi, conv2dTranspose: () => Wv, conv3d: () => Uv, conv3dTranspose: () => Hv, cos: () => qv, cosh: () => Kv, cosineWindow: () => il, cumprod: () => jv, cumsum: () => Xv, denseBincount: () => Yv, depthToSpace: () => Qv, depthwiseConv2d: () => Bp, diag: () => Zv, dilation2d: () => Jv, div: () => Ge, divNoNan: () => ek, dot: () => tk, dropout: () => aH, einsum: () => rk, elu: () => ad, enclosingPowerOfTwo: () => xC, equal: () => sd, erf: () => ok, euclideanNorm: () => ak, exp: () => Co, expandDims: () => Fa, expm1: () => ik, eye: () => id, fft: () => zp, fill: () => Ws, floor: () => ud, floorDiv: () => Jm, fused: () => yC, gather: () => pd, gatherND: () => nH, greater: () => cu, greaterEqual: () => cd, ifft: () => hu, imag: () => Si, image: () => uq, inTopKAsync: () => uH, irfft: () => Fd, isFinite: () => uk, isInf: () => pk, isNaN: () => ck, leakyRelu: () => ld, less: () => lk, lessEqual: () => Vp, linalg: () => pq, linspace: () => mk, localResponseNormalization: () => dk, log: () => Da, log1p: () => md, logSigmoid: () => fk, logSoftmax: () => hk, logSumExp: () => hd, logicalAnd: () => lu, logicalNot: () => gd, logicalOr: () => xd, logicalXor: () => gk, losses: () => cq, lowerBound: () => xk, matMul: () => Xe, max: () => Us, maxPool: () => bd, maxPool3d: () => yk, maxPoolWithArgmax: () => bk, maximum: () => Cd, mean: () => mu, meshgrid: () => Ck, min: () => sl, minimum: () => Sd, mirrorPad: () => Sk, mod: () => wk, moments: () => Ik, movingAverage: () => QG, mul: () => ae, multiRNNCell: () => vk, multinomial: () => kk, neg: () => yr, norm: () => pu, notEqual: () => wd, oneHot: () => tl, ones: () => Gs, onesLike: () => Nk, op: () => N, outerProduct: () => Tk, pad: () => Hs, pad1d: () => _k, pad2d: () => Ek, pad3d: () => $k, pad4d: () => Ak, pool: () => Rk, pow: () => Ra, prelu: () => vd, print: () => Gm, prod: () => Fk, raggedGather: () => Dk, raggedRange: () => Ok, raggedTensorToTensor: () => Pk, rand: () => Mk, randomGamma: () => e1, randomNormal: () => Ed, randomStandardNormal: () => t1, randomUniform: () => $d, range: () => Ni, real: () => $a, reciprocal: () => r1, relu: () => Ti, relu6: () => Ad, reshape: () => z, reverse: () => no, reverse1d: () => o1, reverse2d: () => n1, reverse3d: () => s1, reverse4d: () => a1, rfft: () => Wp, round: () => Rd, rsqrt: () => i1, scalar: () => be, scatterND: () => JG, searchSorted: () => al, selu: () => u1, separableConv2d: () => p1, setdiff1dAsync: () => c1, sigmoid: () => zs, sign: () => l1, signal: () => iq, sin: () => m1, sinh: () => d1, slice: () => He, slice1d: () => f1, slice2d: () => h1, slice3d: () => g1, slice4d: () => x1, softmax: () => y1, softplus: () => fd, spaceToBatchND: () => Id, sparse: () => lq, sparseToDense: () => rH, spectral: () => aq, split: () => Oa, sqrt: () => $r, square: () => Qt, squaredDifference: () => Dd, squeeze: () => Up, stack: () => Sr, step: () => Od, stridedSlice: () => b1, string: () => mq, sub: () => Ne, sum: () => et, tan: () => C1, tanh: () => nl, tensor: () => nr, tensor1d: () => mr, tensor2d: () => _i, tensor3d: () => Xm, tensor4d: () => S1, tensor5d: () => w1, tensor6d: () => I1, tile: () => ki, topk: () => v1, transpose: () => Mp, truncatedNormal: () => k1, unique: () => N1, unsortedSegmentSum: () => T1, unstack: () => so, upperBound: () => _1, variable: () => E1, where: () => os, whereAsync: () => Md, zeros: () => Vr, zerosLike: () => Ut }); var _N = (r, e, t6, o = Ye) => { switch (r.op) { case "BiasAdd": @@ -31831,8 +31831,8 @@ async function process2(input, config3, getTensor = true) { let pixels; let depth = 3; if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && Qv) { - pixels = Qv ? Qv.fromPixels(input) : null; + if (env.browser && Q0) { + pixels = Q0 ? Q0.fromPixels(input) : null; } else { depth = input.data.length / input.height / input.width; const arr = new Uint8Array(input.data.buffer); @@ -31841,12 +31841,12 @@ async function process2(input, config3, getTensor = true) { } else { if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (Qv && env.browser) { + if (Q0 && env.browser) { if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = Qv.fromPixels(outCanvas); + pixels = Q0.fromPixels(outCanvas); } else { tmpCanvas = copy(outCanvas); - pixels = Qv.fromPixels(tmpCanvas); + pixels = Q0.fromPixels(tmpCanvas); } } else { const tempCanvas = copy(outCanvas); @@ -32497,7 +32497,7 @@ async function loadModel(modelPath) { } // package.json -var version = "3.0.0"; +var version = "3.0.1"; // src/tfjs/humangl.ts var config2 = { @@ -37713,7 +37713,7 @@ async function max2d(inputs, minScore) { const max = Us(reshaped, 0); const newScore = (await max.data())[0]; if (newScore > minScore) { - const coordinates = b0(reshaped, 0); + const coordinates = bv(reshaped, 0); const mod = wk(coordinates, width); const x = (await mod.data())[0]; const div = Ge(coordinates, width); @@ -38002,7 +38002,7 @@ function decodeBoxes2(boxOutputs) { t6.ends = xe(t6.centersNormalized, t6.halfBoxSize); t6.startNormalized = ae(t6.starts, inputSizeT); t6.endNormalized = ae(t6.ends, inputSizeT); - const boxes = L0([t6.startNormalized, t6.endNormalized], 1); + const boxes = Lv([t6.startNormalized, t6.endNormalized], 1); Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); return boxes; } @@ -38466,7 +38466,7 @@ async function predict6(image, config3, idx, count2) { obj.gender = gender2[0] <= 0.5 ? "female" : "male"; obj.genderScore = Math.min(0.99, confidence); } - const argmax = b0(resT.find((t6) => t6.shape[1] === 100), 1); + const argmax = bv(resT.find((t6) => t6.shape[1] === 100), 1); const ageIdx = (await argmax.data())[0]; Dt(argmax); const ageT = resT.find((t6) => t6.shape[1] === 100); @@ -38751,7 +38751,7 @@ async function predict11(image, config3, idx, count2) { const redNorm = ae(red, rgb[0]); const greenNorm = ae(green, rgb[1]); const blueNorm = ae(blue, rgb[2]); - const grayscale = g0([redNorm, greenNorm, blueNorm]); + const grayscale = gv([redNorm, greenNorm, blueNorm]); const normalize2 = ae(Ne(grayscale, constants.tf05), 2); return normalize2; }); @@ -42763,7 +42763,7 @@ var HandDetector = class { t6.startPoints = ae(t6.sub, this.inputSizeTensor); t6.add = xe(t6.boxCenterPoints, t6.halfBoxSizes); t6.endPoints = ae(t6.add, this.inputSizeTensor); - const res = L0([t6.startPoints, t6.endPoints], 1); + const res = Lv([t6.startPoints, t6.endPoints], 1); Object.keys(t6).forEach((tensor) => Dt(t6[tensor])); return res; } @@ -43125,7 +43125,7 @@ async function detectHands(input, config3) { t6.filtered = Sr(classScores, 1); Dt(classScores); t6.max = Us(t6.filtered, 1); - t6.argmax = b0(t6.filtered, 1); + t6.argmax = bv(t6.filtered, 1); let id2 = 0; t6.nms = await uq.nonMaxSuppressionAsync(t6.boxes, t6.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); const nms = await t6.nms.data(); @@ -43296,7 +43296,7 @@ var bufferedResult = empty(); var interpolateTime = 0; function calc2(newResult, config3) { var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m2, _n2, _o2, _p, _q2, _r2, _s2, _t, _u2, _v2, _w2; - const t02 = now(); + const t0 = now(); if (!newResult) return empty(); const elapsed = Date.now() - newResult.timestamp; @@ -43423,7 +43423,7 @@ function calc2(newResult, config3) { bufferedResult.width = newResult.width; bufferedResult.height = newResult.height; const t12 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t02) : Math.round(t12 - t02); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t0) : Math.round(t12 - t0); if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; return bufferedResult; @@ -43783,7 +43783,7 @@ async function process4(res, outputShape, config3) { const scores = await scoresT.array(); const featuresT = Up(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); const boxesMaxT = z(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]); - const boxIdxT = b0(boxesMaxT, 2); + const boxIdxT = bv(boxesMaxT, 2); const boxIdx = await boxIdxT.array(); for (let i = 0; i < scoresT.shape[0]; i++) { for (let j = 0; j < (((_b2 = scoresT.shape) == null ? void 0 : _b2[1]) || 0); j++) { @@ -45384,7 +45384,7 @@ async function runCompile(instance) { } async function warmup(instance, userConfig) { await check(instance, false); - const t02 = now(); + const t0 = now(); instance.state = "warmup"; if (userConfig) instance.config = mergeDeep(instance.config, userConfig); @@ -45397,7 +45397,7 @@ async function warmup(instance, userConfig) { const res = await runInference(instance); const t12 = now(); if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t12 - t02), "ms"); + log("warmup", instance.config.warmup, Math.round(t12 - t0), "ms"); instance.emit("warmup"); resolve(res); }); @@ -45572,10 +45572,10 @@ var Human = class { return calc2(result, this.config); } async warmup(userConfig) { - const t02 = now(); + const t0 = now(); const res = await warmup(this, userConfig); const t12 = now(); - this.performance.warmup = Math.trunc(t12 - t02); + this.performance.warmup = Math.trunc(t12 - t0); return res; } async profile(input, userConfig) { diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index 9d4b4d65..3c35b359 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/util/util.ts", "../src/config.ts", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../src/tfjs/load.ts", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", 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i;this.backendName==null&&this.backend;let p,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Db(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=qc(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Db(e)?null:e.backwardsFunc,d;return 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this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Sm(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof va||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let 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l=v(r,\"x\",\"conv2d\",\"float32\"),m=v(e,\"filter\",\"conv2d\",\"float32\"),d=l,f=!1;l.rank===3&&(f=!0,d=z(l,[1,l.shape[0],l.shape[1],l.shape[2]])),E(d.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Pt(\"fused conv2d\",o,a);let h=n===\"NHWC\"?d.shape[3]:d.shape[1];E(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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o=Math.exp(e*t*t);return t<=r?o:0}function q1(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function EH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(e,\"scores\",\"nonMaxSuppressionAsync\"),i=So(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Vd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),mr(l,\"int32\")}var K1=EH;function $H(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=So(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(_n,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var j1=N({nonMaxSuppressionWithScore_:$H});async function AH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=So(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=Wd(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:mr(m,\"int32\"),selectedScores:mr(d)}}var X1=AH;function RH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=So(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(ba,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var Y1=N({nonMaxSuppressionPadded_:RH});async function FH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=So(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=zd(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:mr(f,\"int32\"),validOutputs:be(h,\"int32\")}}var Q1=FH;function DH(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeBilinear\");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=z(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(Mn,i,p);return a?z(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Z1=N({resizeBilinear_:DH});function 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Jn,p,u)}var tN=N({transform_:LH});function BH(r,e,t){E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=z(Ni(0,s,1,\"int32\"),[-1,1]),p=Ni(0,a,1,\"int32\"),u=Ne(i,p),c=lu(Vp(u,be(+e,\"int32\")),cd(u,be(-t,\"int32\"))),l=Vr([s,a],o.dtype);return z(Sr(so(z(o,[-1,s,a])).map(m=>os(c,m,l))),n)}var rN=N({bandPart_:BH});function VH(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must 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n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),ht(n.shape,s.shape,\"Error in hingeLoss: \");let i=be(1);n=Ne(ae(be(2),n),i);let p=Ti(Ne(i,ae(n,s)));return sr(p,a,o)}var uN=N({hingeLoss_:HH});function qH(r,e,t,o=1,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),ht(s.shape,a.shape,\"Error in huberLoss: \");let p=be(o),u=Yt(Ne(a,s)),c=Sd(u,p),l=Ne(u,c),m=xe(ae(be(.5),Qt(c)),ae(p,l));return sr(m,i,n)}var pN=N({huberLoss_:qH});function KH(r,e,t,o=1e-7,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),ht(s.shape,a.shape,\"Error in logLoss: \");let p=be(1),u=be(o),c=yr(ae(s,Da(xe(a,u)))),l=ae(Ne(p,s),Da(xe(Ne(p,a),u))),m=Ne(c,l);return sr(m,i,n)}var cN=N({logLoss_:KH});function jH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let 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a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(wa,a);return{outputIndices:i[0],outputShape:i[1]}}var hN=N({sparseReshape_:eq});function tq(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(pi,a)}var gN=N({sparseSegmentMean_:tq});function rq(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but 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aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:be(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className=\"Adadelta\";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className=\"Adagrad\";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className=\"Adam\";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className=\"Adamax\";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qs.className=\"SGD\";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className=\"Momentum\";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Di.className=\"RMSProp\";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static adagrad(e,t=.1){return new $i(e,t)}};var hMe={sgd:ns.sgd,momentum:ns.momentum,adadelta:ns.adadelta,adagrad:ns.adagrad,rmsprop:ns.rmsprop,adamax:ns.adamax,adam:ns.adam};var dq=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:r=>r())();function CC(){return new Promise(r=>dq(()=>r()))}var S={};Ue(S,{ERF_A1:()=>$q,ERF_A2:()=>Aq,ERF_A3:()=>Rq,ERF_A4:()=>Fq,ERF_A5:()=>Dq,ERF_P:()=>Eq,PARALLELIZE_THRESHOLD:()=>Ud,RowPartitionType:()=>Ks,SELU_SCALE:()=>_q,SELU_SCALEALPHA:()=>Tq,applyActivation:()=>yu,assertAndGetBroadcastShape:()=>Je,assertAxesAreInnerMostDims:()=>DU,assertParamsConsistent:()=>fq,assignToTypedArray:()=>Vq,axesAreInnerMostDims:()=>uC,calculateShapes:()=>Jv,checkEinsumDimSizes:()=>qq,checkPadOnDimRoundingMode:()=>Pt,combineLocations:()=>nk,combineRaggedTensorToTensorShapes:()=>gq,complexWithEvenIndex:()=>Mq,complexWithOddIndex:()=>Lq,computeConv2DInfo:()=>uu,computeConv3DInfo:()=>N0,computeDefaultPad:()=>iC,computeDilation2DInfo:()=>OW,computeOptimalWindowSize:()=>Cq,computeOutAndReduceShapes:()=>FU,computeOutShape:()=>hq,computePool2DInfo:()=>aC,computePool3DInfo:()=>PW,convertConv2DDataFormat:()=>T0,decodeEinsumEquation:()=>Gq,eitherStridesOrDilationsAreOne:()=>lr,expandShapeToKeepDim:()=>Aa,exponent:()=>Wq,exponents:()=>zq,fromStringArrayToUint8:()=>dK,fromUint8ToStringArray:()=>mK,getAxesPermutation:()=>OU,getBroadcastDims:()=>Xv,getComplexWithIndex:()=>Bq,getEinsumComputePath:()=>Kq,getEinsumPermutation:()=>Hq,getFusedBiasGradient:()=>xu,getFusedDyActivation:()=>gu,getImageCenter:()=>Sq,getInnerMostAxes:()=>MU,getPermuted:()=>Iq,getRaggedRank:()=>yq,getReductionAxes:()=>jm,getReshaped:()=>wq,getReshapedPermuted:()=>vq,getRowPartitionTypesHelper:()=>xq,getSliceBeginCoords:()=>kq,getSliceSize:()=>Nq,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Qq,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zq,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jq,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>rK,getSparseReshapeInputOutputMismatchErrorMessage:()=>nK,getSparseReshapeInputOutputMultipleErrorMessage:()=>oK,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eK,getSparseReshapeNegativeOutputDimErrorMessage:()=>tK,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>uK,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sK,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>aK,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>iK,getUndoAxesPermutation:()=>PU,isIdentityPermutation:()=>jq,log:()=>GV,mergeRealAndImagArrays:()=>Oq,prepareAndValidate:()=>Zv,prepareSplitSize:()=>Yq,segment_util:()=>wC,shouldFuse:()=>bu,slice_util:()=>ut,splitRealAndImagArrays:()=>Pq,tupleValuesAreOne:()=>iu,upcastType:()=>dt,validateDefaultValueShape:()=>bq,validateInput:()=>Qm,validateUpdateShape:()=>tC,warn:()=>Os});function fq(r,e){let t=r[0].length;r.forEach((n,s)=>{E(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),E(e>=0&&e`Error in concat${t}D: axis must be between 0 and ${t-1}.`);let o=r[0];r.forEach((n,s)=>{for(let a=0;a`Error in concat${t}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`)})}function hq(r,e){let t=r[0].slice();for(let o=1;o=0)if(i>=0){if(i!==s)throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n+r}] = ${s} but shape[${n+r}] = ${i}`)}else o[a]=s}return o}function xq(r){let e={FIRST_DIM_SIZE:Ks.FIRST_DIM_SIZE,VALUE_ROWIDS:Ks.VALUE_ROWIDS,ROW_LENGTHS:Ks.ROW_LENGTHS,ROW_SPLITS:Ks.ROW_SPLITS,ROW_LIMITS:Ks.ROW_LIMITS,ROW_STARTS:Ks.ROW_STARTS},t=[];for(let o of r)if(o in e)t.push(e[o]);else break;return t}function yq(r){return r.length===0?0:r[0]===Ks.FIRST_DIM_SIZE?r.length-1:r.length}function bq(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function Cq(r){return r<=Ud?r:sp(r,Math.floor(Math.sqrt(r)))}function Sq(r,e,t){let o=t*(typeof r==\"number\"?r:r[0]),n=e*(typeof r==\"number\"?r:r[1]);return[o,n]}function wq(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function vq(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s\",Uq=/->/g,SN=\",\",wN=\"...\";function Gq(r,e){r=r.replace(/\\s/g,\"\");let t=(r.length-r.replace(Uq,\"\").length)/SC.length;if(t<1)throw new Error(\"Equations without an arrow are not supported.\");if(t>1)throw new Error(`Equation must contain exactly one arrow (\"${SC}\").`);let[o,n]=r.split(SC);E(o.indexOf(wN)===-1,()=>`The ellipsis notation (\"${wN}\") is not supported yet.`);let s=o.split(SN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function qq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function Kq(r,e){let t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function Xq(r,e){let t=[];for(let o=0;o\"Number of splits must evenly divide the axis.\"),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);E(n<=1,()=>\"There should be only one negative value in split array.\");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}E(r.shape[t]===e.reduce((a,i)=>a+i),()=>\"The sum of sizes must match the size of the axis dimension.\"),o=e}return o}function Qq(r){return`Received SparseTensor with denseShape[0] = 0 but\n indices.shape[0] = ${r}`}function Zq(r,e){return`indices(${r}, 0) is invalid: ${e} < 0`}function Jq(r,e,t){return`indices(${r}, 0) is invalid: ${e} >= ${t}`}function eK(r,e){return`only one output dimension may be -1, not both ${r} and ${e}`}function tK(r,e){return`size ${r} must be non-negative, not ${e}`}function rK(){return\"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\"}function oK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a SparseTensor with ${t}\n dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`}function nK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a tensor with ${t} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`}function sK(){return\"segment ids must be >= 0\"}function aK(){return\"segment ids are not increasing\"}function iK(r,e){return`Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`}function uK(r,e,t){return`Bad: indices[${r}] == ${e} out of range [0, ${t})`}var wC={};Ue(wC,{collectGatherOpShapeInfo:()=>lK,computeOutShape:()=>cK,segOpComputeOptimalWindowSize:()=>pK});function pK(r,e){let t=!1,o;for(r<=Ud?(o=r,t=!0):o=sp(r,Math.floor(Math.sqrt(r)));!t;)o>e||o===r?t=!0:o=sp(r,o+1);return o}function cK(r,e,t){let o=[],n=r.length;for(let s=0;sn))throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);if(o<0&&(o+=n),o>s)throw new Error(`batchDims (${o}) must be less than rank(x) (\n ${s}).`);if(tAp(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function dK(r){return r.map(e=>gi(e))}var Lt={};Ue(Lt,{nonMaxSuppressionV3Impl:()=>Vd,nonMaxSuppressionV4Impl:()=>zd,nonMaxSuppressionV5Impl:()=>Wd,whereImpl:()=>Pd});var fK=O();fK.registerFlag(\"KEEP_INTERMEDIATE_TENSORS\",()=>!1,r=>{r&&console.warn(\"Keep intermediate tensors is ON. 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=AN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=DN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[be(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ul(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var MN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ul(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function HC(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var BN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=HC(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VN=(r,e,t,o=Ye)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[as(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>as(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[as(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jN=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qC(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>_N(a,i,p));case\"basic_math\":return n(()=>EN(a,i,p));case\"control\":return ON(a,i,p);case\"convolution\":return n(()=>MN(a,i,p));case\"creation\":return n(()=>LN(a,i,p));case\"dynamic\":return BN(a,i,p);case\"evaluation\":return n(()=>VN(a,i,p));case\"image\":return n(()=>UN(a,i,p));case\"graph\":return n(()=>zN(a,i,p));case\"logical\":return n(()=>GN(a,i,p));case\"matrices\":return n(()=>HN(a,i,p));case\"normalization\":return n(()=>qN(a,i,p));case\"ragged\":return n(()=>KN(a,i,p));case\"reduction\":return n(()=>jN(a,i,p));case\"slice_join\":return n(()=>XN(a,i,p));case\"sparse\":return n(()=>YN(a,i,p));case\"spectral\":return n(()=>QN(a,i,p));case\"string\":return n(()=>ZN(a,i,p));case\"transformation\":return n(()=>JN(a,i,p));case\"hash_table\":return WN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var cl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function KC(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((jC(m)||i6(m)||u6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function eT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var n6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],s6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],a6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function jC(r){return n6.indexOf(r.op)>=0}function i6(r){return s6.indexOf(r.op)>=0}function u6(r){return a6.indexOf(r.op)>=0}var Cu=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Cu(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=KC(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return eT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new cl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=vN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new cl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=KC(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=p.filter(C=>!jC(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=ss(l.node.name,o)),n[l.node.name]==null){let d=qC(l.node,n,o,this._resourceManager);m||([m]=ss(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=ss(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var p6=\"?tfjs-format=file\",c6=\"model.json\",ll=class{constructor(e,t={},o=Ea){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cu(pl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=pl.Instance.transformGraph(e.modelInitializer);this.initializer=new Cu(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Dt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function l6(r,e={},t=Ea){if(r==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");e==null&&(e={}),e.fromTFHub&&typeof r==\"string\"&&(r=d6(r));let o=new ll(r,e,t);return await o.load(),o}function m6(r){if(r==null)throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model\");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error(\"modelJSON must be the first element of the array\");if(!n||!(n instanceof ArrayBuffer))throw new Error(\"An ArrayBuffer of weights must be the second element of the array\");if(!(\"modelTopology\"in o))throw new Error(\"Model JSON is missing 'modelTopology'\");if(!(\"weightsManifest\"in o))throw new Error(\"Model JSON is missing 'weightsManifest'\");let s=Ea.getWeightSpecs(o.weightsManifest),a=Ea.getModelArtifactsForJSONSync(o,s,n);e=Ea.fromMemorySync(a)}else if(\"load\"in r)e=r;else if(\"modelTopology\"in r&&\"weightSpecs\"in r&&\"weightData\"in r)e=Ea.fromMemorySync(r);else throw new Error(\"Unknown model format\");let t=new ll(e);return t.load(),t}function d6(r){return r.endsWith(\"/\")||(r=r+\"/\"),`${r}${c6}${p6}`}var f6=\"4.1.0\";function K(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var h6=Lt.whereImpl,Oi=class extends Zr{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Do(this,cr())}nextDataId(){return Oi.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,O().get(\"IS_NODE\")&&S.warn(`\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================`));let n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:o,refCount:1}),n}makeTensorInfo(e,t,o){let n;if(t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return{dataId:n,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,o,n,s){this.data.set(e,{values:t,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:o}=this.data.get(e);if(t===\"complex64\"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return S.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(t,o,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:o}=this.data.get(e);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}memory(){return{unreliable:!0,reasons:[\"The reported memory is an upper bound. 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Qp={};Ue(Qp,{addImpl:()=>QC,bincountImpl:()=>Kp,bincountReduceImpl:()=>uf,castImpl:()=>YC,ceilImpl:()=>ZC,concatImpl:()=>Su,equalImpl:()=>JC,expImpl:()=>tS,expm1Impl:()=>oS,floorImpl:()=>nS,gatherNdImpl:()=>pf,gatherV2Impl:()=>cf,greaterEqualImpl:()=>aS,greaterImpl:()=>sS,lessEqualImpl:()=>uS,lessImpl:()=>iS,linSpaceImpl:()=>lf,logImpl:()=>pS,maxImpl:()=>mf,maximumImpl:()=>cS,minimumImpl:()=>lS,multiplyImpl:()=>ml,negImpl:()=>mS,notEqualImpl:()=>dS,prodImpl:()=>fS,raggedGatherImpl:()=>df,raggedRangeImpl:()=>ff,raggedTensorToTensorImpl:()=>hf,rangeImpl:()=>Iu,rsqrtImpl:()=>hS,scatterImpl:()=>Ma,sigmoidImpl:()=>ET,simpleAbsImpl:()=>XC,sliceImpl:()=>vu,sparseFillEmptyRowsImpl:()=>gf,sparseReshapeImpl:()=>xf,sparseSegmentReductionImpl:()=>Yp,sqrtImpl:()=>RT,squaredDifferenceImpl:()=>xS,stridedSliceImpl:()=>yf,stringNGramsImpl:()=>ku,stringSplitImpl:()=>Nu,stringToHashBucketFastImpl:()=>Tu,subImpl:()=>bS,tileImpl:()=>bf,topKImpl:()=>Cf,transposeImpl:()=>jp,uniqueImpl:()=>Sf});function 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uj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;K([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,w=x-1-c.padInfo.top,k=le(a.shape,\"float32\"),_=1/(d*f),$=t.data.get(n.dataId).values,A=le(n.shape,\"float32\",$);for(let R=0;R=c.outHeight||Math.floor(q)!==q))for(let H=0;H=c.outWidth||Math.floor(j)!==j)continue;let X=A.get(R,q,j,D);V+=X}}k.set(V*_,R,P,M,D)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var i2={kernelName:wm,backendName:\"cpu\",kernelFunc:uj};function pj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||i.shape.length===a.shape.length,()=>\"Batch normalization 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i=s.reduce((x,b)=>x*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Me({inputs:{x:n},backend:t,attrs:{shape:p}}),f=Ct({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Me({inputs:{x:f},backend:t,attrs:{shape:c}}),g=No({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var p2={kernelName:xs,backendName:\"cpu\",kernelFunc:cj};function lj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Kp(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var c2={kernelName:Ja,backendName:\"cpu\",kernelFunc:lj};function mj(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var l2={kernelName:up,backendName:\"cpu\",kernelFunc:mj};var dj=Ie(lo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return ar({inputs:{x:p[0]},backend:t});if(p[0].dtype===\"complex64\"){let h=p.map(w=>wo({inputs:{input:w},backend:t})),g=p.map(w=>Xs({inputs:{input:w},backend:t})),x=Pi({inputs:h,backend:t,attrs:{axis:s}}),b=Pi({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({inputs:{real:x,imag:b},backend:t});return h.forEach(w=>t.disposeIntermediateTensorInfo(w)),g.forEach(w=>t.disposeIntermediateTensorInfo(w)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Me({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=Su(c,i,e[0].dtype,l),d=S.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var h2={kernelName:ys,backendName:\"cpu\",kernelFunc:Pi};function NS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;K([n,s],\"conv2d\");let l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",w=new st(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],A=C?k[1]:k[2],R=C?k[2]:1,D=C?1:k[1],P=w.strides[0],M=C?w.strides[1]:w.strides[2],L=C?w.strides[2]:1,W=C?1:w.strides[1],V=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,q=w.values;for(let H=0;H=m.inHeight)continue;let pe=J*_[0],he=j+ie*A;for(let we=0;we=m.inWidth)continue;let pt=pe+Le*_[1],Oe=he+nt*R,mt=pt;for(let at=0;at=u.inDepth)continue;let H=U*R[0],j=P+q*A[1];for(let X=0;X=u.inHeight)continue;let ie=H+Y*R[1],pe=j+J*A[2];for(let he=0;he=u.inWidth)continue;let nt=ie+$e*R[2],pt=pe+Le*u.inChannels,Oe=nt;for(let mt=0;mtMath.cos(r)),w2={kernelName:qo,backendName:\"cpu\",kernelFunc:Cj};var Sj=Ie(Ko,r=>Math.cosh(r)),I2={kernelName:Ko,backendName:\"cpu\",kernelFunc:Sj};function wj(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=le([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let W=h>1?(P-R)*(l-1)/(h-1):0,V=g>1?(M-D)*(m-1)/(g-1):0;for(let U=0;U1?R*(l-1)+U*W:.5*(R+P)*(l-1);if(q<0||q>l-1){for(let H=0;H1?D*(m-1)+Z*V:.5*(D+M)*(m-1);if(ee<0||ee>m-1){for(let pe=0;pe1?D*(m-1)+H*V:.5*(D+M)*(m-1);if(j<0||j>m-1){for(let ee=0;eex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,w=b.top,k=d.outChannels/d.inChannels,_=new st(d.outShape,n.dtype),$=t.data.get(n.dataId).values,A=t.data.get(s.dataId).values,R=_.values;for(let D=0;D=d.inHeight)continue;let H=U*l[0],j=P+q*c[1];for(let X=0;X=d.inWidth)continue;let ie=H+Y*l[1],pe=j+J*d.inChannels,he=Z,we=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:w,strideWidth:k,filterHeight:_,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:D}=S.computeDilation2DInfo(o.shape,n.shape,s,a,\"NHWC\",i),P=y.sizeFromShape(D),M=D.length,L=y.getArrayFromDType(o.dtype,P);for(let V=0;V=0&&J=0&&peZ&&(Z=ve)}}}let ee=y.locToIndex([V,U,H,X],M,y.computeStrides(D));L[ee]=Z}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),D,o.dtype),shape:D,dtype:o.dtype}}};var D2={kernelName:bb,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===R.length,()=>`Error in ${bb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=ee,Z=J)}}}P[X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var O2={kernelName:yb,backendName:\"cpu\",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,\"NHWC\",p);y.assert(s.rank===R.length,()=>`Error in ${yb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=Y,Z=ie)}}}P[L][X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function La(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;K(n,\"sum\");let i;n.dtype===\"bool\"?i=Io({inputs:{x:n},backend:t,attrs:{dtype:\"int32\"}}):i=ar({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=S.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=Ct({inputs:{x:i},backend:t,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,p)),S.assertAxesAreInnerMostDims(\"sum\",l,m.shape.length);let[d,f]=S.computeOutAndReduceShapes(m.shape,l),h=S.upcastType(m.dtype,\"int32\"),g=Hp(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let w=0;w=0&&(m=La({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var M2={kernelName:ri,backendName:\"cpu\",kernelFunc:$j};function Aj(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;K([o,n],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let p=0;p=1?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,\"float32\",s)}var L2={kernelName:km,backendName:\"cpu\",kernelFunc:Aj};var Rj=S.ERF_P,Fj=S.ERF_A1,Dj=S.ERF_A2,Oj=S.ERF_A3,Pj=S.ERF_A4,Mj=S.ERF_A5,Lj=Ie(ma,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+Rj*t);return e*(1-((((Mj*o+Pj)*o+Oj)*o+Dj)*o+Fj)*o*Math.exp(-t*t))}),B2={kernelName:ma,backendName:\"cpu\",kernelFunc:Lj};function Jp(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Me({inputs:{x:n},backend:t,attrs:{shape:i}})}var V2={kernelName:bs,backendName:\"cpu\",kernelFunc:Jp};var Bj=Be((r,e)=>r/e),hl=Qe(Jo,Bj),gl={kernelName:Jo,backendName:\"cpu\",kernelFunc:hl};function vf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&CMath.floor(r/e)),qj=Qe(sn,Hj,null,\"int32\"),G2={kernelName:sn,backendName:\"cpu\",kernelFunc:qj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=NS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;if(c===\"NCHW\"&&a.shape.length===1&&a.shape[0]!==1){let x=Me({inputs:{x:a},backend:t,attrs:{shape:[a.shape[0],1,1]}});h=js({inputs:{a:h,b:x},backend:t}),t.disposeIntermediateTensorInfo(x)}else h=js({inputs:{a:h,b:a},backend:t});t.disposeIntermediateTensorInfo(g)}if(d){let g=h;if(c===\"NCHW\"&&d===\"prelu\"&&i.shape.length===1&&i.shape[0]!==1){let x=Me({inputs:{x:i},backend:t,attrs:{shape:[i.shape[0],1,1]}});h=_u(t,h,d,x,f),t.disposeIntermediateTensorInfo(x)}else h=_u(t,h,d,i,f);t.disposeIntermediateTensorInfo(g)}return h}var H2={kernelName:ho,backendName:\"cpu\",kernelFunc:Kj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=TS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;h=js({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(d){let g=h;h=_u(t,h,d,i,f),t.disposeIntermediateTensorInfo(g)}return h}var q2={kernelName:go,backendName:\"cpu\",kernelFunc:jj};function Xj(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=y.sizeFromShape(o.shape),a=n.shape,i=a[a.length-1],[p,u,c,l]=S.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(p,o.dtype,[]);let m=t.data.get(n.dataId).values,d=t.bufferSync(o),f=pf(m,d,o.dtype,u,i,c,l,o.shape,s);return t.makeTensorInfo(p,o.dtype,f.values)}var K2={kernelName:un,backendName:\"cpu\",kernelFunc:Xj};function Yj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o;K([n,s],\"gatherV2\");let p=y.parseAxisParam(a,n.shape)[0],u=t.data.get(s.dataId).values,c=n.shape[p];for(let w=0;w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=S.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Me({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Me({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=cf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var j2={kernelName:Ss,backendName:\"cpu\",kernelFunc:Yj};function Qj(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=Me({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=vf(i,!0,t),u=Me({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var X2={kernelName:ni,backendName:\"cpu\",kernelFunc:Qj};var 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Ce=O();Ce.registerFlag(\"HAS_WEBGL\",()=>Ce.getNumber(\"WEBGL_VERSION\")>0);Ce.registerFlag(\"WEBGL_VERSION\",()=>Ff(2)?2:Ff(1)?1:0);Ce.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Ce.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Ce.get(\"WEBGL_VERSION\")===2);Ce.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Ce.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Ce.registerFlag(\"WEBGL_PACK\",()=>Ce.getBool(\"HAS_WEBGL\"));Ce.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_CLIP\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Ce.getBool(\"WEBGL_PACK\"));Ce.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>XS(Ce.getNumber(\"WEBGL_VERSION\")));Ce.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>YS(Ce.getNumber(\"WEBGL_VERSION\")));Ce.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let 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outputColor;\",i=O().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",p=\"\",u=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",e=\"attribute\",t=\"varying\",o=\"varying\",n=\"texture2D\",s=\"gl_FragColor\",a=\"\",i=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,p=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n 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s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function kE(r,e,t=\"index\"){let o=r.map((s,a)=>a),n=B5(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join(\"\")}function nc(r){let e=y.computeStrides(r).map(t=>t.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;\n }\n`}function sc(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Df=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:NE}=S;function TE(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:\"\"};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 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e.isPacked?(u=z5(e.logicalShape,a,t.enableShapeUniforms),c=H5(i)):(u=W5(e.logicalShape,a,t.enableShapeUniforms),c=G5(i)),t.packedInputs&&(l+=Y5),[l,p,c,n,u,s,t.userCode].join(`\n`)}function ic(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return u8(r,e);case 1:return c8(r,e);case 2:return m8(r,e);case 3:return f8(r,e);case 4:return g8(r,e);case 5:return x8(r);case 6:return y8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function _E(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return i8(r);case 1:return p8(r,e);case 2:return l8(r,e);case 3:return d8(r,e);default:return h8(r,e)}}function V5(r,e,t=!1,o){let n=\"\";t?n+=_E(r,o):n+=ic(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=b8(r,e):n+=C8(r,e)),n}function z5(r,e,t){switch(r.length){case 0:return EE();case 1:return Q5(r,e,t);case 2:return s8(r,e,t);case 3:return J5(r,e,t);default:return t8(r,e,t)}}function W5(r,e,t){switch(r.length){case 0:return EE();case 1:return Z5(r,e,t);case 2:return a8(r,e,t);case 3:return e8(r,e,t);case 4:return r8(r,e,t);case 5:return o8(r,e);case 6:return n8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function U5(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function G5(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function H5(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function q5(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${K5}\n ${j5}\n ${X5}\n `}var K5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,j5=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,X5=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Y5=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function EE(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function Q5(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${o[1]}.0);\n }\n `:o[1]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${o[0]}.0);\n }\n `:t?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${o[0]}, ${o[1]}));\n return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);\n }\n `}function Z5(r,e,t){return e[0]===1?t?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${e[1]}.0);\n }\n `:e[1]===1?t?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${e[0]}.0);\n }\n `:t?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${e[0]}, ${e[1]}));\n return resTexRC.x * ${e[1]} + resTexRC.y;\n }\n `}function J5(r,e,t){if(t)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),s=n*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${o[0]}, ${o[1]}));\n int index = resTexRC.x * ${o[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${n});\n int c = imod(index, ${n}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function e8(r,e,t){if(t)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${Au([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let o=us([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n 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t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?Au([\"r\",\"c\",\"d\"],e):us([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=`\n ${Df}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var 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t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,RS(t,e)):this.gl=Wr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=ec(this.gl,s),Ur(this.gl,a))this.textureHalfFloatExtension=ec(this.gl,a);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Ur(this.gl,n))this.colorBufferHalfFloatExtension=ec(this.gl,n);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",Ur(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Ur(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=ow(this.gl),this.indexBuffer=nw(this.gl),this.framebuffer=GS(this.gl),this.textureConfig=Sl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),sw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),aw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),iw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),mw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),lw(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),pw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),uw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>hw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return gw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return fw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=dw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>xw(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rw(t));let o=LS(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),BS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(cw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&wl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&wl(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?HS(this.gl,e,t):qS(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),KS(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ys(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&wl(this.gl,this.program),tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ec(this.gl,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=v8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Il(this.gl,e,this.framebuffer),this.debug&&tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Il(this.gl,this.outputTexture,this.framebuffer),this.debug&&tc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Il(n,e,this.framebuffer),this.debug&&tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function v8(r){let e=0;for(;e`${r}.${t}`)}function $t(r,e){return e===1?[r]:bw(r,e)}function I$(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var lc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${k8(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?sc():nc(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function k8(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?kE([\"r\",\"c\",\"d\"],\"inputShape\"):us([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=k$(t,o),s=N$(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=v$(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=k$(o,n),a=N$(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=v$(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function N8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function v$(r,e,t,o,n){let s=T8(e,o),a;if(n){let[p,u]=Ys(r[0],r[1]);a=p*u}else{let[p,u]=$u(r[0],r[1]);a=p*u}let i=N8(t,s);return a*i}function T8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function _8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function k$(r,e){if(r===ir.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ir.RENDER||r==null)return _8(e);if(r===ir.DOWNLOAD||r===ir.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function N$(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Bt=\"if (isnan(x)) return x;\",T$=\"return x;\",Cw=\"return abs(x);\";var _$=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",E$=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,$$=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Qs=\"return x;\",A$=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var F$=\"return x;\",D$=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,O$=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,P$=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,M$=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Ar=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let t=e.length,o=$t(\"rc\",t),n=_e(t),s=I$(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var $8=Lt.whereImpl,A8=1e-7,R8=1e-4,Zf={};function F8(r){return r in Zf||(Zf[r]={}),Zf[r]}var D8=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),O8=600;function P8(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*O8/1024/1024}var Bi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Fu)t=e;else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Fu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"));t=new Fu(o),this.binaryCache=F8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=P8(),this.texData=new Do(this,cr())}nextDataId(){return Bi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=rc(t),c=new vl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ir.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ir.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Qs):m=new Jt(i,Qs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Qs):f=new Jt(n,Qs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Cl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Ar(s,Qs):d=new Jt(s,Qs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=cr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D8){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Va(e.shape),...za(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Va(t),...za(t)],a=new lc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=rc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Cl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Mi.DENSE){let x=a!=null?a:Cl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Li(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=FE(e,c,l),d=this.getAndSaveBinary(m,()=>AE(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||RE(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=jS(o,p),t.texShape=l),s!=null){let m=rc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ys(l[0],l[1])),p?d=new Vf(m,g):d=new vl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ir.PIXELS:C.usage=ir.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=M8(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await CC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?($f(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=tw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromTexture(e,t,o){let{texture:n,height:s,width:a,channels:i}=e,p=cr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return cr().makeTensorFromDataId(u,t,o,p)}};Bi.nextDataId=0;function M8(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Bi,2);var L9e={forceHalfFloat:L$};var mc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Zs=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var To=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ct(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${_e(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=$t(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var B$={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Rr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var V$={kernelName:ei,backendName:\"webgl\",kernelFunc:Rr};var Sw=\"return (a < 0.) ? b * a : a;\",ww=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(ww,n.shape,a.shape):new io(Sw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var z$={kernelName:mn,backendName:\"webgl\",kernelFunc:B8};var Iw=\"return (a < 0.) ? b * a : a;\",vw=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function V8(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(vw,o.shape,n.shape):new io(Iw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var W$={kernelName:Rn,backendName:\"webgl\",kernelFunc:V8};var _o=\"if (isnan(x)) return x;\";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,$],dt(w.dtype,k.dtype))}),b=Rr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new To(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Wa(r,e=!1){if(r===\"linear\")return e?F$:T$;if(r===\"relu\")return e?O$:E$;if(r===\"elu\")return e?D$:_$;if(r===\"relu6\")return e?P$:$$;if(r===\"prelu\")return e?vw:Iw;if(r===\"leakyrelu\")return e?ww:Sw;if(r===\"sigmoid\")return e?M$:A$;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var dc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ct(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Li(n.shape,p)&&!(c.texture!==null&&Li(c.shape,p))?H$(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var q$={kernelName:Ns,backendName:\"webgl\",kernelFunc:te};var _l=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function W8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Gr(r,e,t,o){let n=W8(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=_e(this.rank),s=bw(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),A=te({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,W=p===\"leakyrelu\",V=p!=null?Wa(p,!0):null,U=M||L||W||V!=null,q;if((d===1||f===1)&&P>Nw&&U===!1){let j=$,X=A;t&&(j=xt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),R.push(j)),o&&(X=xt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(X));let Z=f!==1,ee=f===1,Y=j;Z&&(Y=te({inputs:{x:j},backend:n,attrs:{shape:[D,P,1]}}),R.push(Y));let J=f===1?2:1,ie=X;ee&&(ie=te({inputs:{x:X},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let pe=Tl({inputs:{a:Y,b:ie},backend:n});q=Ou({inputs:{x:pe},backend:n,attrs:{axis:J,keepDims:!0}}),R.push(pe)}else{let j=dt(r.dtype,e.dtype),X=new dc(k,_,[D,d,f],t,o,M,V,L,W),Z=[$,A];if(s!=null&&Z.push(s),L&&Z.push(a),W){let ee=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));Z.push(ee),R.push(ee)}q=n.runWebGLProgram(X,Z,j)}let H=te({inputs:{x:q},backend:n,attrs:{shape:w}});R.push(q);for(let j of R)n.disposeIntermediateTensorInfo(j);return H}function G8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Pu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var Y$={kernelName:fo,backendName:\"webgl\",kernelFunc:G8};var Q$=\"return abs(x);\";function H8(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=Kf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Ar(o.shape,Q$):n=new Jt(o.shape,Q$),t.runWebGLProgram(n,[o],o.dtype)}var Z$={kernelName:gs,backendName:\"webgl\",kernelFunc:H8};var q8=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,K8=ge({opSnippet:q8}),J$={kernelName:sa,backendName:\"webgl\",kernelFunc:K8};var j8=Bt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,backendName:\"webgl\",kernelFunc:X8};var tA=\"return a + b;\",Y8=tt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DE}),rA={kernelName:eo,backendName:\"webgl\",kernelFunc:Y8};var rh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var oh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var oA={kernelName:Mo,backendName:\"webgl\",kernelFunc:nh};function Q8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"all\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Lo,backendName:\"webgl\",kernelFunc:Q8};function Z8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"any\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Bo,backendName:\"webgl\",kernelFunc:Z8};var sh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=_e(p),c=$t(\"coords\",p),l,m;if(a===1){m=p+1;let A=_e(m);l=`\n ${A} sourceLocR = ${A}(${c.join()}, 0);\n ++${c[p-1]};\n ${A} sourceLocG = ${A}(${c.join()}, 0);\n ++${c[p-2]};\n ${A} sourceLocA = ${A}(${c.join()}, 0);\n --${c[p-1]};\n ${A} sourceLocB = ${A}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=$t(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=$t(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=$t(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=$t(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,$=n?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${$}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n ${l}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${k}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function aA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=aA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=iA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,e,o)}function J8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Vo,backendName:\"webgl\",kernelFunc:J8};function eY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Za,backendName:\"webgl\",kernelFunc:eY};var tY=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,rY=ge({opSnippet:tY}),cA={kernelName:ia,backendName:\"webgl\",kernelFunc:rY};var oY=Bt+\"return log(x + sqrt(x * x + 1.0));\",nY=ge({opSnippet:oY}),lA={kernelName:ua,backendName:\"webgl\",kernelFunc:nY};var sY=Bt+`\n return atan(x);\n`,aY=ge({opSnippet:sY}),mA={kernelName:pa,backendName:\"webgl\",kernelFunc:aY};var iY=mc+`\n return atan(a, b);\n`,uY=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Zs+`\n return result;\n`,pY=tt({opSnippet:iY,packedOpSnippet:uY}),dA={kernelName:la,backendName:\"webgl\",kernelFunc:pY};var cY=Bt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelName:ca,backendName:\"webgl\",kernelFunc:lY};var ps=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let A=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${A} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(w=\"avgValue / count\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${w});\n }\n `}},zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",w=\"0.0\";if(C||(w=\"-1.0 / 1e-20\"),o){let D=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / count\");let $=Math.floor(a/4)*4,A=a%4,R=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${w};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${w});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${R}\n }\n\n int xC = xCCorner + ${$};\n if (${A===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${R}\n }\n }\n setOutput(${_});\n }\n }\n `}};function mY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;is(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var hA={kernelName:zo,backendName:\"webgl\",kernelFunc:mY};function dY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new zi(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var gA={kernelName:ip,backendName:\"webgl\",kernelFunc:dY};var uh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function fY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Im,backendName:\"webgl\",kernelFunc:fY};function 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offset);\n }\n `}};var xY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:an,backendName:\"webgl\",kernelFunc:xY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let 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So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function RA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function TY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[RA(o,n.complexTensorInfos.real),RA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var FA={kernelName:pp,backendName:\"webgl\",kernelFunc:TY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${yh(i,u,g)}),\n vec2(${yh(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${yh(i,u,f)}),\n vec2(${yh(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Mu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var DA={kernelName:si,backendName:\"webgl\",kernelFunc:Mu};function fc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ua({inputs:{input:b},backend:t})),f=r.map(b=>Mu({inputs:{input:b},backend:t})),h=fc(d,e,t),g=fc(f,e,t),x=Rr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return te({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=LE(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Qs):new Ar(r[0].shape,Qs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=_Y(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function _Y(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function $w(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):fc(p,s,t)}var OA={kernelName:ys,backendName:\"webgl\",kernelFunc:$w};var hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?w=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:w=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${w}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Nw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Li(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let A=Pu({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Pu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",$),R=te({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Wa(i,!0):null,W=new dc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),V=f?[R,k]:[k,R];if(n&&V.push(n),P&&V.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));V.push(H),w.push(H)}let U=o.runWebGLProgram(W,V,\"float32\"),q=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return q}function EY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new gc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new hc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var PA={kernelName:Go,backendName:\"webgl\",kernelFunc:EY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${a}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var MA={kernelName:cp,backendName:\"webgl\",kernelFunc:$Y};function AY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var LA={kernelName:Ho,backendName:\"webgl\",kernelFunc:AY};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var BA={kernelName:lp,backendName:\"webgl\",kernelFunc:RY};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var VA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var zA={kernelName:mp,backendName:\"webgl\",kernelFunc:DY};var OY=_o+`\n return cos(x);\n`,PY=ge({opSnippet:OY}),WA={kernelName:qo,backendName:\"webgl\",kernelFunc:PY};var MY=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,LY=ge({opSnippet:MY}),UA={kernelName:Ko,backendName:\"webgl\",kernelFunc:LY};var Eh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${w};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var BY=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Eh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},GA={kernelName:Yo,backendName:\"webgl\",kernelFunc:BY};var Lu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Lu||(Lu={}));var El=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Lu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${HA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${_e(s)} coords = getOutputCoords();\n int end = ${qA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${qA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${HA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function HA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function qA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function $h(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=xt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new El(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new El(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=xt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function VY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Prod,n,t,s,a,i)}var KA={kernelName:jo,backendName:\"webgl\",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Sum,n,t,s,a,i)}var jA={kernelName:Xo,backendName:\"webgl\",kernelFunc:zY};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=qf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=OE(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ti,backendName:\"webgl\",kernelFunc:WY};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:Qo,backendName:\"webgl\",kernelFunc:UY};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new yc(l):m=new xc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var QA={kernelName:Zo,backendName:\"webgl\",kernelFunc:GY};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Fh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var ZA={kernelName:dp,backendName:\"webgl\",kernelFunc:HY};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Fh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var JA={kernelName:fp,backendName:\"webgl\",kernelFunc:qY};var Dh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function KY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var eR={kernelName:hp,backendName:\"webgl\",kernelFunc:KY};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var tR={kernelName:gp,backendName:\"webgl\",kernelFunc:jY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ou({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var rR={kernelName:ri,backendName:\"webgl\",kernelFunc:XY};var YY=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",QY=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,ZY=ge({opSnippet:YY,packedOpSnippet:QY}),oR={kernelName:en,backendName:\"webgl\",kernelFunc:ZY};var JY=\"return (b >= 1.0) ? a : a * (b + 1.0);\",eQ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,tQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(eQ,o.shape,n.shape):new io(JY,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},nR={kernelName:km,backendName:\"webgl\",kernelFunc:tQ};var rQ=`\n return vec4(equal(a, b));\n`,oQ=\"return float(a == b);\",nQ=tt({opSnippet:oQ,packedOpSnippet:rQ,dtype:\"bool\",cpuKernelImpl:BE}),sR={kernelName:tn,backendName:\"webgl\",kernelFunc:nQ};var sQ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,aQ=ge({opSnippet:sQ}),aR={kernelName:ma,backendName:\"webgl\",kernelFunc:aQ};var iQ=_o+`\n return exp(x);\n`,uQ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aw=ge({opSnippet:iQ,packedOpSnippet:uQ,cpuKernelImpl:VE,dtype:\"float32\"}),iR={kernelName:rn,backendName:\"webgl\",kernelFunc:Aw};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uR={kernelName:bs,backendName:\"webgl\",kernelFunc:Ph};var pR=\"return exp(x) - 1.0;\",pQ=ge({opSnippet:pR,packedOpSnippet:pR,cpuKernelImpl:zE}),cR={kernelName:da,backendName:\"webgl\",kernelFunc:pQ};var $l=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new $l(\"real\",p,e),c=new $l(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Rr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function cQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var lR={kernelName:oi,backendName:\"webgl\",kernelFunc:cQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ga(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var mR={kernelName:Cs,backendName:\"webgl\",kernelFunc:Ga};var Bh=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var dR={kernelName:on,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var fR=\"return floor(x);\",lQ=ge({opSnippet:fR,packedOpSnippet:fR,cpuKernelImpl:WE}),hR={kernelName:nn,backendName:\"webgl\",kernelFunc:lQ};var mQ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,dQ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,fQ=tt({opSnippet:mQ,packedOpSnippet:dQ,dtype:\"int32\"}),gR={kernelName:sn,backendName:\"webgl\",kernelFunc:fQ};var Vh=class{constructor(e){this.variableNames=[\"A\"];let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var zh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var xR={kernelName:Zi,backendName:\"webgl\",kernelFunc:hQ},bc,Rw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(bc==null||h!==Rw)&&(Rw=h,bc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Rw})),bc.canvas.width=p,bc.canvas.height=u,bc.drawImage(n,0,0,p,u),n=bc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ir.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=te({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return A};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Wa(d,!0):null,R=new gc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Wa(d,!1):null,R=new hc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),$}var yR={kernelName:ho,backendName:\"webgl\",kernelFunc:gQ};function xQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=_e(this.rank),n=bQ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function bQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=GE(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var SR={kernelName:Ss,backendName:\"webgl\",kernelFunc:Fw};var CQ=\"return float(a > b);\",SQ=`\n return vec4(greaterThan(a, b));\n`,wQ=tt({opSnippet:CQ,packedOpSnippet:SQ,cpuKernelImpl:HE,dtype:\"bool\"}),wR={kernelName:pn,backendName:\"webgl\",kernelFunc:wQ};var IQ=\"return float(a >= b);\",vQ=`\n return vec4(greaterThanEqual(a, b));\n`,kQ=tt({opSnippet:IQ,packedOpSnippet:vQ,dtype:\"bool\",cpuKernelImpl:qE}),IR={kernelName:cn,backendName:\"webgl\",kernelFunc:kQ};function NQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var vR={kernelName:ni,backendName:\"webgl\",kernelFunc:NQ};var TQ=\"return float(!isnan(x) && !isinf(x));\",_Q=ge({opSnippet:TQ,dtype:\"bool\"}),kR={kernelName:fa,backendName:\"webgl\",kernelFunc:_Q};var EQ=\"return float(isinf(x));\",$Q=ge({opSnippet:EQ,dtype:\"bool\"}),NR={kernelName:ha,backendName:\"webgl\",kernelFunc:$Q};var AQ=\"return float(isnan(x));\",RQ=ge({opSnippet:AQ,dtype:\"bool\"}),TR={kernelName:ln,backendName:\"webgl\",kernelFunc:RQ};var FQ=\"return float(a < b);\",DQ=`\n return vec4(lessThan(a, b));\n`,OQ=tt({opSnippet:FQ,packedOpSnippet:DQ,cpuKernelImpl:KE,dtype:\"bool\"}),_R={kernelName:dn,backendName:\"webgl\",kernelFunc:OQ};var PQ=\"return float(a <= b);\",MQ=`\n return vec4(lessThanEqual(a, b));\n`,LQ=tt({opSnippet:PQ,packedOpSnippet:MQ,cpuKernelImpl:jE,dtype:\"bool\"}),ER={kernelName:fn,backendName:\"webgl\",kernelFunc:LQ};function BQ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=XE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var $R={kernelName:xp,backendName:\"webgl\",kernelFunc:BQ};var VQ=_o+`\n return x < 0.0 ? 0./0. : log(x);\n`,zQ=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,WQ=ge({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:YE}),AR={kernelName:hn,backendName:\"webgl\",kernelFunc:WQ};var UQ=_o+`\n return log(1.0 + x);\n`,GQ=ge({opSnippet:UQ}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:GQ};var HQ=\"return float(a >= 1.0 && b >= 1.0);\",qQ=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,KQ=tt({opSnippet:HQ,packedOpSnippet:qQ,dtype:\"bool\"}),FR={kernelName:gn,backendName:\"webgl\",kernelFunc:KQ};var jQ=\"return float(!(x >= 1.0));\",XQ=ge({opSnippet:jQ}),DR={kernelName:xn,backendName:\"webgl\",kernelFunc:XQ};var YQ=\"return float(a >= 1.0 || b >= 1.0);\",QQ=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,ZQ=tt({opSnippet:YQ,packedOpSnippet:QQ,dtype:\"bool\"}),OR={kernelName:xa,backendName:\"webgl\",kernelFunc:ZQ};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var JQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},PR={kernelName:yp,backendName:\"webgl\",kernelFunc:JQ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var e7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},MR={kernelName:Nm,backendName:\"webgl\",kernelFunc:e7};function LR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Gr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Dw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var zR={kernelName:Cn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new zi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var WR={kernelName:bp,backendName:\"webgl\",kernelFunc:s7};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new zi(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var UR={kernelName:_m,backendName:\"webgl\",kernelFunc:a7};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;is([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new ps(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new Kh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GR={kernelName:Tm,backendName:\"webgl\",kernelFunc:i7};function HR(r,e,t,o){let n=new ps(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new ps(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var qR={kernelName:Cp,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var mF={kernelName:vs,backendName:\"webgl\",kernelFunc:E7};var Jh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=_e(n),a=t.map(u=>u[0]).join(\",\"),i=t.map((u,c)=>u[0]+e[c]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n }\n `}};var eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=_e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=$t(\"rc\",n),u=$t(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ga({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},dF={kernelName:$n,backendName:\"webgl\",kernelFunc:Lw};var $7=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,A7=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=S.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var hF={kernelName:Fn,backendName:\"webgl\",kernelFunc:F7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=n$(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var gF={kernelName:wp,backendName:\"webgl\",kernelFunc:D7};function O7(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=s$(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var xF={kernelName:Ip,backendName:\"webgl\",kernelFunc:O7};function P7(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=a$(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var yF={kernelName:vp,backendName:\"webgl\",kernelFunc:P7};var Bw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=i$(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},bF={kernelName:ks,backendName:\"webgl\",kernelFunc:Bw};var M7=\"return 1.0 / x;\",L7=ge({opSnippet:M7}),CF={kernelName:Dn,backendName:\"webgl\",kernelFunc:L7};var B7=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,V7=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z7=ge({opSnippet:B7,packedOpSnippet:V7}),SF={kernelName:On,backendName:\"webgl\",kernelFunc:z7};var W7=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,U7=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,G7=ge({opSnippet:W7,packedOpSnippet:U7}),wF={kernelName:Ln,backendName:\"webgl\",kernelFunc:G7};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function H7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var IF={kernelName:Mn,backendName:\"webgl\",kernelFunc:H7};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var vF={kernelName:$m,backendName:\"webgl\",kernelFunc:q7};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var kF={kernelName:Pn,backendName:\"webgl\",kernelFunc:K7};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var NF={kernelName:Em,backendName:\"webgl\",kernelFunc:j7};var ig=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=_e(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=$t(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=_e(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var TF={kernelName:Bn,backendName:\"webgl\",kernelFunc:X7};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var _F={kernelName:es,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Y7=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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eZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Cc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var AF={kernelName:zn,backendName:\"webgl\",kernelFunc:eZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=m$(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var zF={kernelName:ui,backendName:\"webgl\",kernelFunc:xZ};function yZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=d$(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var WF={kernelName:wa,backendName:\"webgl\",kernelFunc:yZ};function bZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var UF={kernelName:pi,backendName:\"webgl\",kernelFunc:bZ};function CZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 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fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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pD={kernelName:kp,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var cD={kernelName:Rs,backendName:\"webgl\",kernelFunc:VZ};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n 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segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function 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Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function $o(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function se(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function T3(r){let e;return e=`\n ${Eee()}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,e}function Eee(){return`\n @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ)\n`}function $ee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n const workgroupSizeX = ${t.workgroupSize[0]}u;\n const workgroupSizeY = ${t.workgroupSize[1]}u;\n const workgroupSizeZ = ${t.workgroupSize[2]}u;\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${F3(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=$3(t);return[_3,o.join(`\n`),E3(e.shape),t.getUserCode(),T3(f)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=Lee(s),o.push(s),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:wc(r[h].dtype,t.isVec4)}>;\n `)}),s!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=Oee(e.shape,t.dispatchLayout),c=[_3+Aee,o.join(`\n`),E3(e.shape),u,Pee(e.shape.length)];t.atomic||c.push(Mee(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>Dee(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=$3(t);return c.push(T3(m)),c.join(`\n`)}function R3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=F3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var _3=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n }\n`,Aee=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function E3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 {\n let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n return vec2(d0, d1);\n }`;let s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`;return`${p}; ${u};`}).join(\"\"),`\n fn getCoordsFromIndex(index : i32) -> ${o} {\n ${s}\n return ${o}(${n.join(\",\")});\n }\n `}function Ree(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n fn ${s}() -> vec4 {\n return vec4(${t}[0]);\n }\n `:`\n fn ${s}() ->f32 {\n return f32(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n fn ${s}(${i}) -> vec4 {\n return vec4(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p}) / 4]);\n }\n `:`\n fn ${s}(${i}) -> f32 {\n return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})]);\n }\n `}function Fee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return vec4(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return vec4(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n return f32(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> f32 {\n return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n }\n `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return get${s}();\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> f32{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${$o(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${$o(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> vec4 {\n var coords = coordsIn;\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> f32 {\n var coords = coordsIn;\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n`}function Dee(r,e,t,o){let n=Ree(r,t);return r.shape.length<=e.length&&(n+=Fee(r,e,t,o)),n}function Oee(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function Pee(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function F3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function wc(r,e){return r===\"float32\"?e?\"vec4\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4\":\"i32\":r}function Mee(r,e,t){let o=r.length,n=wc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n result[flatIndex] = ${n}(value);\n }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex / 4, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex / 4, value);\n }\n `:s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex, value);\n }\n `}return s}function Lee(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function $3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var nI={};Ue(nI,{ArrayBufferToTypedArray:()=>oI,GPUBytesPerElement:()=>rI,MatMulProgramType:()=>Ao,computeDispatch:()=>re,computeWorkPerThreadForConv2d:()=>Ll,computeWorkgroupInfoForMatMul:()=>tI,computeWorkgroupSizeForConv2d:()=>Ml,flatDispatchLayout:()=>ue,isWebGPUSupported:()=>Bl,tilesFitEvenlyIntoShape:()=>Vee});var zu=r=>{let e=1;for(let t=0;tt%r[o]===0)}function re(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(zu(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(zu(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(zu(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function tI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Ml(r,e,t=!1){if(t)return[8,8,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Ll(r,e,t=!1){if(t)return[4,4,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ue(r){return{x:r.map((e,t)=>t)}}function rI(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function oI(r,e){if(e===\"float32\")return new Float32Array(r);if(e===\"int32\")return new Int32Array(r);if(e===\"bool\"||e===\"string\")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${e}`)}function Bl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var Ao;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ao||(Ao={}));var zee=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Wee=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Ui=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Bl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Do(this,cr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Ui.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=oI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=cr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=rI(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[];e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4});let s=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(s,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(s,c,p.data.length).set(p.data):new Float32Array(s,c,p.data.length).set(p.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,s,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Wee(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=R3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=A3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=zee){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Ui(n,s)},3);var ye;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.MAX=12]=\"MAX\",r[r.MIN=13]=\"MIN\",r[r.MOD=14]=\"MOD\",r[r.MUL=15]=\"MUL\",r[r.NOT_EQUAL=16]=\"NOT_EQUAL\",r[r.POW=17]=\"POW\",r[r.PRELU=18]=\"PRELU\",r[r.SQUARED_DIFFERENCE=19]=\"SQUARED_DIFFERENCE\",r[r.SUB=20]=\"SUB\"})(ye||(ye={}));var D3=`\n if (isnan(a)) { return a; }\n if (isnan(b)) { return b; }\n `,O3=`\n if (isNaN.r) {\n resultTemp.r = valueForNaN;\n }\n if (isNaN.g) {\n resultTemp.g = valueForNaN;\n }\n if (isNaN.b) {\n resultTemp.b = valueForNaN;\n }\n if (isNaN.a) {\n resultTemp.a = valueForNaN;\n }\n `,aI=`\n let isNaN = isnanVec4(a) | isnanVec4(b);\n ${O3}\n `,Uee=\"return a + b;\",Gee=\"return areal * breal - aimag * bimag;\",Hee=\"return areal * bimag + aimag * breal;\",qee=\"return a / b;\",Kee=\"return f32(a == b);\",jee=\"return vec4(a == b);\",Xee=\"return f32(a > b);\",Yee=\"return vec4(a > b);\",Qee=\"return f32(a >= b);\",Zee=\"return vec4(a >= b);\",Jee=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,ete=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,tte=\"return f32(a < b);\",rte=\"return vec4(a < b);\",ote=\"return f32(a <= b);\",nte=\"return vec4(a <= b);\",ste=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",ate=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,ite=`\n ${D3}\n if (b == 0.) {\n return uniforms.NAN;\n }\n var resultTemp = a % b;\n if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n return resultTemp;\n } else {\n return (resultTemp + b) % b;\n }\n`,ute=`\n let valueForNaN = uniforms.NAN;\n var resultTemp = vec4(a % b);\n ${aI}\n\n if (b[0] == 0.) {\n resultTemp[0] = uniforms.NAN;\n }\n if (b[1] == 0.) {\n resultTemp[1] = uniforms.NAN;\n }\n if (b[2] == 0.) {\n resultTemp[2] = uniforms.NAN;\n }\n if (b[3] == 0.) {\n resultTemp[3] = uniforms.NAN;\n }\n\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n\n return resultTemp;\n`,pte=\"return a * b;\",cte=`\n if (isnan(a) || isnan(b)) {\n return 1.0;\n }\n return f32(a != b);\n`,lte=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n ${aI}\n\n return resultTemp;\n`,mte=`\n if(a < 0.0 && floor(b) < b) {\n return uniforms.NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n if (round(abs(b) % 2.0) != 1.0) {\n return pow(abs(a), b);\n }\n return sign(a) * pow(abs(a), b);\n`,dte=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n let valueForNaN = uniforms.NAN;\n ${O3}\n return resultTemp;\n`,fte=\"if (a < 0.0) { return b * a; } return a;\",hte=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,gte=\"return (a - b) * (a - b);\",xte=\"return a - b;\";function sI(r,e,t=\"uniforms.NAN\"){let o=e?aI:D3;return e?`\n let valueForNaN = ${t};\n var resultTemp = vec4(${r}(a, b));\n `+o+`\n return resultTemp;\n `:o+`\n return ${r}(a, b);\n `}function Ic(r,e){switch(r){case ye.ADD:return Uee;case ye.ATAN2:return sI(\"atan2\",e);case ye.COMPLEX_MULTIPLY_IMAG:return Hee;case ye.COMPLEX_MULTIPLY_REAL:return Gee;case ye.DIV:return qee;case ye.EQUAL:return e?jee:Kee;case ye.GREATER:return e?Yee:Xee;case ye.GREATER_EQUAL:return e?Zee:Qee;case ye.INT_DIV:return e?ete:Jee;case ye.LESS:return e?rte:tte;case ye.LESS_EQUAL:return e?nte:ote;case ye.LOGICAL_AND:return e?ate:ste;case ye.MAX:return sI(\"max\",e);case ye.MIN:return sI(\"min\",e);case ye.MOD:return e?ute:ite;case ye.MUL:return pte;case ye.NOT_EQUAL:return e?lte:cte;case ye.POW:return e?dte:mte;case ye.PRELU:return e?hte:fte;case ye.SQUARED_DIFFERENCE:return gte;case ye.SUB:return xte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.RSQRT=27]=\"RSQRT\",r[r.SIN=28]=\"SIN\",r[r.SINH=29]=\"SINH\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SQRT=31]=\"SQRT\",r[r.SQUARE=32]=\"SQUARE\",r[r.TAN=33]=\"TAN\",r[r.TANH=34]=\"TANH\",r[r.TO_INT=35]=\"TO_INT\"})(Q||(Q={}));var yte=\"return abs(a);\",bte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Cte=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,Ste=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,wte=\"return asinh(a);\",Ite=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,vte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,kte=\"return ceil(a);\",Nte=\"return cos(a);\",Tte=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,_te=\"return exp(a) - 1.0;\",Ete=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",$te=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,Ate=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${S.ERF_P};\n let a1 = ${S.ERF_A1};\n let a2 = ${S.ERF_A2};\n let a3 = ${S.ERF_A3};\n let a4 = ${S.ERF_A4};\n let a5 = ${S.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,Rte=\"return exp(a);\",Fte=\"return floor(a);\",Dte=\"return f32(!isnan(a) && !isinf(a));\",Ote=\"return f32(isinf(a));\",Pte=\"return f32(isnan(a));\",Mte=\"return a;\",Lte=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,Bte=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Vte=\"return f32(!(a >= 1.0));\",zte=\"return -a;\",Wte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Ute=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Gte=\"return 1.0 / a;\",Hte=\"return select(a, 0.0, a < 0.0);\",qte=\"return clamp(a, 0.0, 6.0);\",Kte=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",jte=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Xte=\"return inverseSqrt(a);\",Yte=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Qte=\"return sin(a);\",Zte=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Jte=\"return sqrt(a);\",ere=\"return a * a;\",tre=\"return tan(a);\",rre=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,ore=\"return f32(i32((a)));\";function Ha(r,e){switch(r){case Q.ABS:return yte;case Q.ACOS:return bte;case Q.ACOSH:return Cte;case Q.ASIN:return Ste;case Q.ASINH:return wte;case Q.ATAN:return Ite;case Q.ATANH:return vte;case Q.COS:return Nte;case Q.COSH:return Tte;case Q.CEIL:return kte;case Q.ELU:return e?$te:Ete;case Q.ERF:return Ate;case Q.EXP:return Rte;case Q.EXPM1:return _te;case Q.FLOOR:return Fte;case Q.IS_FINITE:return Dte;case Q.IS_INF:return Ote;case Q.IS_NAN:return Pte;case Q.LINEAR:return Mte;case Q.LOG:return Lte;case Q.LOG1P:return Bte;case Q.LOGICAL_NOT:return Vte;case Q.NEG:return zte;case Q.LEAKYRELU:return e?Ute:Wte;case Q.RECIPROCAL:return Gte;case Q.RELU:return e?jte:Hte;case Q.RELU6:return e?Kte:qte;case Q.RSQRT:return Xte;case Q.SIGMOID:return Yte;case Q.SIN:return Qte;case Q.SINH:return Zte;case Q.SQRT:return Jte;case Q.SQUARE:return ere;case Q.TAN:return tre;case Q.TANH:return rre;case Q.TO_INT:return ore;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error(`${r}-component is not supported.`)}};function ur(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Ha(Q.LINEAR);else if(r===\"relu\")n=Ha(Q.RELU,t);else if(r===\"elu\")n=Ha(Q.ELU,t);else if(r===\"relu6\")n=Ha(Q.RELU6,t);else if(r===\"prelu\")n=Ic(ye.PRELU,t);else if(r===\"sigmoid\")n=Ha(Q.SIGMOID,t);else if(r===\"leakyrelu\")n=Ha(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Hr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function iI(r,e,t,o,n=!1,s=!1,a=!1,i=1){y.assert(t&&i===1||!t,()=>`transposeA ${t} is not compatible with component size ${i}`);let p=`\n let batch = ${r?\"0\":\"batchIn\"};\n ${t?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,u=o?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n var value = ${kt(i)}(0.0);\n let col = colIn * ${i};\n ${n&&a?p:`\n ${t?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${p}\n }\n `}\n return value;\n }\n\n fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n let col = colIn * ${i};\n let batch = ${e?\"0\":\"batchIn\"};\n var value = ${kt(i)}(0.0);\n ${u}\n return value;\n }\n `}function Vl(r,e,t,o,n,s,a=!1,i=!1,p=!1,u=1){return`\n ${iI(t,o,n,s,a,i,p,u)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) {\n let col = colIn * ${u};\n ${a&&i?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Hr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var nre=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol);\n `,sre=(r,e)=>r?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${e===3?\"\":\"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];\"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n }`;function Wu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const innerElementSize = ${l};\n const tileInner = ${o};\n\n ${se()} {\n let localRow = i32(localId.y);\n let tileRow = ${a?\"0\":\"localRow * rowPerThread\"};\n let tileCol = i32(localId.x);\n\n let globalRow = ${a?\"0\":\"i32(globalId.y) * rowPerThread\"};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${nre(t)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${l===3?\"\":\"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];\"}\n\n ${sre(t,l)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var P3=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,are=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Uu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=r[1]*e[1],p=r[0]*e[0],u=t?i:o,c=t?o:i;y.assert(c%e[1]===0&&u%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let l=c/e[1],m=u/e[0],d=o/e[1],f=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${i};\n let globalColStart = i32(workgroupId.x) * ${p};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n ${P3(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * rowPerThread;\n let tileCol = i32(localId.x) * colPerThread;\n\n let globalRow = i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x) * colPerThread;\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let tileRowA = i32(localId.y) * ${l};\n let tileColA = i32(localId.x) * ${m};\n let tileRowB = i32(localId.y) * ${d};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${P3(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${are(t)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${c}>;\n var mm_Bsub : array, ${o}>;\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const tileInner = ${o};\n\n ${se()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${f}\n }\n `}var ire=r=>r?`\n mm_readA(batch, colA, globalRow),\n mm_readA(batch, colA + 1, globalRow),\n mm_readA(batch, colA + 2, globalRow),\n mm_readA(batch, colA + 3, globalRow)\n `:`\n mm_readA(batch, globalRow, colA),\n mm_readA(batch, globalRow, colA + 1),\n mm_readA(batch, globalRow, colA + 2),\n mm_readA(batch, globalRow, colA + 3)\n `;function ure(r,e=!1){return y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`),`\n const tileSize = ${r[0]*4};\n var mm_Asub : array, ${r[0]}>;\n\n ${se()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / tileSize + 1;\n let batch = i32(globalId.z);\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n let colA = t * tileSize + tileCol * 4;\n mm_Asub[tileCol] = vec4(${ire(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileSize / 4; k = k + 1) {\n let rowB = t * tileSize + k * 4;\n let BCached = vec4(mm_readB(batch, rowB, globalCol),\n mm_readB(batch, rowB + 1, globalCol),\n mm_readB(batch, rowB + 2, globalCol),\n mm_readB(batch, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var _g=class{constructor(e,t,o,n,s=!1,a=!1,i=null,p=null,u=null,c=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];if(this.isVec4=(l%4===0&&!s||t[1]%4===0&&s)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!s,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let f=tI(t[1],l,t[2],s);this.workgroupSize=f.workgroupSize,this.elementsPerThread=f.elementsPerThread}this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let m=i!=null,d=u!=null;m&&this.variableNames.push(\"bias\"),d&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=c,this.transposeA=s,this.transposeB=a,this.addBias=m,this.activation=p,this.hasPreluActivationWeights=d,this.batchAEqualOne=o,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?ure(this.workgroupSize,this.transposeA):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)}\n `}};function pre(){return`\n var sumValues : array;\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) {\n let dataA = mm_readA(batch, row, k);\n let dataB = mm_readB(batch, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = workgroupSizeX / 2u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Eg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize);let u=a!=null,c=p!=null;u&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=t,this.batchBEqualOne=o,this.shaderKey=`matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${pre()}\n `}};function cre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${se()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batch, globalRow, globalColA);\n var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batch, globalRow, globalColA);\n regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var $g=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${cre(this.workgroupSize)}\n `}};var Ag=class{constructor(e,t,o,n,s=!1,a=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(s&&this.outputShape[1]%4===0||!s&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=re(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=s,this.transposeB=a,this.batchAEqualOne=o,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=n=>`\n for (var i = 0; i < ${n}; i = i + 1)\n {\n var oldValue = atomicLoad(&(result[flatIndex + i]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + ${n>1?\"value[i]\":\"value\"};\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n `,t=this.isVec4?4:1;return`\n ${iI(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t)}) {\n let col = colIn * ${t};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n ${e(t)}\n }\n }\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Rg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Hr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Fg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function dr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Fg(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var M3={kernelName:Cs,backendName:\"webgpu\",kernelFunc:dr};function de(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var L3={kernelName:Ns,backendName:\"webgpu\",kernelFunc:de};function Gu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=br.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=de({inputs:{x:r},backend:n,attrs:{shape:k}}),A=de({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=x===1,M=b===1,L=[$,A],W=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],V,U,q=[D,d,f],H=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(H<0){let X=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Z=X>0?X:n.thresholdToIncreaseWorkgroups,ee=D*Math.ceil(d/32)*Math.ceil(f/32);ee<=Z||d<=8&&ee<=Z*2?D*d*f<=128?H=Ao.MatMulReduceProgram:D===1&&m>=2e3?H=Ao.MatMulSplitKProgram:H=Ao.MatMulSmallOutputSizeProgram:H=Ao.MatMulPackedProgram}switch(H){case Ao.MatMulReduceProgram:V=new Eg(q,P,M,t,o,s,p,a);break;case Ao.MatMulSplitKProgram:{if(U=dr({backend:n,attrs:{shape:q,value:0,dtype:r.dtype}}),V=new Ag(q,m,P,M,t,o),s||p){U=n.runWebGPUProgram(V,L,r.dtype,W,U);let Z=new Rg(U.shape,s,p,a),ee=null,Y=[U];s&&Y.push(s),a&&Y.push(a),p===\"leakyrelu\"&&(ee=[{type:\"float32\",data:[i]}],Z.uniforms+=\" alpha : f32,\");let J=n.runWebGPUProgram(Z,Y,U.dtype,ee);R.push(U);let ie=de({inputs:{x:J},backend:n,attrs:{shape:w}});R.push(J);for(let pe of R)n.disposeData(pe.dataId);return ie}break}case Ao.MatMulSmallOutputSizeProgram:V=new $g(k,_,q,t,o,s,p,a);break;case Ao.MatMulPackedProgram:let X=n.adapterInfo.isIntel();V=new _g(k,q,P,M,t,o,s,p,a,X);break;default:throw new Error(`Unsupported MatMulProgramType ${H}.`)}s&&L.push(s),a&&L.push(a),p===\"leakyrelu\"&&(W.push({type:\"float32\",data:[i]}),V.uniforms+=\" alpha : f32,\"),U=n.runWebGPUProgram(V,L,r.dtype,W,U);let j=de({inputs:{x:U},backend:n,attrs:{shape:w}});R.push(U);for(let X of R)n.disposeData(X.dataId);return j}function lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Gu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var B3={kernelName:fo,backendName:\"webgpu\",kernelFunc:lre};var zl=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Ic(this.op,!1)}\n }\n\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Hu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Ic(this.op,this.isVec4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${se(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n let b = getBByOutputIndex(index);\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function Ft(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var V3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Ft};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:t}),p=Ft({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var z3={kernelName:ei,backendName:\"webgpu\",kernelFunc:po};var Ro=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let o=128;this.workgroupSize=[o,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Ha(this.op,!1)}\n }\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function Se({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Ro(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function ot({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ye.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new zl(ye.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new zl(ye.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:W3,castImpl:U3,ceilImpl:G3,concatImpl:H3,equalImpl:q3,expImpl:K3,expm1Impl:j3,floorImpl:X3,gatherNdImpl:Y3,gatherV2Impl:Q3,greaterEqualImpl:Z3,greaterImpl:J3,lessEqualImpl:eM,lessImpl:tM,logImpl:rM,maxImpl:oM,maximumImpl:nM,minimumImpl:sM,multiplyImpl:aM,negImpl:iM,notEqualImpl:uM,prodImpl:pM,rangeImpl:cM,rsqrtImpl:lM,scatterImpl:mM,simpleAbsImpl:dM,sliceImpl:fM,stridedSliceImpl:hM,stringNGramsImpl:gM,subImpl:xM,tileImpl:yM,topKImpl:bM,transposeImpl:CM,uniqueImpl:kNt}=Qp;var mre=Se({opType:Q.ABS,cpuKernelImpl:dM}),SM={kernelName:gs,backendName:\"webgpu\",kernelFunc:mre};var dre=Se({opType:Q.ACOS}),wM={kernelName:sa,backendName:\"webgpu\",kernelFunc:dre};var fre=Se({opType:Q.ACOSH}),IM={kernelName:aa,backendName:\"webgpu\",kernelFunc:fre};var hre=ot({opType:ye.ADD,cpuKernelImpl:W3,supportsComplex:!0}),vM={kernelName:eo,backendName:\"webgpu\",kernelFunc:hre};var Dg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${se(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function gre(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Dg(s);return t.runWebGPUProgram(a,o,n)}var kM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:gre};var Og=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),`\n const tileSize = ${this.workgroupSize[0]};\n var tile : array, ${this.workgroupSize[0]}>;\n ${se()} {\n var x = i32(workgroupId.x) * tileSize + i32(localId.x);\n var y = i32(workgroupId.y) * tileSize + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * tileSize + i32(localId.x);\n y = i32(workgroupId.x) * tileSize + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var Pg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let o=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${o}\n }\n }\n `}};function qr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=oM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=pM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":ka(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Mg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=de({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"all\",t)}var TM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:yre};function bre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"any\",t)}var _M={kernelName:Bo,backendName:\"webgpu\",kernelFunc:bre};var vc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ue(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=re(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${$o(this.inputShape.length-1)}`,t=()=>{let o=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(o+=\"outputCoords,\");else for(let n=0;n u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let reduceLength = ${e()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = getX(${t()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${t()} 0);\n let reduceLength = ${e()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${t()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var EM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var $M={kernelName:Za,backendName:\"webgpu\",kernelFunc:Sre};var wre=Se({opType:Q.ASIN}),AM={kernelName:ia,backendName:\"webgpu\",kernelFunc:wre};var Ire=Se({opType:Q.ASINH}),RM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ire};var vre=Se({opType:Q.ATAN}),FM={kernelName:pa,backendName:\"webgpu\",kernelFunc:vre};var kre=ot({opType:ye.ATAN2}),DM={kernelName:la,backendName:\"webgpu\",kernelFunc:kre};var Nre=Se({opType:Q.ATANH}),OM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Nre};var Wl=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n var count = 0.0;\n\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, coords[3]);\n ${e}\n }\n }\n\n setOutputAtIndex(index, ${t});\n }\n }\n `}};var Lg=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.stride;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Ul(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return qr(n,s,a,\"max\",t)}var PM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Ul};function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"mean\",t)}var MM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:uI};function Bg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Ft({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=de({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=uI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Ul({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=de({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Lg(e):(t===\"avg\"?n=new Wl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Wl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"avg\",t)}var LM={kernelName:zo,backendName:\"webgpu\",kernelFunc:Tre};function _re(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Gu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var BM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:_re};var Vg=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=Ere(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},pI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Ere(r){if(r===1)return\"sourceLoc\";if(r<=6)return pI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function ds(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=fM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Vg(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var VM={kernelName:_s,backendName:\"webgpu\",kernelFunc:ds};var $re=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=de({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=de({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ds({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},zM={kernelName:xs,backendName:\"webgpu\",kernelFunc:$re};var Are=`\n fn bincount_write(index: i32, value: f32) {\n var oldValue = atomicLoad(& (result[index]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + value;\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(\n &(result[index]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n`,Rre=`\n fn bincount_write(index: i32, value: f32) {\n result[index] = value;\n }\n`,kc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Rre:Are}\n ${se(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(index))\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(coord[0], coord[1]))\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=dr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new kc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var WM={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Fre};var cI=ot({opType:ye.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:uM}),UM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:cI};function qa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var GM={kernelName:ai,backendName:\"webgpu\",kernelFunc:qa};function HM(r,e){let t=new Ro(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Ft({inputs:{x:n},backend:t});let a=Vr(n.shape),i=lI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=qa({inputs:{input:n},backend:t}),i=lI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=U3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return HM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=cI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var qM={kernelName:co,backendName:\"webgpu\",kernelFunc:lI};var Dre=Se({opType:Q.CEIL,cpuKernelImpl:G3}),KM={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Dre};var zg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue : vec4;\n for (var i = 0; i < 4; i = i + 1) {\n if (isnan(value[i])) {\n clampedValue[i] = value[i];\n } else {\n clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n }\n }\n\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var Wg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function Ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new zg(n.shape):i=new Wg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var jM={kernelName:lo,backendName:\"webgpu\",kernelFunc:Ore};var Ug=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sqa({inputs:{input:C},backend:t})),h=r.map(C=>qu({inputs:{input:C},backend:t})),g=Nc(f,e,t),x=Nc(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return de({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=H3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Ug(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=de({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Pre(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>de({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function mI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Nc(p,s,t)}var YM={kernelName:ys,backendName:\"webgpu\",kernelFunc:mI};function Mre(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${x} % inChannels;\n var resData = ${kt(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${kt(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),$=r?kt(p):kt(i);return`\n ${ur(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?C:w}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} {\n ${r?w:C}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Hr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var Gg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4\",\"vec4\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Uu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Mre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var Hg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${se(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var qg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${se(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n uniforms.pad[1];\n let xCol = offsetX + uniforms.dilation[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function Kg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=de({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=de({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Kg(s.shape,p);x!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Kg(n.shape,p);x!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Gu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=de({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Bre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],$=new qg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram($,[r],r.dtype,A),D=[];D.push(R);let P=de({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Kg(s.shape,C);U!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Kg(n.shape,C);U!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let W=Gu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),V=de({inputs:{x:W},backend:o,attrs:{shape:t.outShape}});D.push(W);for(let U of D)o.disposeData(U.dataId);return V}function jg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return Lre({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Bre({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Hg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new Gg(t,_,$,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=de({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=de({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Vre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return jg({x:n,filter:s,convInfo:m,backend:o})}var QM={kernelName:Go,backendName:\"webgpu\",kernelFunc:Vre};function zre(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${kt(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${kt(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${kt(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${kt(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var Xg=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize):Uu(this.elementsPerThread,this.workgroupSize);return`\n ${zre(this.isVec4?4:1)}\n ${e}\n `}};var Yg=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n if (${this.isChannelsLast}) {\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n } else {\n let xValue = getDy(batch, d2, idyR, idyC);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Wre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Yg(m);else{f=new Xg(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var ZM={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Wre};var Ure=Se({opType:Q.COS}),JM={kernelName:qo,backendName:\"webgpu\",kernelFunc:Ure};var Gre=Se({opType:Q.COSH}),eL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Gre};var Qg=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Hre=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Qg(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},tL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:Hre};var Ku;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Ku||(Ku={}));var Gl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Ku.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${rL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${oL(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${oL(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${rL(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function rL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function oL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zg(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Gl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Gl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Prod,n,t,s,a,i)}var nL={kernelName:jo,backendName:\"webgpu\",kernelFunc:qre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Sum,n,t,s,a,i)}var sL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Kre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=dr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new kc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var aL={kernelName:ti,backendName:\"webgpu\",kernelFunc:jre};var Jg=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Xre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new Jg(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:Xre};var ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pad;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return`\n ${ur(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n const strideHeight = ${this.convInfo.strideHeight};\n const strideWidth = ${this.convInfo.strideWidth};\n ${se()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var _c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilation[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilation[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Tc(d):(g=new _c(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uL={kernelName:Zo,backendName:\"webgpu\",kernelFunc:Yre};var dI=ot({opType:ye.MUL,cpuKernelImpl:aM,supportsComplex:!0}),pL={kernelName:kn,backendName:\"webgpu\",kernelFunc:dI};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"sum\",t)}var cL={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Hl};function Qre(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Hl({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var lL={kernelName:ri,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Se({opType:Q.ELU}),mL={kernelName:en,backendName:\"webgpu\",kernelFunc:Zre};var Jre=ot({opType:ye.EQUAL,dtype:\"bool\",cpuKernelImpl:q3}),dL={kernelName:tn,backendName:\"webgpu\",kernelFunc:Jre};var eoe=Se({opType:Q.ERF}),fL={kernelName:ma,backendName:\"webgpu\",kernelFunc:eoe};var fI=Se({opType:Q.EXP,cpuKernelImpl:K3,dtype:\"float32\"}),hL={kernelName:rn,backendName:\"webgpu\",kernelFunc:fI};function tx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),de({inputs:{x:s},backend:o,attrs:{shape:i}})}var gL={kernelName:bs,backendName:\"webgpu\",kernelFunc:tx};var toe=Se({opType:Q.EXPM1,cpuKernelImpl:j3}),xL={kernelName:da,backendName:\"webgpu\",kernelFunc:toe};var ql=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function rx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=de({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ql(\"real\",u),l=new ql(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=de({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function roe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!1,t)}var yL={kernelName:oi,backendName:\"webgpu\",kernelFunc:roe};var ox=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var bL={kernelName:on,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ooe=Se({opType:Q.FLOOR,cpuKernelImpl:X3}),CL={kernelName:nn,backendName:\"webgpu\",kernelFunc:ooe};var noe=ot({opType:ye.INT_DIV,dtype:\"int32\"}),SL={kernelName:sn,backendName:\"webgpu\",kernelFunc:noe};var nx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${se(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var wL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:soe},Ec,hI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),sx=new Map;function 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ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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ix=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function 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ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=poe(this.aShape);return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function poe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=de({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var TL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:gI};var coe=ot({opType:ye.GREATER,cpuKernelImpl:J3,dtype:\"bool\"}),_L={kernelName:pn,backendName:\"webgpu\",kernelFunc:coe};var loe=ot({opType:ye.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Z3}),EL={kernelName:cn,backendName:\"webgpu\",kernelFunc:loe};function moe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!0,t)}var $L={kernelName:ni,backendName:\"webgpu\",kernelFunc:moe};var doe=Se({opType:Q.IS_FINITE,dtype:\"bool\"}),AL={kernelName:fa,backendName:\"webgpu\",kernelFunc:doe};var foe=Se({opType:Q.IS_INF,dtype:\"bool\"}),RL={kernelName:ha,backendName:\"webgpu\",kernelFunc:foe};var hoe=Se({opType:Q.IS_NAN,dtype:\"bool\"}),FL={kernelName:ln,backendName:\"webgpu\",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Ro(n.shape,Q.LEAKYRELU);return i.uniforms=\"alpha : f32,\",t.runWebGPUProgram(i,[n],\"float32\",a)}var DL={kernelName:mn,backendName:\"webgpu\",kernelFunc:goe};var xoe=ot({opType:ye.LESS,dtype:\"bool\",cpuKernelImpl:tM}),OL={kernelName:dn,backendName:\"webgpu\",kernelFunc:xoe};var yoe=ot({opType:ye.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:eM}),PL={kernelName:fn,backendName:\"webgpu\",kernelFunc:yoe};var boe=Se({opType:Q.LOG,cpuKernelImpl:rM}),ML={kernelName:hn,backendName:\"webgpu\",kernelFunc:boe};var Coe=Se({opType:Q.LOG1P}),LL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Coe};var Soe=ot({opType:ye.LOGICAL_AND,dtype:\"bool\"}),BL={kernelName:gn,backendName:\"webgpu\",kernelFunc:Soe};var woe=Se({opType:Q.LOGICAL_NOT}),VL={kernelName:xn,backendName:\"webgpu\",kernelFunc:woe};var Ioe=ot({opType:ye.MAX,cpuKernelImpl:nM}),zL={kernelName:bn,backendName:\"webgpu\",kernelFunc:Ioe};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"max\",t)}var WL={kernelName:Cn,backendName:\"webgpu\",kernelFunc:voe};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"min\",t)}var UL={kernelName:wn,backendName:\"webgpu\",kernelFunc:koe};var Noe=ot({opType:ye.MIN,cpuKernelImpl:sM}),GL={kernelName:In,backendName:\"webgpu\",kernelFunc:Noe};var px=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + 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Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Lt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XL={kernelName:_n,backendName:\"webgpu\",kernelFunc:$oe};var cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Aoe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new cx(u,a),l=de({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=de({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YL={kernelName:En,backendName:\"webgpu\",kernelFunc:Aoe};function Kl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=Kl({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QL={kernelName:Fs,backendName:\"webgpu\",kernelFunc:Kl};function ZL(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=ZL({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JL={kernelName:Is,backendName:\"webgpu\",kernelFunc:ZL};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return tx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=tx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=mI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eB={kernelName:vs,backendName:\"webgpu\",kernelFunc:Roe};var lx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let start = ${s};\n let end = ${a};\n let outC = getCoordsFromIndex(index);\n\n if (${i} || ${p}) {\n setOutputAtIndex(index, uniforms.constantValue);\n } else {\n let coords = outC - start;\n setOutputAtIndex(index, getX(${u}));\n }\n }\n }\n `}};var xI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return dr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new lx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tB={kernelName:$n,backendName:\"webgpu\",kernelFunc:xI};var Foe=ot({opType:ye.POW}),rB={kernelName:An,backendName:\"webgpu\",kernelFunc:Foe};function Doe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Hu(ye.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Doe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"prod\",t)}var nB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ooe};var Poe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sB={kernelName:ks,backendName:\"webgpu\",kernelFunc:Poe};var yI=ot({opType:ye.DIV}),aB={kernelName:Jo,backendName:\"webgpu\",kernelFunc:yI};var Moe=Se({opType:Q.RECIPROCAL}),iB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:Moe};var Loe=Se({opType:Q.RELU}),uB={kernelName:On,backendName:\"webgpu\",kernelFunc:Loe};var Boe=Se({opType:Q.RELU6}),pB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Boe};var mx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new mx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Voe};var dx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function zoe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new dx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var lB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:zoe};var fx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=de({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new fx(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=de({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var mB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:Woe};var hx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var dB={kernelName:es,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new hx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Uoe=Se({opType:Q.RSQRT,cpuKernelImpl:lM}),fB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Uoe};var Gi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ue(e),this.dispatch=re(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(c,l)=>`coords[${l}]`).join(\", \")})`,p=(c,l)=>{let m=`atomicAdd(${c}, bitcast(${l}))`;this.type===\"float32\"&&(m=`\n {\n var oldBits = 0;\n var newBits = bitcast(${l});\n loop {\n let info = atomicCompareExchangeWeak(${c}, oldBits, newBits);\n if (info.exchanged) {\n break;\n }\n oldBits = info.old_value;\n let oldValue = bitcast(oldBits);\n let newValue = oldValue + (${l});\n newBits = bitcast(newValue);\n }\n }\n `);let d=`atomicStore(${c}, bitcast(${l}));`;return this.sumDupeIndices?m:d};return`\n ${s}\n\n ${se(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${wc(this.type,!1)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${p(\"&result[flatIndex]\",\"updateValue\")};\n }\n }`}};function Goe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=de({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=de({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=dr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Gi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=de({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var hB={kernelName:zn,backendName:\"webgpu\",kernelFunc:Goe};var gx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new gx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var gB={kernelName:ii,backendName:\"webgpu\",kernelFunc:Hoe};var xx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function qoe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new xx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var xB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:qoe};var Koe=Se({opType:Q.SIGMOID}),yB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Koe};var joe=Se({opType:Q.SIN}),bB={kernelName:Wn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Se({opType:Q.SINH}),CB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Xoe};var bI=ot({opType:ye.SUB,cpuKernelImpl:xM,supportsComplex:!0}),SB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:bI};function Yoe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Ul({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=de({inputs:{x:i},backend:t,attrs:{shape:p}}),c=bI({inputs:{a:n,b:u},backend:t}),l=fI({inputs:{x:c},backend:t}),m=Hl({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=de({inputs:{x:m},backend:t,attrs:{shape:p}}),f=yI({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var wB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},IB={kernelName:Es,backendName:\"webgpu\",kernelFunc:Qoe};var yx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=yM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new yx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var vB={kernelName:to,backendName:\"webgpu\",kernelFunc:CI};function Joe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=mM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=de({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?de({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=de({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=CI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Gi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let $=de({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),$}var kB={kernelName:li,backendName:\"webgpu\",kernelFunc:Joe};function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ds({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NB={kernelName:$s,backendName:\"webgpu\",kernelFunc:ene};var tne=Se({opType:Q.SQRT}),TB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:tne};var _B={kernelName:mi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Ro(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var rne=ot({opType:ye.SQUARED_DIFFERENCE}),EB={kernelName:Kn,backendName:\"webgpu\",kernelFunc:rne};var bx=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function one(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=de({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=ds({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=de({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),A=le(n.shape,n.dtype,$),R=hM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let $=new bx(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram($,[n],n.dtype,A);k=de({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var $B={kernelName:jn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=gM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var AB={kernelName:As,backendName:\"webgpu\",kernelFunc:nne};var sne=Se({opType:Q.TAN}),RB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:sne};var ane=Se({opType:Q.TANH}),FB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:ane};var Cx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function $c(r,e){e!==null&&r.disposeData(e.dataId)}function DB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let A=h(),R=new Cx($),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),$c(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new Sx([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram($,_,\"int32\",R),$c(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ds({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),$c(t,x);let b=gI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});$c(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=de({inputs:{x:f},attrs:{shape:C},backend:t}),$c(t,x);let w=b;return b=de({inputs:{x:b},attrs:{shape:C},backend:t}),$c(t,w),[b,f]}var OB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:ine};var wx=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var PB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB=\"4.1.0\",lne=\"4.1.0\",mne=\"4.1.0\",dne=\"4.1.0\",fne=\"4.1.0\",hne=\"0.0.1-alpha.16\",gne={tfjs:LB,\"tfjs-core\":LB,\"tfjs-converter\":lne,\"tfjs-backend-cpu\":mne,\"tfjs-backend-webgl\":dne,\"tfjs-backend-wasm\":fne,\"tfjs-backend-webgpu\":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,f0 as acos,h0 as acosh,xe as add,g0 as addN,x0 as all,y0 as any,b0 as argMax,C0 as argMin,S0 as asin,w0 as asinh,I0 as atan,v0 as atan2,k0 as atanh,td as avgPool,_0 as avgPool3d,Oie as backend,S as backend_util,E0 as basicLSTMCell,wi as batchNorm,A0 as batchNorm2d,R0 as batchNorm3d,F0 as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,D0 as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Qv as browser,le as buffer,Ke as cast,O0 as ceil,P0 as clipByValue,Br as clone,Tr as complex,gt as concat,M0 as concat1d,L0 as concat2d,B0 as concat3d,V0 as concat4d,z0 as conv1d,vi as conv2d,W0 as conv2dTranspose,U0 as conv3d,H0 as conv3dTranspose,Dne as copyRegisteredKernels,q0 as cos,K0 as cosh,il as cosineWindow,j0 as cumprod,X0 as cumsum,Cr as customGrad,Y0 as denseBincount,eC as deprecationWarn,Q0 as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Z0 as diag,J0 as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,jv as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,p0 as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,hv as tensor_util,d0 as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: true,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') { // TBD replace with navigator.userAgentData once in mainline\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam', 'no devices');\n return;\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam', 'no stream');\n return;\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh.keepInvalid) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=o||lb(t,this.size),this.strides=hs(e)}set(e,...t){t.length===0&&(t=[0]),E(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let o=this.locToIndex(t);this.values[o]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let s=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(s)}t++}let o=e[e.length-1];for(let n=0;nAp(o))}catch(o){throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\")}}return e}dataToGPU(e){return this.throwIfDisposed(),rs().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=rs().readSync(this.dataId);if(this.dtype===\"string\")try{return e.map(t=>Ap(t))}catch(t){throw new Error(\"Failed to decode the string bytes into utf-8. 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it{constructor(e,t,o,n){super(e.shape,e.dtype,e.dataId,n),this.trainable=t,this.name=o}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Pr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);rs().disposeTensor(this),this.dataId=e.dataId,rs().incRef(this,null)}dispose(){rs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(va,Symbol.hasInstance,{value:r=>r instanceof it&&r.assign!=null&&r.assign instanceof Function});var h0={};Ue(h0,{assertTypesMatch:()=>Fb,getTensorsInContainer:()=>Qc,isTensorInList:()=>sz,makeTypesMatch:()=>Re});var _b;(function(r){r.R0=\"R0\",r.R1=\"R1\",r.R2=\"R2\",r.R3=\"R3\",r.R4=\"R4\",r.R5=\"R5\",r.R6=\"R6\"})(_b||(_b={}));var Eb;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"int32\",r.complex64=\"complex64\"})(Eb||(Eb={}));var $b;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})($b||($b={}));var Ab;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(Ab||(Ab={}));var Rb;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Rb||(Rb={}));var nz={float32:Ab,int32:Eb,bool:$b,complex64:Rb};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return nz[r][e]}function ka(r){return dt(r,\"int32\")}function Re(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Fb(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function sz(r,e){return e.some(t=>t.id===r.id)}function Qc(r){let e=[];return f0(r,e,new Set),e}function f0(r,e,t){if(r==null)return;if(r instanceof it){e.push(r);return}if(!az(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),f0(s,e,t))}}function az(r){return Array.isArray(r)||typeof r==\"object\"}function Db(r){return r.kernelName!=null}var Pm=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},xi=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Pm}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Am(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Zr)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return xi.nextTensorId++}nextVariableId(){return xi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(qc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Db(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=qc(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Db(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Cb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Sm(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof va||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Sm(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=Cb(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=ap(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Qc(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));E(s instanceof it,()=>\"The result y returned by f() must be a tensor.\");let a=a0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?iz(s.shape):o,i0(i,a,u=>this.tidy(u),uz);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return E(fs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{E(t.every(i=>i instanceof it),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof it,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(fs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof it),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=ou(),o=await this.backend.time(e);return o.wallMs=ou()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Pm;for(let 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GW(r,e=0){E(r.length>=1,()=>\"Pass at least one tensor to concat\");let t=Na(r,\"tensors\",\"concat\",\"string_or_numeric\");if(t[0].dtype===\"complex64\"&&t.forEach(s=>{if(s.dtype!==\"complex64\")throw new Error(`Cannot concatenate complex64 tensors with a tensor\n with dtype ${s.dtype}. `)}),t.length===1)return Br(t[0]);let o=t,n={axis:e};return T.runKernel(ys,o,n)}var gt=N({concat_:GW});function HW(r){let t={x:v(r,\"x\",\"sigmoid\",\"float32\")};return T.runKernel(Un,t)}var zs=N({sigmoid_:HW});function qW(r,e,t){let o=v(r,\"x\",\"slice\",\"string_or_numeric\");if(o.rank===0)throw new Error(\"Slicing scalar is not possible\");let n={x:o},s={begin:e,size:t};return T.runKernel(_s,n,s)}var He=N({slice_:qW});function KW(r){let t={x:v(r,\"x\",\"tanh\",\"float32\")};return T.runKernel(Qn,t)}var nl=N({tanh_:KW});function jW(r,e,t,o,n,s){let a=v(r,\"forgetBias\",\"basicLSTMCell\"),i=v(e,\"lstmKernel\",\"basicLSTMCell\"),p=v(t,\"lstmBias\",\"basicLSTMCell\"),u=v(o,\"data\",\"basicLSTMCell\"),c=v(n,\"c\",\"basicLSTMCell\"),l=v(s,\"h\",\"basicLSTMCell\"),m=gt([u,l],1),d=Xe(m,i),f=xe(d,p),h=f.shape[0],g=f.shape[1]/4,x=[h,g],b=He(f,[0,0],x),C=He(f,[0,g],x),w=He(f,[0,g*2],x),k=He(f,[0,g*3],x),_=xe(ae(zs(b),nl(C)),ae(c,zs(xe(a,w)))),$=ae(nl(_),zs(k));return[_,$]}var Ev=N({basicLSTMCell_:jW});function XW(r,e,t){let o=v(r,\"x\",\"batchToSpaceND\"),n=e.reduce((i,p)=>i*p);E(o.rank>=1+e.length,()=>`input rank is ${o.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(o.shape[0]%n===0,()=>`input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(\" * \")} === ${n}`);let s={x:o},a={blockShape:e,crops:t};return T.runKernel(xs,s,a)}var rd=N({batchToSpaceND_:XW});function $v(r){let e;return r.rank===0||r.rank===1?e=z(r,[1,1,1,r.size]):r.rank===2?e=z(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function YW(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(i.rank===p.rank,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),E(c==null||i.rank===c.rank,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),E(u==null||i.rank===u.rank,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let m={x:$v(a),scale:u,offset:c,mean:i,variance:p},d={varianceEpsilon:s},f=T.runKernel(an,m,d);return z(f,a.shape)}var wi=N({batchNorm_:YW});function QW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Av=N({batchNorm2d_:QW});function ZW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Rv=N({batchNorm3d_:ZW});function JW(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Fv=N({batchNorm4d_:JW});function eU(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Ja,s,a)}var od=N({bincount_:eU});function tU(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(up,n)}var Dv=N({broadcastArgs_:tU});function rU(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(yt(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Br(t);let i={x:t},p={reps:s};return T.runKernel(to,i,p)}var Ii=N({broadcastTo_:rU});function oU(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(Uo,t)}var Ov=N({ceil_:oU});function Ws(r,e,t){yt(r);let o={shape:r,value:e,dtype:t};return T.runKernel(Cs,{},o)}function nU(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return Ws(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(lo,n,s)}var Pv=N({clipByValue_:nU});function sU(r){return gt(r,0)}var Mv=N({concat1d_:sU});function aU(r,e){return gt(r,e)}var Lv=N({concat2d_:aU});function iU(r,e){return gt(r,e)}var Bv=N({concat3d_:iU});function uU(r,e){return gt(r,e)}var Vv=N({concat4d_:uU});function pU(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Pt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),ht(n.shape,s.shape,\"Error in hingeLoss: \");let i=be(1);n=Ne(ae(be(2),n),i);let p=Ti(Ne(i,ae(n,s)));return sr(p,a,o)}var uN=N({hingeLoss_:HH});function qH(r,e,t,o=1,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),ht(s.shape,a.shape,\"Error in huberLoss: \");let p=be(o),u=Yt(Ne(a,s)),c=Sd(u,p),l=Ne(u,c),m=xe(ae(be(.5),Qt(c)),ae(p,l));return sr(m,i,n)}var pN=N({huberLoss_:qH});function KH(r,e,t,o=1e-7,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),ht(s.shape,a.shape,\"Error in logLoss: \");let p=be(1),u=be(o),c=yr(ae(s,Da(xe(a,u)))),l=ae(Ne(p,s),Da(xe(Ne(p,a),u))),m=Ne(c,l);return sr(m,i,n)}var cN=N({logLoss_:KH});function jH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),ht(n.shape,s.shape,\"Error in meanSquaredError: \");let i=Dd(n,s);return sr(i,a,o)}var lN=N({meanSquaredError_:jH});function XH(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");ht(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=Ti(o),s=ae(o,t),a=md(Co(yr(Yt(o))));return xe(Ne(n,s),a)}function YH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),a=v(e,\"logits\",\"sigmoidCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"sigmoidCrossEntropy\")),ht(s.shape,a.shape,\"Error in sigmoidCrossEntropy: \"),o>0){let u=be(o),c=be(1),l=be(.5);s=xe(ae(s,Ne(c,u)),ae(l,u))}let p=XH(s,a);return sr(p,i,n)}var mN=N({sigmoidCrossEntropy_:YH});function QH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Cr((n,s,a)=>{let p=hd(s,[t],!0),u=Ne(Ke(s,\"float32\"),p);a([n,u]);let c=yr(ae(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Aa(d.shape,[t]);return[ae(z(d,x),Ne(Ke(h,\"float32\"),Co(g))),ae(z(d,x),Ne(Co(g),Ke(h,\"float32\")))]}}})(r,e)}function ZH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),ht(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=be(o),c=be(1),l=be(s.shape[1]);s=xe(ae(s,Ne(c,u)),Ge(u,l))}let p=QH(s,a);return sr(p,i,n)}var dN=N({softmaxCrossEntropy_:ZH});function JH(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(ui,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var fN=N({sparseFillEmptyRows_:JH});function eq(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(wa,a);return{outputIndices:i[0],outputShape:i[1]}}var hN=N({sparseReshape_:eq});function tq(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(pi,a)}var gN=N({sparseSegmentMean_:tq});function rq(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ci,a)}var xN=N({sparseSegmentSum_:rq});function oq(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(As,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var yN=N({stringNGrams_:oq});function nq(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(di,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var bN=N({stringSplit_:nq});function sq(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(fi,n,o)}var CN=N({stringToHashBucketFast_:sq});var aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:be(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className=\"Adadelta\";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className=\"Adagrad\";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className=\"Adam\";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className=\"Adamax\";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qs.className=\"SGD\";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className=\"Momentum\";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let 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s=1;s\",Uq=/->/g,SN=\",\",wN=\"...\";function Gq(r,e){r=r.replace(/\\s/g,\"\");let t=(r.length-r.replace(Uq,\"\").length)/SC.length;if(t<1)throw new Error(\"Equations without an arrow are not supported.\");if(t>1)throw new Error(`Equation must contain exactly one arrow (\"${SC}\").`);let[o,n]=r.split(SC);E(o.indexOf(wN)===-1,()=>`The ellipsis notation (\"${wN}\") is not supported yet.`);let s=o.split(SN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function qq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function Kq(r,e){let 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o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"dtype\",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case\"TensorArrayScatterV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case\"TensorArrayConcatV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id),s=I(\"dtype\",r,e,t);return[n.concat(s)]}case\"TensorArraySplitV3\":{let o=I(\"tensorArrayId\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"lengths\",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case\"TensorArraySizeV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return[be(n.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let o=I(\"tensorArrayId\",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case\"TensorListSetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"tensor\",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case\"TensorListGetItem\":{let o=I(\"tensorListId\",r,e,t),n=I(\"index\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let o=I(\"indices\",r,e,t),n=I(\"tensor\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"numElements\",r,e,t),i=FN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let o=I(\"elementShape\",r,e,t),n=I(\"elementDType\",r,e,t),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let a=I(s,r,e,t),i=r.op===\"TensorListReserve\"?-1:a,p=RN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case\"TensorListGather\":{let o=I(\"tensorListId\",r,e,t),n=I(\"indices\",r,e,t),s=I(\"elementShape\",r,e,t),a=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case\"TensorListStack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=AN(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=DN(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[be(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PN(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=ul(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var MN=(r,e,t,o=Ye)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=ul(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=ul(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LN=(r,e,t,o=Ye)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function HC(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var BN=async(r,e,t,o,n=Ye)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=HC(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t),c=I(\"padToMaxOutputSize\",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case\"Where\":{let s=n.cast(I(\"condition\",r,e,t),\"bool\"),a=[await n.whereAsync(s)];return s.dispose(),a}case\"ListDiff\":return n.setdiff1dAsync(I(\"x\",r,e,t),I(\"y\",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VN=(r,e,t,o=Ye)=>{switch(r.op){case\"LowerBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.lowerBound(n,s)]}case\"TopKV2\":{let n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zN=(r,e,t,o=Ye)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Gt(r.name,e,t)||n];case\"Placeholder\":return[Gt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[as(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>as(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[as(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UN=(r,e,t,o=Ye)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HN=(r,e,t,o=Ye)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];case\"SparseToDense\":return[o.sparseToDense(I(\"sparseIndices\",r,e,t),I(\"outputShape\",r,e,t),I(\"sparseValues\",r,e,t),I(\"defaultValue\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jN=(r,e,t,o=Ye)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qC(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>_N(a,i,p));case\"basic_math\":return n(()=>EN(a,i,p));case\"control\":return ON(a,i,p);case\"convolution\":return n(()=>MN(a,i,p));case\"creation\":return n(()=>LN(a,i,p));case\"dynamic\":return BN(a,i,p);case\"evaluation\":return n(()=>VN(a,i,p));case\"image\":return n(()=>UN(a,i,p));case\"graph\":return n(()=>zN(a,i,p));case\"logical\":return n(()=>GN(a,i,p));case\"matrices\":return n(()=>HN(a,i,p));case\"normalization\":return n(()=>qN(a,i,p));case\"ragged\":return n(()=>KN(a,i,p));case\"reduction\":return n(()=>jN(a,i,p));case\"slice_join\":return n(()=>XN(a,i,p));case\"sparse\":return n(()=>YN(a,i,p));case\"spectral\":return n(()=>QN(a,i,p));case\"string\":return n(()=>ZN(a,i,p));case\"transformation\":return n(()=>JN(a,i,p));case\"hash_table\":return WN(a,i,p,o);case\"custom\":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var cl=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function KC(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((jC(m)||i6(m)||u6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function eT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var n6=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],s6=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],a6=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function jC(r){return n6.indexOf(r.op)>=0}function i6(r){return s6.indexOf(r.op)>=0}function u6(r){return a6.indexOf(r.op)>=0}var Cu=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Cu(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+\"--\"+n.join(this.SEPERATOR)}compile(e,t){let o=KC(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return eT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new cl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(o[e].forEach(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!==\"control\"){let u=vN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new cl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=KC(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=p.filter(C=>!jC(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C=\"\";throw l!=null&&(C=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=ss(l.node.name,o)),n[l.node.name]==null){let d=qC(l.node,n,o,this._resourceManager);m||([m]=ss(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=ss(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var p6=\"?tfjs-format=file\",c6=\"model.json\",ll=class{constructor(e,t={},o=Ea){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else 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{\n return 0;\n }\n `}function Q5(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${o[1]}.0);\n }\n `:o[1]===1?t?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${o[0]}.0);\n }\n `:t?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${o[0]}, ${o[1]}));\n return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);\n }\n `}function Z5(r,e,t){return e[0]===1?t?`\n int getOutputCoords() {\n return int(resultUV.x 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`}function C8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n=\"get\"+o+\"AtOutCoords\",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`\n float ${n}() {\n return sampleTexture(${t}, resultUV);\n }\n `;let u=_e(p),c=NE(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];i===0?m=\"\":p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(`\n`);let f=\"\";return p<2&&i>0?f=\"coords\":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(\", \"),`\n float ${n}() {\n ${u} coords = getOutputCoords();\n ${m}\n return get${o}(${f});\n }\n `}function _e(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Of(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(\", \")}function AE(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=TE(n,a,e),p=MS(r.gl,i),u=r.createProgram(p);return O().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},tw(r,e,u))}function tw(r,e,t){let o={},n={},s={},a=[],i,p,u,c=null,l=null;l=r.getUniformLocation(t,\"NAN\",!1),O().getNumber(\"WEBGL_VERSION\")===1&&(c=r.getUniformLocation(t,\"INFINITY\",!1));let m=!1;for(let d=0;d{a[f]=r.getUniformLocation(t,d.name,m)}),{uniformLocations:o,customUniformLocations:a,infLoc:c,nanLoc:l,inShapesLocations:n,inTexShapesLocations:s,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:p}}function $E(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} 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Pf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Mi.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?Au([\"r\",\"c\",\"d\"],e):us([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${t.output} = result;\n }\n `}};var Mf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Mi.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?Au([\"r\",\"c\",\"d\"],e):us([\"r\",\"c\",\"d\"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var Lf=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=`\n ${Df}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var 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i=0;icw,createBufferFromOutputTexture:()=>dw,createFloat16MatrixTexture:()=>aw,createFloat16PackedMatrixTexture:()=>pw,createFloat32MatrixTexture:()=>sw,createIndexBuffer:()=>nw,createPackedMatrixTexture:()=>uw,createUnsignedBytesMatrixTexture:()=>iw,createVertexBuffer:()=>ow,createVertexShader:()=>rw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>hw,downloadFloat32MatrixFromBuffer:()=>fw,downloadMatrixFromPackedOutputTexture:()=>xw,downloadPackedMatrixFromBuffer:()=>gw,getInternalFormatForFloat16MatrixTexture:()=>Wf,getInternalFormatForFloat16PackedMatrixTexture:()=>Hf,getInternalFormatForFloat32MatrixTexture:()=>zf,getInternalFormatForPackedMatrixTexture:()=>Gf,getInternalFormatForUnsignedBytesMatrixTexture:()=>Uf,uploadDenseMatrixToTexture:()=>lw,uploadPixelDataToTexture:()=>mw});function rw(r){let e=St(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`;return PS(r,t)}function ow(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return VS(r,e)}function nw(r){let e=new Uint16Array([0,1,2,2,1,3]);return zS(r,e)}function kl(r,e,t,o,n,s){US(e,t);let a=WS(r),i=r.TEXTURE_2D;return ce(r,()=>r.bindTexture(i,a)),ce(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ce(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ce(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),ce(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),O().getNumber(\"WEBGL_VERSION\")===1?ce(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)):ce(r,()=>r.texStorage2D(i,1,o,e,t)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:a,texShape:[t,e]}}function zf(r){return r.internalFormatFloat}function sw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,zf(o),o.textureFormatFloat,r.FLOAT)}function Wf(r){return r.internalFormatHalfFloat}function aw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,Wf(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Uf(r){return r.downloadTextureFormat}function iw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,Uf(o),r.RGBA,r.UNSIGNED_BYTE)}function Gf(r){return r.internalFormatPackedFloat}function uw(r,e,t,o){let[n,s]=Ys(e,t);return kl(r,n,s,Gf(o),r.RGBA,r.FLOAT)}function Hf(r){return r.internalFormatPackedHalfFloat}function pw(r,e,t,o){let[n,s]=Ys(e,t);return kl(r,n,s,Hf(o),r.RGBA,o.textureTypeHalfFloat)}function cw(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Af(r,e,\"clipSpacePos\",t,3,20,0)&&Af(r,e,\"uv\",t,2,20,12)}function lw(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,p=s.internalFormatPackedFloat),a.set(n),O().getNumber(\"WEBGL_VERSION\")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t,o,r.RGBA,i,a)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,p,t,o,0,r.RGBA,i,a)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function mw(r,e,t){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?O().getNumber(\"WEBGL_VERSION\")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t.width,t.height,r.RGBA,r.UNSIGNED_BYTE,t.data)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):O().getNumber(\"WEBGL_VERSION\")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,t)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function dw(r,e,t,o){let n=r.createBuffer();ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function fw(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function hw(r,e,t,o){let[n,s]=$u(e,t),a=4,i=new Uint8Array(bE(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function gw(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(CE(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function xw(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Fu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=O().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,RS(t,e)):this.gl=Wr(t),e=this.gl,O().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),O().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=ec(this.gl,s),Ur(this.gl,a))this.textureHalfFloatExtension=ec(this.gl,a);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),Ur(this.gl,n))this.colorBufferHalfFloatExtension=ec(this.gl,n);else if(O().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",Ur(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Ur(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=ow(this.gl),this.indexBuffer=nw(this.gl),this.framebuffer=GS(this.gl),this.textureConfig=Sl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),sw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),aw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),iw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),mw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),lw(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),pw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),uw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>hw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return gw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return fw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=dw(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(O().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>xw(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rw(t));let o=LS(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),BS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(cw(t,n,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&wl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&wl(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?HS(this.gl,e,t):qS(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),KS(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ys(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&wl(this.gl,this.program),tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ec(this.gl,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=v8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Il(this.gl,e,this.framebuffer),this.debug&&tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Il(this.gl,this.outputTexture,this.framebuffer),this.debug&&tc(this.gl)):Rf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Il(n,e,this.framebuffer),this.debug&&tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function v8(r){let e=0;for(;e`${r}.${t}`)}function $t(r,e){return e===1?[r]:bw(r,e)}function I$(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var lc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${k8(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?sc():nc(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function k8(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?kE([\"r\",\"c\",\"d\"],\"inputShape\"):us([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=k$(t,o),s=N$(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=v$(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=k$(o,n),a=N$(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=v$(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function N8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function v$(r,e,t,o,n){let s=T8(e,o),a;if(n){let[p,u]=Ys(r[0],r[1]);a=p*u}else{let[p,u]=$u(r[0],r[1]);a=p*u}let i=N8(t,s);return a*i}function T8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function _8(r){return O().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function k$(r,e){if(r===ir.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ir.RENDER||r==null)return _8(e);if(r===ir.DOWNLOAD||r===ir.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function N$(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Bt=\"if (isnan(x)) return x;\",T$=\"return x;\",Cw=\"return abs(x);\";var _$=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",E$=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,$$=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Qs=\"return x;\",A$=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var F$=\"return x;\",D$=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,O$=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,P$=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,M$=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Ar=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Qf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let t=e.length,o=$t(\"rc\",t),n=_e(t),s=I$(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var $8=Lt.whereImpl,A8=1e-7,R8=1e-4,Zf={};function F8(r){return r in Zf||(Zf[r]={}),Zf[r]}var D8=O().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),O8=600;function P8(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*O8/1024/1024}var Bi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Fu)t=e;else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"),e);t=new Fu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Wr(O().getNumber(\"WEBGL_VERSION\"));t=new Fu(o),this.binaryCache=F8(O().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=P8(),this.texData=new Do(this,cr())}nextDataId(){return Bi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=rc(t),c=new vl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||O().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:ir.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(O().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:ir.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Ar(i,Qs):m=new Jt(i,Qs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Qs):f=new Jt(n,Qs);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(O().getBool(\"DEBUG\")&&!O().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&O().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&O().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Cl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Ar(s,Qs):d=new Jt(s,Qs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=cr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D8){return O().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Va(e.shape),...za(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Va(t),...za(t)],a=new lc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=rc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Cl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Mi.DENSE){let x=a!=null?a:Cl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Li(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=FE(e,c,l),d=this.getAndSaveBinary(m,()=>AE(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get(\"ENGINE_COMPILE_ONLY\")||RE(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(O().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!O().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=O().getBool(\"DEBUG\");O().set(\"DEBUG\",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(O().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=jS(o,p),t.texShape=l),s!=null){let m=rc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ys(l[0],l[1])),p?d=new Vf(m,g):d=new vl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ir.PIXELS:C.usage=ir.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,O().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=M8(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await CC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?($f(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=tw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromTexture(e,t,o){let{texture:n,height:s,width:a,channels:i}=e,p=cr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return cr().makeTensorFromDataId(u,t,o,p)}};Bi.nextDataId=0;function M8(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Bi,2);var L9e={forceHalfFloat:L$};var mc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var io=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Zs=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var To=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ct(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${_e(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=$t(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function At(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var B$={kernelName:mo,backendName:\"webgl\",kernelFunc:At};function Rr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var V$={kernelName:ei,backendName:\"webgl\",kernelFunc:Rr};var Sw=\"return (a < 0.) ? b * a : a;\",ww=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(ww,n.shape,a.shape):new io(Sw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var z$={kernelName:mn,backendName:\"webgl\",kernelFunc:B8};var Iw=\"return (a < 0.) ? b * a : a;\",vw=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function V8(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(vw,o.shape,n.shape):new io(Iw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var W$={kernelName:Rn,backendName:\"webgl\",kernelFunc:V8};var _o=\"if (isnan(x)) return x;\";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,$],dt(w.dtype,k.dtype))}),b=Rr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?S.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new To(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Wa(r,e=!1){if(r===\"linear\")return e?F$:T$;if(r===\"relu\")return e?O$:E$;if(r===\"elu\")return e?D$:_$;if(r===\"relu6\")return e?P$:$$;if(r===\"prelu\")return e?vw:Iw;if(r===\"leakyrelu\")return e?ww:Sw;if(r===\"sigmoid\")return e?M$:A$;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var dc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ct(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",w=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Li(n.shape,p)&&!(c.texture!==null&&Li(c.shape,p))?H$(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var q$={kernelName:Ns,backendName:\"webgl\",kernelFunc:te};var _l=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var Jf=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function W8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Gr(r,e,t,o){let n=W8(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=_e(this.rank),s=bw(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),A=te({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,W=p===\"leakyrelu\",V=p!=null?Wa(p,!0):null,U=M||L||W||V!=null,q;if((d===1||f===1)&&P>Nw&&U===!1){let j=$,X=A;t&&(j=xt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),R.push(j)),o&&(X=xt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(X));let Z=f!==1,ee=f===1,Y=j;Z&&(Y=te({inputs:{x:j},backend:n,attrs:{shape:[D,P,1]}}),R.push(Y));let J=f===1?2:1,ie=X;ee&&(ie=te({inputs:{x:X},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let pe=Tl({inputs:{a:Y,b:ie},backend:n});q=Ou({inputs:{x:pe},backend:n,attrs:{axis:J,keepDims:!0}}),R.push(pe)}else{let j=dt(r.dtype,e.dtype),X=new dc(k,_,[D,d,f],t,o,M,V,L,W),Z=[$,A];if(s!=null&&Z.push(s),L&&Z.push(a),W){let ee=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));Z.push(ee),R.push(ee)}q=n.runWebGLProgram(X,Z,j)}let H=te({inputs:{x:q},backend:n,attrs:{shape:w}});R.push(q);for(let j of R)n.disposeIntermediateTensorInfo(j);return H}function G8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Pu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var Y$={kernelName:fo,backendName:\"webgl\",kernelFunc:G8};var Q$=\"return abs(x);\";function H8(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=Kf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Ar(o.shape,Q$):n=new Jt(o.shape,Q$),t.runWebGLProgram(n,[o],o.dtype)}var Z$={kernelName:gs,backendName:\"webgl\",kernelFunc:H8};var q8=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,K8=ge({opSnippet:q8}),J$={kernelName:sa,backendName:\"webgl\",kernelFunc:K8};var j8=Bt+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,backendName:\"webgl\",kernelFunc:X8};var tA=\"return a + b;\",Y8=tt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DE}),rA={kernelName:eo,backendName:\"webgl\",kernelFunc:Y8};var rh=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n float result = ${n};\n setOutput(result);\n }\n `}};var oh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool(\"WEBGL_PACK\")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var oA={kernelName:Mo,backendName:\"webgl\",kernelFunc:nh};function Q8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"all\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Lo,backendName:\"webgl\",kernelFunc:Q8};function Z8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims(\"any\",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,\"any\",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Bo,backendName:\"webgl\",kernelFunc:Z8};var sh=class{constructor(e,t,o){this.variableNames=[\"A\"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,a];let i=t===\"max\"?\">\":\"<\",p=o?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var ah=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=_e(p),c=$t(\"coords\",p),l,m;if(a===1){m=p+1;let A=_e(m);l=`\n ${A} sourceLocR = ${A}(${c.join()}, 0);\n ++${c[p-1]};\n ${A} sourceLocG = ${A}(${c.join()}, 0);\n ++${c[p-2]};\n ${A} sourceLocA = ${A}(${c.join()}, 0);\n --${c[p-1]};\n ${A} sourceLocB = ${A}(${c.join()}, 0);\n --${c[p-2]};`}else m=p,l=`\n ${u} sourceLocR = coords;\n ++${c[p-1]};\n ${u} sourceLocG = coords;\n ++${c[p-2]};\n ${u} sourceLocA = coords;\n --${c[p-1]};\n ${u} sourceLocB = coords;\n --${c[p-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),f=\".\"+d[m-1],h=d.map(A=>\"int \"+A),g=$t(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=$t(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=$t(\"sourceLocB\",m-1).concat(\"inIdx.b\"),C=$t(\"sourceLocA\",m-1).concat(\"inIdx.a\"),w=o===\"max\"?\"greaterThan\":\"lessThan\",k=n?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${C.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,$=n?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${$}\n void main() {\n ${u} coords = getOutputCoords();\n bool hasNextCol = ${c[p-1]} < ${i[p-1]-1};\n bool hasNextRow = ${c[p-2]} < ${i[p-2]-1};\n ${l}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${k}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function aA(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,\"int32\");if(c.shape[1]===1)return c;let l=aA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,\"int32\");if(u.shape.length===e.shape.length){let c=iA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,e,o)}function J8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Vo,backendName:\"webgl\",kernelFunc:J8};function eY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=ih(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Za,backendName:\"webgl\",kernelFunc:eY};var tY=Bt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,rY=ge({opSnippet:tY}),cA={kernelName:ia,backendName:\"webgl\",kernelFunc:rY};var oY=Bt+\"return log(x + sqrt(x * x + 1.0));\",nY=ge({opSnippet:oY}),lA={kernelName:ua,backendName:\"webgl\",kernelFunc:nY};var sY=Bt+`\n return atan(x);\n`,aY=ge({opSnippet:sY}),mA={kernelName:pa,backendName:\"webgl\",kernelFunc:aY};var iY=mc+`\n return atan(a, b);\n`,uY=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Zs+`\n return result;\n`,pY=tt({opSnippet:iY,packedOpSnippet:uY}),dA={kernelName:la,backendName:\"webgl\",kernelFunc:pY};var cY=Bt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelName:ca,backendName:\"webgl\",kernelFunc:lY};var ps=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let A=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${A} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(w=\"avgValue / count\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${w});\n }\n `}},zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",w=\"0.0\";if(C||(w=\"-1.0 / 1e-20\"),o){let D=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / count\");let $=Math.floor(a/4)*4,A=a%4,R=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${w};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${w});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${R}\n }\n\n int xC = xCCorner + ${$};\n if (${A===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${R}\n } else if (${A===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${R}\n }\n }\n setOutput(${_});\n }\n }\n `}};function mY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;is(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var hA={kernelName:zo,backendName:\"webgl\",kernelFunc:mY};function dY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new zi(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var gA={kernelName:ip,backendName:\"webgl\",kernelFunc:dY};var uh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},ph=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function fY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Im,backendName:\"webgl\",kernelFunc:fY};function hY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;is([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:wm,backendName:\"webgl\",kernelFunc:hY};function gY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Pu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var bA={kernelName:Wo,backendName:\"webgl\",kernelFunc:gY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var lh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var xY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:an,backendName:\"webgl\",kernelFunc:xY};var mh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=_e(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=yY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Tw[i]} = start[${i}] + coords.${Tw[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},Tw=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function yY(r){if(r===1)return\"sourceLoc\";if(r<=6)return Tw.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=_e(this.rank),o=$t(\"coords\",this.rank),n=$t(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < 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i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=cs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},wA={kernelName:xs,backendName:\"webgl\",kernelFunc:CY};function SY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=qf(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var IA={kernelName:Ja,backendName:\"webgl\",kernelFunc:SY};function wY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return 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a=Ua({inputs:{input:n},backend:t}),i=Ew({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=PE(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return TA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=_w({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var _A={kernelName:co,backendName:\"webgl\",kernelFunc:Ew};var EA=\"return ceil(x);\",kY=ge({opSnippet:EA,packedOpSnippet:EA,cpuKernelImpl:ME}),$A={kernelName:Uo,backendName:\"webgl\",kernelFunc:kY};var fh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var hh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function NY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;O().getBool(\"WEBGL_PACK_CLIP\")?i=new hh(n.shape):i=new fh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var AA={kernelName:lo,backendName:\"webgl\",kernelFunc:NY};var gh=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function RA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function TY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[RA(o,n.complexTensorInfos.real),RA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var FA={kernelName:pp,backendName:\"webgl\",kernelFunc:TY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) {\n return getChannel(\n getT${h}(${yh(i,u,g)}),\n vec2(${yh(c,u,g)}));\n }`}let d=p.length,f=p[p.length-1];m+=`\n return getChannel(\n getT${d}(${yh(i,u,f)}),\n vec2(${yh(c,u,f)}));`,this.userCode=`\n float getValue(${i.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n ${a[n-1]} = ${a[n-1]} + 1;\n if (${a[n-1]} < ${o[n-1]}) {\n result.g = getValue(${a});\n }\n\n ${a[n-2]} = ${a[n-2]} + 1;\n if (${a[n-2]} < ${o[n-2]}) {\n result.a = getValue(${a});\n }\n\n ${a[n-1]} = ${a[n-1]} - 1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Mu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var DA={kernelName:si,backendName:\"webgl\",kernelFunc:Mu};function fc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>Ua({inputs:{input:b},backend:t})),f=r.map(b=>Mu({inputs:{input:b},backend:t})),h=fc(d,e,t),g=fc(f,e,t),x=Rr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return te({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=LE(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Qs):new Ar(r[0].shape,Qs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=_Y(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function _Y(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function $w(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):fc(p,s,t)}var OA={kernelName:ys,backendName:\"webgl\",kernelFunc:$w};var hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,w=\"\",k=\"\";o&&(n?w=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?w=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:w=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${w}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Ch=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Nw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(Li(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let A=Pu({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Pu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],\"float32\",$),R=te({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i===\"leakyrelu\",L=i?Wa(i,!0):null,W=new dc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),V=f?[R,k]:[k,R];if(n&&V.push(n),P&&V.push(s),M){let H=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));V.push(H),w.push(H)}let U=o.runWebGLProgram(W,V,\"float32\"),q=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return q}function EY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let h=new gc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new hc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var PA={kernelName:Go,backendName:\"webgl\",kernelFunc:EY};var kh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${a}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Th=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},_h=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var MA={kernelName:cp,backendName:\"webgl\",kernelFunc:$Y};function AY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var LA={kernelName:Ho,backendName:\"webgl\",kernelFunc:AY};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var BA={kernelName:lp,backendName:\"webgl\",kernelFunc:RY};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var VA={kernelName:vm,backendName:\"webgl\",kernelFunc:FY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var zA={kernelName:mp,backendName:\"webgl\",kernelFunc:DY};var OY=_o+`\n return cos(x);\n`,PY=ge({opSnippet:OY}),WA={kernelName:qo,backendName:\"webgl\",kernelFunc:PY};var MY=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,LY=ge({opSnippet:MY}),UA={kernelName:Ko,backendName:\"webgl\",kernelFunc:LY};var Eh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${w};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var BY=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Eh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},GA={kernelName:Yo,backendName:\"webgl\",kernelFunc:BY};var Lu;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Lu||(Lu={}));var El=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===Lu.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${HA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${_e(s)} coords = getOutputCoords();\n int end = ${qA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${qA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${HA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function HA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function qA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function $h(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=xt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new El(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new El(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=xt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function VY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Prod,n,t,s,a,i)}var KA={kernelName:jo,backendName:\"webgl\",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Sum,n,t,s,a,i)}var jA={kernelName:Xo,backendName:\"webgl\",kernelFunc:zY};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=qf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=OE(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ti,backendName:\"webgl\",kernelFunc:WY};var Ah=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:Qo,backendName:\"webgl\",kernelFunc:UY};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new yc(l):m=new xc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var QA={kernelName:Zo,backendName:\"webgl\",kernelFunc:GY};var Rh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Fh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var ZA={kernelName:dp,backendName:\"webgl\",kernelFunc:HY};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Fh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var JA={kernelName:fp,backendName:\"webgl\",kernelFunc:qY};var Dh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function KY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var eR={kernelName:hp,backendName:\"webgl\",kernelFunc:KY};var Oh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var tR={kernelName:gp,backendName:\"webgl\",kernelFunc:jY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ou({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var rR={kernelName:ri,backendName:\"webgl\",kernelFunc:XY};var YY=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",QY=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,ZY=ge({opSnippet:YY,packedOpSnippet:QY}),oR={kernelName:en,backendName:\"webgl\",kernelFunc:ZY};var JY=\"return (b >= 1.0) ? a : a * (b + 1.0);\",eQ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,tQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new To(eQ,o.shape,n.shape):new io(JY,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},nR={kernelName:km,backendName:\"webgl\",kernelFunc:tQ};var rQ=`\n return vec4(equal(a, b));\n`,oQ=\"return float(a == b);\",nQ=tt({opSnippet:oQ,packedOpSnippet:rQ,dtype:\"bool\",cpuKernelImpl:BE}),sR={kernelName:tn,backendName:\"webgl\",kernelFunc:nQ};var sQ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,aQ=ge({opSnippet:sQ}),aR={kernelName:ma,backendName:\"webgl\",kernelFunc:aQ};var iQ=_o+`\n return exp(x);\n`,uQ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aw=ge({opSnippet:iQ,packedOpSnippet:uQ,cpuKernelImpl:VE,dtype:\"float32\"}),iR={kernelName:rn,backendName:\"webgl\",kernelFunc:Aw};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uR={kernelName:bs,backendName:\"webgl\",kernelFunc:Ph};var pR=\"return exp(x) - 1.0;\",pQ=ge({opSnippet:pR,packedOpSnippet:pR,cpuKernelImpl:zE}),cR={kernelName:da,backendName:\"webgl\",kernelFunc:pQ};var $l=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function Mh(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new $l(\"real\",p,e),c=new $l(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Rr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function cQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var lR={kernelName:oi,backendName:\"webgl\",kernelFunc:cQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ga(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var mR={kernelName:Cs,backendName:\"webgl\",kernelFunc:Ga};var Bh=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var dR={kernelName:on,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var fR=\"return floor(x);\",lQ=ge({opSnippet:fR,packedOpSnippet:fR,cpuKernelImpl:WE}),hR={kernelName:nn,backendName:\"webgl\",kernelFunc:lQ};var mQ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,dQ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,fQ=tt({opSnippet:mQ,packedOpSnippet:dQ,dtype:\"int32\"}),gR={kernelName:sn,backendName:\"webgl\",kernelFunc:fQ};var Vh=class{constructor(e){this.variableNames=[\"A\"];let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var zh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=St(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var xR={kernelName:Zi,backendName:\"webgl\",kernelFunc:hQ},bc,Rw=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function hQ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(bc==null||h!==Rw)&&(Rw=h,bc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Rw})),bc.canvas.width=p,bc.canvas.height=u,bc.drawImage(n,0,0,p,u),n=bc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=ir.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool(\"WEBGL_PACK\")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d===\"leakyrelu\",_=()=>{let A=[n,s],R=(D,P)=>{if(P===\"NCHW\"&&D.shape.length===1&&D.shape[0]!==1){let M=te({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));A.push(D),b.push(D)}return A};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&O().getBool(\"WEBGL_EXP_CONV\")){let A=d?Wa(d,!0):null,R=new gc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,\"float32\",D)}else if(O().getBool(\"WEBGL_CONV_IM2COL\"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Wa(d,!1):null,R=new hc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),$}var yR={kernelName:ho,backendName:\"webgl\",kernelFunc:gQ};function xQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Uh=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=_e(this.rank),n=bQ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function bQ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),w=GE(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var SR={kernelName:Ss,backendName:\"webgl\",kernelFunc:Fw};var CQ=\"return float(a > b);\",SQ=`\n return vec4(greaterThan(a, b));\n`,wQ=tt({opSnippet:CQ,packedOpSnippet:SQ,cpuKernelImpl:HE,dtype:\"bool\"}),wR={kernelName:pn,backendName:\"webgl\",kernelFunc:wQ};var IQ=\"return float(a >= b);\",vQ=`\n return vec4(greaterThanEqual(a, b));\n`,kQ=tt({opSnippet:IQ,packedOpSnippet:vQ,dtype:\"bool\",cpuKernelImpl:qE}),IR={kernelName:cn,backendName:\"webgl\",kernelFunc:kQ};function NQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var vR={kernelName:ni,backendName:\"webgl\",kernelFunc:NQ};var TQ=\"return float(!isnan(x) && !isinf(x));\",_Q=ge({opSnippet:TQ,dtype:\"bool\"}),kR={kernelName:fa,backendName:\"webgl\",kernelFunc:_Q};var EQ=\"return float(isinf(x));\",$Q=ge({opSnippet:EQ,dtype:\"bool\"}),NR={kernelName:ha,backendName:\"webgl\",kernelFunc:$Q};var AQ=\"return float(isnan(x));\",RQ=ge({opSnippet:AQ,dtype:\"bool\"}),TR={kernelName:ln,backendName:\"webgl\",kernelFunc:RQ};var FQ=\"return float(a < b);\",DQ=`\n return vec4(lessThan(a, b));\n`,OQ=tt({opSnippet:FQ,packedOpSnippet:DQ,cpuKernelImpl:KE,dtype:\"bool\"}),_R={kernelName:dn,backendName:\"webgl\",kernelFunc:OQ};var PQ=\"return float(a <= b);\",MQ=`\n return vec4(lessThanEqual(a, b));\n`,LQ=tt({opSnippet:PQ,packedOpSnippet:MQ,cpuKernelImpl:jE,dtype:\"bool\"}),ER={kernelName:fn,backendName:\"webgl\",kernelFunc:LQ};function BQ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=XE(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var $R={kernelName:xp,backendName:\"webgl\",kernelFunc:BQ};var VQ=_o+`\n return x < 0.0 ? 0./0. : log(x);\n`,zQ=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,WQ=ge({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:YE}),AR={kernelName:hn,backendName:\"webgl\",kernelFunc:WQ};var UQ=_o+`\n return log(1.0 + x);\n`,GQ=ge({opSnippet:UQ}),RR={kernelName:ga,backendName:\"webgl\",kernelFunc:GQ};var HQ=\"return float(a >= 1.0 && b >= 1.0);\",qQ=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,KQ=tt({opSnippet:HQ,packedOpSnippet:qQ,dtype:\"bool\"}),FR={kernelName:gn,backendName:\"webgl\",kernelFunc:KQ};var jQ=\"return float(!(x >= 1.0));\",XQ=ge({opSnippet:jQ}),DR={kernelName:xn,backendName:\"webgl\",kernelFunc:XQ};var YQ=\"return float(a >= 1.0 || b >= 1.0);\",QQ=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,ZQ=tt({opSnippet:YQ,packedOpSnippet:QQ,dtype:\"bool\"}),OR={kernelName:xa,backendName:\"webgl\",kernelFunc:ZQ};var Gh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var JQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},PR={kernelName:yp,backendName:\"webgl\",kernelFunc:JQ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var e7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},MR={kernelName:Nm,backendName:\"webgl\",kernelFunc:e7};function LR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Gr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Dw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var zR={kernelName:Cn,backendName:\"webgl\",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new zi(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var WR={kernelName:bp,backendName:\"webgl\",kernelFunc:s7};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},jh=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new zi(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var UR={kernelName:_m,backendName:\"webgl\",kernelFunc:a7};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;is([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new ps(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new Kh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GR={kernelName:Tm,backendName:\"webgl\",kernelFunc:i7};function HR(r,e,t,o){let n=new ps(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new ps(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var qR={kernelName:Cp,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var mF={kernelName:vs,backendName:\"webgl\",kernelFunc:E7};var Jh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=_e(n),a=t.map(u=>u[0]).join(\",\"),i=t.map((u,c)=>u[0]+e[c]).join(\",\"),p=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,n);if(n===1){this.userCode=`\n int start = ${a};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${i});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${p}));\n }\n }\n `}};var eg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=_e(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=$t(\"rc\",n),u=$t(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ga({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},dF={kernelName:$n,backendName:\"webgl\",kernelFunc:Lw};var $7=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,A7=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=S.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var hF={kernelName:Fn,backendName:\"webgl\",kernelFunc:F7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=n$(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var gF={kernelName:wp,backendName:\"webgl\",kernelFunc:D7};function O7(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=s$(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var xF={kernelName:Ip,backendName:\"webgl\",kernelFunc:O7};function P7(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=a$(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var yF={kernelName:vp,backendName:\"webgl\",kernelFunc:P7};var Bw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=i$(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},bF={kernelName:ks,backendName:\"webgl\",kernelFunc:Bw};var M7=\"return 1.0 / x;\",L7=ge({opSnippet:M7}),CF={kernelName:Dn,backendName:\"webgl\",kernelFunc:L7};var B7=Bt+`\n return (x < 0.0) ? 0.0 : x;\n`,V7=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z7=ge({opSnippet:B7,packedOpSnippet:V7}),SF={kernelName:On,backendName:\"webgl\",kernelFunc:z7};var W7=Bt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,U7=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,G7=ge({opSnippet:W7,packedOpSnippet:U7}),wF={kernelName:Ln,backendName:\"webgl\",kernelFunc:G7};var tg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var rg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function H7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var IF={kernelName:Mn,backendName:\"webgl\",kernelFunc:H7};var og=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var vF={kernelName:$m,backendName:\"webgl\",kernelFunc:q7};var ng=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var kF={kernelName:Pn,backendName:\"webgl\",kernelFunc:K7};var ag=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var NF={kernelName:Em,backendName:\"webgl\",kernelFunc:j7};var ig=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=_e(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var ug=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=$t(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=_e(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return At({inputs:{x:n},backend:t});let p=O().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var TF={kernelName:Bn,backendName:\"webgl\",kernelFunc:X7};var pg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var _F={kernelName:es,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Y7=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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eZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Cc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var AF={kernelName:zn,backendName:\"webgl\",kernelFunc:eZ};var cg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=m$(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var zF={kernelName:ui,backendName:\"webgl\",kernelFunc:xZ};function yZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=d$(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var WF={kernelName:wa,backendName:\"webgl\",kernelFunc:yZ};function bZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var UF={kernelName:pi,backendName:\"webgl\",kernelFunc:bZ};function CZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 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fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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pD={kernelName:kp,backendName:\"webgl\",kernelFunc:BZ};function VZ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var cD={kernelName:Rs,backendName:\"webgl\",kernelFunc:VZ};var xg=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n 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segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function 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Rt(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function $o(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function se(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function T3(r){let e;return e=`\n ${Eee()}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,e}function Eee(){return`\n @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ)\n`}function $ee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(`\n const workgroupSizeX = ${t.workgroupSize[0]}u;\n const workgroupSizeY = ${t.workgroupSize[1]}u;\n const workgroupSizeZ = ${t.workgroupSize[2]}u;\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${F3(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} +\n localIndex);\n `}\n }\n `),t.isFromPixels){o.push(`\n struct Uniform {\n size : i32,\n numChannels : i32,\n outShapeStrides : vec2,\n };\n\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n @group(0) @binding(2) var uniforms: Uniform;\n `);let f=$3(t);return[_3,o.join(`\n`),E3(e.shape),t.getUserCode(),T3(f)].join(`\n`)}let s=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=`\n outShapeStrides: ${p}, `,t.size&&(s+=\"size : i32, \"),t.uniforms&&(s+=t.uniforms),s+=\"};\",s=Lee(s),o.push(s),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:wc(r[h].dtype,t.isVec4)}>;\n `)}),s!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=Oee(e.shape,t.dispatchLayout),c=[_3+Aee,o.join(`\n`),E3(e.shape),u,Pee(e.shape.length)];t.atomic||c.push(Mee(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>Dee(f,e.shape,t.variableTypes?t.variableTypes[h]===\"vec4\":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=$3(t);return c.push(T3(m)),c.join(`\n`)}function R3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=F3(r)?\"flatDispatch\":\"\";return n+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+e.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,n}var _3=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n var res: i32 = a / b;\n let modulo: i32 = a % b;\n if (sign < 0. && modulo != 0) {\n res = res - 1;\n }\n return res;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n }\n`,Aee=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function E3(r){let e=r.length;if(e<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 {\n let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n return vec2(d0, d1);\n }`;let s;return s=\"var index2 = index;\"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`;return`${p}; ${u};`}).join(\"\"),`\n fn getCoordsFromIndex(index : i32) -> ${o} {\n ${s}\n return ${o}(${n.join(\",\")});\n }\n `}function Ree(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return e?`\n fn ${s}() -> vec4 {\n return vec4(${t}[0]);\n }\n `:`\n fn ${s}() ->f32 {\n return f32(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),e?`\n fn ${s}(${i}) -> vec4 {\n return vec4(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p}) / 4]);\n }\n `:`\n fn ${s}(${i}) -> f32 {\n return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})]);\n }\n `}function Fee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return vec4(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return vec4(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n return f32(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> f32 {\n return f32(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n }\n `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> vec4 {\n return get${s}();\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> f32{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${$o(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${$o(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?`\n fn ${a}Index(globalIndex : i32) -> vec4 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> vec4 {\n var coords = coordsIn;\n ${m}\n return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];\n }\n `:`\n fn ${a}Index(globalIndex : i32) -> f32 {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> f32 {\n var coords = coordsIn;\n ${m}\n return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);\n }\n`}function Dee(r,e,t,o){let n=Ree(r,t);return r.shape.length<=e.length&&(n+=Fee(r,e,t,o)),n}function Oee(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function Pee(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function F3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function wc(r,e){return r===\"float32\"?e?\"vec4\":\"f32\":r===\"int32\"||r===\"bool\"?e?\"vec4\":\"i32\":r}function Mee(r,e,t){let o=r.length,n=wc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : vec4) {\n result[flatIndex] = ${n}(value);\n }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n result[flatIndex] = ${n}(value);\n }\n fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n result[flatIndex] = ${n}(value);\n }`,o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=Rt(o);t?s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex / 4, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : vec4) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex / 4, value);\n }\n `:s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : f32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : i32) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex, value);\n }\n `}return s}function Lee(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function $3(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var nI={};Ue(nI,{ArrayBufferToTypedArray:()=>oI,GPUBytesPerElement:()=>rI,MatMulProgramType:()=>Ao,computeDispatch:()=>re,computeWorkPerThreadForConv2d:()=>Ll,computeWorkgroupInfoForMatMul:()=>tI,computeWorkgroupSizeForConv2d:()=>Ml,flatDispatchLayout:()=>ue,isWebGPUSupported:()=>Bl,tilesFitEvenlyIntoShape:()=>Vee});var zu=r=>{let e=1;for(let t=0;tt%r[o]===0)}function re(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(zu(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(zu(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(zu(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function tI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Ml(r,e,t=!1){if(t)return[8,8,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Ll(r,e,t=!1){if(t)return[4,4,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ue(r){return{x:r.map((e,t)=>t)}}function rI(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function oI(r,e){if(e===\"float32\")return new Float32Array(r);if(e===\"int32\")return new Int32Array(r);if(e===\"bool\"||e===\"string\")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${e}`)}function Bl(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var Ao;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Ao||(Ao={}));var zee=O().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Wee=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},Ui=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Bl())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has(\"timestamp-query-inside-passes\"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Do(this,cr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Ui.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if(\"texture\"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=oI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n===\"complex64\")throw new Error(\"Does not support reading buffer for complex64 dtype.\");if(a==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=cr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn(\"This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.\");let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if(\"texture\"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=rI(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[];e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4});let s=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(s,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(s,c,p.data.length).set(p.data):new Float32Array(s,c,p.data.length).set(p.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,s,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Wee(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h=\"int32\";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{if(h.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=R3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=A3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=zee){return O().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let r={powerPreference:O().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has(\"timestamp-query-inside-passes\")&&(t.requiredFeatures=[\"timestamp-query-inside-passes\"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Ui(n,s)},3);var ye;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.EQUAL=5]=\"EQUAL\",r[r.GREATER=6]=\"GREATER\",r[r.GREATER_EQUAL=7]=\"GREATER_EQUAL\",r[r.INT_DIV=8]=\"INT_DIV\",r[r.LESS=9]=\"LESS\",r[r.LESS_EQUAL=10]=\"LESS_EQUAL\",r[r.LOGICAL_AND=11]=\"LOGICAL_AND\",r[r.MAX=12]=\"MAX\",r[r.MIN=13]=\"MIN\",r[r.MOD=14]=\"MOD\",r[r.MUL=15]=\"MUL\",r[r.NOT_EQUAL=16]=\"NOT_EQUAL\",r[r.POW=17]=\"POW\",r[r.PRELU=18]=\"PRELU\",r[r.SQUARED_DIFFERENCE=19]=\"SQUARED_DIFFERENCE\",r[r.SUB=20]=\"SUB\"})(ye||(ye={}));var D3=`\n if (isnan(a)) { return a; }\n if (isnan(b)) { return b; }\n `,O3=`\n if (isNaN.r) {\n resultTemp.r = valueForNaN;\n }\n if (isNaN.g) {\n resultTemp.g = valueForNaN;\n }\n if (isNaN.b) {\n resultTemp.b = valueForNaN;\n }\n if (isNaN.a) {\n resultTemp.a = valueForNaN;\n }\n `,aI=`\n let isNaN = isnanVec4(a) | isnanVec4(b);\n ${O3}\n `,Uee=\"return a + b;\",Gee=\"return areal * breal - aimag * bimag;\",Hee=\"return areal * bimag + aimag * breal;\",qee=\"return a / b;\",Kee=\"return f32(a == b);\",jee=\"return vec4(a == b);\",Xee=\"return f32(a > b);\",Yee=\"return vec4(a > b);\",Qee=\"return f32(a >= b);\",Zee=\"return vec4(a >= b);\",Jee=`\n let s = sign(a) * sign(b);\n let ia = i32(round(a));\n let ib = i32(round(b));\n return f32(idiv(ia, ib, s));\n`,ete=`\n let ia = vec4(round(a));\n let ib = vec4(round(b));\n let cond = ib != vec4(0);\n var resultTemp = vec4(0);\n let s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(resultTemp);\n`,tte=\"return f32(a < b);\",rte=\"return vec4(a < b);\",ote=\"return f32(a <= b);\",nte=\"return vec4(a <= b);\",ste=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",ate=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,ite=`\n ${D3}\n if (b == 0.) {\n return uniforms.NAN;\n }\n var resultTemp = a % b;\n if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {\n return resultTemp;\n } else {\n return (resultTemp + b) % b;\n }\n`,ute=`\n let valueForNaN = uniforms.NAN;\n var resultTemp = vec4(a % b);\n ${aI}\n\n if (b[0] == 0.) {\n resultTemp[0] = uniforms.NAN;\n }\n if (b[1] == 0.) {\n resultTemp[1] = uniforms.NAN;\n }\n if (b[2] == 0.) {\n resultTemp[2] = uniforms.NAN;\n }\n if (b[3] == 0.) {\n resultTemp[3] = uniforms.NAN;\n }\n\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n\n return resultTemp;\n`,pte=\"return a * b;\",cte=`\n if (isnan(a) || isnan(b)) {\n return 1.0;\n }\n return f32(a != b);\n`,lte=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n ${aI}\n\n return resultTemp;\n`,mte=`\n if(a < 0.0 && floor(b) < b) {\n return uniforms.NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n if (round(abs(b) % 2.0) != 1.0) {\n return pow(abs(a), b);\n }\n return sign(a) * pow(abs(a), b);\n`,dte=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n let valueForNaN = uniforms.NAN;\n ${O3}\n return resultTemp;\n`,fte=\"if (a < 0.0) { return b * a; } return a;\",hte=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,gte=\"return (a - b) * (a - b);\",xte=\"return a - b;\";function sI(r,e,t=\"uniforms.NAN\"){let o=e?aI:D3;return e?`\n let valueForNaN = ${t};\n var resultTemp = vec4(${r}(a, b));\n `+o+`\n return resultTemp;\n `:o+`\n return ${r}(a, b);\n `}function Ic(r,e){switch(r){case ye.ADD:return Uee;case ye.ATAN2:return sI(\"atan2\",e);case ye.COMPLEX_MULTIPLY_IMAG:return Hee;case ye.COMPLEX_MULTIPLY_REAL:return Gee;case ye.DIV:return qee;case ye.EQUAL:return e?jee:Kee;case ye.GREATER:return e?Yee:Xee;case ye.GREATER_EQUAL:return e?Zee:Qee;case ye.INT_DIV:return e?ete:Jee;case ye.LESS:return e?rte:tte;case ye.LESS_EQUAL:return e?nte:ote;case ye.LOGICAL_AND:return e?ate:ste;case ye.MAX:return sI(\"max\",e);case ye.MIN:return sI(\"min\",e);case ye.MOD:return e?ute:ite;case ye.MUL:return pte;case ye.NOT_EQUAL:return e?lte:cte;case ye.POW:return e?dte:mte;case ye.PRELU:return e?hte:fte;case ye.SQUARED_DIFFERENCE:return gte;case ye.SUB:return xte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.RSQRT=27]=\"RSQRT\",r[r.SIN=28]=\"SIN\",r[r.SINH=29]=\"SINH\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SQRT=31]=\"SQRT\",r[r.SQUARE=32]=\"SQUARE\",r[r.TAN=33]=\"TAN\",r[r.TANH=34]=\"TANH\",r[r.TO_INT=35]=\"TO_INT\"})(Q||(Q={}));var yte=\"return abs(a);\",bte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,Cte=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,Ste=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,wte=\"return asinh(a);\",Ite=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,vte=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,kte=\"return ceil(a);\",Nte=\"return cos(a);\",Tte=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,_te=\"return exp(a) - 1.0;\",Ete=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",$te=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,Ate=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${S.ERF_P};\n let a1 = ${S.ERF_A1};\n let a2 = ${S.ERF_A2};\n let a3 = ${S.ERF_A3};\n let a4 = ${S.ERF_A4};\n let a5 = ${S.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,Rte=\"return exp(a);\",Fte=\"return floor(a);\",Dte=\"return f32(!isnan(a) && !isinf(a));\",Ote=\"return f32(isinf(a));\",Pte=\"return f32(isnan(a));\",Mte=\"return a;\",Lte=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,Bte=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Vte=\"return f32(!(a >= 1.0));\",zte=\"return -a;\",Wte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Ute=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Gte=\"return 1.0 / a;\",Hte=\"return select(a, 0.0, a < 0.0);\",qte=\"return clamp(a, 0.0, 6.0);\",Kte=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",jte=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Xte=\"return inverseSqrt(a);\",Yte=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Qte=\"return sin(a);\",Zte=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Jte=\"return sqrt(a);\",ere=\"return a * a;\",tre=\"return tan(a);\",rre=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,ore=\"return f32(i32((a)));\";function Ha(r,e){switch(r){case Q.ABS:return yte;case Q.ACOS:return bte;case Q.ACOSH:return Cte;case Q.ASIN:return Ste;case Q.ASINH:return wte;case Q.ATAN:return Ite;case Q.ATANH:return vte;case Q.COS:return Nte;case Q.COSH:return Tte;case Q.CEIL:return kte;case Q.ELU:return e?$te:Ete;case Q.ERF:return Ate;case Q.EXP:return Rte;case Q.EXPM1:return _te;case Q.FLOOR:return Fte;case Q.IS_FINITE:return Dte;case Q.IS_INF:return Ote;case Q.IS_NAN:return Pte;case Q.LINEAR:return Mte;case Q.LOG:return Lte;case Q.LOG1P:return Bte;case Q.LOGICAL_NOT:return Vte;case Q.NEG:return zte;case Q.LEAKYRELU:return e?Ute:Wte;case Q.RECIPROCAL:return Gte;case Q.RELU:return e?jte:Hte;case Q.RELU6:return e?Kte:qte;case Q.RSQRT:return Xte;case Q.SIGMOID:return Yte;case Q.SIN:return Qte;case Q.SINH:return Zte;case Q.SQRT:return Jte;case Q.SQUARE:return ere;case Q.TAN:return tre;case Q.TANH:return rre;case Q.TO_INT:return ore;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return\"f32\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error(`${r}-component is not supported.`)}};function ur(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Ha(Q.LINEAR);else if(r===\"relu\")n=Ha(Q.RELU,t);else if(r===\"elu\")n=Ha(Q.ELU,t);else if(r===\"relu6\")n=Ha(Q.RELU6,t);else if(r===\"prelu\")n=Ic(ye.PRELU,t);else if(r===\"sigmoid\")n=Ha(Q.SIGMOID,t);else if(r===\"leakyrelu\")n=Ha(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Hr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function iI(r,e,t,o,n=!1,s=!1,a=!1,i=1){y.assert(t&&i===1||!t,()=>`transposeA ${t} is not compatible with component size ${i}`);let p=`\n let batch = ${r?\"0\":\"batchIn\"};\n ${t?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,u=o?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n var value = ${kt(i)}(0.0);\n let col = colIn * ${i};\n ${n&&a?p:`\n ${t?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${p}\n }\n `}\n return value;\n }\n\n fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} {\n let col = colIn * ${i};\n let batch = ${e?\"0\":\"batchIn\"};\n var value = ${kt(i)}(0.0);\n ${u}\n return value;\n }\n `}function Vl(r,e,t,o,n,s,a=!1,i=!1,p=!1,u=1){return`\n ${iI(t,o,n,s,a,i,p,u)}\n fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) {\n let col = colIn * ${u};\n ${a&&i?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Hr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var nre=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart / innerElementSize + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRow + innerRow,\n kStart / innerElementSize + inputCol);\n `,sre=(r,e)=>r?`\n let ACached0 = mm_Asub[k * innerElementSize][localRow];\n let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n ${e===3?\"\":\"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];\"}\n for (var i = 0; i < rowPerThread; i = i + 1) {\n acc[i] = BCached0 * ACached0[i] + acc[i];\n acc[i] = BCached1 * ACached1[i] + acc[i];\n acc[i] = BCached2 * ACached2[i] + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n }`:`\n for (var i = 0; i < rowPerThread; i = i + 1) {\n let ACached = mm_Asub[tileRow + i][k];\n acc[i] = BCached0 * ACached.x + acc[i];\n acc[i] = BCached1 * ACached.y + acc[i];\n acc[i] = BCached2 * ACached.z + acc[i];\n ${e===3?\"\":\"acc[i] = BCached3 * ACached.w + acc[i];\"}\n }`;function Wu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const innerElementSize = ${l};\n const tileInner = ${o};\n\n ${se()} {\n let localRow = i32(localId.y);\n let tileRow = ${a?\"0\":\"localRow * rowPerThread\"};\n let tileCol = i32(localId.x);\n\n let globalRow = ${a?\"0\":\"i32(globalId.y) * rowPerThread\"};\n let globalCol = i32(globalId.x);\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, rowPerThread>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${nre(t)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n ${l===3?\"\":\"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];\"}\n\n ${sre(t,l)}\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var P3=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batch,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,are=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Uu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=r[1]*e[1],p=r[0]*e[0],u=t?i:o,c=t?o:i;y.assert(c%e[1]===0&&u%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let l=c/e[1],m=u/e[0],d=o/e[1],f=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${i};\n let globalColStart = i32(workgroupId.x) * ${p};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n ${P3(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n ACached * BCached[innerCol];\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * rowPerThread;\n let tileCol = i32(localId.x) * colPerThread;\n\n let globalRow = i32(globalId.y) * rowPerThread;\n let globalCol = i32(globalId.x) * colPerThread;\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let tileRowA = i32(localId.y) * ${l};\n let tileColA = i32(localId.x) * ${m};\n let tileRowB = i32(localId.y) * ${d};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${P3(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + tileInner;\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < tileInner; k = k + 1) {\n for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n ${are(t)}\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${c}>;\n var mm_Bsub : array, ${o}>;\n const rowPerThread = ${r[1]};\n const colPerThread = ${r[0]};\n const tileInner = ${o};\n\n ${se()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:\"(uniforms.dimInner - 1) / tileInner + 1\"};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, rowPerThread>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${f}\n }\n `}var ire=r=>r?`\n mm_readA(batch, colA, globalRow),\n mm_readA(batch, colA + 1, globalRow),\n mm_readA(batch, colA + 2, globalRow),\n mm_readA(batch, colA + 3, globalRow)\n `:`\n mm_readA(batch, globalRow, colA),\n mm_readA(batch, globalRow, colA + 1),\n mm_readA(batch, globalRow, colA + 2),\n mm_readA(batch, globalRow, colA + 3)\n `;function ure(r,e=!1){return y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`),`\n const tileSize = ${r[0]*4};\n var mm_Asub : array, ${r[0]}>;\n\n ${se()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / tileSize + 1;\n let batch = i32(globalId.z);\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t = t + 1) {\n // Load one tile of A into local memory.\n let colA = t * tileSize + tileCol * 4;\n mm_Asub[tileCol] = vec4(${ire(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < tileSize / 4; k = k + 1) {\n let rowB = t * tileSize + k * 4;\n let BCached = vec4(mm_readB(batch, rowB, globalCol),\n mm_readB(batch, rowB + 1, globalCol),\n mm_readB(batch, rowB + 2, globalCol),\n mm_readB(batch, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var _g=class{constructor(e,t,o,n,s=!1,a=!1,i=null,p=null,u=null,c=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];if(this.isVec4=(l%4===0&&!s||t[1]%4===0&&s)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!s,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let f=tI(t[1],l,t[2],s);this.workgroupSize=f.workgroupSize,this.elementsPerThread=f.elementsPerThread}this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let m=i!=null,d=u!=null;m&&this.variableNames.push(\"bias\"),d&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=c,this.transposeA=s,this.transposeB=a,this.addBias=m,this.activation=p,this.hasPreluActivationWeights=d,this.batchAEqualOne=o,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?ure(this.workgroupSize,this.transposeA):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)}\n `}};function pre(){return`\n var sumValues : array;\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) {\n let dataA = mm_readA(batch, row, k);\n let dataB = mm_readB(batch, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = workgroupSizeX / 2u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Eg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize);let u=a!=null,c=p!=null;u&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=t,this.batchBEqualOne=o,this.shaderKey=`matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${pre()}\n `}};function cre(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${se()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batch, globalRow, globalColA);\n var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batch, globalRow, globalColA);\n regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var $g=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}\n ${cre(this.workgroupSize)}\n `}};var Ag=class{constructor(e,t,o,n,s=!1,a=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(s&&this.outputShape[1]%4===0||!s&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=re(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=s,this.transposeB=a,this.batchAEqualOne=o,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=n=>`\n for (var i = 0; i < ${n}; i = i + 1)\n {\n var oldValue = atomicLoad(&(result[flatIndex + i]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + ${n>1?\"value[i]\":\"value\"};\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n `,t=this.isVec4?4:1;return`\n ${iI(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)}\n fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t)}) {\n let col = colIn * ${t};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n ${e(t)}\n }\n }\n ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Rg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights)}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Hr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var Fg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function dr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Fg(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var M3={kernelName:Cs,backendName:\"webgpu\",kernelFunc:dr};function de(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var L3={kernelName:Ns,backendName:\"webgpu\",kernelFunc:de};function Gu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=br.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=de({inputs:{x:r},backend:n,attrs:{shape:k}}),A=de({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=x===1,M=b===1,L=[$,A],W=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],V,U,q=[D,d,f],H=O().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(H<0){let X=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Z=X>0?X:n.thresholdToIncreaseWorkgroups,ee=D*Math.ceil(d/32)*Math.ceil(f/32);ee<=Z||d<=8&&ee<=Z*2?D*d*f<=128?H=Ao.MatMulReduceProgram:D===1&&m>=2e3?H=Ao.MatMulSplitKProgram:H=Ao.MatMulSmallOutputSizeProgram:H=Ao.MatMulPackedProgram}switch(H){case Ao.MatMulReduceProgram:V=new Eg(q,P,M,t,o,s,p,a);break;case Ao.MatMulSplitKProgram:{if(U=dr({backend:n,attrs:{shape:q,value:0,dtype:r.dtype}}),V=new Ag(q,m,P,M,t,o),s||p){U=n.runWebGPUProgram(V,L,r.dtype,W,U);let Z=new Rg(U.shape,s,p,a),ee=null,Y=[U];s&&Y.push(s),a&&Y.push(a),p===\"leakyrelu\"&&(ee=[{type:\"float32\",data:[i]}],Z.uniforms+=\" alpha : f32,\");let J=n.runWebGPUProgram(Z,Y,U.dtype,ee);R.push(U);let ie=de({inputs:{x:J},backend:n,attrs:{shape:w}});R.push(J);for(let pe of R)n.disposeData(pe.dataId);return ie}break}case Ao.MatMulSmallOutputSizeProgram:V=new $g(k,_,q,t,o,s,p,a);break;case Ao.MatMulPackedProgram:let X=n.adapterInfo.isIntel();V=new _g(k,q,P,M,t,o,s,p,a,X);break;default:throw new Error(`Unsupported MatMulProgramType ${H}.`)}s&&L.push(s),a&&L.push(a),p===\"leakyrelu\"&&(W.push({type:\"float32\",data:[i]}),V.uniforms+=\" alpha : f32,\"),U=n.runWebGPUProgram(V,L,r.dtype,W,U);let j=de({inputs:{x:U},backend:n,attrs:{shape:w}});R.push(U);for(let X of R)n.disposeData(X.dataId);return j}function lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Gu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var B3={kernelName:fo,backendName:\"webgpu\",kernelFunc:lre};var zl=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Ic(this.op,!1)}\n }\n\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Hu=class{constructor(e,t,o){this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type=\"shared\",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type=\"vec4\",this.workPerThread=4):(this.isVec4=!1,this.type=\"plain\",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Ic(this.op,this.isVec4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${se(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n let b = getBByOutputIndex(index);\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function Ft(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var V3={kernelName:mo,backendName:\"webgpu\",kernelFunc:Ft};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:t}),p=Ft({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var z3={kernelName:ei,backendName:\"webgpu\",kernelFunc:po};var Ro=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let o=128;this.workgroupSize=[o,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Ha(this.op,!1)}\n }\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function Se({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Ro(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function ot({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ye.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new zl(ye.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new zl(ye.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?S.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:W3,castImpl:U3,ceilImpl:G3,concatImpl:H3,equalImpl:q3,expImpl:K3,expm1Impl:j3,floorImpl:X3,gatherNdImpl:Y3,gatherV2Impl:Q3,greaterEqualImpl:Z3,greaterImpl:J3,lessEqualImpl:eM,lessImpl:tM,logImpl:rM,maxImpl:oM,maximumImpl:nM,minimumImpl:sM,multiplyImpl:aM,negImpl:iM,notEqualImpl:uM,prodImpl:pM,rangeImpl:cM,rsqrtImpl:lM,scatterImpl:mM,simpleAbsImpl:dM,sliceImpl:fM,stridedSliceImpl:hM,stringNGramsImpl:gM,subImpl:xM,tileImpl:yM,topKImpl:bM,transposeImpl:CM,uniqueImpl:kNt}=Qp;var mre=Se({opType:Q.ABS,cpuKernelImpl:dM}),SM={kernelName:gs,backendName:\"webgpu\",kernelFunc:mre};var dre=Se({opType:Q.ACOS}),wM={kernelName:sa,backendName:\"webgpu\",kernelFunc:dre};var fre=Se({opType:Q.ACOSH}),IM={kernelName:aa,backendName:\"webgpu\",kernelFunc:fre};var hre=ot({opType:ye.ADD,cpuKernelImpl:W3,supportsComplex:!0}),vM={kernelName:eo,backendName:\"webgpu\",kernelFunc:hre};var Dg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${se(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function gre(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Dg(s);return t.runWebGPUProgram(a,o,n)}var kM={kernelName:Mo,backendName:\"webgpu\",kernelFunc:gre};var Og=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),`\n const tileSize = ${this.workgroupSize[0]};\n var tile : array, ${this.workgroupSize[0]}>;\n ${se()} {\n var x = i32(workgroupId.x) * tileSize + i32(localId.x);\n var y = i32(workgroupId.y) * tileSize + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * tileSize + i32(localId.x);\n y = i32(workgroupId.x) * tileSize + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var Pg=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let o=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${o}\n }\n }\n `}};function qr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=oM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=pM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o===\"mean\"?\"float32\":ka(r.dtype),w=[{type:\"int32\",data:[h]}],k=new Mg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=de({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"all\",t)}var TM={kernelName:Lo,backendName:\"webgpu\",kernelFunc:yre};function bre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"any\",t)}var _M={kernelName:Bo,backendName:\"webgpu\",kernelFunc:bre};var vc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ue(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type=\"plain\",this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=re(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${$o(this.inputShape.length-1)}`,t=()=>{let o=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(o+=\"outputCoords,\");else for(let n=0;n u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${se(\"index\")} {\n let outputIndex = index / i32(workgroupSizeX);\n let reduceLength = ${e()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + i32(workgroupSizeX)) {\n let candidate = getX(${t()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), workgroupSizeX);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${t()} 0);\n let reduceLength = ${e()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${t()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var EM={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var $M={kernelName:Za,backendName:\"webgpu\",kernelFunc:Sre};var wre=Se({opType:Q.ASIN}),AM={kernelName:ia,backendName:\"webgpu\",kernelFunc:wre};var Ire=Se({opType:Q.ASINH}),RM={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ire};var vre=Se({opType:Q.ATAN}),FM={kernelName:pa,backendName:\"webgpu\",kernelFunc:vre};var kre=ot({opType:ye.ATAN2}),DM={kernelName:la,backendName:\"webgpu\",kernelFunc:kre};var Nre=Se({opType:Q.ATANH}),OM={kernelName:ca,backendName:\"webgpu\",kernelFunc:Nre};var Wl=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n var count = 0.0;\n\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, coords[3]);\n ${e}\n }\n }\n\n setOutputAtIndex(index, ${t});\n }\n }\n `}};var Lg=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.stride;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};function Ul(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return qr(n,s,a,\"max\",t)}var PM={kernelName:yn,backendName:\"webgpu\",kernelFunc:Ul};function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,\"mean\",t)}var MM={kernelName:Sn,backendName:\"webgpu\",kernelFunc:uI};function Bg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Ft({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=de({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=uI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=Ul({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=de({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Lg(e):(t===\"avg\"?n=new Wl(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Wl(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"avg\",t)}var LM={kernelName:zo,backendName:\"webgpu\",kernelFunc:Tre};function _re(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Gu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var BM={kernelName:Wo,backendName:\"webgpu\",kernelFunc:_re};var Vg=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Rt(this.rank),t=Ere(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},pI=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Ere(r){if(r===1)return\"sourceLoc\";if(r<=6)return pI.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function ds(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=fM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Vg(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var VM={kernelName:_s,backendName:\"webgpu\",kernelFunc:ds};var $re=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=de({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=de({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ds({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},zM={kernelName:xs,backendName:\"webgpu\",kernelFunc:$re};var Are=`\n fn bincount_write(index: i32, value: f32) {\n var oldValue = atomicLoad(& (result[index]));\n var exchanged = false;\n for (; !exchanged;) {\n let newValueF32 = bitcast(oldValue) + value;\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(\n &(result[index]), oldValue, newValue);\n oldValue = res.old_value;\n exchanged = res.exchanged;\n }\n }\n`,Rre=`\n fn bincount_write(index: i32, value: f32) {\n result[index] = value;\n }\n`,kc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Rre:Are}\n ${se(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(index))\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"f32(getW(coord[0], coord[1]))\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=dr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new kc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var WM={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Fre};var cI=ot({opType:ye.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:uM}),UM={kernelName:Nn,backendName:\"webgpu\",kernelFunc:cI};function qa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var GM={kernelName:ai,backendName:\"webgpu\",kernelFunc:qa};function HM(r,e){let t=new Ro(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Ft({inputs:{x:n},backend:t});let a=Vr(n.shape),i=lI({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=qa({inputs:{input:n},backend:t}),i=lI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=U3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return HM(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=cI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var qM={kernelName:co,backendName:\"webgpu\",kernelFunc:lI};var Dre=Se({opType:Q.CEIL,cpuKernelImpl:G3}),KM={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Dre};var zg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue : vec4;\n for (var i = 0; i < 4; i = i + 1) {\n if (isnan(value[i])) {\n clampedValue[i] = value[i];\n } else {\n clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n }\n }\n\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var Wg=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function Ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new zg(n.shape):i=new Wg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var jM={kernelName:lo,backendName:\"webgpu\",kernelFunc:Ore};var Ug=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;sqa({inputs:{input:C},backend:t})),h=r.map(C=>qu({inputs:{input:C},backend:t})),g=Nc(f,e,t),x=Nc(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return de({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=H3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Ug(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=de({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Pre(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>de({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function mI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Nc(p,s,t)}var YM={kernelName:ys,backendName:\"webgpu\",kernelFunc:mI};function Mre(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return\"resData = x[xIndex];\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = x[xIndex / 4];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return\"return W[row * uniforms.wShape[3] + colIn];\";case 4:return\"return W[row * uniforms.wShape[3] / 4 + colIn];\";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n let xCh = ${x} % inChannels;\n var resData = ${kt(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${kt(i)}(0.0);`:o&&t?`\n let col = colIn * ${i};\n ${b}`:`\n let col = colIn * ${i};\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),$=r?kt(p):kt(i);return`\n ${ur(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {\n ${r?C:w}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} {\n ${r?w:C}\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {\n let col = colIn * ${u};\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Hr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var Gg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=[\"f32\",\"vec4\"]):(this.innerElementSize=4,this.variableTypes=[\"vec4\",\"vec4\"]),s&&(this.variableNames.push(\"bias\"),this.variableTypes.push(\"vec4\")),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableTypes.push(\"vec4\"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Uu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Mre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var Hg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${ur(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${se(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var qg=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${se(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];\n let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -\n uniforms.pad[1];\n let xCol = offsetX + uniforms.dilation[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function Kg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=de({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=de({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Kg(s.shape,p);x!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Kg(n.shape,p);x!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Gu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=de({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Bre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],$=new qg(_,C),A=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],R=o.runWebGPUProgram($,[r],r.dtype,A),D=[];D.push(R);let P=de({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Kg(s.shape,C);U!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Kg(n.shape,C);U!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let W=Gu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),V=de({inputs:{x:W},backend:o,attrs:{shape:t.outShape}});D.push(W);for(let U of D)o.disposeData(U.dataId);return V}function jg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return Lre({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Bre({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Hg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[A]});let R=o.adapterInfo.isIntel();g=new Gg(t,_,$,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=de({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=de({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Vre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return jg({x:n,filter:s,convInfo:m,backend:o})}var QM={kernelName:Go,backendName:\"webgpu\",kernelFunc:Vre};function zre(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${kt(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${kt(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${kt(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {\n let col = colIn * ${r};\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${kt(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {\n let col = colIn * ${r};\n if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var Xg=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=[\"vec4\",\"f32\"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize):Uu(this.elementsPerThread,this.workgroupSize);return`\n ${zre(this.isVec4?4:1)}\n ${e}\n `}};var Yg=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n if (${this.isChannelsLast}) {\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n } else {\n let xValue = getDy(batch, d2, idyR, idyC);\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Wre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Yg(m);else{f=new Xg(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var ZM={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Wre};var Ure=Se({opType:Q.COS}),JM={kernelName:qo,backendName:\"webgpu\",kernelFunc:Ure};var Gre=Se({opType:Q.COSH}),eL={kernelName:Ko,backendName:\"webgpu\",kernelFunc:Gre};var Qg=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Hre=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Qg(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},tL={kernelName:Yo,backendName:\"webgpu\",kernelFunc:Hre};var Ku;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Ku||(Ku={}));var Gl=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Ku.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${rL(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${oL(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${oL(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${rL(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function rL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function oL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zg(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Gl(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Gl(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Prod,n,t,s,a,i)}var nL={kernelName:jo,backendName:\"webgpu\",kernelFunc:qre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Sum,n,t,s,a,i)}var sL={kernelName:Xo,backendName:\"webgpu\",kernelFunc:Kre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=dr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new kc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var aL={kernelName:ti,backendName:\"webgpu\",kernelFunc:jre};var Jg=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Xre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new Jg(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iL={kernelName:Qo,backendName:\"webgpu\",kernelFunc:Xre};var ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${se()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pad;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2,\",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return`\n ${ur(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n const strideHeight = ${this.convInfo.strideHeight};\n const strideWidth = ${this.convInfo.strideWidth};\n ${se()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var _c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${ur(this.activation,this.hasPreluActivation,!1,4)}\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.stride - uniforms.pad;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilation[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilation[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilation[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilation[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Hr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Tc(d):(g=new _c(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uL={kernelName:Zo,backendName:\"webgpu\",kernelFunc:Yre};var dI=ot({opType:ye.MUL,cpuKernelImpl:aM,supportsComplex:!0}),pL={kernelName:kn,backendName:\"webgpu\",kernelFunc:dI};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"sum\",t)}var cL={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Hl};function Qre(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Hl({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var lL={kernelName:ri,backendName:\"webgpu\",kernelFunc:Qre};var Zre=Se({opType:Q.ELU}),mL={kernelName:en,backendName:\"webgpu\",kernelFunc:Zre};var Jre=ot({opType:ye.EQUAL,dtype:\"bool\",cpuKernelImpl:q3}),dL={kernelName:tn,backendName:\"webgpu\",kernelFunc:Jre};var eoe=Se({opType:Q.ERF}),fL={kernelName:ma,backendName:\"webgpu\",kernelFunc:eoe};var fI=Se({opType:Q.EXP,cpuKernelImpl:K3,dtype:\"float32\"}),hL={kernelName:rn,backendName:\"webgpu\",kernelFunc:fI};function tx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),de({inputs:{x:s},backend:o,attrs:{shape:i}})}var gL={kernelName:bs,backendName:\"webgpu\",kernelFunc:tx};var toe=Se({opType:Q.EXPM1,cpuKernelImpl:j3}),xL={kernelName:da,backendName:\"webgpu\",kernelFunc:toe};var ql=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function rx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=de({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ql(\"real\",u),l=new ql(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=de({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function roe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!1,t)}var yL={kernelName:oi,backendName:\"webgpu\",kernelFunc:roe};var ox=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var bL={kernelName:on,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ooe=Se({opType:Q.FLOOR,cpuKernelImpl:X3}),CL={kernelName:nn,backendName:\"webgpu\",kernelFunc:ooe};var noe=ot({opType:ye.INT_DIV,dtype:\"int32\"}),SL={kernelName:sn,backendName:\"webgpu\",kernelFunc:noe};var nx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${se(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var wL={kernelName:Zi,backendName:\"webgpu\",kernelFunc:soe},Ec,hI=O().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\"),sx=new Map;function 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ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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ix=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function 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ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=poe(this.aShape);return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function poe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=de({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var TL={kernelName:Ss,backendName:\"webgpu\",kernelFunc:gI};var coe=ot({opType:ye.GREATER,cpuKernelImpl:J3,dtype:\"bool\"}),_L={kernelName:pn,backendName:\"webgpu\",kernelFunc:coe};var loe=ot({opType:ye.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Z3}),EL={kernelName:cn,backendName:\"webgpu\",kernelFunc:loe};function moe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!0,t)}var $L={kernelName:ni,backendName:\"webgpu\",kernelFunc:moe};var doe=Se({opType:Q.IS_FINITE,dtype:\"bool\"}),AL={kernelName:fa,backendName:\"webgpu\",kernelFunc:doe};var foe=Se({opType:Q.IS_INF,dtype:\"bool\"}),RL={kernelName:ha,backendName:\"webgpu\",kernelFunc:foe};var hoe=Se({opType:Q.IS_NAN,dtype:\"bool\"}),FL={kernelName:ln,backendName:\"webgpu\",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Ro(n.shape,Q.LEAKYRELU);return i.uniforms=\"alpha : f32,\",t.runWebGPUProgram(i,[n],\"float32\",a)}var DL={kernelName:mn,backendName:\"webgpu\",kernelFunc:goe};var xoe=ot({opType:ye.LESS,dtype:\"bool\",cpuKernelImpl:tM}),OL={kernelName:dn,backendName:\"webgpu\",kernelFunc:xoe};var yoe=ot({opType:ye.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:eM}),PL={kernelName:fn,backendName:\"webgpu\",kernelFunc:yoe};var boe=Se({opType:Q.LOG,cpuKernelImpl:rM}),ML={kernelName:hn,backendName:\"webgpu\",kernelFunc:boe};var Coe=Se({opType:Q.LOG1P}),LL={kernelName:ga,backendName:\"webgpu\",kernelFunc:Coe};var Soe=ot({opType:ye.LOGICAL_AND,dtype:\"bool\"}),BL={kernelName:gn,backendName:\"webgpu\",kernelFunc:Soe};var woe=Se({opType:Q.LOGICAL_NOT}),VL={kernelName:xn,backendName:\"webgpu\",kernelFunc:woe};var Ioe=ot({opType:ye.MAX,cpuKernelImpl:nM}),zL={kernelName:bn,backendName:\"webgpu\",kernelFunc:Ioe};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,\"max\",t)}var WL={kernelName:Cn,backendName:\"webgpu\",kernelFunc:voe};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"min\",t)}var UL={kernelName:wn,backendName:\"webgpu\",kernelFunc:koe};var Noe=ot({opType:ye.MIN,cpuKernelImpl:sM}),GL={kernelName:In,backendName:\"webgpu\",kernelFunc:Noe};var px=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + 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Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Lt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XL={kernelName:_n,backendName:\"webgpu\",kernelFunc:$oe};var cx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${se(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Aoe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new cx(u,a),l=de({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=de({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YL={kernelName:En,backendName:\"webgpu\",kernelFunc:Aoe};function Kl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=Kl({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QL={kernelName:Fs,backendName:\"webgpu\",kernelFunc:Kl};function ZL(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=qa({inputs:{input:o},backend:t}),s=ZL({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JL={kernelName:Is,backendName:\"webgpu\",kernelFunc:ZL};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return tx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=tx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=mI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eB={kernelName:vs,backendName:\"webgpu\",kernelFunc:Roe};var lx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:\"\"}`).join(\",\"),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?\"any(outC < start)\":\"outC < start\",p=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let start = ${s};\n let end = ${a};\n let outC = getCoordsFromIndex(index);\n\n if (${i} || ${p}) {\n setOutputAtIndex(index, uniforms.constantValue);\n } else {\n let coords = outC - start;\n setOutputAtIndex(index, getX(${u}));\n }\n }\n }\n `}};var xI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return dr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new lx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tB={kernelName:$n,backendName:\"webgpu\",kernelFunc:xI};var Foe=ot({opType:ye.POW}),rB={kernelName:An,backendName:\"webgpu\",kernelFunc:Foe};function Doe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Hu(ye.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oB={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Doe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,\"prod\",t)}var nB={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ooe};var Poe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sB={kernelName:ks,backendName:\"webgpu\",kernelFunc:Poe};var yI=ot({opType:ye.DIV}),aB={kernelName:Jo,backendName:\"webgpu\",kernelFunc:yI};var Moe=Se({opType:Q.RECIPROCAL}),iB={kernelName:Dn,backendName:\"webgpu\",kernelFunc:Moe};var Loe=Se({opType:Q.RELU}),uB={kernelName:On,backendName:\"webgpu\",kernelFunc:Loe};var Boe=Se({opType:Q.RELU6}),pB={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Boe};var mx=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new mx(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cB={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Voe};var dx=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function zoe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new dx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var lB={kernelName:Pn,backendName:\"webgpu\",kernelFunc:zoe};var fx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=de({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new fx(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=de({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var mB={kernelName:Bn,backendName:\"webgpu\",kernelFunc:Woe};var hx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var dB={kernelName:es,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new hx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Uoe=Se({opType:Q.RSQRT,cpuKernelImpl:lM}),fB={kernelName:Vn,backendName:\"webgpu\",kernelFunc:Uoe};var Gi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ue(e),this.dispatch=re(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(c,l)=>`coords[${l}]`).join(\", \")})`,p=(c,l)=>{let m=`atomicAdd(${c}, bitcast(${l}))`;this.type===\"float32\"&&(m=`\n {\n var oldBits = 0;\n var newBits = bitcast(${l});\n loop {\n let info = atomicCompareExchangeWeak(${c}, oldBits, newBits);\n if (info.exchanged) {\n break;\n }\n oldBits = info.old_value;\n let oldValue = bitcast(oldBits);\n let newValue = oldValue + (${l});\n newBits = bitcast(newValue);\n }\n }\n `);let d=`atomicStore(${c}, bitcast(${l}));`;return this.sumDupeIndices?m:d};return`\n ${s}\n\n ${se(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${wc(this.type,!1)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${p(\"&result[flatIndex]\",\"updateValue\")};\n }\n }`}};function Goe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=de({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=de({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=dr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new Gi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=de({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var hB={kernelName:zn,backendName:\"webgpu\",kernelFunc:Goe};var gx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new gx([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var gB={kernelName:ii,backendName:\"webgpu\",kernelFunc:Hoe};var xx=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function qoe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new xx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var xB={kernelName:Ts,backendName:\"webgpu\",kernelFunc:qoe};var Koe=Se({opType:Q.SIGMOID}),yB={kernelName:Un,backendName:\"webgpu\",kernelFunc:Koe};var joe=Se({opType:Q.SIN}),bB={kernelName:Wn,backendName:\"webgpu\",kernelFunc:joe};var Xoe=Se({opType:Q.SINH}),CB={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Xoe};var bI=ot({opType:ye.SUB,cpuKernelImpl:xM,supportsComplex:!0}),SB={kernelName:Xn,backendName:\"webgpu\",kernelFunc:bI};function Yoe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Ul({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=de({inputs:{x:i},backend:t,attrs:{shape:p}}),c=bI({inputs:{a:n,b:u},backend:t}),l=fI({inputs:{x:c},backend:t}),m=Hl({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=de({inputs:{x:m},backend:t,attrs:{shape:p}}),f=yI({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var wB={kernelName:qn,backendName:\"webgpu\",kernelFunc:Yoe};var Qoe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},IB={kernelName:Es,backendName:\"webgpu\",kernelFunc:Qoe};var yx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=yM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new yx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var vB={kernelName:to,backendName:\"webgpu\",kernelFunc:CI};function Joe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=mM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=de({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?de({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=de({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=CI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Gi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let $=de({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),$}var kB={kernelName:li,backendName:\"webgpu\",kernelFunc:Joe};function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ds({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NB={kernelName:$s,backendName:\"webgpu\",kernelFunc:ene};var tne=Se({opType:Q.SQRT}),TB={kernelName:Gn,backendName:\"webgpu\",kernelFunc:tne};var _B={kernelName:mi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Ro(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var rne=ot({opType:ye.SQUARED_DIFFERENCE}),EB={kernelName:Kn,backendName:\"webgpu\",kernelFunc:rne};var bx=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function one(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=de({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=ds({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=de({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),A=le(n.shape,n.dtype,$),R=hM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let $=new bx(d),A=[{type:\"int32\",data:b},{type:\"int32\",data:w}],R=t.runWebGPUProgram($,[n],n.dtype,A);k=de({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var $B={kernelName:jn,backendName:\"webgpu\",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=gM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var AB={kernelName:As,backendName:\"webgpu\",kernelFunc:nne};var sne=Se({opType:Q.TAN}),RB={kernelName:Yn,backendName:\"webgpu\",kernelFunc:sne};var ane=Se({opType:Q.TANH}),FB={kernelName:Qn,backendName:\"webgpu\",kernelFunc:ane};var Cx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function $c(r,e){e!==null&&r.disposeData(e.dataId)}function DB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let A=h(),R=new Cx($),P=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,\"int32\",P),$c(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new Sx([c,k/2]),R=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],D=f;f=t.runWebGPUProgram($,_,\"int32\",R),$c(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ds({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),$c(t,x);let b=gI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});$c(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=de({inputs:{x:f},attrs:{shape:C},backend:t}),$c(t,x);let w=b;return b=de({inputs:{x:b},attrs:{shape:C},backend:t}),$c(t,w),[b,f]}var OB={kernelName:Zn,backendName:\"webgpu\",kernelFunc:ine};var wx=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${se(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let w=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",w)}var PB={kernelName:Jn,backendName:\"webgpu\",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:\"webgpu\",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB=\"4.1.0\",lne=\"4.1.0\",mne=\"4.1.0\",dne=\"4.1.0\",fne=\"4.1.0\",hne=\"0.0.1-alpha.16\",gne={tfjs:LB,\"tfjs-core\":LB,\"tfjs-converter\":lne,\"tfjs-backend-cpu\":mne,\"tfjs-backend-webgl\":dne,\"tfjs-backend-wasm\":fne,\"tfjs-backend-webgpu\":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,fv as acos,hv as acosh,xe as add,gv as addN,xv as all,yv as any,bv as argMax,Cv as argMin,Sv as asin,wv as asinh,Iv as atan,vv as atan2,kv as atanh,td as avgPool,_v as avgPool3d,Oie as backend,S as backend_util,Ev as basicLSTMCell,wi as batchNorm,Av as batchNorm2d,Rv as batchNorm3d,Fv as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,Dv as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Q0 as browser,le as buffer,Ke as cast,Ov as ceil,Pv as clipByValue,Br as clone,Tr as complex,gt as concat,Mv as concat1d,Lv as concat2d,Bv as concat3d,Vv as concat4d,zv as conv1d,vi as conv2d,Wv as conv2dTranspose,Uv as conv3d,Hv as conv3dTranspose,Dne as copyRegisteredKernels,qv as cos,Kv as cosh,il as cosineWindow,jv as cumprod,Xv as cumsum,Cr as customGrad,Yv as denseBincount,eC as deprecationWarn,Qv as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Zv as diag,Jv as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,j0 as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,pv as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,h0 as tensor_util,dv as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: true,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') { // TBD replace with navigator.userAgentData once in mainline\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam', 'no devices');\n return;\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam', 'no stream');\n return;\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh.keepInvalid) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += m.sizeFromManifest;\n totalSizeWeights += m.sizeLoadedWeights;\n totalSizeLoading += m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded() {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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255, 200, 0.3)":lt.color,t.fill())}}function Hce(e,t){var a;if(lt.drawGaze&&((a=e.rotation)==null?void 0:a.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*So(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*So(e.rotation.angle.pitch)/90,s=new Path2D(` M ${e.box[0]+e.box[2]/2} ${e.box[1]} C diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js index 46f3e0a7..20d34e24 100644 --- a/dist/human.node-gpu.js +++ b/dist/human.node-gpu.js @@ -4,316 +4,7 @@ author: ' */ -"use strict"; -var __create = Object.create; -var __defProp = Object.defineProperty; -var __getOwnPropDesc = Object.getOwnPropertyDescriptor; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __getProtoOf = Object.getPrototypeOf; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __commonJS = (cb, mod3) => function __require() { - return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; -}; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => { - __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); - return value; -}; -var __accessCheck = (obj, member, msg) => { - if (!member.has(obj)) - throw TypeError("Cannot " + msg); -}; -var __privateGet = (obj, member, getter) => { - __accessCheck(obj, member, "read from private field"); - return getter ? getter.call(obj) : member.get(obj); -}; -var __privateAdd = (obj, member, value) => { - if (member.has(obj)) - throw TypeError("Cannot add the same private member more than once"); - member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -}; -var __privateSet = (obj, member, value, setter) => { - __accessCheck(obj, member, "write to private field"); - setter ? setter.call(obj, value) : member.set(obj, value); - return value; -}; - -// dist/tfjs.esm.js -var require_tfjs_esm = __commonJS({ - "dist/tfjs.esm.js"(exports, module2) { - "use strict"; - var __defProp2 = Object.defineProperty; - var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; - var __getOwnPropNames2 = Object.getOwnPropertyNames; - var __hasOwnProp2 = Object.prototype.hasOwnProperty; - var __export2 = (target, all2) => { - for (var name in all2) - __defProp2(target, name, { get: all2[name], enumerable: true }); - }; - var __copyProps2 = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames2(from)) - if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_gpu_exports = {}; - __export2(tf_node_gpu_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_gpu_exports); - __reExport(tf_node_gpu_exports, require("@tensorflow/tfjs-node-gpu"), module2.exports); - var version4 = "4.1.0"; - var version22 = "4.1.0"; - var version32 = "4.1.0"; - var version42 = "4.1.0"; - var version5 = "4.1.0"; - var version6 = "0.0.1-alpha.16"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) - console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) - throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") - return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) - msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) - msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) - log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) - prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) - prev[key] = mergeDeep(pVal, oVal); - else - prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - 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modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: 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E1="4.1.0",qo="4.1.0",Uo="4.1.0",Yo="4.1.0",Ko="4.1.0",Jo="0.0.1-alpha.16",Qo={tfjs:E1,"tfjs-core":E1,"tfjs-converter":qo,"tfjs-backend-cpu":Uo,"tfjs-backend-webgl":Yo,"tfjs-backend-wasm":Ko,"tfjs-backend-webgpu":Jo}});var da={};ze(da,{Env:()=>w2,Human:()=>v1,default:()=>v1,defaults:()=>Ye,draw:()=>et,empty:()=>he,env:()=>R,match:()=>Nt,models:()=>T1});module.exports=Fo(da);var ae=Z(H());function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function z1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Ut(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Ut(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ye={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var I0=Z(H());var N=Z(H());var S1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -323,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var j1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -336,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,N1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -349,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,I1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -363,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,O1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -387,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,L1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -410,6055 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) - return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) - this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) - this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) - return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) - return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) - source = sourceTexture; - else - source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) - return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) - return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) - sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb2 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; - const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") - throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document === "undefined") - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } - } else { - if (typeof env.Canvas !== "undefined") - c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") - c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) - log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) - throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) - throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb2, 0); - tf2.dispose(rgb2); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) - throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) - log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) - log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) - targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) - targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) - targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) - targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) - throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) - inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) - outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) - fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) - log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) - fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) - fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) - fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) - fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) - fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) - fx.add("hue", config3.filter.hue); - if (config3.filter.negative) - fx.add("negative"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.vintage) - fx.add("brownie"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.kodachrome) - fx.add("kodachrome"); - if (config3.filter.technicolor) - fx.add("technicolor"); - if (config3.filter.polaroid) - fx.add("polaroid"); - if (config3.filter.pixelate !== 0) - fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) - outCanvas = fx.apply(inCanvas); - else - outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) - fx = null; - env.filter = !!fx; - } - if (!getTensor) - return { tensor: null, canvas: outCanvas }; - if (!outCanvas) - throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) - tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb2; - } - if (!pixels) - throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) - return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) - log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) - log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - __publicField(this, "browser"); - __publicField(this, "node"); - __publicField(this, "worker"); - __publicField(this, "platform", ""); - __publicField(this, "agent", ""); - __publicField(this, "backends", []); - __publicField(this, "initial"); - __publicField(this, "filter"); - __publicField(this, "tfjs"); - __publicField(this, "offscreen"); - __publicField(this, "perfadd", false); - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - __publicField(this, "kernels", []); - __privateAdd(this, _canvas, void 0); - __privateAdd(this, _image, void 0); - __privateAdd(this, _imageData, void 0); - this.browser = typeof navigator !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined") { - const raw = navigator.userAgent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = navigator.userAgent.replace(raw[0], ""); - if (this.platform[1]) - this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) - Object.defineProperty(this, "cpu", { value: cpu }); - else - this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - __publicField(this, "config"); - __publicField(this, "element"); - __publicField(this, "stream"); - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) - this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) - this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) - this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) - this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) - this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) - this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) - log("webcam", "cannot get dom element", webcamConfig.element); - return; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) - log("webcam", "unknown dom element", webcamConfig.element); - return; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) - requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) - requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) - requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) - log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) - log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) - return; - if (this.element.paused) - await this.element.play(); - else - this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) - log("webcam", "no devices"); - return; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return; - } - if (!this.stream) { - if (this.config.debug) - log("webcam", "no stream"); - return; - } - this.element.srcObject = this.stream; - const ready3 = new Promise((resolve) => { - if (!this.element) - resolve(false); - else - this.element.onloadeddata = () => resolve(true); - }); - await ready3; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - }); - __publicField(this, "pause", () => { - if (this.element) - this.element.pause(); - }); - __publicField(this, "play", async () => { - if (this.element) - await this.element.play(); - }); - __publicField(this, "stop", () => { - if (this.config.debug) - log("webcam", "stop"); - if (this.track) - this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - get track() { - if (!this.stream) - return void 0; - return this.stream.getVideoTracks()[0]; - } - get capabilities() { - if (!this.track) - return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - get constraints() { - if (!this.track) - return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - get settings() { - if (!this.stream) - return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - get label() { - if (!this.track) - return ""; - return this.track.label; - } - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) - log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) - modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - let loaded = false; - try { - model23.findIOHandler(); - if (options.debug) - log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) - model23.loadSync(artifacts); - else - model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; - if (options.verbose) - log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) - log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.0.0"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - desynchronized: true - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) - return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") - return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) - tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) - log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) - return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) - return "webgpu"; - if (env.webgl.supported && env.webgl.backend) - return "webgl"; - if (env.wasm.supported && env.wasm.backend) - return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) - log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) - instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) - log("running inside web worker"); - } - if (env.browser && instance.config.backend === "tensorflow") { - if (instance.config.debug) - log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { - if (instance.config.debug) - log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) - log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) - log("available backends:", available); - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) - log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) - log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) - tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) - log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") - tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else - throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) - log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) - defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) - tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) - continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) - log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - env.initial = false; - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) - log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) - log("draw error: invalid canvas"); - else if (!input.getContext) - log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d"); - if (!ctx) - log("draw error: cannot get canvas context"); - else - return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") - return opt.color; - const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - labelColor: "rgba(173, 216, 230, 1)", - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - rightEyeIris: [473, 474, 475, 476, 477], - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } -]; -var UV468 = [ - [0.499976992607117, 0.652534008026123], - [0.500025987625122, 0.547487020492554], - [0.499974012374878, 0.602371990680695], - [0.482113003730774, 0.471979022026062], - [0.500150978565216, 0.527155995368958], - [0.499909996986389, 0.498252987861633], - [0.499523013830185, 0.40106201171875], - [0.289712011814117, 0.380764007568359], - [0.499954998493195, 0.312398016452789], - [0.499987006187439, 0.269918978214264], - [0.500023007392883, 0.107050001621246], - [0.500023007392883, 0.666234016418457], - [0.5000159740448, 0.679224014282227], - [0.500023007392883, 0.692348003387451], - [0.499976992607117, 0.695277988910675], - [0.499976992607117, 0.70593398809433], - 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94, - 2, - 370, - 289, - 305, - 455, - 339, - 254, - 448, - 359, - 255, - 446, - 254, - 253, - 449, - 253, - 252, - 450, - 252, - 256, - 451, - 256, - 341, - 452, - 414, - 413, - 463, - 286, - 441, - 414, - 286, - 258, - 441, - 258, - 257, - 442, - 257, - 259, - 443, - 259, - 260, - 444, - 260, - 467, - 445, - 309, - 459, - 250, - 305, - 289, - 290, - 305, - 290, - 460, - 401, - 376, - 435, - 309, - 250, - 392, - 376, - 411, - 433, - 453, - 341, - 464, - 357, - 453, - 465, - 343, - 357, - 412, - 437, - 343, - 399, - 344, - 360, - 440, - 420, - 437, - 456, - 360, - 420, - 363, - 361, - 401, - 288, - 265, - 372, - 353, - 390, - 339, - 249, - 339, - 448, - 255 -]; -var VTX68 = [ - 127, - 234, - 132, - 58, - 172, - 150, - 149, - 148, - 152, - 377, - 378, - 379, - 397, - 288, - 361, - 454, - 356, - 70, - 63, - 105, - 66, - 107, - 336, - 296, - 334, - 293, - 300, - 168, - 6, - 195, - 4, - 98, - 97, - 2, - 326, - 327, - 33, - 160, - 158, - 133, - 153, - 144, - 362, - 385, - 387, - 263, - 373, - 380, - 57, - 40, - 37, - 0, - 267, - 270, - 287, - 321, - 314, - 17, - 84, - 91, - 78, - 81, - 13, - 311, - 308, - 402, - 14, - 178 -]; -var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; -var VTX7 = [33, 133, 362, 263, 1, 78, 308]; -var UV68 = VTX68.map((x) => UV468[x]); -var UV33 = VTX33.map((x) => UV468[x]); -var UV7 = VTX7.map((x) => UV468[x]); -function connectionsToIndices(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 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336, - 285, - 417 -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) - return; - let l = localOptions.faceLabels.slice(); - if (f.score) - l = replace(l, "[score]", 100 * f.score); - if (f.gender) - l = replace(l, "[gender]", f.gender); - if (f.genderScore) - l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) - l = replace(l, "[age]", f.age); - if (f.distance) - l = replace(l, "[distance]", 100 * f.distance); - if (f.real) - l = replace(l, "[real]", 100 * f.real); - if (f.live) - l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) - emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - } - if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) - 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Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(R.browser&&N.browser)l=N.browser?N.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=N.tensor(f,[e.height,e.width,c],"int32")}else if((!c2||R0.width!==c2.width||R0.height!==c2.height)&&(c2=te(R0.width,R0.height)),N.browser&&R.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=N.browser.fromPixels(R0):(c2=Z2(R0),l=N.browser.fromPixels(c2));else{let M=Z2(R0).getContext("2d").getImageData(0,0,s,A);c=M.data.length/s/A;let T=new Uint8Array(M.data.buffer);l=N.tensor(T,[s,A,c])}if(c===4){let f=N.slice3d(l,[0,0,0],[-1,-1,3]);N.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let x=N.cast(l,"float32"),i=t.filter.equalization?await G2(x):N.expandDims(x,0);if(N.dispose([l,x]),t.filter.autoBrightness){let f=N.max(i),b=await 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O0={cacheModels:!0,cacheSupported:!0,verbose:!0,debug:!1,modelBasePath:""},E0={};async function os(e,t){return O0.debug&&u("load model fetch:",e,t),fetch(e,t)}function F1(e){O0.cacheModels=e.cacheModels,O0.verbose=e.debug,O0.modelBasePath=e.modelBasePath}async function O(e){var c,x,i,y;let t=z1(O0.modelBasePath,e||"");t.toLowerCase().endsWith(".json")||(t+=".json");let n=t.includes("/")?t.split("/"):t.split("\\"),o=n[n.length-1].replace(".json",""),r="indexeddb://"+o;E0[o]={name:o,sizeFromManifest:0,sizeLoadedWeights:0,sizeDesired:Qt[o],inCache:!1,url:""},O0.cacheSupported=typeof indexedDB!="undefined";let s={};try{s=O0.cacheSupported&&O0.cacheModels?await d2.io.listModels():{}}catch(d){O0.cacheSupported=!1}E0[o].inCache=O0.cacheSupported&&O0.cacheModels&&Object.keys(s).includes(r),E0[o].url=E0[o].inCache?r:t;let A=typeof fetch=="undefined"?{}:{fetchFunc:(d,p)=>os(d,p)},a=new d2.GraphModel(E0[o].url,A),l=!1;try{a.findIOHandler(),O0.debug&&u("model load 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f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints && f.mesh.length >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) - continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) - continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) - curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) - return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);$t(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function J2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)Z1(r,c,o)}}}function Q2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let x=o.fingerLabels.slice();x=_(x,"[label]",l),ne(r,x,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let x=0;x0?x-1:0][0],c[x>0?x-1:0][1]),r.lineTo(c[x][0],c[x][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function _2(e,t,n){var s;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function $2(e,t,n){var r;let o=a0(x0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let x=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",x),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6467,7523 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) - return; - const ctx = getCanvasContext(output); - if (!ctx) - return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) - return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - "leftEyeInside", - "leftEye", - "leftEyeOutside", - "rightEyeInside", - "rightEye", - "rightEyeOutside", - "leftEar", - "rightEar", - "leftMouth", - "rightMouth", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftPinky", - "rightPinky", - "leftIndex", - "rightIndex", - "leftThumb", - "rightThumb", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle", - "leftHeel", - "rightHeel", - "leftFoot", - "rightFoot", - "bodyCenter", - "bodyTop", - "leftPalm", - "leftHand", - "rightPalm", - "rightHand" -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) - model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) - log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) - model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) - log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - height, - width, - [0, 0] - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - kpt4.positionRaw[1] / width + cropBox[0], - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) - return null; - const t2 = {}; - [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) - return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) - continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) - model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) - return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) - continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) - model4 = null; - if (!model4) - model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) - return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) - resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) - product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) - column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) - product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) - anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var faceBoxScaleFactor = 1.4; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) - model5 = null; - if (!model5) - model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) - return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) - return []; - const t2 = {}; - t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const rawBox = { - startPoint: [points[0], points[1]], - endPoint: [points[2], points[3]], - landmarks: await b.landmarks.array(), - confidence - }; - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); - const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); - const squaredBox = squarifyBox(enlargedBox); - boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var irisEnlarge = 2.3; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) - model6 = null; - if (!model6) - model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) - inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize) { - if (!(model6 == null ? void 0 : model6["executor"])) - return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - }; - for (const val of Object.values(t2)) { - if (!val) - return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) - rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) - rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - if (!(model7 == null ? void 0 : model7["executor"])) - return []; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) - face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } else if (!model7) { - if (config3.debug) - log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh.keepInvalid) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) - face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) - faces.push(face4); - else - tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) - model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) - model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) - model7 = await loadModel(config3.face.attention.modelPath); - else - model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -async function load6(config3) { - var _a; - if (env.initial) - model8 = null; - if (!model8) - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model8["modelUrl"]); - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) - return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) - obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) - model9 = null; - if (!model9) - model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input) { - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) - return tensor6; - const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) - return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) - log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) - inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) - return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) - return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) - silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) - silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) - model10 = null; - if (!model10) - model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) - return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) - model11 = null; - if (!model11) - model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) - return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) - model12 = null; - if (!model12) - model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) - return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) - return; - const t2 = {}; - const box = [[0, 0.1, 0.9, 0.9]]; - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) - [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) - obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) - age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) - model13 = null; - if (!model13) - model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) - log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) - return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) - return; - const t2 = {}; - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) - model14 = null; - if (!model14) - model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) - log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) - return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) - return; - const t2 = {}; - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - t2.enhance = tf22.tidy(() => { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb[0]); - const greenNorm = tf22.mul(green, rgb[1]); - const blueNorm = tf22.mul(blue, rgb[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) - model15 = null; - if (!model15) - model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) - return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) - model16 = null; - if (!model16) - model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) - log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) - return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) - return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - thetaZ = Math.asin(r10); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) - thetaX = 0; - if (Number.isNaN(thetaY)) - thetaY = 0; - if (Number.isNaN(thetaZ)) - thetaZ = 0; - return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) - return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!f) - return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) - return []; - if (!faces) - return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) - faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) - delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) - res.age = descRes.age; - if (descRes.gender) - res.gender = descRes.gender; - if (descRes.genderScore) - res.genderScore = descRes.genderScore; - if (descRes.descriptor) - res.embedding = descRes.descriptor; - if (descRes.race) - res.race = descRes.race; - if (emotionRes) - res.emotion = emotionRes; - if (antispoofRes) - res.real = antispoofRes; - if (livenessRes) - res.live = livenessRes; - if (irisSize > 0) - res.distance = irisSize; - if (rotation) - res.rotation = rotation; - if (tensor6) - res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) - delete instance.performance.face; - if (instance.performance.age) - delete instance.performance.age; - if (instance.performance.gender) - delete instance.performance.gender; - if (instance.performance.emotion) - delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") - this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) - this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) - slope = -slope; - else if (slope > 0) - slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) - return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) - return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) - isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) - isDiagonal = 1 * weightageAt; - else - isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) - cos_in = 1; - else if (cos_in < -1) - cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) - fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) - fingerCurl = FingerCurl.half; - else - fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalUpLeft; - else - estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalDownLeft; - else - estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) - voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) - voteDiagonal += options3.DISTANCE_VOTE_POWER; - else - voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) - return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) - return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) - return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) - poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) - gestures.push({ face: i, gesture: "facing center" }); - else - gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) - gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) - gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) - gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) - gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) - continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) - center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (leftIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (rightIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) - gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - gestures.push({ iris: i, gesture: "looking up" }); - if (center) - gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) - fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) - gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - 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{ x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - 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{ x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (config3.skipAllowed) - this.skipped++; - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) - useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) - continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) - hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -async function predict14(input, config3) { - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) - return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) - box[0] = pt[0]; - if (pt[1] < box[1]) - box[1] = pt[1]; - if (pt[0] > box[2]) - box[2] = pt[0]; - if (pt[1] > box[3]) - box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function load15(config3) { - var _a, _b; - if (env.initial) { - handDetectorModel = null; - handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, - config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null - ]); - } else { - if (config3.debug) - log("cached model:", handDetectorModel["modelUrl"]); - if (config3.debug) - log("cached model:", handPoseModel["modelUrl"]); - } - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) - handPipeline = new HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect2(config3) { - var _a; - if (env.initial) - models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) - models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) - return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) - hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) - return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - const t0 = now(); - if (!newResult) - return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) - bufferedResult.canvas = newResult.canvas; - if (newResult.error) - bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) - coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) - coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) - bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) - bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) - bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load16(config3) { - if (!model17 || env.initial) - model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) - model17 = await load16(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) - return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - return (options4.multiplier || 20) * sum3; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) - return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.max(Math.min(norm, 1), 0); - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) - break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) - continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - cache5.padding = [ - [0, 0], - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - [0, 0] - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load17(config3) { - var _a; - if (env.initial) - model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) - log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) - inputSize8 = 256; - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (totalScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) - bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) - return []; - if (!config3.skipAllowed) - cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load18(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) - log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) - return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) - objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) - j++; - if (!this.less(k, j)) - break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) - return min2; - if (a > max5) - return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) - break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) - continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) - queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) - return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) - result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) - continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) - poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) - return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) - return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) - tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) - return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load19(config3) { - if (!model20 || env.initial) - model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load20(config3) { - if (!model21 || env.initial) - model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb2, a], -1); - tf35.dispose([rgb2, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) - model21 = await load20(config3); - if (!(model21 == null ? void 0 : model21["executor"])) - return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) - init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load21(config3) { - if (!model22 || env.initial) - model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) - model22 = await load21(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) - return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) - return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) - ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) - log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += m.sizeFromManifest; - totalSizeWeights += m.sizeLoadedWeights; - totalSizeLoading += m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) - this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) - this.reset(); - if (instance) - this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; - if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) - [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) - promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) - continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) - continue; - const res = validateModel(this.instance, model23, defined); - if (res) - missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) - person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) - person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var r5=0;function Fs(e,t,n){let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;sa5,kpt:()=>A5});var A5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],a5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,U1,Gs=5,nt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function Y1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var q1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),q1[0]),A=D.mul(D.div(A,t2),q1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function Xs(e,t,n,o){var c,x;let r=[],s={};s.boxes=Zs(e,U1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function ot(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var F0,l5=256,i5=Number.MAX_SAFE_INTEGER,qs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},st=[],Oe=[[0,0],[0,0],[0,0],[0,0]],Q1=0,_1=e=>1-1/(1+Math.exp(e)),e3=e=>Y1(e);async function t3(e){if(R.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await O(e.body.modelPath);let t=F0!=null&&F0.executor?Object.values(F0.modelSignature.inputs):void 0;l5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return F0}function $1(e,t,n){var s,A;let o={};if(!((s=e==null?void 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W0.all)n[W0.getName(o)]={curl:Be.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function En(e){let t=[];if(!e||e.length===0)return t;let n=wn(e);for(let o of Tn){let r=o.matchAgainst(n.curls,n.directions);r>=gA&&t.push({name:o.name,confidence:r})}return t}var zn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Sn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},jn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||b>.06)&&(d=!1),f>b?f>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let M=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],T=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(T<.01||M<.01||T>.022||M>.022)&&(d=!1),(T<.01||M<.01)&&t.push({iris:A,gesture:"looking down"}),(T>.022||M>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Nn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Pt(e,t=1.5){let n=L2(e),o=Mt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function kt(e){let t=L2(e),n=Mt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function PA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Dn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return PA(n)}var In=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ze(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},f=Wn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Gn=1.65,Vn=[0,5,9,13,17,1,2],SA=0,jA=2,Zn=0,Et=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>J5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Pt(kt(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Pt(kt(n),Gn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=K5(o,[0,0]),c=a.map(d=>[...J5(d,l),d[2]]),x=Fn(r),i=[...L2(n),1],y=[Ze(i,x[0]),Ze(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Zn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,x,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let x=Pt(kt(c),Gn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Xn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,qn;async function Q5(e,t){let n=await qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Rt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Un(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",a2.modelUrl),e.debug&&u("cached model:",i2.modelUrl));let t=a2?new wt(a2):void 0;return t&&i2&&(qn=new Et(t,i2)),[a2,i2]}var Q=Z(H());var f0=[null,null],IA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],OA=["hand","fist","pinch","point","face","tip","pinchtip"],Kn=4,Jn=1.6,LA=512,CA=1.4,zt=Number.MAX_SAFE_INTEGER,_5=0,ke=[0,0],y0={boxes:[],hands:[]},Qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function _n(e){var t;if(R.initial&&(f0[0]=null),f0[0])e.debug&&u("cached model:",f0[0].modelUrl);else{Y2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),f0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=f0[0].executor?Object.values(f0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[0]}async function $n(e){var t;if(R.initial&&(f0[1]=null),f0[1])e.debug&&u("cached model:",f0[1].modelUrl);else{f0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=f0[1].executor?Object.values(f0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[1]}async function WA(e,t){let n=[];if(!e||!f0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,LA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await f0[0].executeAsync(o.cast,IA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[Kn]),a.splice(Kn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=Q.slice(o.boxes,y,1),p=await d.data();Q.dispose(d);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=ot(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=x[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function $5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Rt(o.keypoints);for(let i of Object.keys(Qn))o.annotations[i]=Qn[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function e1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],zt++;let n=(t.hand.skipTime||0)>g()-_5,o=zt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-_5,l=zt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):(y0.boxes=await WA(e,t),_5=g(),y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))),zt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[x].fingerScore&&y0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=ot(i.box,Jn),d=ot(i.boxRaw,Jn);y0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var C2={};ze(C2,{connected:()=>jt,horizontal:()=>t1,kpt:()=>St,relative:()=>o1,vertical:()=>n1});var St=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],t1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],n1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],o1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),r1=0;function to(e,t){var 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be.executeAsync(b0,ta),l;switch(t.segmentation.mode||"default"){case"default":l=u1(n,o);break;case"alpha":l=u1(null,o);break;case"foreground":l=u1(n,null);break;case"state":l=na(r);break;default:l=F.tensor(0)}return F.dispose([b0.src,n,o,b0.r1i,b0.r2i,b0.r3i,b0.r4i]),[b0.r1i,b0.r2i,b0.r3i,b0.r4i]=[r,s,A,a],l}var k0=Z(H());var N0;async function g1(e){return!N0||R.initial?N0=await O(e.segmentation.modelPath):e.debug&&u("cached model:",N0.modelUrl),N0}async function Eo(e,t){var r;if(N0||(N0=await g1(t)),!(N0!=null&&N0.executor)||!((r=N0==null?void 0:N0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=k0.image.resizeBilinear(e,[N0.inputs[0].shape?N0.inputs[0].shape[1]:0,N0.inputs[0].shape?N0.inputs[0].shape[2]:0],!1),n.norm=k0.div(n.resize,C.tf255),n.res=N0.execute(n.norm),n.squeeze=k0.squeeze(n.res,[0]),n.alpha=k0.image.resizeBilinear(n.squeeze,[e.shape[1]||0,e.shape[2]||0]),n.mul=k0.mul(n.alpha,C.tf255);let o;switch(t.segmentation.mode||"default"){case"default":n.input=k0.squeeze(e),n.concat=k0.concat([n.input,n.mul],-1),o=k0.cast(n.concat,"int32");break;case"alpha":o=k0.cast(n.mul,"int32");break;default:o=k0.tensor(0)}return Object.keys(n).forEach(s=>k0.dispose(n[s])),o}function Ft(e,t,n){var c,x;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((x=l==null?void 0:l.graph)!=null&&x.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&u("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return e.config.debug&&A.length>0&&u("model validation failed:",n,A),A.length>0?{name:n,missing:A,ops:s,url:a}:null}var F2=class{constructor(t){k(this,"instance");k(this,"models",{});this.models={},this.instance=t}stats(){let t=0,n=0,o=0;for(let s of Object.values(E0))t+=s.sizeFromManifest,n+=s.sizeLoadedWeights,o+=s.sizeDesired;let r=o>0?n/o:0;return{numLoadedModels:Object.values(E0).length,numDefinedModels:Object.keys(this.models).length,percentageLoaded:r,totalSizeFromManifest:t,totalSizeWeights:n,totalSizeLoading:o,modelStats:Object.values(E0)}}reset(){for(let t of Object.keys(this.models))this.models[t]=null}async load(t){var o,r,s,A,a,l,c,x,i,y,d,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0,v;R.initial&&this.reset(),t&&(this.instance=t);let n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?b3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)==null?void 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0:c.enabled)&&!this.models.facemesh?S3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)==null?void 0:x.enabled)&&!this.models.gear?_3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)==null?void 0:i.enabled)&&!this.models.ssrnetage?nn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)==null?void 0:y.enabled)&&!this.models.ssrnetgender?An(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)==null?void 0:d.enabled)&&!this.models.mobilefacenet?xn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)==null?void 0:p.enabled)&&!this.models.insightface?un(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)==null?void 0:f.includes("blazepose"))?t3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?e3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)==null?void 0:b.includes("efficientpose"))?a3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)==null?void 0:M.includes("movenet"))?co(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)==null?void 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0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var d0=Z(H());var Bt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14134,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; -var body3 = ` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Ht=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -14703,563 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`; - -// src/warmup.ts -async function warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") - img = new Image(); - else if (env.Image) - img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) - ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) - img.src = src; - else - resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") - img = atob(face3); - else - img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) - log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") - res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) - res = await warmupCanvas(instance); - else - res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) - return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) - continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) - shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) - res.forEach((t2) => tf37.dispose(t2)); - else - tf37.dispose(res); - } catch (e) { - if (instance.config.debug) - log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) - log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) - log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) - instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - constructor(userConfig) { - __publicField(this, "version"); - __publicField(this, "config"); - __publicField(this, "result"); - __publicField(this, "state"); - __publicField(this, "process"); - __publicField(this, "tf"); - __publicField(this, "env", env); - __publicField(this, "draw", draw_exports); - __publicField(this, "match", match_exports); - __publicField(this, "models"); - __publicField(this, "events"); - __publicField(this, "faceTriangulation"); - __publicField(this, "faceUVMap"); - __publicField(this, "performance"); - __privateAdd(this, _numTensors, void 0); - __privateAdd(this, _analyzeMemoryLeaks, void 0); - __privateAdd(this, _checkSanity, void 0); - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) - return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) - log(...msg, leaked); - }); - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) - return null; - if (!input) - return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) - return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - __publicField(this, "webcam", new WebCam()); - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) - this.events.dispatchEvent(new Event(event)); - }); - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) - log(`version: ${this.version}`); - if (this.config.debug) - log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) - log("environment:", envTemp); - } - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) - this.config = mergeDeep(this.config, userConfig); - return msgs; - } - now() { - return now(); - } - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) - return null; - const processed = await process2(input, this.config); - if (!processed.tensor) - return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) - tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) - tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) - tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) - log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) - log("configuration:", this.config); - if (this.config.debug) - log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) - log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) - this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - next(result = this.result) { - return calc2(result, this.config); - } - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) - kernels[kernel.name] += ms; - else - kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) - log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) - this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) - this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) - this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) - delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) - faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) - bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) - bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) - bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) - delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) - bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) - bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) - bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) - bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) - handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) - handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) - delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) - handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) - handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) - objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) - objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) - delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) - objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) - objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) - [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) - this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) - delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) - log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) - await this.detect(element); - if (delay > 0) - await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) - requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) - log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); +2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Bt);break;case"body":case"full":n=await t(Ht);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return 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Image!="undefined"||R.Canvas!==void 0?t=await Aa(e):t=await aa(e),t}async function la(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let d=(y==null?void 0:y.modelSignature)&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])==null?void 0:l.shape)?[...y.inputs[0].shape]:[1,64,64,3],p=(y==null?void 0:y.modelSignature)&&((x=(c=y==null?void 0:y.inputs)==null?void 0:c[0])==null?void 0:x.dtype)?y.inputs[0].dtype:"float32";for(let b=0;bd0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let A=d0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function jo(e,t){await j2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?he():new Promise(async o=>{await e.models.load(),await la(e);let r=await ia(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var M2,B2,H2,Gt,Ue,v1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",et);k(this,"match",Nt);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");me(this,M2,void 0);me(this,B2,void 0);me(this,H2,void 0);k(this,"analyze",(...t)=>{if(!G0(this,B2))return;let n=this.tf.engine().state.numTensors,o=G0(this,M2);ge(this,M2,n);let r=n-o;r!==0&&u(...t,r)});me(this,Gt,t=>{if(!G0(this,H2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"webcam",new U2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});me(this,Ue,{});let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Ye.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ye.modelBasePath=R.browser?"../models/":"file://models/",this.version=_t,Object.defineProperty(this,"version",{value:_t}),this.config=JSON.parse(JSON.stringify(Ye)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=a0(this.config,t)),F1(this.config),this.tf=ae,this.state="idle",ge(this,M2,0),ge(this,B2,!1),ge(this,H2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new F2(this),s5(),this.result=he(),this.process={tensor:null,canvas:null},this.faceTriangulation=j3,this.faceUVMap=N3,Ft(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ye)),this.config.backend=t,Jt(),R.initial=!0}validate(t){let n=Ut(Ye,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return X2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await X2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await ko(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await no(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Eo(o.tensor,this.config)),ae.dispose(o.tensor),r}compare(t,n){return D1(this.config,t,n)}async init(){await j2(this,!0),await this.tf.ready(),Jt()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await j2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return to(t,this.config)}async warmup(t){let n=g(),o=await jo(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,I,q,t0,G,$,A0,v,V,n0,U,g0,m0,B,X;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=G0(this,Gt).call(this,t);s&&(u(s,t),this.emit("error"),o(he(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await X2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(he("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await W1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?p1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?c5(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?u5(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?l1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await p1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await c5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await u5(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await l1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((t0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&t0.includes("handdetect")?x=this.config.hand.enabled?Q5(a.tensor,d):[]:($=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&$.includes("handtrack")&&(x=this.config.hand.enabled?e1(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(v=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&v.includes("handdetect")?x=this.config.hand.enabled?await Q5(a.tensor,d):[]:(n0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&n0.includes("handtrack")&&(x=this.config.hand.enabled?await e1(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?i=this.config.object.enabled?d1(a.tensor,this.config):[]:(g0=this.config.object.modelPath)!=null&&g0.includes("centernet")&&(i=this.config.object.enabled?y5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(m0=this.config.object.modelPath)!=null&&m0.includes("nanodet")?i=this.config.object.enabled?await d1(a.tensor,this.config):[]:(B=this.config.object.modelPath)!=null&&B.includes("centernet")&&(i=this.config.object.enabled?await y5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...Sn(l),...zn(c),...Nn(x),...jn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let f=((X=this.process.tensor)==null?void 0:X.shape)||[0,0,0,0];this.result={face:l,body:c,hand:x,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return So(l,c,x,p,f)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,Ue)[t.id]||(this.config.debug&&u("video start",t.id),G0(this,Ue)[t.id]=!0),!t.paused&&G0(this,Ue)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,Ue)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),G0(this,Ue)[t.id]=!1)}};M2=new WeakMap,B2=new WeakMap,H2=new WeakMap,Gt=new WeakMap,Ue=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js index fc47fa86..8538b4b5 100644 --- a/dist/human.node-wasm.js +++ b/dist/human.node-wasm.js @@ -4,318 +4,7 @@ author: ' */ -"use strict"; -var __create = Object.create; -var __defProp = Object.defineProperty; -var __getOwnPropDesc = Object.getOwnPropertyDescriptor; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __getProtoOf = Object.getPrototypeOf; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __commonJS = (cb, mod3) => function __require() { - return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; -}; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => { - __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); - return value; -}; -var __accessCheck = (obj, member, msg) => { - if (!member.has(obj)) - throw TypeError("Cannot " + msg); -}; -var __privateGet = (obj, member, getter) => { - __accessCheck(obj, member, "read from private field"); - return getter ? getter.call(obj) : member.get(obj); -}; -var __privateAdd = (obj, member, value) => { - if (member.has(obj)) - throw TypeError("Cannot add the same private member more than once"); - member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -}; -var __privateSet = (obj, member, value, setter) => { - __accessCheck(obj, member, "write to private field"); - setter ? setter.call(obj, value) : member.set(obj, value); - return value; -}; - -// dist/tfjs.esm.js -var require_tfjs_esm = __commonJS({ - "dist/tfjs.esm.js"(exports, module2) { - "use strict"; - var __defProp2 = Object.defineProperty; - var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; - var __getOwnPropNames2 = Object.getOwnPropertyNames; - var __hasOwnProp2 = Object.prototype.hasOwnProperty; - var __export2 = (target, all2) => { - for (var name in all2) - __defProp2(target, name, { get: all2[name], enumerable: true }); - }; - var __copyProps2 = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames2(from)) - if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_wasm_exports = {}; - __export2(tf_node_wasm_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_wasm_exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-core"), module2.exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-converter"), module2.exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports); - var version4 = "4.1.0"; - var version22 = "4.1.0"; - var version32 = "4.1.0"; - var version42 = "4.1.0"; - var version5 = "4.1.0"; - var version6 = "0.0.1-alpha.16"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) - console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) - throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") - return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) - msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) - msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) - log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) - prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) - prev[key] = mergeDeep(pVal, oVal); - else - prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: true, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - iouThreshold: 0.1, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/util/env.ts -var tf3 = __toESM(require_tfjs_esm()); - -// src/image/image.ts -var tf2 = __toESM(require_tfjs_esm()); - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +"use strict";var Io=Object.create;var k2=Object.defineProperty;var Oo=Object.getOwnPropertyDescriptor;var Lo=Object.getOwnPropertyNames;var Co=Object.getPrototypeOf,Wo=Object.prototype.hasOwnProperty;var Do=(e,t,n)=>t in e?k2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Fo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ze=(e,t)=>{for(var n in t)k2(e,n,{get:t[n],enumerable:!0})},w1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Lo(t))!Wo.call(e,r)&&r!==n&&k2(e,r,{get:()=>t[r],enumerable:!(o=Oo(t,r))||o.enumerable});return e};var Z=(e,t,n)=>(n=e!=null?Io(Co(e)):{},w1(t||!e||!e.__esModule?k2(n,"default",{value:e,enumerable:!0}):n,e)),Bo=e=>w1(k2({},"__esModule",{value:!0}),e);var 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n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Yt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Yt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let 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Ye={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var I0=Z(H());var N=Z(H());var j1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -325,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var N1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -338,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,I1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -351,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,O1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -365,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,L1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -389,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,C1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -412,6055 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) - return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) - this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) - this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) - return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) - return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) - source = sourceTexture; - else - source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) - return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) - return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) - sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb2 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; - const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") - throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document === "undefined") - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } - } else { - if (typeof env.Canvas !== "undefined") - c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") - c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) - log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) - throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) - throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb2, 0); - tf2.dispose(rgb2); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) - throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) - log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) - log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) - targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) - targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) - targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) - targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) - throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) - inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) - outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) - fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) - log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) - fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) - fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) - fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) - fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) - fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) - fx.add("hue", config3.filter.hue); - if (config3.filter.negative) - fx.add("negative"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.vintage) - fx.add("brownie"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.kodachrome) - fx.add("kodachrome"); - if (config3.filter.technicolor) - fx.add("technicolor"); - if (config3.filter.polaroid) - fx.add("polaroid"); - if (config3.filter.pixelate !== 0) - fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) - outCanvas = fx.apply(inCanvas); - else - outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) - fx = null; - env.filter = !!fx; - } - if (!getTensor) - return { tensor: null, canvas: outCanvas }; - if (!outCanvas) - throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) - tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb2; - } - if (!pixels) - throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) - return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) - log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) - log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - __publicField(this, "browser"); - __publicField(this, "node"); - __publicField(this, "worker"); - __publicField(this, "platform", ""); - __publicField(this, "agent", ""); - __publicField(this, "backends", []); - __publicField(this, "initial"); - __publicField(this, "filter"); - __publicField(this, "tfjs"); - __publicField(this, "offscreen"); - __publicField(this, "perfadd", false); - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - __publicField(this, "kernels", []); - __privateAdd(this, _canvas, void 0); - __privateAdd(this, _image, void 0); - __privateAdd(this, _imageData, void 0); - this.browser = typeof navigator !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined") { - const raw = navigator.userAgent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = navigator.userAgent.replace(raw[0], ""); - if (this.platform[1]) - this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) - Object.defineProperty(this, "cpu", { value: cpu }); - else - this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - __publicField(this, "config"); - __publicField(this, "element"); - __publicField(this, "stream"); - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) - this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) - this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) - this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) - this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) - this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) - this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) - log("webcam", "cannot get dom element", webcamConfig.element); - return; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) - log("webcam", "unknown dom element", webcamConfig.element); - return; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) - requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) - requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) - requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) - log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) - log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) - return; - if (this.element.paused) - await this.element.play(); - else - this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) - log("webcam", "no devices"); - return; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return; - } - if (!this.stream) { - if (this.config.debug) - log("webcam", "no stream"); - return; - } - this.element.srcObject = this.stream; - const ready3 = new Promise((resolve) => { - if (!this.element) - resolve(false); - else - this.element.onloadeddata = () => resolve(true); - }); - await ready3; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - }); - __publicField(this, "pause", () => { - if (this.element) - this.element.pause(); - }); - __publicField(this, "play", async () => { - if (this.element) - await this.element.play(); - }); - __publicField(this, "stop", () => { - if (this.config.debug) - log("webcam", "stop"); - if (this.track) - this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - get track() { - if (!this.stream) - return void 0; - return this.stream.getVideoTracks()[0]; - } - get capabilities() { - if (!this.track) - return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - get constraints() { - if (!this.track) - return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - get settings() { - if (!this.stream) - return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - get label() { - if (!this.track) - return ""; - return this.track.label; - } - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) - log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) - modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - let loaded = false; - try { - model23.findIOHandler(); - if (options.debug) - log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) - model23.loadSync(artifacts); - else - model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; - if (options.verbose) - log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) - log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.0.0"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - desynchronized: true - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) - return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") - return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) - tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) - log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) - return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) - return "webgpu"; - if (env.webgl.supported && env.webgl.backend) - return "webgl"; - if (env.wasm.supported && env.wasm.backend) - return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) - log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) - instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) - log("running inside web worker"); - } - if (env.browser && instance.config.backend === "tensorflow") { - if (instance.config.debug) - log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { - if (instance.config.debug) - log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) - log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) - log("available backends:", available); - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) - log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) - log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) - tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) - log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") - tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else - throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) - log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) - defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) - tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) - continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) - log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - env.initial = false; - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) - log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) - log("draw error: invalid canvas"); - else if (!input.getContext) - log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d"); - if (!ctx) - log("draw error: cannot get canvas context"); - else - return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") - return opt.color; - const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - labelColor: "rgba(173, 216, 230, 1)", - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - rightEyeIris: [473, 474, 475, 476, 477], - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } -]; -var UV468 = [ - [0.499976992607117, 0.652534008026123], - [0.500025987625122, 0.547487020492554], - [0.499974012374878, 0.602371990680695], - [0.482113003730774, 0.471979022026062], - [0.500150978565216, 0.527155995368958], - [0.499909996986389, 0.498252987861633], - [0.499523013830185, 0.40106201171875], - [0.289712011814117, 0.380764007568359], - [0.499954998493195, 0.312398016452789], - [0.499987006187439, 0.269918978214264], - [0.500023007392883, 0.107050001621246], - [0.500023007392883, 0.666234016418457], - [0.5000159740448, 0.679224014282227], - [0.500023007392883, 0.692348003387451], - [0.499976992607117, 0.695277988910675], - [0.499976992607117, 0.70593398809433], - 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94, - 2, - 370, - 289, - 305, - 455, - 339, - 254, - 448, - 359, - 255, - 446, - 254, - 253, - 449, - 253, - 252, - 450, - 252, - 256, - 451, - 256, - 341, - 452, - 414, - 413, - 463, - 286, - 441, - 414, - 286, - 258, - 441, - 258, - 257, - 442, - 257, - 259, - 443, - 259, - 260, - 444, - 260, - 467, - 445, - 309, - 459, - 250, - 305, - 289, - 290, - 305, - 290, - 460, - 401, - 376, - 435, - 309, - 250, - 392, - 376, - 411, - 433, - 453, - 341, - 464, - 357, - 453, - 465, - 343, - 357, - 412, - 437, - 343, - 399, - 344, - 360, - 440, - 420, - 437, - 456, - 360, - 420, - 363, - 361, - 401, - 288, - 265, - 372, - 353, - 390, - 339, - 249, - 339, - 448, - 255 -]; -var VTX68 = [ - 127, - 234, - 132, - 58, - 172, - 150, - 149, - 148, - 152, - 377, - 378, - 379, - 397, - 288, - 361, - 454, - 356, - 70, - 63, - 105, - 66, - 107, - 336, - 296, - 334, - 293, - 300, - 168, - 6, - 195, - 4, - 98, - 97, - 2, - 326, - 327, - 33, - 160, - 158, - 133, - 153, - 144, - 362, - 385, - 387, - 263, - 373, - 380, - 57, - 40, - 37, - 0, - 267, - 270, - 287, - 321, - 314, - 17, - 84, - 91, - 78, - 81, - 13, - 311, - 308, - 402, - 14, - 178 -]; -var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; -var VTX7 = [33, 133, 362, 263, 1, 78, 308]; -var UV68 = VTX68.map((x) => UV468[x]); -var UV33 = VTX33.map((x) => UV468[x]); -var UV7 = VTX7.map((x) => UV468[x]); -function connectionsToIndices(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - 246, - 161, - 160, - 159, - 158, - 157, - 173, - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - 247, - 30, - 29, - 27, - 28, - 56, - 190, - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - 113, - 225, - 224, - 223, - 222, - 221, - 189, - 35, - 124, - 46, - 53, - 52, - 65, - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - 466, - 388, - 387, - 386, - 385, - 384, - 398, - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - 467, - 260, - 259, - 257, - 258, - 286, - 414, - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - 342, - 445, - 444, - 443, - 442, - 441, - 413, - 265, - 353, - 276, - 283, - 282, - 295, - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) - return; - let l = localOptions.faceLabels.slice(); - if (f.score) - l = replace(l, "[score]", 100 * f.score); - if (f.gender) - l = replace(l, "[gender]", f.gender); - if (f.genderScore) - l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) - l = replace(l, "[age]", f.age); - if (f.distance) - l = replace(l, "[distance]", 100 * f.distance); - if (f.real) - l = replace(l, "[real]", 100 * f.real); - if (f.live) - l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) - emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - } - if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) - 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Qt(){J0.inputSum=0,J0.cacheDiff=1,J0.sumMethod=0,J0.inputTensor=void 0}function te(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function q2(e,t){let n=t||te(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function U2(e,t,n=!0){var y,x,p;if(!e)return t.debug&&u("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof N.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e 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Image(){return G0(this,j2)}set Image(t){ge(this,j2,t),globalThis.Image=t}get ImageData(){return G0(this,N2)}set ImageData(t){ge(this,N2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(I0.engine().registryFactory);try{this.tensorflow={version:I0.backend().binding?I0.backend().binding.TF_Version:void 0,gpu:I0.backend().binding?I0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await I0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await I0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=te(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=I0.getKernelsForBackend(I0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};S2=new WeakMap,j2=new WeakMap,N2=new WeakMap;var R=new z2;var K2=class{constructor(){k(this,"config");k(this,"element");k(this,"stream");k(this,"devices",[]);k(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});k(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else{this.config.debug&&u("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof 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navigator.mediaDevices.getUserMedia(n)}catch(A){u("webcam",A);return}if(!this.stream){this.config.debug&&u("webcam","no stream");return}this.element.srcObject=this.stream,await new Promise(A=>{this.element?this.element.onloadeddata=()=>A(!0):A(!1)}),await this.element.play(),this.config.debug&&u("webcam",{width:this.width,height:this.height,label:this.label,stream:this.stream,track:this.track,settings:this.settings,constraints:this.constraints,capabilities:this.capabilities})});k(this,"pause",()=>{this.element&&this.element.pause()});k(this,"play",async()=>{this.element&&await this.element.play()});k(this,"stop",()=>{this.config.debug&&u("webcam","stop"),this.track&&this.track.stop()});this.config={element:void 0,debug:!0,mode:"front",crop:!1,width:0,height:0}}get track(){if(!!this.stream)return this.stream.getVideoTracks()[0]}get capabilities(){if(!!this.track)return this.track.getCapabilities?this.track.getCapabilities():void 0}get constraints(){if(!!this.track)return 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n=K.faceLabels.slice();if(e.score&&(n=_(n,"[score]",100*e.score)),e.gender&&(n=_(n,"[gender]",e.gender)),e.genderScore&&(n=_(n,"[genderScore]",100*e.genderScore)),e.age&&(n=_(n,"[age]",e.age)),e.distance&&(n=_(n,"[distance]",100*e.distance)),e.real&&(n=_(n,"[real]",100*e.real)),e.live&&(n=_(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let y=e.emotion.map(x=>`${Math.trunc(100*x.score)}% ${x.emotion}`);y.length>3&&(y.length=3),n=_(n,"[emotions]",y.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(n=_(n,"[roll]",Ke(e.rotation.angle.roll))),(a=(A=e.rotation)==null?void 0:A.angle)!=null&&a.yaw&&(n=_(n,"[yaw]",Ke(e.rotation.angle.yaw))),(c=(l=e.rotation)==null?void 0:l.angle)!=null&&c.pitch&&(n=_(n,"[pitch]",Ke(e.rotation.angle.pitch))),(i=(d=e.rotation)==null?void 0:d.gaze)!=null&&i.bearing&&(n=_(n,"[gaze]",Ke(e.rotation.gaze.bearing))),ne(t,n,e.box[0],e.box[1],K)}function Is(e,t){var n,o,r,s;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}}function Os(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ke(e.rotation.angle.pitch)/90,s=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),A=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints && f.mesh.length >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) - continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) - continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) - curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) - return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];t5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];t5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);e5(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function _2(e,t,n){var s,A;let o=a0(d0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)X1(r,c,o)}}}function $2(e,t,n){var s,A;let o=a0(d0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let d=o.fingerLabels.slice();d=_(d,"[label]",l),ne(r,d,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let d=0;d0?d-1:0][0],c[d>0?d-1:0][1]),r.lineTo(c[d][0],c[d][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function et(e,t,n){var s;let o=a0(d0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function tt(e,t,n){var r;let o=a0(d0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let d=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",d),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6469,7523 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) - return; - const ctx = getCanvasContext(output); - if (!ctx) - return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) - return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - "leftEyeInside", - "leftEye", - "leftEyeOutside", - "rightEyeInside", - "rightEye", - "rightEyeOutside", - "leftEar", - "rightEar", - "leftMouth", - "rightMouth", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftPinky", - "rightPinky", - "leftIndex", - "rightIndex", - "leftThumb", - "rightThumb", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle", - "leftHeel", - "rightHeel", - "leftFoot", - "rightFoot", - "bodyCenter", - "bodyTop", - "leftPalm", - "leftHand", - "rightPalm", - "rightHand" -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) - model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) - log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) - model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) - log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - height, - width, - [0, 0] - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - kpt4.positionRaw[1] / width + cropBox[0], - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) - return null; - const t2 = {}; - [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) - return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) - continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) - model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) - return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) - continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) - model4 = null; - if (!model4) - model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) - return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) - resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) - product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) - column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) - product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) - anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var faceBoxScaleFactor = 1.4; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) - model5 = null; - if (!model5) - model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) - return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) - return []; - const t2 = {}; - t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const rawBox = { - startPoint: [points[0], points[1]], - endPoint: [points[2], points[3]], - landmarks: await b.landmarks.array(), - confidence - }; - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); - const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); - const squaredBox = squarifyBox(enlargedBox); - boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var irisEnlarge = 2.3; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) - model6 = null; - if (!model6) - model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) - inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize) { - if (!(model6 == null ? void 0 : model6["executor"])) - return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - }; - for (const val of Object.values(t2)) { - if (!val) - return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) - rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) - rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - if (!(model7 == null ? void 0 : model7["executor"])) - return []; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) - face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } else if (!model7) { - if (config3.debug) - log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh.keepInvalid) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) - face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) - faces.push(face4); - else - tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) - model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) - model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) - model7 = await loadModel(config3.face.attention.modelPath); - else - model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -async function load6(config3) { - var _a; - if (env.initial) - model8 = null; - if (!model8) - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model8["modelUrl"]); - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) - return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) - obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) - model9 = null; - if (!model9) - model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input) { - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) - return tensor6; - const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) - return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) - log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) - inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) - return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) - return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) - silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) - silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) - model10 = null; - if (!model10) - model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) - return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) - model11 = null; - if (!model11) - model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) - return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) - model12 = null; - if (!model12) - model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) - return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) - return; - const t2 = {}; - const box = [[0, 0.1, 0.9, 0.9]]; - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) - [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) - obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) - age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) - model13 = null; - if (!model13) - model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) - log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) - return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) - return; - const t2 = {}; - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) - model14 = null; - if (!model14) - model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) - log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) - return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) - return; - const t2 = {}; - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - t2.enhance = tf22.tidy(() => { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb[0]); - const greenNorm = tf22.mul(green, rgb[1]); - const blueNorm = tf22.mul(blue, rgb[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) - model15 = null; - if (!model15) - model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) - return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) - model16 = null; - if (!model16) - model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) - log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) - return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) - return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - thetaZ = Math.asin(r10); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) - thetaX = 0; - if (Number.isNaN(thetaY)) - thetaY = 0; - if (Number.isNaN(thetaZ)) - thetaZ = 0; - return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) - return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!f) - return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) - return []; - if (!faces) - return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) - faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) - delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) - res.age = descRes.age; - if (descRes.gender) - res.gender = descRes.gender; - if (descRes.genderScore) - res.genderScore = descRes.genderScore; - if (descRes.descriptor) - res.embedding = descRes.descriptor; - if (descRes.race) - res.race = descRes.race; - if (emotionRes) - res.emotion = emotionRes; - if (antispoofRes) - res.real = antispoofRes; - if (livenessRes) - res.live = livenessRes; - if (irisSize > 0) - res.distance = irisSize; - if (rotation) - res.rotation = rotation; - if (tensor6) - res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) - delete instance.performance.face; - if (instance.performance.age) - delete instance.performance.age; - if (instance.performance.gender) - delete instance.performance.gender; - if (instance.performance.emotion) - delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") - this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) - this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) - slope = -slope; - else if (slope > 0) - slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) - return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) - return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) - isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) - isDiagonal = 1 * weightageAt; - else - isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) - cos_in = 1; - else if (cos_in < -1) - cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) - fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) - fingerCurl = FingerCurl.half; - else - fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalUpLeft; - else - estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalDownLeft; - else - estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) - voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) - voteDiagonal += options3.DISTANCE_VOTE_POWER; - else - voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) - return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) - return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) - return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) - poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) - gestures.push({ face: i, gesture: "facing center" }); - else - gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) - gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) - gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) - gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) - gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) - continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) - center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (leftIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (rightIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) - gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - gestures.push({ iris: i, gesture: "looking up" }); - if (center) - gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) - fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) - gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - 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{ x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - 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{ x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (config3.skipAllowed) - this.skipped++; - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) - useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) - continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) - hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -async function predict14(input, config3) { - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) - return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) - box[0] = pt[0]; - if (pt[1] < box[1]) - box[1] = pt[1]; - if (pt[0] > box[2]) - box[2] = pt[0]; - if (pt[1] > box[3]) - box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function load15(config3) { - var _a, _b; - if (env.initial) { - handDetectorModel = null; - handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, - config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null - ]); - } else { - if (config3.debug) - log("cached model:", handDetectorModel["modelUrl"]); - if (config3.debug) - log("cached model:", handPoseModel["modelUrl"]); - } - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) - handPipeline = new HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect2(config3) { - var _a; - if (env.initial) - models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) - models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) - return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) - hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) - return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - const t0 = now(); - if (!newResult) - return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) - bufferedResult.canvas = newResult.canvas; - if (newResult.error) - bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) - coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) - coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) - bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) - bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) - bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load16(config3) { - if (!model17 || env.initial) - model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) - model17 = await load16(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) - return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - return (options4.multiplier || 20) * sum3; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) - return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.max(Math.min(norm, 1), 0); - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) - break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) - continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - cache5.padding = [ - [0, 0], - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - [0, 0] - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load17(config3) { - var _a; - if (env.initial) - model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) - log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) - inputSize8 = 256; - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (totalScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) - bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) - return []; - if (!config3.skipAllowed) - cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load18(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) - log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) - return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) - objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) - j++; - if (!this.less(k, j)) - break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) - return min2; - if (a > max5) - return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) - break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) - continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) - queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) - return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) - result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) - continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) - poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) - return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) - return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) - tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) - return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load19(config3) { - if (!model20 || env.initial) - model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load20(config3) { - if (!model21 || env.initial) - model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb2, a], -1); - tf35.dispose([rgb2, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) - model21 = await load20(config3); - if (!(model21 == null ? void 0 : model21["executor"])) - return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) - init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load21(config3) { - if (!model22 || env.initial) - model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) - model22 = await load21(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) - return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) - return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) - ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) - log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += m.sizeFromManifest; - totalSizeWeights += m.sizeLoadedWeights; - totalSizeLoading += m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) - this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) - this.reset(); - if (instance) - this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; - if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) - [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) - promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) - continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) - continue; - const res = validateModel(this.instance, model23, defined); - if (res) - missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) - person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) - person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var s5=0;function Fs(e,t,n){let o=a0(d0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;si5,kpt:()=>a5});var a5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],i5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,Y1,Gs=5,rt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function K1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var U1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),U1[0]),A=D.mul(D.div(A,t2),U1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),d=D.add(l,A);return D.stack([a,l,c,d],1)})}async function Xs(e,t,n,o){var c,d;let r=[],s={};s.boxes=Zs(e,Y1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var F0,c5=256,l5=Number.MAX_SAFE_INTEGER,qs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Oe=[[0,0],[0,0],[0,0],[0,0]],_1=0,$1=e=>1-1/(1+Math.exp(e)),t3=e=>K1(e);async function n3(e){if(R.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await O(e.body.modelPath);let t=F0!=null&&F0.executor?Object.values(F0.modelSignature.inputs):void 0;c5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return F0}function e3(e,t,n){var s,A;let o={};if(!((s=e==null?void 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W0.all)n[W0.getName(o)]={curl:Be.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function zn(e){let t=[];if(!e||e.length===0)return t;let n=En(e);for(let o of vn){let r=o.matchAgainst(n.curls,n.directions);r>=gA&&t.push({name:o.name,confidence:r})}return t}var Sn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},jn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Nn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||b>.06)&&(x=!1),f>b?f>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let M=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],T=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(T<.01||M<.01||T>.022||M>.022)&&(x=!1),(T<.01||M<.01)&&t.push({iris:A,gesture:"looking down"}),(T>.022||M>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},In=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function wt(e,t=1.5){let n=W2(e),o=kt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function Et(e){let t=W2(e),n=kt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function PA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Fn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return PA(n)}var On=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ze(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=Dn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Vn=1.65,Zn=[0,5,9,13,17,1,2],SA=0,jA=2,Xn=0,St=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>Q5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return wt(Et(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=wt(Et(n),Vn);o.palmLandmarks=[];for(let r=0;r[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=J5(o,[0,0]),c=a.map(x=>[...Q5(x,l),x[2]]),d=Bn(r),i=[...W2(n),1],y=[Ze(i,d[0]),Ze(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Xn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,d,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let d=wt(Et(c),Vn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,Un;async function _5(e,t){let n=await Un.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Pt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Yn(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",a2.modelUrl),e.debug&&u("cached model:",i2.modelUrl));let t=a2?new zt(a2):void 0;return t&&i2&&(Un=new St(t,i2)),[a2,i2]}var Q=Z(H());var f0=[null,null],IA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],OA=["hand","fist","pinch","point","face","tip","pinchtip"],Jn=4,Qn=1.6,LA=512,CA=1.4,jt=Number.MAX_SAFE_INTEGER,$5=0,ke=[0,0],y0={boxes:[],hands:[]},_n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function $n(e){var t;if(R.initial&&(f0[0]=null),f0[0])e.debug&&u("cached model:",f0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),f0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=f0[0].executor?Object.values(f0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[0]}async function eo(e){var t;if(R.initial&&(f0[1]=null),f0[1])e.debug&&u("cached model:",f0[1].modelUrl);else{f0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=f0[1].executor?Object.values(f0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[1]}async function WA(e,t){let n=[];if(!e||!f0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,LA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await f0[0].executeAsync(o.cast,IA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[Jn]),a.splice(Jn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=Q.slice(o.boxes,y,1),p=await x.data();Q.dispose(x);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=st(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=d[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function e1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Pt(o.keypoints);for(let i of Object.keys(_n))o.annotations[i]=_n[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function t1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],jt++;let n=(t.hand.skipTime||0)>g()-$5,o=jt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-$5,l=jt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(d=>e1(e,d,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(d=>e1(e,d,t))):(y0.boxes=await WA(e,t),$5=g(),y0.hands=await Promise.all(y0.boxes.map(d=>e1(e,d,t))),jt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[d].fingerScore&&y0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=st(i.box,Qn),x=st(i.boxRaw,Qn);y0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var D2={};ze(D2,{connected:()=>It,horizontal:()=>n1,kpt:()=>Nt,relative:()=>r1,vertical:()=>o1});var Nt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],n1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],o1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],r1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],It={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),s1=0;function no(e,t){var 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o;switch(t.segmentation.mode||"default"){case"default":n.input=k0.squeeze(e),n.concat=k0.concat([n.input,n.mul],-1),o=k0.cast(n.concat,"int32");break;case"alpha":o=k0.cast(n.mul,"int32");break;default:o=k0.tensor(0)}return Object.keys(n).forEach(s=>k0.dispose(n[s])),o}function Ht(e,t,n){var c,d;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((d=l==null?void 0:l.graph)!=null&&d.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&u("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return e.config.debug&&A.length>0&&u("model validation failed:",n,A),A.length>0?{name:n,missing:A,ops:s,url:a}:null}var H2=class{constructor(t){k(this,"instance");k(this,"models",{});this.models={},this.instance=t}stats(){let t=0,n=0,o=0;for(let s of Object.values(E0))t+=s.sizeFromManifest,n+=s.sizeLoadedWeights,o+=s.sizeDesired;let r=o>0?n/o:0;return{numLoadedModels:Object.values(E0).length,numDefinedModels:Object.keys(this.models).length,percentageLoaded:r,totalSizeFromManifest:t,totalSizeWeights:n,totalSizeLoading:o,modelStats:Object.values(E0)}}reset(){for(let t of Object.keys(this.models))this.models[t]=null}async load(t){var o,r,s,A,a,l,c,d,i,y,x,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0,v;R.initial&&this.reset(),t&&(this.instance=t);let n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?g3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)==null?void 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0:c.enabled)&&!this.models.facemesh?j3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((d=this.instance.config.face.gear)==null?void 0:d.enabled)&&!this.models.gear?$3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)==null?void 0:i.enabled)&&!this.models.ssrnetage?on(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)==null?void 0:y.enabled)&&!this.models.ssrnetgender?an(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((x=this.instance.config.face.mobilefacenet)==null?void 0:x.enabled)&&!this.models.mobilefacenet?yn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)==null?void 0:p.enabled)&&!this.models.insightface?hn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)==null?void 0:f.includes("blazepose"))?n3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?t3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)==null?void 0:b.includes("efficientpose"))?i3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)==null?void 0:M.includes("movenet"))?xo(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)==null?void 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0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var x0=Z(H());var Gt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14136,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; -var body3 = ` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Vt=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -14705,563 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`; - -// src/warmup.ts -async function warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") - img = new Image(); - else if (env.Image) - img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) - ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) - img.src = src; - else - resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") - img = atob(face3); - else - img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) - log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") - res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) - res = await warmupCanvas(instance); - else - res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) - return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) - continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) - shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) - res.forEach((t2) => tf37.dispose(t2)); - else - tf37.dispose(res); - } catch (e) { - if (instance.config.debug) - log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) - log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) - log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) - instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - constructor(userConfig) { - __publicField(this, "version"); - __publicField(this, "config"); - __publicField(this, "result"); - __publicField(this, "state"); - __publicField(this, "process"); - __publicField(this, "tf"); - __publicField(this, "env", env); - __publicField(this, "draw", draw_exports); - __publicField(this, "match", match_exports); - __publicField(this, "models"); - __publicField(this, "events"); - __publicField(this, "faceTriangulation"); - __publicField(this, "faceUVMap"); - __publicField(this, "performance"); - __privateAdd(this, _numTensors, void 0); - __privateAdd(this, _analyzeMemoryLeaks, void 0); - __privateAdd(this, _checkSanity, void 0); - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) - return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) - log(...msg, leaked); - }); - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) - return null; - if (!input) - return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) - return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - __publicField(this, "webcam", new WebCam()); - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) - this.events.dispatchEvent(new Event(event)); - }); - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) - log(`version: ${this.version}`); - if (this.config.debug) - log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) - log("environment:", envTemp); - } - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) - this.config = mergeDeep(this.config, userConfig); - return msgs; - } - now() { - return now(); - } - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) - return null; - const processed = await process2(input, this.config); - if (!processed.tensor) - return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) - tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) - tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) - tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) - log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) - log("configuration:", this.config); - if (this.config.debug) - log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) - log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) - this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - next(result = this.result) { - return calc2(result, this.config); - } - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) - kernels[kernel.name] += ms; - else - kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) - log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) - this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) - this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) - this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) - delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) - faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) - bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) - bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) - bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) - delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) - bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) - bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) - bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) - bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) - handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) - handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) - delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) - handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) - handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) - objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) - objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) - delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) - objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) - objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) - [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) - this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) - delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) - log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) - await this.detect(element); - if (delay > 0) - await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) - requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) - log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); +2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Gt);break;case"body":case"full":n=await t(Vt);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return 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A=x0.engine().state.numTensors;A-o>0&&u("tensor leak:",A-o)}async function No(e,t){await I2(e,!1);let n=g();return e.state="warmup",t&&(e.config=a0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?he():new Promise(async o=>{await e.models.load(),await la(e);let r=await ia(e),s=g();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var M2,G2,V2,Zt,Ue,R1=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env",R);k(this,"draw",nt);k(this,"match",Ot);k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");me(this,M2,void 0);me(this,G2,void 0);me(this,V2,void 0);k(this,"analyze",(...t)=>{if(!G0(this,G2))return;let n=this.tf.engine().state.numTensors,o=G0(this,M2);ge(this,M2,n);let r=n-o;r!==0&&u(...t,r)});me(this,Zt,t=>{if(!G0(this,V2))return null;if(!t)return"input is not 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H2(this),A5(),this.result=he(),this.process={tensor:null,canvas:null},this.faceTriangulation=N3,this.faceUVMap=I3,Ht(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ye)),this.config.backend=t,Qt(),R.initial=!0}validate(t){let n=Yt(Ye,t||this.config);return n.length===0&&(this.config=a0(this.config,t)),n}now(){return g()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=a0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await wo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await oo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await zo(o.tensor,this.config)),ae.dispose(o.tensor),r}compare(t,n){return F1(this.config,t,n)}async init(){await I2(this,!0),await this.tf.ready(),Qt()}async load(t){this.state="load";let n=g(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=a0(this.config,t)),this.env.initial&&(await I2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(g()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return no(t,this.config)}async warmup(t){let n=g(),o=await No(this,t),r=g();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,M,T,m,h,S,P,I,q,t0,G,$,A0,v,V,n0,U,g0,m0,B,X;this.state="config";let r;this.config=a0(this.config,n),this.state="check";let s=G0(this,Zt).call(this,t);s&&(u(s,t),this.emit("error"),o(he(s)));let A=g();await this.load(),r=g(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o(he("could not convert input to tensor"));return}this.emit("image"),r=g(),this.config.skipAllowed=await D1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?U5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await U5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?a0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?u1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?x5(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?c=this.config.body.enabled?h5(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?c1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=g(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?await u1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?c=this.config.body.enabled?await x5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await h5(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await c1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?a0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((t0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&t0.includes("handdetect")?d=this.config.hand.enabled?_5(a.tensor,x):[]:($=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&$.includes("handtrack")&&(d=this.config.hand.enabled?t1(a.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(v=(A0=this.config.hand.detector)==null?void 0:A0.modelPath)!=null&&v.includes("handdetect")?d=this.config.hand.enabled?await _5(a.tensor,x):[]:(n0=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&n0.includes("handtrack")&&(d=this.config.hand.enabled?await t1(a.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?i=this.config.object.enabled?d1(a.tensor,this.config):[]:(g0=this.config.object.modelPath)!=null&&g0.includes("centernet")&&(i=this.config.object.enabled?f5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(m0=this.config.object.modelPath)!=null&&m0.includes("nanodet")?i=this.config.object.enabled?await d1(a.tensor,this.config):[]:(B=this.config.object.modelPath)!=null&&B.includes("centernet")&&(i=this.config.object.enabled?await f5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,d,i]=await Promise.all([l,c,d,i])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(r=g(),p=[...jn(l),...Sn(c),...In(d),...Nn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-A):Math.trunc(g()-A);let f=((X=this.process.tensor)==null?void 0:X.shape)||[0,0,0,0];this.result={face:l,body:c,hand:d,gesture:p,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return jo(l,c,d,p,f)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,Ue)[t.id]||(this.config.debug&&u("video start",t.id),G0(this,Ue)[t.id]=!0),!t.paused&&G0(this,Ue)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,Ue)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),G0(this,Ue)[t.id]=!1)}};M2=new WeakMap,G2=new WeakMap,V2=new WeakMap,Zt=new WeakMap,Ue=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node.js b/dist/human.node.js index d426a85e..af2d1a9e 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -4,316 +4,7 @@ author: ' */ -"use strict"; -var __create = Object.create; -var __defProp = Object.defineProperty; -var __getOwnPropDesc = Object.getOwnPropertyDescriptor; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __getProtoOf = Object.getPrototypeOf; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __commonJS = (cb, mod3) => function __require() { - return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; -}; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => { - __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); - return value; -}; -var __accessCheck = (obj, member, msg) => { - if (!member.has(obj)) - throw TypeError("Cannot " + msg); -}; -var __privateGet = (obj, member, getter) => { - __accessCheck(obj, member, "read from private field"); - return getter ? getter.call(obj) : member.get(obj); -}; -var __privateAdd = (obj, member, value) => { - if (member.has(obj)) - throw TypeError("Cannot add the same private member more than once"); - member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -}; -var __privateSet = (obj, member, value, setter) => { - __accessCheck(obj, member, "write to private field"); - setter ? setter.call(obj, value) : member.set(obj, value); - return value; -}; - -// dist/tfjs.esm.js -var require_tfjs_esm = __commonJS({ - "dist/tfjs.esm.js"(exports, module2) { - "use strict"; - var __defProp2 = Object.defineProperty; - var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; - var __getOwnPropNames2 = Object.getOwnPropertyNames; - var __hasOwnProp2 = Object.prototype.hasOwnProperty; - var __export2 = (target, all2) => { - for (var name in all2) - __defProp2(target, name, { get: all2[name], enumerable: true }); - }; - var __copyProps2 = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames2(from)) - if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_exports = {}; - __export2(tf_node_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_exports); - __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports); - var version4 = "4.1.0"; - var version22 = "4.1.0"; - var version32 = "4.1.0"; - var version42 = "4.1.0"; - var version5 = "4.1.0"; - var version6 = "0.0.1-alpha.16"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) - console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) - throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") - return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) - msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) - msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) - log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) - prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) - prev[key] = mergeDeep(pVal, oVal); - else - prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: true, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - iouThreshold: 0.1, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/util/env.ts -var tf3 = __toESM(require_tfjs_esm()); - -// src/image/image.ts -var tf2 = __toESM(require_tfjs_esm()); - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +"use strict";var No=Object.create;var k2=Object.defineProperty;var Io=Object.getOwnPropertyDescriptor;var Oo=Object.getOwnPropertyNames;var Lo=Object.getPrototypeOf,Co=Object.prototype.hasOwnProperty;var Wo=(e,t,n)=>t in e?k2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Do=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ze=(e,t)=>{for(var n in t)k2(e,n,{get:t[n],enumerable:!0})},k1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Oo(t))!Co.call(e,r)&&r!==n&&k2(e,r,{get:()=>t[r],enumerable:!(o=Io(t,r))||o.enumerable});return e};var Z=(e,t,n)=>(n=e!=null?No(Lo(e)):{},k1(t||!e||!e.__esModule?k2(n,"default",{value:e,enumerable:!0}):n,e)),Fo=e=>k1(k2({},"__esModule",{value:!0}),e);var 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performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Ut(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Ut(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function a0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=a0(s,A):n[r]=A}),n),{})}var Ye={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var I0=Z(H());var N=Z(H());var S1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -323,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var j1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -336,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,N1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -349,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,I1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -363,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,O1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -387,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,L1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -410,6055 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) - return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) - this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) - this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) - return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) - return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) - source = sourceTexture; - else - source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) - return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) - return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) - return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) - sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb2 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb2[0]), tf.min(rgb2[1]), tf.min(rgb2[2])]; - const max5 = [tf.max(rgb2[0]), tf.max(rgb2[1]), tf.max(rgb2[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb2[0], min2[0]), tf.sub(rgb2[1], min2[1]), tf.sub(rgb2[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb2, ...min2, ...max5, rgb2, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") - throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document === "undefined") - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } - } else { - if (typeof env.Canvas !== "undefined") - c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") - c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) - log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) - throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) - throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb2, 0); - tf2.dispose(rgb2); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) - throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) - log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) - log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) - targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) - targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) - targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) - targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) - throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) - inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) - outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) - fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) - log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) - fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) - fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) - fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) - fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) - fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) - fx.add("hue", config3.filter.hue); - if (config3.filter.negative) - fx.add("negative"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.vintage) - fx.add("brownie"); - if (config3.filter.sepia) - fx.add("sepia"); - if (config3.filter.kodachrome) - fx.add("kodachrome"); - if (config3.filter.technicolor) - fx.add("technicolor"); - if (config3.filter.polaroid) - fx.add("polaroid"); - if (config3.filter.pixelate !== 0) - fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) - outCanvas = fx.apply(inCanvas); - else - outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) - fx = null; - env.filter = !!fx; - } - if (!getTensor) - return { tensor: null, canvas: outCanvas }; - if (!outCanvas) - throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) - tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb2; - } - if (!pixels) - throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) - return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) - log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) - log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - __publicField(this, "browser"); - __publicField(this, "node"); - __publicField(this, "worker"); - __publicField(this, "platform", ""); - __publicField(this, "agent", ""); - __publicField(this, "backends", []); - __publicField(this, "initial"); - __publicField(this, "filter"); - __publicField(this, "tfjs"); - __publicField(this, "offscreen"); - __publicField(this, "perfadd", false); - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - __publicField(this, "kernels", []); - __privateAdd(this, _canvas, void 0); - __privateAdd(this, _image, void 0); - __privateAdd(this, _imageData, void 0); - this.browser = typeof navigator !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined") { - const raw = navigator.userAgent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = navigator.userAgent.replace(raw[0], ""); - if (this.platform[1]) - this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) - Object.defineProperty(this, "cpu", { value: cpu }); - else - this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - __publicField(this, "config"); - __publicField(this, "element"); - __publicField(this, "stream"); - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) - this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) - this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) - this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) - this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) - this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) - this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) - log("webcam", "cannot get dom element", webcamConfig.element); - return; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) - log("webcam", "unknown dom element", webcamConfig.element); - return; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) - requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) - requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) - requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) - log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) - log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) - return; - if (this.element.paused) - await this.element.play(); - else - this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) - log("webcam", "no devices"); - return; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return; - } - if (!this.stream) { - if (this.config.debug) - log("webcam", "no stream"); - return; - } - this.element.srcObject = this.stream; - const ready3 = new Promise((resolve) => { - if (!this.element) - resolve(false); - else - this.element.onloadeddata = () => resolve(true); - }); - await ready3; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - }); - __publicField(this, "pause", () => { - if (this.element) - this.element.pause(); - }); - __publicField(this, "play", async () => { - if (this.element) - await this.element.play(); - }); - __publicField(this, "stop", () => { - if (this.config.debug) - log("webcam", "stop"); - if (this.track) - this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - get track() { - if (!this.stream) - return void 0; - return this.stream.getVideoTracks()[0]; - } - get capabilities() { - if (!this.track) - return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - get constraints() { - if (!this.track) - return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - get settings() { - if (!this.stream) - return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - get label() { - if (!this.track) - return ""; - return this.track.label; - } - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) - log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) - modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - let loaded = false; - try { - model23.findIOHandler(); - if (options.debug) - log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) - model23.loadSync(artifacts); - else - model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0; - if (options.verbose) - log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) - log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.0.0"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - desynchronized: true - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) - return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") - return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) - tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) - log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) - return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) - return "webgpu"; - if (env.webgl.supported && env.webgl.backend) - return "webgl"; - if (env.wasm.supported && env.wasm.backend) - return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) - log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) - instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) - log("running inside web worker"); - } - if (env.browser && instance.config.backend === "tensorflow") { - if (instance.config.debug) - log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { - if (instance.config.debug) - log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) - log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) - log("available backends:", available); - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) - log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) - log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) - tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) - log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") - tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else - throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) - log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) - log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) - defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) - tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) - tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) - continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) - log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) - log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - env.initial = false; - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) - log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) - log("draw error: invalid canvas"); - else if (!input.getContext) - log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d"); - if (!ctx) - log("draw error: cannot get canvas context"); - else - return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") - return opt.color; - const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) - return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - labelColor: "rgba(173, 216, 230, 1)", - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - rightEyeIris: [473, 474, 475, 476, 477], - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } -]; -var UV468 = [ - [0.499976992607117, 0.652534008026123], - [0.500025987625122, 0.547487020492554], - [0.499974012374878, 0.602371990680695], - [0.482113003730774, 0.471979022026062], - [0.500150978565216, 0.527155995368958], - [0.499909996986389, 0.498252987861633], - [0.499523013830185, 0.40106201171875], - [0.289712011814117, 0.380764007568359], - [0.499954998493195, 0.312398016452789], - [0.499987006187439, 0.269918978214264], - [0.500023007392883, 0.107050001621246], - [0.500023007392883, 0.666234016418457], - [0.5000159740448, 0.679224014282227], - [0.500023007392883, 0.692348003387451], - [0.499976992607117, 0.695277988910675], - [0.499976992607117, 0.70593398809433], - 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94, - 2, - 370, - 289, - 305, - 455, - 339, - 254, - 448, - 359, - 255, - 446, - 254, - 253, - 449, - 253, - 252, - 450, - 252, - 256, - 451, - 256, - 341, - 452, - 414, - 413, - 463, - 286, - 441, - 414, - 286, - 258, - 441, - 258, - 257, - 442, - 257, - 259, - 443, - 259, - 260, - 444, - 260, - 467, - 445, - 309, - 459, - 250, - 305, - 289, - 290, - 305, - 290, - 460, - 401, - 376, - 435, - 309, - 250, - 392, - 376, - 411, - 433, - 453, - 341, - 464, - 357, - 453, - 465, - 343, - 357, - 412, - 437, - 343, - 399, - 344, - 360, - 440, - 420, - 437, - 456, - 360, - 420, - 363, - 361, - 401, - 288, - 265, - 372, - 353, - 390, - 339, - 249, - 339, - 448, - 255 -]; -var VTX68 = [ - 127, - 234, - 132, - 58, - 172, - 150, - 149, - 148, - 152, - 377, - 378, - 379, - 397, - 288, - 361, - 454, - 356, - 70, - 63, - 105, - 66, - 107, - 336, - 296, - 334, - 293, - 300, - 168, - 6, - 195, - 4, - 98, - 97, - 2, - 326, - 327, - 33, - 160, - 158, - 133, - 153, - 144, - 362, - 385, - 387, - 263, - 373, - 380, - 57, - 40, - 37, - 0, - 267, - 270, - 287, - 321, - 314, - 17, - 84, - 91, - 78, - 81, - 13, - 311, - 308, - 402, - 14, - 178 -]; -var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; -var VTX7 = [33, 133, 362, 263, 1, 78, 308]; -var UV68 = VTX68.map((x) => UV468[x]); -var UV33 = VTX33.map((x) => UV468[x]); -var UV7 = VTX7.map((x) => UV468[x]); -function connectionsToIndices(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - 246, - 161, - 160, - 159, - 158, - 157, - 173, - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - 247, - 30, - 29, - 27, - 28, - 56, - 190, - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - 113, - 225, - 224, - 223, - 222, - 221, - 189, - 35, - 124, - 46, - 53, - 52, - 65, - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - 466, - 388, - 387, - 386, - 385, - 384, - 398, - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - 467, - 260, - 259, - 257, - 258, - 286, - 414, - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - 342, - 445, - 444, - 443, - 442, - 441, - 413, - 265, - 353, - 276, - 283, - 282, - 295, - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) - return; - let l = localOptions.faceLabels.slice(); - if (f.score) - l = replace(l, "[score]", 100 * f.score); - if (f.gender) - l = replace(l, "[gender]", f.gender); - if (f.genderScore) - l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) - l = replace(l, "[age]", f.age); - if (f.distance) - l = replace(l, "[distance]", 100 * f.distance); - if (f.real) - l = replace(l, "[real]", 100 * f.real); - if (f.live) - l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) - emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - } - if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) - 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Image(){return G0(this,z2)}set Image(t){ge(this,z2,t),globalThis.Image=t}get ImageData(){return G0(this,S2)}set ImageData(t){ge(this,S2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(I0.engine().registryFactory);try{this.tensorflow={version:I0.backend().binding?I0.backend().binding.TF_Version:void 0,gpu:I0.backend().binding?I0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await I0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await I0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=te(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof 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U2=class{constructor(){k(this,"config");k(this,"element");k(this,"stream");k(this,"devices",[]);k(this,"enumerate",async()=>{try{let t=await navigator.mediaDevices.enumerateDevices();this.devices=t.filter(n=>n.kind==="videoinput")}catch(t){this.devices=[]}return this.devices});k(this,"start",async t=>{var r,s;if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.id&&(this.config.id=t==null?void 0:t.id),t!=null&&t.element)if(typeof t.element=="string"){let A=document.getElementById(t.element);if(A&&A instanceof HTMLVideoElement)this.element=A;else{this.config.debug&&u("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof 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navigator.mediaDevices.getUserMedia(n)}catch(A){u("webcam",A);return}if(!this.stream){this.config.debug&&u("webcam","no stream");return}this.element.srcObject=this.stream,await new Promise(A=>{this.element?this.element.onloadeddata=()=>A(!0):A(!1)}),await this.element.play(),this.config.debug&&u("webcam",{width:this.width,height:this.height,label:this.label,stream:this.stream,track:this.track,settings:this.settings,constraints:this.constraints,capabilities:this.capabilities})});k(this,"pause",()=>{this.element&&this.element.pause()});k(this,"play",async()=>{this.element&&await this.element.play()});k(this,"stop",()=>{this.config.debug&&u("webcam","stop"),this.track&&this.track.stop()});this.config={element:void 0,debug:!0,mode:"front",crop:!1,width:0,height:0}}get track(){if(!!this.stream)return this.stream.getVideoTracks()[0]}get capabilities(){if(!!this.track)return this.track.getCapabilities?this.track.getCapabilities():void 0}get constraints(){if(!!this.track)return 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n=K.faceLabels.slice();if(e.score&&(n=_(n,"[score]",100*e.score)),e.gender&&(n=_(n,"[gender]",e.gender)),e.genderScore&&(n=_(n,"[genderScore]",100*e.genderScore)),e.age&&(n=_(n,"[age]",e.age)),e.distance&&(n=_(n,"[distance]",100*e.distance)),e.real&&(n=_(n,"[real]",100*e.real)),e.live&&(n=_(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let y=e.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);y.length>3&&(y.length=3),n=_(n,"[emotions]",y.join(" "))}(s=(r=e.rotation)==null?void 0:r.angle)!=null&&s.roll&&(n=_(n,"[roll]",Ke(e.rotation.angle.roll))),(a=(A=e.rotation)==null?void 0:A.angle)!=null&&a.yaw&&(n=_(n,"[yaw]",Ke(e.rotation.angle.yaw))),(c=(l=e.rotation)==null?void 0:l.angle)!=null&&c.pitch&&(n=_(n,"[pitch]",Ke(e.rotation.angle.pitch))),(i=(x=e.rotation)==null?void 0:x.gaze)!=null&&i.bearing&&(n=_(n,"[gaze]",Ke(e.rotation.gaze.bearing))),ne(t,n,e.box[0],e.box[1],K)}function Is(e,t){var n,o,r,s;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=K.useDepth?"rgba(255, 200, 255, 0.3)":K.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),K.fillPolygons&&(t.fillStyle=K.useDepth?"rgba(255, 255, 200, 0.3)":K.color,t.fill())}}function Os(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Ke(e.rotation.angle.pitch)/90,s=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),A=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints && f.mesh.length >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) - continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) - continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) - curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) - return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function Ls(e,t){var n;if(K.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];e5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Cs(e,t){if(K.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<_e.length/3;n++){let o=[_e[n*3+0],_e[n*3+1],_e[n*3+2]].map(r=>e.mesh[r]);$t(t,o,K)}Is(e,t)}}function Ws(e,t){if(K.drawPoints&&e.mesh.length>=468)for(let n=0;n0&&(Ws(r,o),Cs(r,o),Os(r,o),Ls(r,o))}}function J2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=_(l,"[score]",100*t[a].score),ne(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=_(c,"[label]",l.part),c=_(c,"[score]",100*l.score),ne(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)Z1(r,c,o)}}}function Q2(e,t,n){var s,A;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=_(l,"[label]",a.label),l=_(l,"[score]",100*a.score),ne(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Je(l[2],o),Te(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let x=o.fingerLabels.slice();x=_(x,"[label]",l),ne(r,x,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let x=0;x0?x-1:0][0],c[x>0?x-1:0][1]),r.lineTo(c[x][0],c[x][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function _2(e,t,n){var s;let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,pe(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=_(a,"[label]",A.label),a=_(a,"[score]",100*A.score),ne(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function $2(e,t,n){var r;let o=a0(x0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=Q0(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let x=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=_(i,"[where]",l[0]),i=_(i,"[who]",x),i=_(i,"[what]",c[1]),ne(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Ie={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6467,7523 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) - return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) - return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) - return; - const ctx = getCanvasContext(output); - if (!ctx) - return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) - return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - "leftEyeInside", - "leftEye", - "leftEyeOutside", - "rightEyeInside", - "rightEye", - "rightEyeOutside", - "leftEar", - "rightEar", - "leftMouth", - "rightMouth", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftPinky", - "rightPinky", - "leftIndex", - "rightIndex", - "leftThumb", - "rightThumb", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle", - "leftHeel", - "rightHeel", - "leftFoot", - "rightFoot", - "bodyCenter", - "bodyTop", - "leftPalm", - "leftHand", - "rightPalm", - "rightHand" -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) - model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) - log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) - model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) - log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - height, - width, - [0, 0] - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - kpt4.positionRaw[1] / width + cropBox[0], - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) - return null; - const t2 = {}; - [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) - return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) - continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) - model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) - return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) - continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) - model4 = null; - if (!model4) - model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) - return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) - resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) - product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) - column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) - product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) - anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var faceBoxScaleFactor = 1.4; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) - model5 = null; - if (!model5) - model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) - return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) - return []; - const t2 = {}; - t2.resized = tf13.image.resizeBilinear(inputImage, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf05); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const rawBox = { - startPoint: [points[0], points[1]], - endPoint: [points[2], points[3]], - landmarks: await b.landmarks.array(), - confidence - }; - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]); - const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); - const squaredBox = squarifyBox(enlargedBox); - boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var irisEnlarge = 2.3; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) - model6 = null; - if (!model6) - model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) - inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize) { - if (!(model6 == null ? void 0 : model6["executor"])) - return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - }; - for (const val of Object.values(t2)) { - if (!val) - return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) - rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) - rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) - rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - if (!(model7 == null ? void 0 : model7["executor"])) - return []; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) - face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } else if (!model7) { - if (config3.debug) - log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh.keepInvalid) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.mesh = box.landmarks.map((pt) => [ - (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), - (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() - ]); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) - face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) - faces.push(face4); - else - tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) - model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) - model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) - model7 = await loadModel(config3.face.attention.modelPath); - else - model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -async function load6(config3) { - var _a; - if (env.initial) - model8 = null; - if (!model8) - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model8["modelUrl"]); - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) - return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) - obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) - model9 = null; - if (!model9) - model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input) { - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) - return tensor6; - const crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) - return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) - log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) - inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) - return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) - return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) - silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) - silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) - model10 = null; - if (!model10) - model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) - return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) - model11 = null; - if (!model11) - model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) - return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) - model12 = null; - if (!model12) - model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) - return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) - return; - const t2 = {}; - const box = [[0, 0.1, 0.9, 0.9]]; - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled) - [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2)) - obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) - age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) - model13 = null; - if (!model13) - model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) - log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) - return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) - return; - const t2 = {}; - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) - model14 = null; - if (!model14) - model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) - log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) - return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) - return; - const t2 = {}; - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - t2.enhance = tf22.tidy(() => { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb[0]); - const greenNorm = tf22.mul(green, rgb[1]); - const blueNorm = tf22.mul(blue, rgb[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - const normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled) - t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) - model15 = null; - if (!model15) - model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) - log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) - return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) - model16 = null; - if (!model16) - model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) - log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) - return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) - return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - thetaZ = Math.asin(r10); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) - thetaX = 0; - if (Number.isNaN(thetaY)) - thetaY = 0; - if (Number.isNaN(thetaZ)) - thetaZ = 0; - return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) - return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!f) - return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) - return []; - if (!faces) - return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) - faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) - delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) - res.age = descRes.age; - if (descRes.gender) - res.gender = descRes.gender; - if (descRes.genderScore) - res.genderScore = descRes.genderScore; - if (descRes.descriptor) - res.embedding = descRes.descriptor; - if (descRes.race) - res.race = descRes.race; - if (emotionRes) - res.emotion = emotionRes; - if (antispoofRes) - res.real = antispoofRes; - if (livenessRes) - res.live = livenessRes; - if (irisSize > 0) - res.distance = irisSize; - if (rotation) - res.rotation = rotation; - if (tensor6) - res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) - delete instance.performance.face; - if (instance.performance.age) - delete instance.performance.age; - if (instance.performance.gender) - delete instance.performance.gender; - if (instance.performance.emotion) - delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") - this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) - this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) - slope = -slope; - else if (slope > 0) - slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) - return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) - return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) - isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) - isDiagonal = 1 * weightageAt; - else - isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) - cos_in = 1; - else if (cos_in < -1) - cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) - fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) - fingerCurl = FingerCurl.half; - else - fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) - estimatedDirection = FingerDirection.horizontalLeft; - else - estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) - estimatedDirection = FingerDirection.verticalDown; - else - estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalUpLeft; - else - estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) - estimatedDirection = FingerDirection.diagonalDownLeft; - else - estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) - voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) - voteDiagonal += options3.DISTANCE_VOTE_POWER; - else - voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) - return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) - return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) - return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) - poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) - gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) - gestures.push({ face: i, gesture: "facing center" }); - else - gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) - gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) - gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) - gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) - gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) - continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) - center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (leftIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (rightIrisCenterX > 0.05) - gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) - gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) - gestures.push({ iris: i, gesture: "looking up" }); - if (center) - gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) - return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) - fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) - gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - 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{ x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - 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{ x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (config3.skipAllowed) - this.skipped++; - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) - useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) - continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) - hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -async function predict14(input, config3) { - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) - return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) - box[0] = pt[0]; - if (pt[1] < box[1]) - box[1] = pt[1]; - if (pt[0] > box[2]) - box[2] = pt[0]; - if (pt[1] > box[3]) - box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function load15(config3) { - var _a, _b; - if (env.initial) { - handDetectorModel = null; - handPoseModel = null; - } - if (!handDetectorModel || !handPoseModel) { - [handDetectorModel, handPoseModel] = await Promise.all([ - config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, - config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null - ]); - } else { - if (config3.debug) - log("cached model:", handDetectorModel["modelUrl"]); - if (config3.debug) - log("cached model:", handPoseModel["modelUrl"]); - } - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) - handPipeline = new HandPipeline(handDetector, handPoseModel); - return [handDetectorModel, handPoseModel]; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect2(config3) { - var _a; - if (env.initial) - models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) - models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) - log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) - return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) - hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) - return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - const t0 = now(); - if (!newResult) - return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) - bufferedResult.canvas = newResult.canvas; - if (newResult.error) - bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) - coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) - coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) - bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) - bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) - bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load16(config3) { - if (!model17 || env.initial) - model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) - model17 = await load16(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) - return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - return (options4.multiplier || 20) * sum3; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) - return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.max(Math.min(norm, 1), 0); - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) - break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) - continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) - return input; - cache5.padding = [ - [0, 0], - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - [0, 0] - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load17(config3) { - var _a; - if (env.initial) - model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) - log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) - inputSize8 = 256; - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (totalScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) - pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) - bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) - return []; - if (!config3.skipAllowed) - cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load18(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) - log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) - return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) - return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) - objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) - j++; - if (!this.less(k, j)) - break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) - return min2; - if (a > max5) - return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) - break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) - continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) - queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) - return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) - result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) - continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) - poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) - return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) - return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) - tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) - return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load19(config3) { - if (!model20 || env.initial) - model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) - log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load20(config3) { - if (!model21 || env.initial) - model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb2 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb2, a], -1); - tf35.dispose([rgb2, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) - model21 = await load20(config3); - if (!(model21 == null ? void 0 : model21["executor"])) - return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) - init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load21(config3) { - if (!model22 || env.initial) - model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) - log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) - model22 = await load21(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) - return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) - return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) - return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) - ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) - log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += m.sizeFromManifest; - totalSizeWeights += m.sizeLoadedWeights; - totalSizeLoading += m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) - this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) - this.reset(); - if (instance) - this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load17(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load19(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect2(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null; - if ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect")) - [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null]; - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load18(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load16(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load20(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) - promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) - continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) - continue; - const res = validateModel(this.instance, model23, defined); - if (res) - missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) - person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) - person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) - person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) - person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) - person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var r5=0;function Fs(e,t,n){let o=a0(x0,n);if(!t||!e)return;let r=Q0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let s=0;sa5,kpt:()=>A5});var A5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],a5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(H());var _0,t2=224,U1,Gs=5,nt=[8,16,32,32,32];function Vs(){let e=[],t=0;for(;tn.x)),y:D.tensor1d(e.map(n=>n.y))}}async function Y1(e){if(R.initial&&(_0=null),!_0&&e.body.detector&&e.body.detector.modelPath){_0=await O(e.body.detector.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;t2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&_0&&u("cached model:",_0.modelUrl);return Vs(),_0}var q1=[5,5];function Zs(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,t2),t.x),r=D.add(D.div(r,t2),t.y),s=D.mul(D.div(s,t2),q1[0]),A=D.mul(D.div(A,t2),q1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),x=D.add(l,A);return D.stack([a,l,c,x],1)})}async function Xs(e,t,n,o){var c,x;let r=[],s={};s.boxes=Zs(e,U1),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function ot(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var F0,l5=256,i5=Number.MAX_SAFE_INTEGER,qs={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},st=[],Oe=[[0,0],[0,0],[0,0],[0,0]],Q1=0,_1=e=>1-1/(1+Math.exp(e)),e3=e=>Y1(e);async function t3(e){if(R.initial&&(F0=null),F0)e.debug&&u("cached model:",F0.modelUrl);else{F0=await O(e.body.modelPath);let t=F0!=null&&F0.executor?Object.values(F0.modelSignature.inputs):void 0;l5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return F0}function $1(e,t,n){var s,A;let o={};if(!((s=e==null?void 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W0.all)n[W0.getName(o)]={curl:Be.getName(t.curls[o]),direction:c0.getName(t.directions[o])};return n}function En(e){let t=[];if(!e||e.length===0)return t;let n=wn(e);for(let o of Tn){let r=o.matchAgainst(n.curls,n.directions);r>=gA&&t.push({name:o.name,confidence:r})}return t}var zn=e=>{if(!e)return[];let t=[];for(let n=0;nl.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&o&&r&&o.position[1]l.part==="leftShoulder"),a=e[n].keypoints.find(l=>l.part==="rightShoulder");A&&a&&Math.abs(A.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${A.position[1]>a.position[1]?"left":"right"}`})}return t},Sn=e=>{if(!e)return[];let t=[];for(let n=0;n450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},jn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||b>.06)&&(d=!1),f>b?f>.05&&t.push({iris:A,gesture:"looking right"}):b>.05&&t.push({iris:A,gesture:"looking left"});let M=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],T=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(T<.01||M<.01||T>.022||M>.022)&&(d=!1),(T<.01||M<.01)&&t.push({iris:A,gesture:"looking down"}),(T>.022||M>.022)&&t.push({iris:A,gesture:"looking up"}),d&&t.push({iris:A,gesture:"looking center"})}return t},Nn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Pt(e,t=1.5){let n=L2(e),o=Mt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function kt(e){let t=L2(e),n=Mt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function PA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Dn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return PA(n)}var In=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ze(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),p={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},f=Wn(p,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(b=>W.dispose(c[b]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var U0=Z(H());var zA=5,Gn=1.65,Vn=[0,5,9,13,17,1,2],SA=0,jA=2,Zn=0,Et=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>J5([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Pt(kt(r),zA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Pt(kt(n),Gn);o.palmLandmarks=[];for(let r=0;r[A[0]*(d[0]-this.inputSize/2),A[1]*(d[1]-this.inputSize/2),A[2]*d[2]]),l=K5(o,[0,0]),c=a.map(d=>[...J5(d,l),d[2]]),x=Fn(r),i=[...L2(n),1],y=[Ze(i,x[0]),Ze(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>g()-Zn,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let S=U0.reshape(m,[-1,3]),P=await S.array();U0.dispose(m),U0.dispose(S);let I=this.transformRawCoords(P,f,x,p),q=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...q,confidence:h};let t0={landmarks:I,confidence:h,boxConfidence:c.confidence,fingerConfidence:h,box:{topLeft:q.startPoint,bottomRight:q.endPoint}};a.push(t0)}else this.storedBoxes[l]=null;U0.dispose(m)}else{let x=Pt(kt(c),Gn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:x.startPoint,bottomRight:x.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Xn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},a2,i2,qn;async function Q5(e,t){let n=await qn.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let x of A)x[0]a[2]&&(a[2]=x[0]),x[1]>a[3]&&(a[3]=x[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Rt(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function Un(e){var n,o;R.initial&&(a2=null,i2=null),!a2||!i2?[a2,i2]=await Promise.all([e.hand.enabled?O((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?O((o=e.hand.skeleton)==null?void 0:o.modelPath):null]):(e.debug&&u("cached model:",a2.modelUrl),e.debug&&u("cached model:",i2.modelUrl));let t=a2?new wt(a2):void 0;return t&&i2&&(qn=new Et(t,i2)),[a2,i2]}var Q=Z(H());var f0=[null,null],IA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],OA=["hand","fist","pinch","point","face","tip","pinchtip"],Kn=4,Jn=1.6,LA=512,CA=1.4,zt=Number.MAX_SAFE_INTEGER,_5=0,ke=[0,0],y0={boxes:[],hands:[]},Qn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function _n(e){var t;if(R.initial&&(f0[0]=null),f0[0])e.debug&&u("cached model:",f0[0].modelUrl);else{Y2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),f0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=f0[0].executor?Object.values(f0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[0]}async function $n(e){var t;if(R.initial&&(f0[1]=null),f0[1])e.debug&&u("cached model:",f0[1].modelUrl);else{f0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=f0[1].executor?Object.values(f0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return f0[1]}async function WA(e,t){let n=[];if(!e||!f0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,LA),A=Math.round(s*r/8)*8;o.resize=Q.image.resizeBilinear(e,[s,A]),o.cast=Q.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await f0[0].executeAsync(o.cast,IA),o.boxes=Q.squeeze(o.rawBoxes,[0,2]),o.scores=Q.squeeze(o.rawScores,[0]);let a=Q.unstack(o.scores,1);Q.dispose(a[Kn]),a.splice(Kn,1),o.filtered=Q.stack(a,1),Q.dispose(a),o.max=Q.max(o.filtered,1),o.argmax=Q.argMax(o.filtered,1);let l=0;o.nms=await Q.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=Q.slice(o.boxes,y,1),p=await d.data();Q.dispose(d);let f=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=ot(f,CA),M=[Math.trunc(f[0]*ke[0]),Math.trunc(f[1]*ke[1]),Math.trunc(f[2]*ke[0]),Math.trunc(f[3]*ke[1])],T=x[y],m=OA[i[y]],h={id:l++,score:T,box:M,boxRaw:b,label:m};n.push(h)}return Object.keys(o).forEach(y=>Q.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function $5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&f0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=Q.div(r.crop,C.tf255),[r.score,r.keypoints]=f0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ke[0]*(i[0]+t.boxRaw[0]),ke[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Rt(o.keypoints);for(let i of Object.keys(Qn))o.annotations[i]=Qn[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>Q.dispose(r[l]))}return o}async function e1(e,t){var r,s;if(!((r=f0[0])!=null&&r.executor)||!((s=f0[1])!=null&&s.executor)||!f0[0].inputs[0].shape||!f0[1].inputs[0].shape)return[];ke=[e.shape[2]||0,e.shape[1]||0],zt++;let n=(t.hand.skipTime||0)>g()-_5,o=zt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?y0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>g()-_5,l=zt<3*(t.hand.skipFrames||0);t.skipAllowed&&y0.hands.length===t.hand.maxDetected?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):t.skipAllowed&&a&&l&&y0.hands.length>0?y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))):(y0.boxes=await WA(e,t),_5=g(),y0.hands=await Promise.all(y0.boxes.map(x=>$5(e,x,t))),zt=0);let c=[...y0.boxes];if(y0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&i.box[3]/(e.shape[1]||1)>.05&&y0.hands[x].fingerScore&&y0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=ot(i.box,Jn),d=ot(i.boxRaw,Jn);y0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var C2={};ze(C2,{connected:()=>jt,horizontal:()=>t1,kpt:()=>St,relative:()=>o1,vertical:()=>n1});var St=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],t1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],n1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],o1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var w=he(),r1=0;function to(e,t){var 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be.executeAsync(b0,ta),l;switch(t.segmentation.mode||"default"){case"default":l=u1(n,o);break;case"alpha":l=u1(null,o);break;case"foreground":l=u1(n,null);break;case"state":l=na(r);break;default:l=F.tensor(0)}return F.dispose([b0.src,n,o,b0.r1i,b0.r2i,b0.r3i,b0.r4i]),[b0.r1i,b0.r2i,b0.r3i,b0.r4i]=[r,s,A,a],l}var k0=Z(H());var N0;async function g1(e){return!N0||R.initial?N0=await O(e.segmentation.modelPath):e.debug&&u("cached model:",N0.modelUrl),N0}async function Eo(e,t){var r;if(N0||(N0=await g1(t)),!(N0!=null&&N0.executor)||!((r=N0==null?void 0:N0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=k0.image.resizeBilinear(e,[N0.inputs[0].shape?N0.inputs[0].shape[1]:0,N0.inputs[0].shape?N0.inputs[0].shape[2]:0],!1),n.norm=k0.div(n.resize,C.tf255),n.res=N0.execute(n.norm),n.squeeze=k0.squeeze(n.res,[0]),n.alpha=k0.image.resizeBilinear(n.squeeze,[e.shape[1]||0,e.shape[2]||0]),n.mul=k0.mul(n.alpha,C.tf255);let o;switch(t.segmentation.mode||"default"){case"default":n.input=k0.squeeze(e),n.concat=k0.concat([n.input,n.mul],-1),o=k0.cast(n.concat,"int32");break;case"alpha":o=k0.cast(n.mul,"int32");break;default:o=k0.tensor(0)}return Object.keys(n).forEach(s=>k0.dispose(n[s])),o}function Ft(e,t,n){var c,x;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((x=l==null?void 0:l.graph)!=null&&x.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&u("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return e.config.debug&&A.length>0&&u("model validation failed:",n,A),A.length>0?{name:n,missing:A,ops:s,url:a}:null}var F2=class{constructor(t){k(this,"instance");k(this,"models",{});this.models={},this.instance=t}stats(){let t=0,n=0,o=0;for(let s of Object.values(E0))t+=s.sizeFromManifest,n+=s.sizeLoadedWeights,o+=s.sizeDesired;let r=o>0?n/o:0;return{numLoadedModels:Object.values(E0).length,numDefinedModels:Object.keys(this.models).length,percentageLoaded:r,totalSizeFromManifest:t,totalSizeWeights:n,totalSizeLoading:o,modelStats:Object.values(E0)}}reset(){for(let t of Object.keys(this.models))this.models[t]=null}async load(t){var o,r,s,A,a,l,c,x,i,y,d,p,f,b,M,T,m,h,S,P,I,q,t0,G,$,A0,v;R.initial&&this.reset(),t&&(this.instance=t);let n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?b3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)==null?void 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0:c.enabled)&&!this.models.facemesh?S3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)==null?void 0:x.enabled)&&!this.models.gear?_3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)==null?void 0:i.enabled)&&!this.models.ssrnetage?nn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)==null?void 0:y.enabled)&&!this.models.ssrnetgender?An(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((d=this.instance.config.face.mobilefacenet)==null?void 0:d.enabled)&&!this.models.mobilefacenet?xn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)==null?void 0:p.enabled)&&!this.models.insightface?un(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)==null?void 0:f.includes("blazepose"))?t3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?e3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)==null?void 0:b.includes("efficientpose"))?a3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((M=this.instance.config.body.modelPath)==null?void 0:M.includes("movenet"))?co(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)==null?void 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0:y.box);let T=Math.min(...f),m=Math.min(...b);p.box=[T,m,Math.max(...f)-T,Math.max(...b)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(p.boxRaw=[p.box[0]/r[2],p.box[1]/r[1],p.box[2]/r[2],p.box[3]/r[1]]),A.push(p)}return A}var d0=Z(H());var Bt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14134,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; -var body3 = ` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,Ht=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -14703,563 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`; - -// src/warmup.ts -async function warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") - img = new Image(); - else if (env.Image) - img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) - ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) - img.src = src; - else - resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") - img = atob(face3); - else - img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) - log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") - res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) - res = await warmupCanvas(instance); - else - res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) - return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) - continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) - shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) - res.forEach((t2) => tf37.dispose(t2)); - else - tf37.dispose(res); - } catch (e) { - if (instance.config.debug) - log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) - log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) - log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) - instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) - log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - constructor(userConfig) { - __publicField(this, "version"); - __publicField(this, "config"); - __publicField(this, "result"); - __publicField(this, "state"); - __publicField(this, "process"); - __publicField(this, "tf"); - __publicField(this, "env", env); - __publicField(this, "draw", draw_exports); - __publicField(this, "match", match_exports); - __publicField(this, "models"); - __publicField(this, "events"); - __publicField(this, "faceTriangulation"); - __publicField(this, "faceUVMap"); - __publicField(this, "performance"); - __privateAdd(this, _numTensors, void 0); - __privateAdd(this, _analyzeMemoryLeaks, void 0); - __privateAdd(this, _checkSanity, void 0); - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) - return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) - log(...msg, leaked); - }); - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) - return null; - if (!input) - return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) - return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - __publicField(this, "webcam", new WebCam()); - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) - this.events.dispatchEvent(new Event(event)); - }); - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) - log(`version: ${this.version}`); - if (this.config.debug) - log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) - log("environment:", envTemp); - } - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) - this.config = mergeDeep(this.config, userConfig); - return msgs; - } - now() { - return now(); - } - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) - return null; - const processed = await process2(input, this.config); - if (!processed.tensor) - return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) - tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) - tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) - tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) - this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) - log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) - log("configuration:", this.config); - if (this.config.debug) - log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) - log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) - this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - next(result = this.result) { - return calc2(result, this.config); - } - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) - kernels[kernel.name] += ms; - else - kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) - log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) - this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) - this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) - this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) - delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) - faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) - bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) - bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) - bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) - bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) - delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) - bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) - bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) - bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) - bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) - handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) - handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) - delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) - handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) - handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) - objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) - objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) - delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) - objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) - objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) - [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) - this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) - delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) - log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) - await this.detect(element); - if (delay > 0) - await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) - requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) - log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); +2Q==`;async function sa(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(A=>A.blob()),n,o;switch(e.config.warmup){case"face":n=await t(Bt);break;case"body":case"full":n=await t(Ht);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return 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Image!="undefined"||R.Canvas!==void 0?t=await Aa(e):t=await aa(e),t}async function la(e){var a,l,c,x;if(!d0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=d0.getBackend(),n=d0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;d0.env().set("ENGINE_COMPILE_ONLY",!0);let o=d0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let d=(y==null?void 0:y.modelSignature)&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])==null?void 0:l.shape)?[...y.inputs[0].shape]:[1,64,64,3],p=(y==null?void 0:y.modelSignature)&&((x=(c=y==null?void 0:y.inputs)==null?void 0:c[0])==null?void 0:x.dtype)?y.inputs[0].dtype:"float32";for(let b=0;bd0.dispose(M)):d0.dispose(b)}catch(b){e.config.debug&&u("compile fail model:",i)}d0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:s.length}),d0.env().set("ENGINE_COMPILE_ONLY",!1);let 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p,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Db(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=qc(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Db(e)?null:e.backwardsFunc,d;return 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this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Sm(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof va||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let 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a=v(r,"forgetBias","basicLSTMCell"),i=v(e,"lstmKernel","basicLSTMCell"),p=v(t,"lstmBias","basicLSTMCell"),u=v(o,"data","basicLSTMCell"),c=v(n,"c","basicLSTMCell"),l=v(s,"h","basicLSTMCell"),m=gt([u,l],1),d=Xe(m,i),f=xe(d,p),h=f.shape[0],g=f.shape[1]/4,x=[h,g],b=He(f,[0,0],x),C=He(f,[0,g],x),w=He(f,[0,g*2],x),k=He(f,[0,g*3],x),_=xe(ae(zs(b),nl(C)),ae(c,zs(xe(a,w)))),$=ae(nl(_),zs(k));return[_,$]}var E0=N({basicLSTMCell_:jW});function XW(r,e,t){let o=v(r,"x","batchToSpaceND"),n=e.reduce((i,p)=>i*p);E(o.rank>=1+e.length,()=>`input rank is ${o.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(o.shape[0]%n===0,()=>`input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`);let s={x:o},a={blockShape:e,crops:t};return T.runKernel(xs,s,a)}var rd=N({batchToSpaceND_:XW});function $0(r){let e;return r.rank===0||r.rank===1?e=z(r,[1,1,1,r.size]):r.rank===2?e=z(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function YW(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;o!=null&&(c=v(o,"offset","batchNorm")),E(i.rank===p.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:$0(a),scale:u,offset:c,mean:i,variance:p},d={varianceEpsilon:s},f=T.runKernel(an,m,d);return z(f,a.shape)}var wi=N({batchNorm_:YW});function QW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var A0=N({batchNorm2d_:QW});function ZW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var R0=N({batchNorm3d_:ZW});function JW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var F0=N({batchNorm4d_:JW});function eU(r,e,t){let o=v(r,"x","bincount"),n=v(e,"weights","bincount");E(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Ja,s,a)}var od=N({bincount_:eU});function tU(r,e){let t=v(r,"s0","broadcastArgs","int32"),o=v(e,"s1","broadcastArgs","int32");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(up,n)}var D0=N({broadcastArgs_:tU});function rU(r,e){let t=v(r,"broadcastTo","x"),o=t.shape;if(yt(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Br(t);let i={x:t},p={reps:s};return T.runKernel(to,i,p)}var Ii=N({broadcastTo_:rU});function oU(r){let t={x:v(r,"x","ceil","float32")};return T.runKernel(Uo,t)}var O0=N({ceil_:oU});function Ws(r,e,t){yt(r);let o={shape:r,value:e,dtype:t};return T.runKernel(Cs,{},o)}function nU(r,e,t){let o=v(r,"x","clipByValue");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return Ws(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(lo,n,s)}var P0=N({clipByValue_:nU});function sU(r){return gt(r,0)}var M0=N({concat1d_:sU});function aU(r,e){return gt(r,e)}var L0=N({concat2d_:aU});function iU(r,e){return gt(r,e)}var B0=N({concat3d_:iU});function uU(r,e){return gt(r,e)}var V0=N({concat4d_:uU});function pU(r,e,t,o,n="NHWC",s=[1,1],a){let i=v(r,"x","conv2d","float32"),p=v(e,"filter","conv2d","float32"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Pt("conv2d",o,a);let l=n==="NHWC"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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n=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");E(n.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ti,a,i)}var Y0=N({denseBincount_:CU});function SU(r,e,t="NHWC"){let o=v(r,"x","depthToSpace","float32"),n=t==="NHWC"?o.shape[1]:o.shape[2],s=t==="NHWC"?o.shape[2]:o.shape[3],a=t==="NHWC"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying +Expected: ${s}.`)}typeof expect!="undefined"&&expect().nothing()}function lW(r,e){r().then(()=>e.fail(),()=>e()),typeof expect!="undefined"&&expect().nothing()}function mW(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return Po(r)||Po(r[0])||Po(e)||Po(e[0])?oC(r,t,(o,n)=>o==n):oC(r,e,(o,n)=>nC(o,n,0))}function lv(r,e,t){if(t==null&&(t=Zm()),!nC(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);typeof expect!="undefined"&&expect().nothing()}function nC(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function dW(r,e,t){for(let o=0;ot)throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t}`)}function fW(r,e){let t=new Float32Array(r),o=new Float32Array(e);if(t.length!==o.length)throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t.length}`);for(let n=0;n{e.addEventListener("loadeddata",o=>t(e)),e.load()})}async function gW(r){await r.play(),"requestVideoFrameCallback"in r&&await new Promise(e=>{r.requestVideoFrameCallback(e)})}var 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a=o.size/n.size,i=Ge(et(s),et(n));return a>1?Ge(i,be(a)):i}}if(t===Et.SUM_BY_NONZERO_WEIGHTS){if(n==null)return Ge(et(s),be(o.size));{let a=ae(n,Gs(o.shape)),i=Ke(et(wd(a,be(0))),"float32");return Ge(et(s),i)}}throw Error(`Unknown reduction: ${t}`)}var sr=N({computeWeightedLoss_:WH});function UH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),ht(n.shape,s.shape,"Error in absoluteDifference: ");let i=Yt(Ne(n,s));return sr(i,a,o)}var aN=N({absoluteDifference_:UH});function GH(r,e,t,o,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),ht(s.shape,a.shape,"Error in cosineDistance: ");let p=be(1),u=Ne(p,et(ae(s,a),t,!0));return sr(u,i,n)}var iN=N({cosineDistance_:GH});function HH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),ht(n.shape,s.shape,"Error in hingeLoss: ");let i=be(1);n=Ne(ae(be(2),n),i);let p=Ti(Ne(i,ae(n,s)));return sr(p,a,o)}var uN=N({hingeLoss_:HH});function qH(r,e,t,o=1,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),ht(s.shape,a.shape,"Error in huberLoss: ");let p=be(o),u=Yt(Ne(a,s)),c=Sd(u,p),l=Ne(u,c),m=xe(ae(be(.5),Qt(c)),ae(p,l));return sr(m,i,n)}var pN=N({huberLoss_:qH});function KH(r,e,t,o=1e-7,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),ht(s.shape,a.shape,"Error in logLoss: ");let p=be(1),u=be(o),c=yr(ae(s,Da(xe(a,u)))),l=ae(Ne(p,s),Da(xe(Ne(p,a),u))),m=Ne(c,l);return sr(m,i,n)}var cN=N({logLoss_:KH});function jH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),ht(n.shape,s.shape,"Error in meanSquaredError: ");let i=Dd(n,s);return sr(i,a,o)}var lN=N({meanSquaredError_:jH});function XH(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");ht(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Ti(o),s=ae(o,t),a=md(Co(yr(Yt(o))));return xe(Ne(n,s),a)}function YH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),ht(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=be(o),c=be(1),l=be(.5);s=xe(ae(s,Ne(c,u)),ae(l,u))}let p=XH(s,a);return sr(p,i,n)}var mN=N({sigmoidCrossEntropy_:YH});function QH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Cr((n,s,a)=>{let p=hd(s,[t],!0),u=Ne(Ke(s,"float32"),p);a([n,u]);let c=yr(ae(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Aa(d.shape,[t]);return[ae(z(d,x),Ne(Ke(h,"float32"),Co(g))),ae(z(d,x),Ne(Co(g),Ke(h,"float32")))]}}})(r,e)}function ZH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),ht(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=be(o),c=be(1),l=be(s.shape[1]);s=xe(ae(s,Ne(c,u)),Ge(u,l))}let p=QH(s,a);return sr(p,i,n)}var dN=N({softmaxCrossEntropy_:ZH});function JH(r,e,t,o){let n=v(r,"indices","sparseFillEmptyRows","int32"),s=v(e,"values","sparseFillEmptyRows"),a=v(t,"denseShape","sparseFillEmptyRows","int32"),i=v(o,"defaultValue","sparseFillEmptyRows",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(ui,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var fN=N({sparseFillEmptyRows_:JH});function eq(r,e,t){let o=v(r,"inputIndices","sparseReshape","int32"),n=v(e,"inputShape","sparseReshape","int32"),s=v(t,"newShape","sparseReshape","int32");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape @@ -29,10 +29,10 @@ Expected: ${s}.`)}typeof expect!="undefined"&&expect().nothing()}function lW(r,e ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(pi,a)}var gN=N({sparseSegmentMean_:tq});function rq(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ci,a)}var xN=N({sparseSegmentSum_:rq});function oq(r,e,t,o,n,s,a,i){let p=v(r,"data","stringNGrams","string");if(p.dtype!=="string")throw new Error("Data must be of datatype string");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(As,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var yN=N({stringNGrams_:oq});function nq(r,e,t=!0){let o=v(r,"input","stringSplit","string"),n=v(e,"delimiter","stringSplit","string");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(di,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var bN=N({stringSplit_:nq});function sq(r,e){let t=v(r,"input","stringToHashBucketFast","string"),o={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let n={input:t};return T.runKernel(fi,n,o)}var CN=N({stringToHashBucketFast_:sq});var aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className="Adadelta";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className="Adagrad";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className="Adam";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className="Adamax";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qs.className="SGD";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className="Momentum";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Di.className="RMSProp";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static adagrad(e,t=.1){return new $i(e,t)}};var hMe={sgd:ns.sgd,momentum:ns.momentum,adadelta:ns.adadelta,adagrad:ns.adagrad,rmsprop:ns.rmsprop,adamax:ns.adamax,adam:ns.adam};var dq=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function CC(){return new Promise(r=>dq(()=>r()))}var S={};Ue(S,{ERF_A1:()=>$q,ERF_A2:()=>Aq,ERF_A3:()=>Rq,ERF_A4:()=>Fq,ERF_A5:()=>Dq,ERF_P:()=>Eq,PARALLELIZE_THRESHOLD:()=>Ud,RowPartitionType:()=>Ks,SELU_SCALE:()=>_q,SELU_SCALEALPHA:()=>Tq,applyActivation:()=>yu,assertAndGetBroadcastShape:()=>Je,assertAxesAreInnerMostDims:()=>DU,assertParamsConsistent:()=>fq,assignToTypedArray:()=>Vq,axesAreInnerMostDims:()=>uC,calculateShapes:()=>Jv,checkEinsumDimSizes:()=>qq,checkPadOnDimRoundingMode:()=>Pt,combineLocations:()=>nk,combineRaggedTensorToTensorShapes:()=>gq,complexWithEvenIndex:()=>Mq,complexWithOddIndex:()=>Lq,computeConv2DInfo:()=>uu,computeConv3DInfo:()=>N0,computeDefaultPad:()=>iC,computeDilation2DInfo:()=>OW,computeOptimalWindowSize:()=>Cq,computeOutAndReduceShapes:()=>FU,computeOutShape:()=>hq,computePool2DInfo:()=>aC,computePool3DInfo:()=>PW,convertConv2DDataFormat:()=>T0,decodeEinsumEquation:()=>Gq,eitherStridesOrDilationsAreOne:()=>lr,expandShapeToKeepDim:()=>Aa,exponent:()=>Wq,exponents:()=>zq,fromStringArrayToUint8:()=>dK,fromUint8ToStringArray:()=>mK,getAxesPermutation:()=>OU,getBroadcastDims:()=>Xv,getComplexWithIndex:()=>Bq,getEinsumComputePath:()=>Kq,getEinsumPermutation:()=>Hq,getFusedBiasGradient:()=>xu,getFusedDyActivation:()=>gu,getImageCenter:()=>Sq,getInnerMostAxes:()=>MU,getPermuted:()=>Iq,getRaggedRank:()=>yq,getReductionAxes:()=>jm,getReshaped:()=>wq,getReshapedPermuted:()=>vq,getRowPartitionTypesHelper:()=>xq,getSliceBeginCoords:()=>kq,getSliceSize:()=>Nq,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Qq,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zq,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jq,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>rK,getSparseReshapeInputOutputMismatchErrorMessage:()=>nK,getSparseReshapeInputOutputMultipleErrorMessage:()=>oK,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eK,getSparseReshapeNegativeOutputDimErrorMessage:()=>tK,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>uK,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sK,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>aK,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>iK,getUndoAxesPermutation:()=>PU,isIdentityPermutation:()=>jq,log:()=>GV,mergeRealAndImagArrays:()=>Oq,prepareAndValidate:()=>Zv,prepareSplitSize:()=>Yq,segment_util:()=>wC,shouldFuse:()=>bu,slice_util:()=>ut,splitRealAndImagArrays:()=>Pq,tupleValuesAreOne:()=>iu,upcastType:()=>dt,validateDefaultValueShape:()=>bq,validateInput:()=>Qm,validateUpdateShape:()=>tC,warn:()=>Os});function fq(r,e){let t=r[0].length;r.forEach((n,s)=>{E(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),E(e>=0&&e`Error in concat${t}D: axis must be between 0 and ${t-1}.`);let o=r[0];r.forEach((n,s)=>{for(let a=0;a`Error in concat${t}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`)})}function hq(r,e){let t=r[0].slice();for(let o=1;o=0)if(i>=0){if(i!==s)throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n+r}] = ${s} but shape[${n+r}] = ${i}`)}else o[a]=s}return o}function xq(r){let e={FIRST_DIM_SIZE:Ks.FIRST_DIM_SIZE,VALUE_ROWIDS:Ks.VALUE_ROWIDS,ROW_LENGTHS:Ks.ROW_LENGTHS,ROW_SPLITS:Ks.ROW_SPLITS,ROW_LIMITS:Ks.ROW_LIMITS,ROW_STARTS:Ks.ROW_STARTS},t=[];for(let o of r)if(o in e)t.push(e[o]);else break;return t}function yq(r){return r.length===0?0:r[0]===Ks.FIRST_DIM_SIZE?r.length-1:r.length}function bq(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function Cq(r){return r<=Ud?r:sp(r,Math.floor(Math.sqrt(r)))}function Sq(r,e,t){let o=t*(typeof r=="number"?r:r[0]),n=e*(typeof r=="number"?r:r[1]);return[o,n]}function wq(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function vq(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s/g,SN=",",wN="...";function Gq(r,e){r=r.replace(/\s/g,"");let t=(r.length-r.replace(Uq,"").length)/SC.length;if(t<1)throw new Error("Equations without an arrow are not supported.");if(t>1)throw new Error(`Equation must contain exactly one arrow ("${SC}").`);let[o,n]=r.split(SC);E(o.indexOf(wN)===-1,()=>`The ellipsis notation ("${wN}") is not supported yet.`);let s=o.split(SN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function qq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function Kq(r,e){let t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function Xq(r,e){let t=[];for(let o=0;o"Number of splits must evenly divide the axis."),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);E(n<=1,()=>"There should be only one negative value in split array.");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}E(r.shape[t]===e.reduce((a,i)=>a+i),()=>"The sum of sizes must match the size of the axis dimension."),o=e}return o}function Qq(r){return`Received SparseTensor with denseShape[0] = 0 but + ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ci,a)}var xN=N({sparseSegmentSum_:rq});function oq(r,e,t,o,n,s,a,i){let p=v(r,"data","stringNGrams","string");if(p.dtype!=="string")throw new Error("Data must be of datatype string");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(As,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var yN=N({stringNGrams_:oq});function nq(r,e,t=!0){let o=v(r,"input","stringSplit","string"),n=v(e,"delimiter","stringSplit","string");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(di,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var bN=N({stringSplit_:nq});function sq(r,e){let t=v(r,"input","stringToHashBucketFast","string"),o={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let n={input:t};return T.runKernel(fi,n,o)}var CN=N({stringToHashBucketFast_:sq});var aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Dt(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ei.className="Adadelta";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className="Adagrad";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Ai.className="Adam";Er(Ai);var Ri=class extends wr{constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ri.className="Adamax";Er(Ri);var qs=class extends wr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};qs.className="SGD";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className="Momentum";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Di.className="RMSProp";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static adagrad(e,t=.1){return new $i(e,t)}};var hMe={sgd:ns.sgd,momentum:ns.momentum,adadelta:ns.adadelta,adagrad:ns.adagrad,rmsprop:ns.rmsprop,adamax:ns.adamax,adam:ns.adam};var dq=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function CC(){return new Promise(r=>dq(()=>r()))}var S={};Ue(S,{ERF_A1:()=>$q,ERF_A2:()=>Aq,ERF_A3:()=>Rq,ERF_A4:()=>Fq,ERF_A5:()=>Dq,ERF_P:()=>Eq,PARALLELIZE_THRESHOLD:()=>Ud,RowPartitionType:()=>Ks,SELU_SCALE:()=>_q,SELU_SCALEALPHA:()=>Tq,applyActivation:()=>yu,assertAndGetBroadcastShape:()=>Je,assertAxesAreInnerMostDims:()=>DU,assertParamsConsistent:()=>fq,assignToTypedArray:()=>Vq,axesAreInnerMostDims:()=>uC,calculateShapes:()=>J0,checkEinsumDimSizes:()=>qq,checkPadOnDimRoundingMode:()=>Pt,combineLocations:()=>nk,combineRaggedTensorToTensorShapes:()=>gq,complexWithEvenIndex:()=>Mq,complexWithOddIndex:()=>Lq,computeConv2DInfo:()=>uu,computeConv3DInfo:()=>Nv,computeDefaultPad:()=>iC,computeDilation2DInfo:()=>OW,computeOptimalWindowSize:()=>Cq,computeOutAndReduceShapes:()=>FU,computeOutShape:()=>hq,computePool2DInfo:()=>aC,computePool3DInfo:()=>PW,convertConv2DDataFormat:()=>Tv,decodeEinsumEquation:()=>Gq,eitherStridesOrDilationsAreOne:()=>lr,expandShapeToKeepDim:()=>Aa,exponent:()=>Wq,exponents:()=>zq,fromStringArrayToUint8:()=>dK,fromUint8ToStringArray:()=>mK,getAxesPermutation:()=>OU,getBroadcastDims:()=>X0,getComplexWithIndex:()=>Bq,getEinsumComputePath:()=>Kq,getEinsumPermutation:()=>Hq,getFusedBiasGradient:()=>xu,getFusedDyActivation:()=>gu,getImageCenter:()=>Sq,getInnerMostAxes:()=>MU,getPermuted:()=>Iq,getRaggedRank:()=>yq,getReductionAxes:()=>jm,getReshaped:()=>wq,getReshapedPermuted:()=>vq,getRowPartitionTypesHelper:()=>xq,getSliceBeginCoords:()=>kq,getSliceSize:()=>Nq,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Qq,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zq,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jq,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>rK,getSparseReshapeInputOutputMismatchErrorMessage:()=>nK,getSparseReshapeInputOutputMultipleErrorMessage:()=>oK,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eK,getSparseReshapeNegativeOutputDimErrorMessage:()=>tK,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>uK,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sK,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>aK,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>iK,getUndoAxesPermutation:()=>PU,isIdentityPermutation:()=>jq,log:()=>GV,mergeRealAndImagArrays:()=>Oq,prepareAndValidate:()=>Z0,prepareSplitSize:()=>Yq,segment_util:()=>wC,shouldFuse:()=>bu,slice_util:()=>ut,splitRealAndImagArrays:()=>Pq,tupleValuesAreOne:()=>iu,upcastType:()=>dt,validateDefaultValueShape:()=>bq,validateInput:()=>Qm,validateUpdateShape:()=>tC,warn:()=>Os});function fq(r,e){let t=r[0].length;r.forEach((n,s)=>{E(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),E(e>=0&&e`Error in concat${t}D: axis must be between 0 and ${t-1}.`);let o=r[0];r.forEach((n,s)=>{for(let a=0;a`Error in concat${t}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`)})}function hq(r,e){let t=r[0].slice();for(let o=1;o=0)if(i>=0){if(i!==s)throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n+r}] = ${s} but shape[${n+r}] = ${i}`)}else o[a]=s}return o}function xq(r){let e={FIRST_DIM_SIZE:Ks.FIRST_DIM_SIZE,VALUE_ROWIDS:Ks.VALUE_ROWIDS,ROW_LENGTHS:Ks.ROW_LENGTHS,ROW_SPLITS:Ks.ROW_SPLITS,ROW_LIMITS:Ks.ROW_LIMITS,ROW_STARTS:Ks.ROW_STARTS},t=[];for(let o of r)if(o in e)t.push(e[o]);else break;return t}function yq(r){return r.length===0?0:r[0]===Ks.FIRST_DIM_SIZE?r.length-1:r.length}function bq(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function Cq(r){return r<=Ud?r:sp(r,Math.floor(Math.sqrt(r)))}function Sq(r,e,t){let o=t*(typeof r=="number"?r:r[0]),n=e*(typeof r=="number"?r:r[1]);return[o,n]}function wq(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function vq(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s/g,SN=",",wN="...";function Gq(r,e){r=r.replace(/\s/g,"");let t=(r.length-r.replace(Uq,"").length)/SC.length;if(t<1)throw new Error("Equations without an arrow are not supported.");if(t>1)throw new Error(`Equation must contain exactly one arrow ("${SC}").`);let[o,n]=r.split(SC);E(o.indexOf(wN)===-1,()=>`The ellipsis notation ("${wN}") is not supported yet.`);let s=o.split(SN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function qq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function Kq(r,e){let t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function Xq(r,e){let t=[];for(let o=0;o"Number of splits must evenly divide the axis."),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);E(n<=1,()=>"There should be only one negative value in split array.");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}E(r.shape[t]===e.reduce((a,i)=>a+i),()=>"The sum of sizes must match the size of the axis dimension."),o=e}return o}function Qq(r){return`Received SparseTensor with denseShape[0] = 0 but indices.shape[0] = ${r}`}function Zq(r,e){return`indices(${r}, 0) is invalid: ${e} < 0`}function Jq(r,e,t){return`indices(${r}, 0) is invalid: ${e} >= ${t}`}function eK(r,e){return`only one output dimension may be -1, not both ${r} and ${e}`}function tK(r,e){return`size ${r} must be non-negative, not ${e}`}function rK(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function oK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a SparseTensor with ${t} dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`}function nK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a tensor with ${t} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`}function sK(){return"segment ids must be >= 0"}function aK(){return"segment ids are not increasing"}function iK(r,e){return`Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`}function uK(r,e,t){return`Bad: indices[${r}] == ${e} out of range [0, ${t})`}var wC={};Ue(wC,{collectGatherOpShapeInfo:()=>lK,computeOutShape:()=>cK,segOpComputeOptimalWindowSize:()=>pK});function pK(r,e){let t=!1,o;for(r<=Ud?(o=r,t=!0):o=sp(r,Math.floor(Math.sqrt(r)));!t;)o>e||o===r?t=!0:o=sp(r,o+1);return o}function cK(r,e,t){let o=[],n=r.length;for(let s=0;sn))throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);if(o<0&&(o+=n),o>s)throw new Error(`batchDims (${o}) must be less than rank(x) ( - ${s}).`);if(tAp(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function dK(r){return r.map(e=>gi(e))}var Lt={};Ue(Lt,{nonMaxSuppressionV3Impl:()=>Vd,nonMaxSuppressionV4Impl:()=>zd,nonMaxSuppressionV5Impl:()=>Wd,whereImpl:()=>Pd});var fK=O();fK.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,r=>{r&&console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. 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${t}`);let n=r.shape.slice(1);zr(n,e,"TensorList shape mismatch: ");let s=so(r);return new Pa(s,e,o)}function RN(r,e,t,o){return new Pa([],r,e,o)}function FN(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n} vs. ${o})`);let s=new Pa([],t,r.dtype,o),a=so(r,0);return e.forEach((i,p)=>{s.setItem(i,a[p])}),s}function DN(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to @@ -7291,4 +7291,4 @@ return a / b;`,x7=` setOutputAtIndex(index, outputValue); } } - `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let w=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",w)}var PB={kernelName:Jn,backendName:"webgpu",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:"webgpu",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB="4.1.0",lne="4.1.0",mne="4.1.0",dne="4.1.0",fne="4.1.0",hne="0.0.1-alpha.16",gne={tfjs:LB,"tfjs-core":LB,"tfjs-converter":lne,"tfjs-backend-cpu":mne,"tfjs-backend-webgl":dne,"tfjs-backend-wasm":fne,"tfjs-backend-webgpu":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,f0 as acos,h0 as acosh,xe as add,g0 as addN,x0 as all,y0 as any,b0 as argMax,C0 as argMin,S0 as asin,w0 as asinh,I0 as atan,v0 as atan2,k0 as atanh,td as avgPool,_0 as avgPool3d,Oie as backend,S as backend_util,E0 as basicLSTMCell,wi as batchNorm,A0 as batchNorm2d,R0 as batchNorm3d,F0 as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,D0 as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Qv as browser,le as buffer,Ke as cast,O0 as ceil,P0 as clipByValue,Br as clone,Tr as complex,gt as concat,M0 as concat1d,L0 as concat2d,B0 as concat3d,V0 as concat4d,z0 as conv1d,vi as conv2d,W0 as conv2dTranspose,U0 as conv3d,H0 as conv3dTranspose,Dne as copyRegisteredKernels,q0 as cos,K0 as cosh,il as cosineWindow,j0 as cumprod,X0 as cumsum,Cr as customGrad,Y0 as denseBincount,eC as deprecationWarn,Q0 as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Z0 as diag,J0 as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,jv as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,p0 as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,hv as tensor_util,d0 as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike}; + `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let w=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",w)}var PB={kernelName:Jn,backendName:"webgpu",kernelFunc:une};function pne(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:"webgpu",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB="4.1.0",lne="4.1.0",mne="4.1.0",dne="4.1.0",fne="4.1.0",hne="0.0.1-alpha.16",gne={tfjs:LB,"tfjs-core":LB,"tfjs-converter":lne,"tfjs-backend-cpu":mne,"tfjs-backend-webgl":dne,"tfjs-backend-wasm":fne,"tfjs-backend-webgpu":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,fv as acos,hv as acosh,xe as add,gv as addN,xv as all,yv as any,bv as argMax,Cv as argMin,Sv as asin,wv as asinh,Iv as atan,vv as atan2,kv as atanh,td as avgPool,_v as avgPool3d,Oie as backend,S as backend_util,Ev as basicLSTMCell,wi as batchNorm,Av as batchNorm2d,Rv as batchNorm3d,Fv as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,Dv as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Q0 as browser,le as buffer,Ke as cast,Ov as ceil,Pv as clipByValue,Br as clone,Tr as complex,gt as concat,Mv as concat1d,Lv as concat2d,Bv as concat3d,Vv as concat4d,zv as conv1d,vi as conv2d,Wv as conv2dTranspose,Uv as conv3d,Hv as conv3dTranspose,Dne as copyRegisteredKernels,qv as cos,Kv as cosh,il as cosineWindow,jv as cumprod,Xv as cumsum,Cr as customGrad,Yv as denseBincount,eC as deprecationWarn,Qv as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Zv as diag,Jv as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,j0 as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,pv as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,h0 as tensor_util,dv as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index 5366c1a3..ecf8470d 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,35 +4,4 @@ author: ' */ - -// node_modules/.pnpm/@tensorflow+tfjs-core@4.1.0/node_modules/@tensorflow/tfjs-core/package.json -var version = "4.1.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-converter@4.1.0_npjwttp6o2hhjgfcmiedqvkgoa/node_modules/@tensorflow/tfjs-converter/package.json -var version2 = "4.1.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.1.0_npjwttp6o2hhjgfcmiedqvkgoa/node_modules/@tensorflow/tfjs-backend-cpu/package.json -var version3 = "4.1.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.1.0_npjwttp6o2hhjgfcmiedqvkgoa/node_modules/@tensorflow/tfjs-backend-webgl/package.json -var version4 = "4.1.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.1.0_npjwttp6o2hhjgfcmiedqvkgoa/node_modules/@tensorflow/tfjs-backend-wasm/package.json -var version5 = "4.1.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.16_npjwttp6o2hhjgfcmiedqvkgoa/node_modules/@tensorflow/tfjs-backend-webgpu/package.json -var version6 = "0.0.1-alpha.16"; - -// tfjs/tf-version.ts -var version7 = { - tfjs: version, - "tfjs-core": version, - "tfjs-converter": version2, - "tfjs-backend-cpu": version3, - "tfjs-backend-webgl": version4, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 -}; -export { - version7 as version -}; +var e="4.1.0";var s="4.1.0";var t="4.1.0";var n="4.1.0";var r="4.1.0";var i="0.0.1-alpha.16";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; diff --git a/test/build.log b/test/build.log index 76d44587..206ea15b 100644 --- a/test/build.log +++ b/test/build.log @@ -1,1493 +1,50 @@ -2022-11-21 13:51:35 DATA:  Build {"name":"@vladmandic/human","version":"3.0.0"} -2022-11-21 13:51:35 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.0"} -2022-11-21 13:51:35 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2022-11-21 13:51:35 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.15","typescript":"4.9.3","typedoc":"0.23.21","eslint":"8.28.0"} -2022-11-21 13:51:35 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2022-11-21 13:51:35 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":670150,"outputBytes":317295} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":670154,"outputBytes":317299} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671102,"outputBytes":317410} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":669896,"outputBytes":315871} -2022-11-21 13:51:35 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1138192} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1807418,"outputBytes":1450432} -2022-11-21 13:51:35 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1807418,"outputBytes":1893456} -2022-11-21 13:51:39 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} -2022-11-21 13:51:41 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":77,"generated":true} -2022-11-21 13:51:41 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6088,"outputBytes":2880} -2022-11-21 13:51:41 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17572,"outputBytes":9456} -2022-11-21 13:51:49 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":168,"errors":0,"warnings":0} -2022-11-21 13:51:49 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2022-11-21 13:51:49 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2022-11-21 13:51:49 INFO:  Done... -2022-11-21 13:51:49 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2022-11-21 13:51:49 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2022-11-21 13:51:49 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2022-11-21 13:51:49 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2022-11-21 13:51:49 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2022-11-21 13:51:50 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":204} -2022-11-21 13:51:50 STATE: Filter: {"input":"types/human.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.esm.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.node.d.ts"} -2022-11-21 13:51:50 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2022-11-21 13:51:50 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2022-11-21 13:51:50 STATE: Models {"folder":"./models","models":12} -2022-11-21 13:51:50 STATE: Models {"folder":"../human-models/models","models":41} -2022-11-21 13:51:50 STATE: Models {"folder":"../blazepose/model/","models":4} -2022-11-21 13:51:50 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2022-11-21 13:51:50 STATE: Models {"folder":"../efficientpose/models","models":3} -2022-11-21 13:51:50 STATE: Models {"folder":"../insightface/models","models":5} -2022-11-21 13:51:50 STATE: Models {"folder":"../movenet/models","models":3} -2022-11-21 13:51:50 STATE: Models {"folder":"../nanodet/models","models":4} -2022-11-21 13:51:51 STATE: Models: {"count":55,"totalSize":372917743} -2022-11-21 13:51:51 INFO:  Human Build complete... {"logFile":"test/build.log"} -2022-11-22 07:18:34 INFO:  @vladmandic/human version 3.0.0 -2022-11-22 07:18:34 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 -2022-11-22 07:18:34 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.0"} -2022-11-22 07:18:34 INFO:  Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2022-11-22 07:18:34 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.15","typescript":"4.9.3","typedoc":"0.23.21","eslint":"8.28.0"} -2022-11-22 07:18:34 INFO:  Build: {"profile":"development","steps":["serve","watch","compile"]} -2022-11-22 07:18:34 STATE: WebServer: {"ssl":false,"port":8000,"root":"."} -2022-11-22 07:18:34 STATE: WebServer: {"ssl":true,"port":8001,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"} -2022-11-22 07:18:34 STATE: Watch: {"locations":["src/**/*","tfjs/**/*","demo/**/*.ts"]} -2022-11-22 07:18:34 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1357} -2022-11-22 07:18:34 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1565,"outputBytes":1786} -2022-11-22 07:18:34 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":671012,"outputBytes":504714} -2022-11-22 07:18:34 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1573,"outputBytes":1810} -2022-11-22 07:18:34 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":671036,"outputBytes":504734} -2022-11-22 07:18:34 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1661,"outputBytes":1992} -2022-11-22 07:18:34 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671218,"outputBytes":504925} -2022-11-22 07:18:34 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2371,"outputBytes":923} -2022-11-22 07:18:34 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670149,"outputBytes":507406} -2022-11-22 07:18:35 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2371,"outputBytes":1138192} -2022-11-22 07:18:35 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1807418,"outputBytes":1450432} -2022-11-22 07:18:35 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1807418,"outputBytes":1893463} -2022-11-22 07:18:35 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6088,"outputBytes":4160} -2022-11-22 07:18:35 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17572,"outputBytes":13919} -2022-11-22 07:18:35 INFO:  Listening... -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"text/html","size":2042,"url":"/test/test-browser-iife.html","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"text/javascript","size":1450432,"url":"/dist/human.js","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"text/javascript","size":4583,"url":"/test/test-browser-iife.js","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"font/woff2","size":181500,"url":"/assets/lato-light.woff2","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"application/octet-stream","size":2023432,"url":"/models/handlandmark-lite.bin","remote":"::1"} -2022-11-22 07:19:59 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"image/jpeg","size":32320,"url":"/samples/in/ai-body.jpg","remote":"::1"} -2022-11-22 07:20:00 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"image/jpeg","size":32320,"url":"/samples/in/ai-body.jpg","remote":"::1"} -2022-11-22 07:20:11 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"image/jpeg","size":32320,"url":"/samples/in/ai-body.jpg","remote":"::1"} -2022-11-22 07:20:19 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"text/html","size":2010,"url":"/test/test-browser-esm.html","remote":"::1"} -2022-11-22 07:20:19 DATA:  HTTPS: 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STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":671475,"outputBytes":505576} -2022-11-22 09:21:49 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1573,"outputBytes":1810} -2022-11-22 09:21:49 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":671499,"outputBytes":505596} -2022-11-22 09:21:49 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1661,"outputBytes":1992} -2022-11-22 09:21:49 STATE: Compile: 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-2022-11-22 09:42:14 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670541,"outputBytes":508115} -2022-11-22 09:42:14 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2371,"outputBytes":1138192} -2022-11-22 09:42:14 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1807810,"outputBytes":1450757} -2022-11-22 09:42:14 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1807810,"outputBytes":1894181} -2022-11-22 09:42:14 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6082,"outputBytes":4154} -2022-11-22 09:42:14 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17572,"outputBytes":13919} -2022-11-22 09:54:32 INFO:  Watch: {"event":"modify","input":"src/util/env.ts"} -2022-11-22 09:54:32 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1357} -2022-11-22 09:54:32 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1565,"outputBytes":1786} -2022-11-22 09:54:32 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":671404,"outputBytes":505423} -2022-11-22 09:54:32 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1573,"outputBytes":1810} -2022-11-22 09:54:32 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":671428,"outputBytes":505443} -2022-11-22 09:54:32 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1661,"outputBytes":1992} -2022-11-22 09:54:32 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671610,"outputBytes":505634} -2022-11-22 09:54:32 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2371,"outputBytes":923} -2022-11-22 09:54:32 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670541,"outputBytes":508115} -2022-11-22 09:54:33 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2371,"outputBytes":1138192} -2022-11-22 09:54:33 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1807810,"outputBytes":1450757} -2022-11-22 09:54:33 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1807810,"outputBytes":1894181} -2022-11-22 09:54:33 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6082,"outputBytes":4154} -2022-11-22 09:54:33 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17572,"outputBytes":13919} +2022-11-22 10:34:25 DATA:  Build {"name":"@vladmandic/human","version":"3.0.1"} +2022-11-22 10:34:25 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.1"} +2022-11-22 10:34:25 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2022-11-22 10:34:25 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.15","typescript":"4.9.3","typedoc":"0.23.21","eslint":"8.28.0"} +2022-11-22 10:34:25 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2022-11-22 10:34:25 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2022-11-22 10:34:25 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} +2022-11-22 10:34:25 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} +2022-11-22 10:34:25 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":670542,"outputBytes":317619} +2022-11-22 10:34:25 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} +2022-11-22 10:34:25 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":670546,"outputBytes":317623} +2022-11-22 10:34:25 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} +2022-11-22 10:34:25 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":671494,"outputBytes":317734} +2022-11-22 10:34:25 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} +2022-11-22 10:34:25 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":670288,"outputBytes":316195} +2022-11-22 10:34:26 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1138192} +2022-11-22 10:34:26 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1807810,"outputBytes":1450757} +2022-11-22 10:34:26 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1807810,"outputBytes":1894174} +2022-11-22 10:34:30 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} +2022-11-22 10:34:31 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":77,"generated":true} +2022-11-22 10:34:31 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6082,"outputBytes":2872} +2022-11-22 10:34:31 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17572,"outputBytes":9456} +2022-11-22 10:34:40 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":169,"errors":0,"warnings":0} +2022-11-22 10:34:40 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2022-11-22 10:34:40 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2022-11-22 10:34:40 INFO:  Done... +2022-11-22 10:34:40 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2022-11-22 10:34:40 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2022-11-22 10:34:40 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2022-11-22 10:34:40 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2022-11-22 10:34:40 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2022-11-22 10:34:41 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":204} +2022-11-22 10:34:41 STATE: Filter: {"input":"types/human.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.esm.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.node.d.ts"} +2022-11-22 10:34:41 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2022-11-22 10:34:41 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2022-11-22 10:34:41 STATE: Models {"folder":"./models","models":12} +2022-11-22 10:34:41 STATE: Models {"folder":"../human-models/models","models":41} +2022-11-22 10:34:41 STATE: Models {"folder":"../blazepose/model/","models":4} +2022-11-22 10:34:41 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2022-11-22 10:34:41 STATE: Models {"folder":"../efficientpose/models","models":3} +2022-11-22 10:34:41 STATE: Models {"folder":"../insightface/models","models":5} +2022-11-22 10:34:41 STATE: Models {"folder":"../movenet/models","models":3} +2022-11-22 10:34:41 STATE: Models {"folder":"../nanodet/models","models":4} +2022-11-22 10:34:42 STATE: Models: {"count":55,"totalSize":372917743} +2022-11-22 10:34:42 INFO:  Human Build complete... {"logFile":"test/build.log"} diff --git a/test/test.log b/test/test.log index 94489222..b5126470 100644 --- a/test/test.log +++ b/test/test.log @@ -1,1000 +1,1001 @@ -2022-11-22 10:27:49 INFO:  @vladmandic/human version 3.0.0 -2022-11-22 10:27:49 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 -2022-11-22 10:27:49 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]}] -2022-11-22 10:27:49 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start -2022-11-22 10:27:50 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start -2022-11-22 10:27:51 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start -2022-11-22 10:27:51 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start -2022-11-22 10:27:52 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start -2022-11-22 10:27:53 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start -2022-11-22 10:27:55 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start -2022-11-22 10:28:06 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start -2022-11-22 10:28:07 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start -2022-11-22 10:28:49 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] -2022-11-22 10:28:49 INFO:  -2022-11-22 10:28:49 INFO:  test-node-load.js start -2022-11-22 10:28:49 INFO:  test-node-load.js load start {"human":"3.0.0","tf":"4.1.0","progress":0} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":29,"progress":0.12865823126207815} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":62,"progress":0.4726970548784964} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":75,"progress":0.68355493315821} -2022-11-22 10:28:49 STATE: test-node-load.js passed {"progress":1} -2022-11-22 10:28:49 INFO:  test-node-load.js load final {"progress":1} -2022-11-22 10:28:49 DATA:  test-node-load.js load interval {"elapsed":446,"progress":1} -2022-11-22 10:28:49 INFO:  -2022-11-22 10:28:49 INFO:  test-node-gear.js start -2022-11-22 10:28:49 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] -2022-11-22 10:28:50 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg -2022-11-22 10:28:50 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} -2022-11-22 10:28:50 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg -2022-11-22 10:28:50 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} -2022-11-22 10:28:51 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg -2022-11-22 10:28:51 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} -2022-11-22 10:28:51 INFO:  -2022-11-22 10:28:51 INFO:  test-backend-node.js start -2022-11-22 10:28:51 INFO:  test-backend-node.js test: configuration validation -2022-11-22 10:28:51 STATE: test-backend-node.js passed: configuration default validation [] -2022-11-22 10:28:51 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-11-22 10:28:51 INFO:  test-backend-node.js test: model load -2022-11-22 10:28:51 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2022-11-22 10:28:51 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-11-22 10:28:51 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-11-22 10:28:51 INFO:  test-backend-node.js test: warmup -2022-11-22 10:28:51 STATE: test-backend-node.js passed: create human -2022-11-22 10:28:51 INFO:  test-backend-node.js human version: 3.0.0 -2022-11-22 10:28:51 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 -2022-11-22 10:28:51 INFO:  test-backend-node.js tfjs version: 4.1.0 -2022-11-22 10:28:51 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} -2022-11-22 10:28:51 STATE: test-backend-node.js passed: set backend: tensorflow -2022-11-22 10:28:51 STATE: test-backend-node.js tensors 1785 -2022-11-22 10:28:51 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 -2022-11-22 10:28:51 STATE: test-backend-node.js passed: load models 11 -2022-11-22 10:28:51 STATE: test-backend-node.js passed: warmup: none default -2022-11-22 10:28:51 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-11-22 10:28:51 DATA:  test-backend-node.js result: performance: load: null total: null -2022-11-22 10:28:51 STATE: test-backend-node.js passed: warmup none result match -2022-11-22 10:28:51 STATE: test-backend-node.js event: image -2022-11-22 10:28:51 STATE: test-backend-node.js event: detect -2022-11-22 10:28:51 STATE: test-backend-node.js event: warmup -2022-11-22 10:28:51 STATE: test-backend-node.js passed: warmup: face default -2022-11-22 10:28:51 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2022-11-22 10:28:51 DATA:  test-backend-node.js result: performance: load: null total: 419 -2022-11-22 10:28:51 STATE: test-backend-node.js passed: warmup face result match -2022-11-22 10:28:51 STATE: test-backend-node.js event: image -2022-11-22 10:28:52 STATE: test-backend-node.js event: detect -2022-11-22 10:28:52 STATE: test-backend-node.js event: warmup -2022-11-22 10:28:52 STATE: test-backend-node.js passed: warmup: body default -2022-11-22 10:28:52 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:52 DATA:  test-backend-node.js result: performance: load: null total: 336 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: warmup body result match -2022-11-22 10:28:52 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-11-22 10:28:52 INFO:  test-backend-node.js test: details verification -2022-11-22 10:28:52 STATE: test-backend-node.js start default -2022-11-22 10:28:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:52 STATE: test-backend-node.js event: image -2022-11-22 10:28:52 STATE: test-backend-node.js event: detect -2022-11-22 10:28:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default -2022-11-22 10:28:52 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:52 DATA:  test-backend-node.js result: performance: load: null total: 319 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face length 1 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face score 1 0.93 1 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face arrays 4 478 1024 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details face liveness 0.83 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details body length 1 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details body 0.92 17 6 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details hand length 1 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details hand 0.51 0.73 point -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details hand arrays 21 5 7 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details gesture length 7 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details object length 1 -2022-11-22 10:28:52 STATE: test-backend-node.js passed: details object 0.72 person -2022-11-22 10:28:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2022-11-22 10:28:52 STATE: test-backend-node.js event: image -2022-11-22 10:28:53 STATE: test-backend-node.js event: detect -2022-11-22 10:28:53 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-11-22 10:28:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2022-11-22 10:28:53 STATE: test-backend-node.js event: image -2022-11-22 10:28:53 STATE: test-backend-node.js event: detect -2022-11-22 10:28:53 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-11-22 10:28:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:53 STATE: test-backend-node.js event: image -2022-11-22 10:28:54 STATE: test-backend-node.js event: detect -2022-11-22 10:28:54 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-11-22 10:28:54 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:54 STATE: test-backend-node.js event: image -2022-11-22 10:28:54 STATE: test-backend-node.js event: detect -2022-11-22 10:28:54 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-11-22 10:28:54 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-11-22 10:28:54 STATE: test-backend-node.js event: image -2022-11-22 10:28:55 STATE: test-backend-node.js event: detect -2022-11-22 10:28:55 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-11-22 10:28:55 INFO:  test-backend-node.js test default -2022-11-22 10:28:55 STATE: test-backend-node.js start async -2022-11-22 10:28:55 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:55 STATE: test-backend-node.js event: image -2022-11-22 10:28:55 STATE: test-backend-node.js event: detect -2022-11-22 10:28:55 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async -2022-11-22 10:28:55 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:55 DATA:  test-backend-node.js result: performance: load: null total: 354 -2022-11-22 10:28:55 STATE: test-backend-node.js passed: default result face match 1 female 0.97 -2022-11-22 10:28:55 INFO:  test-backend-node.js test sync -2022-11-22 10:28:55 STATE: test-backend-node.js start sync -2022-11-22 10:28:56 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:56 STATE: test-backend-node.js event: image -2022-11-22 10:28:56 STATE: test-backend-node.js event: detect -2022-11-22 10:28:56 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync -2022-11-22 10:28:56 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:56 DATA:  test-backend-node.js result: performance: load: null total: 323 -2022-11-22 10:28:56 STATE: test-backend-node.js passed: default sync 1 female 0.97 -2022-11-22 10:28:56 INFO:  test-backend-node.js test: image process -2022-11-22 10:28:56 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:28:56 STATE: test-backend-node.js passed: image input null [1,256,256,3] -2022-11-22 10:28:56 INFO:  test-backend-node.js test: image null -2022-11-22 10:28:56 STATE: test-backend-node.js passed: invalid input could not convert input to tensor -2022-11-22 10:28:56 INFO:  test-backend-node.js test face similarity -2022-11-22 10:28:56 STATE: test-backend-node.js start face similarity -2022-11-22 10:28:56 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:28:56 STATE: test-backend-node.js event: image -2022-11-22 10:28:56 STATE: test-backend-node.js event: detect -2022-11-22 10:28:56 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity -2022-11-22 10:28:56 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-11-22 10:28:56 DATA:  test-backend-node.js result: performance: load: null total: 296 -2022-11-22 10:28:56 STATE: test-backend-node.js start face similarity -2022-11-22 10:28:56 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:56 STATE: test-backend-node.js event: image -2022-11-22 10:28:57 STATE: test-backend-node.js event: detect -2022-11-22 10:28:57 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity -2022-11-22 10:28:57 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:57 DATA:  test-backend-node.js result: performance: load: null total: 298 -2022-11-22 10:28:57 STATE: test-backend-node.js start face similarity -2022-11-22 10:28:57 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:28:57 STATE: test-backend-node.js event: image -2022-11-22 10:28:57 STATE: test-backend-node.js event: detect -2022-11-22 10:28:57 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-11-22 10:28:57 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-11-22 10:28:57 DATA:  test-backend-node.js result: performance: load: null total: 278 -2022-11-22 10:28:57 STATE: test-backend-node.js passed: face descriptor -2022-11-22 10:28:57 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} -2022-11-22 10:28:57 INFO:  test-backend-node.js test object -2022-11-22 10:28:57 STATE: test-backend-node.js start object -2022-11-22 10:28:57 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:57 STATE: test-backend-node.js event: image -2022-11-22 10:28:58 STATE: test-backend-node.js event: detect -2022-11-22 10:28:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:28:58 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:58 DATA:  test-backend-node.js result: performance: load: null total: 303 -2022-11-22 10:28:58 STATE: test-backend-node.js passed: centernet -2022-11-22 10:28:58 STATE: test-backend-node.js start object -2022-11-22 10:28:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:59 STATE: test-backend-node.js event: image -2022-11-22 10:28:59 STATE: test-backend-node.js event: detect -2022-11-22 10:28:59 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:28:59 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:28:59 DATA:  test-backend-node.js result: performance: load: null total: 312 -2022-11-22 10:28:59 STATE: test-backend-node.js passed: nanodet -2022-11-22 10:28:59 INFO:  test-backend-node.js test sensitive -2022-11-22 10:28:59 STATE: test-backend-node.js start sensitive -2022-11-22 10:28:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:28:59 STATE: test-backend-node.js event: image -2022-11-22 10:28:59 STATE: test-backend-node.js event: detect -2022-11-22 10:28:59 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive -2022-11-22 10:28:59 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:28:59 DATA:  test-backend-node.js result: performance: load: null total: 259 -2022-11-22 10:28:59 STATE: test-backend-node.js passed: sensitive result match -2022-11-22 10:28:59 STATE: test-backend-node.js passed: sensitive face result match -2022-11-22 10:28:59 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2022-11-22 10:28:59 STATE: test-backend-node.js passed: sensitive body result match -2022-11-22 10:28:59 STATE: test-backend-node.js passed: sensitive hand result match -2022-11-22 10:28:59 INFO:  test-backend-node.js test body -2022-11-22 10:28:59 STATE: test-backend-node.js start blazepose -2022-11-22 10:29:01 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:01 STATE: test-backend-node.js event: image -2022-11-22 10:29:02 STATE: test-backend-node.js event: detect -2022-11-22 10:29:02 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose -2022-11-22 10:29:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-11-22 10:29:02 DATA:  test-backend-node.js result: performance: load: null total: 348 -2022-11-22 10:29:02 STATE: test-backend-node.js passed: blazepose -2022-11-22 10:29:02 STATE: test-backend-node.js start efficientpose -2022-11-22 10:29:03 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:03 STATE: test-backend-node.js event: image -2022-11-22 10:29:03 STATE: test-backend-node.js event: detect -2022-11-22 10:29:03 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-11-22 10:29:03 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-11-22 10:29:03 DATA:  test-backend-node.js result: performance: load: null total: 323 -2022-11-22 10:29:03 STATE: test-backend-node.js passed: efficientpose -2022-11-22 10:29:03 STATE: test-backend-node.js start posenet -2022-11-22 10:29:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:04 STATE: test-backend-node.js event: image -2022-11-22 10:29:04 STATE: test-backend-node.js event: detect -2022-11-22 10:29:04 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet -2022-11-22 10:29:04 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-11-22 10:29:04 DATA:  test-backend-node.js result: performance: load: null total: 254 -2022-11-22 10:29:04 STATE: test-backend-node.js passed: posenet -2022-11-22 10:29:04 STATE: test-backend-node.js start movenet -2022-11-22 10:29:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:04 STATE: test-backend-node.js event: image -2022-11-22 10:29:05 STATE: test-backend-node.js event: detect -2022-11-22 10:29:05 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet -2022-11-22 10:29:05 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:05 DATA:  test-backend-node.js result: performance: load: null total: 256 -2022-11-22 10:29:05 STATE: test-backend-node.js passed: movenet -2022-11-22 10:29:05 INFO:  test-backend-node.js test face matching -2022-11-22 10:29:05 STATE: test-backend-node.js passed: face database 40 -2022-11-22 10:29:05 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} -2022-11-22 10:29:05 INFO:  test-backend-node.js test face similarity alternative -2022-11-22 10:29:05 STATE: test-backend-node.js start face embeddings -2022-11-22 10:29:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:05 STATE: test-backend-node.js event: image -2022-11-22 10:29:06 STATE: test-backend-node.js event: detect -2022-11-22 10:29:06 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:06 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:06 DATA:  test-backend-node.js result: performance: load: null total: 291 -2022-11-22 10:29:06 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} -2022-11-22 10:29:06 STATE: test-backend-node.js start face embeddings -2022-11-22 10:29:07 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:07 STATE: test-backend-node.js event: image -2022-11-22 10:29:07 STATE: test-backend-node.js event: detect -2022-11-22 10:29:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:07 DATA:  test-backend-node.js result: performance: load: null total: 307 -2022-11-22 10:29:07 STATE: test-backend-node.js passed: insightface {"embedding":512} -2022-11-22 10:29:07 INFO:  test-backend-node.js test face attention -2022-11-22 10:29:07 STATE: test-backend-node.js start face attention -2022-11-22 10:29:08 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:08 STATE: test-backend-node.js event: image -2022-11-22 10:29:08 STATE: test-backend-node.js event: detect -2022-11-22 10:29:08 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention -2022-11-22 10:29:08 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:08 DATA:  test-backend-node.js result: performance: load: null total: 300 -2022-11-22 10:29:08 STATE: test-backend-node.js passed: face attention -2022-11-22 10:29:08 INFO:  test-backend-node.js test detectors -2022-11-22 10:29:08 STATE: test-backend-node.js start detectors -2022-11-22 10:29:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:08 STATE: test-backend-node.js event: image -2022-11-22 10:29:08 STATE: test-backend-node.js event: detect -2022-11-22 10:29:08 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors -2022-11-22 10:29:08 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:08 DATA:  test-backend-node.js result: performance: load: null total: 179 -2022-11-22 10:29:08 STATE: test-backend-node.js passed: detector result face match -2022-11-22 10:29:08 STATE: test-backend-node.js passed: detector result hand match -2022-11-22 10:29:08 INFO:  test-backend-node.js test: multi-instance -2022-11-22 10:29:08 STATE: test-backend-node.js start multi instance -2022-11-22 10:29:09 STATE: test-backend-node.js event: image -2022-11-22 10:29:09 STATE: test-backend-node.js event: detect -2022-11-22 10:29:09 STATE: test-backend-node.js passed: detect: random multi instance -2022-11-22 10:29:09 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-11-22 10:29:09 DATA:  test-backend-node.js result: performance: load: null total: 132 -2022-11-22 10:29:09 INFO:  test-backend-node.js test: first instance -2022-11-22 10:29:09 STATE: test-backend-node.js start multi instance -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:29:09 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:09 DATA:  test-backend-node.js result: performance: load: null total: 154 -2022-11-22 10:29:09 INFO:  test-backend-node.js test: second instance -2022-11-22 10:29:09 STATE: test-backend-node.js start multi instance -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:29:09 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:09 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:09 DATA:  test-backend-node.js result: performance: load: null total: 145 -2022-11-22 10:29:09 INFO:  test-backend-node.js test: concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js start concurrent -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:10 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:29:10 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:29:10 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} -2022-11-22 10:29:10 STATE: test-backend-node.js event: image -2022-11-22 10:29:10 STATE: test-backend-node.js event: image -2022-11-22 10:29:10 STATE: test-backend-node.js event: image -2022-11-22 10:29:11 STATE: test-backend-node.js event: detect -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js event: detect -2022-11-22 10:29:11 STATE: test-backend-node.js event: detect -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:11 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:11 DATA:  test-backend-node.js result: performance: load: null total: 1330 -2022-11-22 10:29:11 INFO:  test-backend-node.js test: monkey-patch -2022-11-22 10:29:11 STATE: test-backend-node.js event: image -2022-11-22 10:29:11 STATE: test-backend-node.js event: detect -2022-11-22 10:29:11 STATE: test-backend-node.js passed: monkey patch -2022-11-22 10:29:12 STATE: test-backend-node.js passed: segmentation [262144] -2022-11-22 10:29:12 STATE: test-backend-node.js passeed: equal usage -2022-11-22 10:29:12 INFO:  test-backend-node.js test: input compare -2022-11-22 10:29:12 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} -2022-11-22 10:29:12 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 -2022-11-22 10:29:12 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} -2022-11-22 10:29:12 INFO:  test-backend-node.js tensors 4441 -2022-11-22 10:29:12 INFO:  test-backend-node.js test complete: 21405 ms -2022-11-22 10:29:12 INFO:  -2022-11-22 10:29:12 INFO:  test-backend-node-gpu.js start -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js test: configuration validation -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: configuration default validation [] -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js test: model load -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js test: warmup -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: create human -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js human version: 3.0.0 -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js tfjs version: 4.1.0 -2022-11-22 10:29:13 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: set backend: tensorflow -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js tensors 1785 -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: load models 11 -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: warmup: none default -2022-11-22 10:29:13 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-11-22 10:29:13 DATA:  test-backend-node-gpu.js result: performance: load: null total: null -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js passed: warmup none result match -2022-11-22 10:29:13 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js event: warmup -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js passed: warmup: face default -2022-11-22 10:29:15 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} -2022-11-22 10:29:15 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1910 -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js passed: warmup face result match -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js event: warmup -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js passed: warmup: body default -2022-11-22 10:29:15 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:15 DATA:  test-backend-node-gpu.js result: performance: load: null total: 154 -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js passed: warmup body result match -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-11-22 10:29:15 INFO:  test-backend-node-gpu.js test: details verification -2022-11-22 10:29:15 STATE: test-backend-node-gpu.js start default -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default -2022-11-22 10:29:16 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:16 DATA:  test-backend-node-gpu.js result: performance: load: null total: 143 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face length 1 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details body length 1 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details hand length 1 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details gesture length 7 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details object length 1 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: details object 0.72 person -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:16 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-11-22 10:29:17 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:17 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:17 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:17 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-11-22 10:29:18 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:18 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-11-22 10:29:19 INFO:  test-backend-node-gpu.js test default -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js start async -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async -2022-11-22 10:29:19 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:19 DATA:  test-backend-node-gpu.js result: performance: load: null total: 147 -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 -2022-11-22 10:29:19 INFO:  test-backend-node-gpu.js test sync -2022-11-22 10:29:19 STATE: test-backend-node-gpu.js start sync -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: performance: load: null total: 162 -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 -2022-11-22 10:29:20 INFO:  test-backend-node-gpu.js test: image process -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] -2022-11-22 10:29:20 INFO:  test-backend-node-gpu.js test: image null -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor -2022-11-22 10:29:20 INFO:  test-backend-node-gpu.js test face similarity -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js start face similarity -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: performance: load: null total: 144 -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js start face similarity -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:20 DATA:  test-backend-node-gpu.js result: performance: load: null total: 152 -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js start face similarity -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:20 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-11-22 10:29:21 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-11-22 10:29:21 DATA:  test-backend-node-gpu.js result: performance: load: null total: 136 -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: face descriptor -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475002983522097,0.5570879556505012],"descriptors":[1024,1024,1024]} -2022-11-22 10:29:21 INFO:  test-backend-node-gpu.js test object -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js start object -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:29:21 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:21 DATA:  test-backend-node-gpu.js result: performance: load: null total: 160 -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js passed: centernet -2022-11-22 10:29:21 STATE: test-backend-node-gpu.js start object -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:29:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 660 -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js passed: nanodet -2022-11-22 10:29:22 INFO:  test-backend-node-gpu.js test sensitive -2022-11-22 10:29:22 STATE: test-backend-node-gpu.js start sensitive -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive -2022-11-22 10:29:23 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:23 DATA:  test-backend-node-gpu.js result: performance: load: null total: 133 -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: sensitive result match -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: sensitive face result match -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: sensitive body result match -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js passed: sensitive hand result match -2022-11-22 10:29:23 INFO:  test-backend-node-gpu.js test body -2022-11-22 10:29:23 STATE: test-backend-node-gpu.js start blazepose -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose -2022-11-22 10:29:25 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-11-22 10:29:25 DATA:  test-backend-node-gpu.js result: performance: load: null total: 293 -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js passed: blazepose -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js start efficientpose -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:25 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:27 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:27 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-11-22 10:29:27 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-11-22 10:29:27 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1384 -2022-11-22 10:29:27 STATE: test-backend-node-gpu.js passed: efficientpose -2022-11-22 10:29:27 STATE: test-backend-node-gpu.js start posenet -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet -2022-11-22 10:29:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-11-22 10:29:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 159 -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: posenet -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js start movenet -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet -2022-11-22 10:29:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 137 -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: movenet -2022-11-22 10:29:28 INFO:  test-backend-node-gpu.js test face matching -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: face database 40 -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829338043932047}} {"second":{"index":4,"similarity":0.5002928781584631}} {"third":{"index":4,"similarity":0.5402934771672516}} -2022-11-22 10:29:28 INFO:  test-backend-node-gpu.js test face similarity alternative -2022-11-22 10:29:28 STATE: test-backend-node-gpu.js start face embeddings -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:29 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:29 DATA:  test-backend-node-gpu.js result: performance: load: null total: 176 -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} -2022-11-22 10:29:29 STATE: test-backend-node-gpu.js start face embeddings -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:30 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:30 DATA:  test-backend-node-gpu.js result: performance: load: null total: 193 -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} -2022-11-22 10:29:30 INFO:  test-backend-node-gpu.js test face attention -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js start face attention -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention -2022-11-22 10:29:30 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:30 DATA:  test-backend-node-gpu.js result: performance: load: null total: 245 -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: face attention -2022-11-22 10:29:30 INFO:  test-backend-node-gpu.js test detectors -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js start detectors -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:30 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 113 -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detector result face match -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detector result hand match -2022-11-22 10:29:31 INFO:  test-backend-node-gpu.js test: multi-instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start multi instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detect: random multi instance -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 65 -2022-11-22 10:29:31 INFO:  test-backend-node-gpu.js test: first instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start multi instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 87 -2022-11-22 10:29:31 INFO:  test-backend-node-gpu.js test: second instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start multi instance -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:31 DATA:  test-backend-node-gpu.js result: performance: load: null total: 85 -2022-11-22 10:29:31 INFO:  test-backend-node-gpu.js test: concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js start concurrent -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:31 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:32 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 708 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:33 DATA:  test-backend-node-gpu.js result: performance: load: null total: 709 -2022-11-22 10:29:33 INFO:  test-backend-node-gpu.js test: monkey-patch -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js event: image -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js event: detect -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: monkey patch -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: segmentation [262144] -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passeed: equal usage -2022-11-22 10:29:33 INFO:  test-backend-node-gpu.js test: input compare -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} -2022-11-22 10:29:33 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 -2022-11-22 10:29:33 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} -2022-11-22 10:29:33 INFO:  test-backend-node-gpu.js tensors 4441 -2022-11-22 10:29:33 INFO:  test-backend-node-gpu.js test complete: 20579 ms -2022-11-22 10:29:34 INFO:  -2022-11-22 10:29:34 INFO:  test-backend-node-wasm.js start -2022-11-22 10:29:34 DATA:  test-backend-node-wasm.js stdout: 2022-11-22 10:29:34 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.1.0/dist/ -2022-11-22 10:29:34 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ -2022-11-22 10:29:34 INFO:  test-backend-node-wasm.js test: configuration validation -2022-11-22 10:29:34 STATE: test-backend-node-wasm.js passed: configuration default validation [] -2022-11-22 10:29:34 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] -2022-11-22 10:29:34 INFO:  test-backend-node-wasm.js test: model load -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"}] -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js test: warmup -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: create human -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js human version: 3.0.0 -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js tfjs version: 4.1.0 -2022-11-22 10:29:36 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: set backend: wasm -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js tensors 1785 -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: load models 11 -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: warmup: none default -2022-11-22 10:29:36 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} -2022-11-22 10:29:36 DATA:  test-backend-node-wasm.js result: performance: load: null total: null -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js passed: warmup none result match -2022-11-22 10:29:36 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js event: warmup -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js passed: warmup: face default -2022-11-22 10:29:37 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-11-22 10:29:37 DATA:  test-backend-node-wasm.js result: performance: load: null total: 489 -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js passed: warmup face result match -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js event: warmup -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js passed: warmup: body default -2022-11-22 10:29:37 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:37 DATA:  test-backend-node-wasm.js result: performance: load: null total: 351 -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js passed: warmup body result match -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} -2022-11-22 10:29:37 INFO:  test-backend-node-wasm.js test: details verification -2022-11-22 10:29:37 STATE: test-backend-node-wasm.js start default -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default -2022-11-22 10:29:38 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:38 DATA:  test-backend-node-wasm.js result: performance: load: null total: 325 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face length 1 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details body length 1 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details hand length 1 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details gesture length 7 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details object length 1 -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: details object 0.72 person -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:38 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:39 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 -2022-11-22 10:29:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 -2022-11-22 10:29:41 INFO:  test-backend-node-wasm.js test default -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js start async -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:41 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async -2022-11-22 10:29:42 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:42 DATA:  test-backend-node-wasm.js result: performance: load: null total: 411 -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 -2022-11-22 10:29:42 INFO:  test-backend-node-wasm.js test sync -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js start sync -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync -2022-11-22 10:29:42 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:42 DATA:  test-backend-node-wasm.js result: performance: load: null total: 465 -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 -2022-11-22 10:29:42 INFO:  test-backend-node-wasm.js test: image process -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] -2022-11-22 10:29:42 INFO:  test-backend-node-wasm.js test: image null -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor -2022-11-22 10:29:42 INFO:  test-backend-node-wasm.js test face similarity -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js start face similarity -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:42 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:43 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:43 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity -2022-11-22 10:29:43 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} -2022-11-22 10:29:43 DATA:  test-backend-node-wasm.js result: performance: load: null total: 423 -2022-11-22 10:29:43 STATE: test-backend-node-wasm.js start face similarity -2022-11-22 10:29:43 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:43 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity -2022-11-22 10:29:44 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:44 DATA:  test-backend-node-wasm.js result: performance: load: null total: 416 -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js start face similarity -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity -2022-11-22 10:29:44 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} -2022-11-22 10:29:44 DATA:  test-backend-node-wasm.js result: performance: load: null total: 390 -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: face descriptor -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} -2022-11-22 10:29:44 INFO:  test-backend-node-wasm.js test object -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js start object -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:44 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:45 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:45 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:29:45 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} -2022-11-22 10:29:45 DATA:  test-backend-node-wasm.js result: performance: load: null total: 412 -2022-11-22 10:29:45 STATE: test-backend-node-wasm.js passed: centernet -2022-11-22 10:29:45 STATE: test-backend-node-wasm.js start object -2022-11-22 10:29:46 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} -2022-11-22 10:29:46 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:46 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object -2022-11-22 10:29:47 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:47 DATA:  test-backend-node-wasm.js result: performance: load: null total: 268 -2022-11-22 10:29:47 ERROR: test-backend-node-wasm.js failed: nanodet [] -2022-11-22 10:29:47 INFO:  test-backend-node-wasm.js test sensitive -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js start sensitive -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive -2022-11-22 10:29:47 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:47 DATA:  test-backend-node-wasm.js result: performance: load: null total: 280 -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: sensitive result match -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: sensitive face result match -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: sensitive body result match -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js passed: sensitive hand result match -2022-11-22 10:29:47 INFO:  test-backend-node-wasm.js test body -2022-11-22 10:29:47 STATE: test-backend-node-wasm.js start blazepose -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose -2022-11-22 10:29:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.99,"keypoints":39} -2022-11-22 10:29:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 399 -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js passed: blazepose -2022-11-22 10:29:51 STATE: test-backend-node-wasm.js start efficientpose -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose -2022-11-22 10:29:52 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.75,"keypoints":13} -2022-11-22 10:29:52 DATA:  test-backend-node-wasm.js result: performance: load: null total: 656 -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js passed: efficientpose -2022-11-22 10:29:52 STATE: test-backend-node-wasm.js start posenet -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet -2022-11-22 10:29:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.96,"keypoints":16} -2022-11-22 10:29:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 272 -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js passed: posenet -2022-11-22 10:29:53 STATE: test-backend-node-wasm.js start movenet -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet -2022-11-22 10:29:54 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:54 DATA:  test-backend-node-wasm.js result: performance: load: null total: 232 -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js passed: movenet -2022-11-22 10:29:54 INFO:  test-backend-node-wasm.js test face matching -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js passed: face database 40 -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} -2022-11-22 10:29:54 INFO:  test-backend-node-wasm.js test face similarity alternative -2022-11-22 10:29:54 STATE: test-backend-node-wasm.js start face embeddings -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:55 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:55 DATA:  test-backend-node-wasm.js result: performance: load: null total: 248 -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} -2022-11-22 10:29:55 STATE: test-backend-node-wasm.js start face embeddings -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings -2022-11-22 10:29:56 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:56 DATA:  test-backend-node-wasm.js result: performance: load: null total: 269 -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} -2022-11-22 10:29:56 INFO:  test-backend-node-wasm.js test face attention -2022-11-22 10:29:56 STATE: test-backend-node-wasm.js start face attention -2022-11-22 10:29:57 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 110 -2022-11-22 10:29:57 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} -2022-11-22 10:29:57 INFO:  test-backend-node-wasm.js test detectors -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js start detectors -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 116 -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detector result face match -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detector result hand match -2022-11-22 10:29:57 INFO:  test-backend-node-wasm.js test: multi-instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js start multi instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detect: random multi instance -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 97 -2022-11-22 10:29:57 INFO:  test-backend-node-wasm.js test: first instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js start multi instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:57 DATA:  test-backend-node-wasm.js result: performance: load: null total: 110 -2022-11-22 10:29:57 INFO:  test-backend-node-wasm.js test: second instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js start multi instance -2022-11-22 10:29:57 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance -2022-11-22 10:29:58 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:29:58 DATA:  test-backend-node-wasm.js result: performance: load: null total: 110 -2022-11-22 10:29:58 INFO:  test-backend-node-wasm.js test: concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js start concurrent -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} -2022-11-22 10:29:59 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:59 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:29:59 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} -2022-11-22 10:30:00 DATA:  test-backend-node-wasm.js result: performance: load: null total: 993 -2022-11-22 10:30:00 INFO:  test-backend-node-wasm.js test: monkey-patch -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js event: image -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js event: detect -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: monkey patch -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: segmentation [262144] -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passeed: equal usage -2022-11-22 10:30:00 INFO:  test-backend-node-wasm.js test: input compare -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} -2022-11-22 10:30:00 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 -2022-11-22 10:30:00 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} -2022-11-22 10:30:00 INFO:  test-backend-node-wasm.js tensors 4443 -2022-11-22 10:30:00 INFO:  test-backend-node-wasm.js test complete: 26158 ms -2022-11-22 10:30:00 STATE: all tests complete -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"test-node-gear.js","passed":3,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} -2022-11-22 10:30:00 INFO:  status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} -2022-11-22 10:30:00 INFO:  failures {"count":2} -2022-11-22 10:30:00 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} -2022-11-22 10:30:00 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} +2022-11-22 10:35:11 INFO:  @vladmandic/human version 3.0.1 +2022-11-22 10:35:11 INFO:  User: vlado Platform: linux Arch: x64 Node: v19.1.0 +2022-11-22 10:35:11 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]},{"cmd":"../demo/nodejs/node-bench.js","args":[]}] +2022-11-22 10:35:11 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start +2022-11-22 10:35:12 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start +2022-11-22 10:35:13 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start +2022-11-22 10:35:14 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start +2022-11-22 10:35:14 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start +2022-11-22 10:35:15 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start +2022-11-22 10:35:17 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start +2022-11-22 10:35:29 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start +2022-11-22 10:35:30 INFO:  {"cmd":"../demo/nodejs/node-bench.js","args":[]} start +2022-11-22 10:35:46 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] +2022-11-22 10:35:46 INFO:  +2022-11-22 10:35:46 INFO:  test-node-load.js start +2022-11-22 10:35:46 INFO:  test-node-load.js load start {"human":"3.0.1","tf":"4.1.0","progress":0} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":25,"progress":0.12865823126207815} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":37,"progress":0.33826045252170456} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":60,"progress":0.4726970548784964} +2022-11-22 10:35:46 DATA:  test-node-load.js load interval {"elapsed":73,"progress":0.68355493315821} +2022-11-22 10:35:46 STATE: test-node-load.js passed {"progress":1} +2022-11-22 10:35:46 INFO:  test-node-load.js load final {"progress":1} +2022-11-22 10:35:47 DATA:  test-node-load.js load interval {"elapsed":432,"progress":1} +2022-11-22 10:35:47 INFO:  +2022-11-22 10:35:47 INFO:  test-node-gear.js start +2022-11-22 10:35:47 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] +2022-11-22 10:35:48 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg +2022-11-22 10:35:48 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} +2022-11-22 10:35:48 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg +2022-11-22 10:35:48 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} +2022-11-22 10:35:48 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg +2022-11-22 10:35:48 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} +2022-11-22 10:35:48 INFO:  +2022-11-22 10:35:48 INFO:  test-backend-node.js start +2022-11-22 10:35:48 INFO:  test-backend-node.js test: configuration validation +2022-11-22 10:35:48 STATE: test-backend-node.js passed: configuration default validation [] +2022-11-22 10:35:48 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2022-11-22 10:35:48 INFO:  test-backend-node.js test: model load +2022-11-22 10:35:48 STATE: test-backend-node.js passed: models loaded 11 11 [{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2022-11-22 10:35:48 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2022-11-22 10:35:48 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2022-11-22 10:35:48 INFO:  test-backend-node.js test: warmup +2022-11-22 10:35:48 STATE: test-backend-node.js passed: create human +2022-11-22 10:35:48 INFO:  test-backend-node.js human version: 3.0.1 +2022-11-22 10:35:48 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v19.1.0 +2022-11-22 10:35:48 INFO:  test-backend-node.js tfjs version: 4.1.0 +2022-11-22 10:35:48 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} +2022-11-22 10:35:48 STATE: test-backend-node.js passed: set backend: tensorflow +2022-11-22 10:35:48 STATE: test-backend-node.js tensors 1785 +2022-11-22 10:35:48 STATE: test-backend-node.js result: defined models: 11 loaded models: 11 +2022-11-22 10:35:48 STATE: test-backend-node.js passed: load models 11 +2022-11-22 10:35:48 STATE: test-backend-node.js passed: warmup: none default +2022-11-22 10:35:48 DATA:  test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2022-11-22 10:35:48 DATA:  test-backend-node.js result: performance: load: null total: null +2022-11-22 10:35:48 STATE: test-backend-node.js passed: warmup none result match +2022-11-22 10:35:48 STATE: test-backend-node.js event: image +2022-11-22 10:35:49 STATE: test-backend-node.js event: detect +2022-11-22 10:35:49 STATE: test-backend-node.js event: warmup +2022-11-22 10:35:49 STATE: test-backend-node.js passed: warmup: face default +2022-11-22 10:35:49 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2022-11-22 10:35:49 DATA:  test-backend-node.js result: performance: load: null total: 419 +2022-11-22 10:35:49 STATE: test-backend-node.js passed: warmup face result match +2022-11-22 10:35:49 STATE: test-backend-node.js event: image +2022-11-22 10:35:49 STATE: test-backend-node.js event: detect +2022-11-22 10:35:49 STATE: test-backend-node.js event: warmup +2022-11-22 10:35:49 STATE: test-backend-node.js passed: warmup: body default +2022-11-22 10:35:49 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:49 DATA:  test-backend-node.js result: performance: load: null total: 350 +2022-11-22 10:35:49 STATE: test-backend-node.js passed: warmup body result match +2022-11-22 10:35:49 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2022-11-22 10:35:49 INFO:  test-backend-node.js test: details verification +2022-11-22 10:35:49 STATE: test-backend-node.js start default +2022-11-22 10:35:49 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:49 STATE: test-backend-node.js event: image +2022-11-22 10:35:50 STATE: test-backend-node.js event: detect +2022-11-22 10:35:50 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default +2022-11-22 10:35:50 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:50 DATA:  test-backend-node.js result: performance: load: null total: 318 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face length 1 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face score 1 0.93 1 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 2.34 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face arrays 4 478 1024 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details face liveness 0.83 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details body length 1 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details body 0.92 17 6 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details hand length 1 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details hand 0.51 0.73 point +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details hand arrays 21 5 7 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details gesture length 7 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details object length 1 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: details object 0.72 person +2022-11-22 10:35:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2022-11-22 10:35:50 STATE: test-backend-node.js event: image +2022-11-22 10:35:50 STATE: test-backend-node.js event: detect +2022-11-22 10:35:50 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2022-11-22 10:35:50 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2022-11-22 10:35:50 STATE: test-backend-node.js event: image +2022-11-22 10:35:51 STATE: test-backend-node.js event: detect +2022-11-22 10:35:51 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 +2022-11-22 10:35:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:51 STATE: test-backend-node.js event: image +2022-11-22 10:35:51 STATE: test-backend-node.js event: detect +2022-11-22 10:35:51 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2022-11-22 10:35:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:51 STATE: test-backend-node.js event: image +2022-11-22 10:35:52 STATE: test-backend-node.js event: detect +2022-11-22 10:35:52 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 +2022-11-22 10:35:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2022-11-22 10:35:52 STATE: test-backend-node.js event: image +2022-11-22 10:35:52 STATE: test-backend-node.js event: detect +2022-11-22 10:35:52 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2022-11-22 10:35:52 INFO:  test-backend-node.js test default +2022-11-22 10:35:52 STATE: test-backend-node.js start async +2022-11-22 10:35:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:52 STATE: test-backend-node.js event: image +2022-11-22 10:35:53 STATE: test-backend-node.js event: detect +2022-11-22 10:35:53 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async +2022-11-22 10:35:53 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:53 DATA:  test-backend-node.js result: performance: load: null total: 296 +2022-11-22 10:35:53 STATE: test-backend-node.js passed: default result face match 1 female 0.97 +2022-11-22 10:35:53 INFO:  test-backend-node.js test sync +2022-11-22 10:35:53 STATE: test-backend-node.js start sync +2022-11-22 10:35:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:53 STATE: test-backend-node.js event: image +2022-11-22 10:35:53 STATE: test-backend-node.js event: detect +2022-11-22 10:35:53 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync +2022-11-22 10:35:53 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:53 DATA:  test-backend-node.js result: performance: load: null total: 307 +2022-11-22 10:35:53 STATE: test-backend-node.js passed: default sync 1 female 0.97 +2022-11-22 10:35:53 INFO:  test-backend-node.js test: image process +2022-11-22 10:35:53 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:35:53 STATE: test-backend-node.js passed: image input null [1,256,256,3] +2022-11-22 10:35:53 INFO:  test-backend-node.js test: image null +2022-11-22 10:35:53 STATE: test-backend-node.js passed: invalid input could not convert input to tensor +2022-11-22 10:35:53 INFO:  test-backend-node.js test face similarity +2022-11-22 10:35:53 STATE: test-backend-node.js start face similarity +2022-11-22 10:35:53 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:35:53 STATE: test-backend-node.js event: image +2022-11-22 10:35:53 STATE: test-backend-node.js event: detect +2022-11-22 10:35:53 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity +2022-11-22 10:35:53 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2022-11-22 10:35:53 DATA:  test-backend-node.js result: performance: load: null total: 286 +2022-11-22 10:35:53 STATE: test-backend-node.js start face similarity +2022-11-22 10:35:54 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:54 STATE: test-backend-node.js event: image +2022-11-22 10:35:54 STATE: test-backend-node.js event: detect +2022-11-22 10:35:54 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity +2022-11-22 10:35:54 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:54 DATA:  test-backend-node.js result: performance: load: null total: 294 +2022-11-22 10:35:54 STATE: test-backend-node.js start face similarity +2022-11-22 10:35:54 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:35:54 STATE: test-backend-node.js event: image +2022-11-22 10:35:54 STATE: test-backend-node.js event: detect +2022-11-22 10:35:54 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity +2022-11-22 10:35:54 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2022-11-22 10:35:54 DATA:  test-backend-node.js result: performance: load: null total: 270 +2022-11-22 10:35:54 STATE: test-backend-node.js passed: face descriptor +2022-11-22 10:35:54 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727441595492046,0.556793560189727],"descriptors":[1024,1024,1024]} +2022-11-22 10:35:54 INFO:  test-backend-node.js test object +2022-11-22 10:35:54 STATE: test-backend-node.js start object +2022-11-22 10:35:54 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:54 STATE: test-backend-node.js event: image +2022-11-22 10:35:55 STATE: test-backend-node.js event: detect +2022-11-22 10:35:55 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:35:55 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:55 DATA:  test-backend-node.js result: performance: load: null total: 293 +2022-11-22 10:35:55 STATE: test-backend-node.js passed: centernet +2022-11-22 10:35:55 STATE: test-backend-node.js start object +2022-11-22 10:35:56 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:56 STATE: test-backend-node.js event: image +2022-11-22 10:35:56 STATE: test-backend-node.js event: detect +2022-11-22 10:35:56 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:35:56 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:35:56 DATA:  test-backend-node.js result: performance: load: null total: 337 +2022-11-22 10:35:56 STATE: test-backend-node.js passed: nanodet +2022-11-22 10:35:56 INFO:  test-backend-node.js test sensitive +2022-11-22 10:35:56 STATE: test-backend-node.js start sensitive +2022-11-22 10:35:56 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:56 STATE: test-backend-node.js event: image +2022-11-22 10:35:57 STATE: test-backend-node.js event: detect +2022-11-22 10:35:57 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive +2022-11-22 10:35:57 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:35:57 DATA:  test-backend-node.js result: performance: load: null total: 259 +2022-11-22 10:35:57 STATE: test-backend-node.js passed: sensitive result match +2022-11-22 10:35:57 STATE: test-backend-node.js passed: sensitive face result match +2022-11-22 10:35:57 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2022-11-22 10:35:57 STATE: test-backend-node.js passed: sensitive body result match +2022-11-22 10:35:57 STATE: test-backend-node.js passed: sensitive hand result match +2022-11-22 10:35:57 INFO:  test-backend-node.js test body +2022-11-22 10:35:57 STATE: test-backend-node.js start blazepose +2022-11-22 10:35:59 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:35:59 STATE: test-backend-node.js event: image +2022-11-22 10:35:59 STATE: test-backend-node.js event: detect +2022-11-22 10:35:59 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose +2022-11-22 10:35:59 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2022-11-22 10:35:59 DATA:  test-backend-node.js result: performance: load: null total: 335 +2022-11-22 10:35:59 STATE: test-backend-node.js passed: blazepose +2022-11-22 10:35:59 STATE: test-backend-node.js start efficientpose +2022-11-22 10:36:00 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:00 STATE: test-backend-node.js event: image +2022-11-22 10:36:00 STATE: test-backend-node.js event: detect +2022-11-22 10:36:00 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose +2022-11-22 10:36:00 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2022-11-22 10:36:00 DATA:  test-backend-node.js result: performance: load: null total: 322 +2022-11-22 10:36:00 STATE: test-backend-node.js passed: efficientpose +2022-11-22 10:36:00 STATE: test-backend-node.js start posenet +2022-11-22 10:36:01 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:01 STATE: test-backend-node.js event: image +2022-11-22 10:36:01 STATE: test-backend-node.js event: detect +2022-11-22 10:36:01 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet +2022-11-22 10:36:01 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2022-11-22 10:36:01 DATA:  test-backend-node.js result: performance: load: null total: 268 +2022-11-22 10:36:01 STATE: test-backend-node.js passed: posenet +2022-11-22 10:36:01 STATE: test-backend-node.js start movenet +2022-11-22 10:36:01 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:01 STATE: test-backend-node.js event: image +2022-11-22 10:36:01 STATE: test-backend-node.js event: detect +2022-11-22 10:36:01 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet +2022-11-22 10:36:01 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:01 DATA:  test-backend-node.js result: performance: load: null total: 257 +2022-11-22 10:36:01 STATE: test-backend-node.js passed: movenet +2022-11-22 10:36:01 INFO:  test-backend-node.js test face matching +2022-11-22 10:36:01 STATE: test-backend-node.js passed: face database 40 +2022-11-22 10:36:01 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852251220577}} {"second":{"index":4,"similarity":0.5002052057057577}} {"third":{"index":4,"similarity":0.5401588464054732}} +2022-11-22 10:36:01 INFO:  test-backend-node.js test face similarity alternative +2022-11-22 10:36:01 STATE: test-backend-node.js start face embeddings +2022-11-22 10:36:02 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:02 STATE: test-backend-node.js event: image +2022-11-22 10:36:02 STATE: test-backend-node.js event: detect +2022-11-22 10:36:02 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:02 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:02 DATA:  test-backend-node.js result: performance: load: null total: 276 +2022-11-22 10:36:02 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} +2022-11-22 10:36:02 STATE: test-backend-node.js start face embeddings +2022-11-22 10:36:03 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:03 STATE: test-backend-node.js event: image +2022-11-22 10:36:03 STATE: test-backend-node.js event: detect +2022-11-22 10:36:03 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:03 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:03 DATA:  test-backend-node.js result: performance: load: null total: 298 +2022-11-22 10:36:03 STATE: test-backend-node.js passed: insightface {"embedding":512} +2022-11-22 10:36:03 INFO:  test-backend-node.js test face attention +2022-11-22 10:36:03 STATE: test-backend-node.js start face attention +2022-11-22 10:36:04 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:04 STATE: test-backend-node.js event: image +2022-11-22 10:36:04 STATE: test-backend-node.js event: detect +2022-11-22 10:36:04 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention +2022-11-22 10:36:04 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:04 DATA:  test-backend-node.js result: performance: load: null total: 275 +2022-11-22 10:36:04 STATE: test-backend-node.js passed: face attention +2022-11-22 10:36:04 INFO:  test-backend-node.js test detectors +2022-11-22 10:36:04 STATE: test-backend-node.js start detectors +2022-11-22 10:36:04 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:04 STATE: test-backend-node.js event: image +2022-11-22 10:36:04 STATE: test-backend-node.js event: detect +2022-11-22 10:36:04 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors +2022-11-22 10:36:04 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:04 DATA:  test-backend-node.js result: performance: load: null total: 178 +2022-11-22 10:36:04 STATE: test-backend-node.js passed: detector result face match +2022-11-22 10:36:04 STATE: test-backend-node.js passed: detector result hand match +2022-11-22 10:36:04 INFO:  test-backend-node.js test: multi-instance +2022-11-22 10:36:04 STATE: test-backend-node.js start multi instance +2022-11-22 10:36:04 STATE: test-backend-node.js event: image +2022-11-22 10:36:05 STATE: test-backend-node.js event: detect +2022-11-22 10:36:05 STATE: test-backend-node.js passed: detect: random multi instance +2022-11-22 10:36:05 DATA:  test-backend-node.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2022-11-22 10:36:05 DATA:  test-backend-node.js result: performance: load: null total: 129 +2022-11-22 10:36:05 INFO:  test-backend-node.js test: first instance +2022-11-22 10:36:05 STATE: test-backend-node.js start multi instance +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:05 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:05 DATA:  test-backend-node.js result: performance: load: null total: 155 +2022-11-22 10:36:05 INFO:  test-backend-node.js test: second instance +2022-11-22 10:36:05 STATE: test-backend-node.js start multi instance +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:05 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:05 DATA:  test-backend-node.js result: performance: load: null total: 143 +2022-11-22 10:36:05 INFO:  test-backend-node.js test: concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js start concurrent +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:05 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:06 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:36:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:36:06 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} +2022-11-22 10:36:06 STATE: test-backend-node.js event: image +2022-11-22 10:36:06 STATE: test-backend-node.js event: image +2022-11-22 10:36:06 STATE: test-backend-node.js event: image +2022-11-22 10:36:07 STATE: test-backend-node.js event: detect +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1295 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1295 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1295 +2022-11-22 10:36:07 STATE: test-backend-node.js event: detect +2022-11-22 10:36:07 STATE: test-backend-node.js event: detect +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1296 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1296 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1295 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1296 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1296 +2022-11-22 10:36:07 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:07 DATA:  test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:07 DATA:  test-backend-node.js result: performance: load: null total: 1295 +2022-11-22 10:36:07 INFO:  test-backend-node.js test: monkey-patch +2022-11-22 10:36:07 STATE: test-backend-node.js event: image +2022-11-22 10:36:07 STATE: test-backend-node.js event: detect +2022-11-22 10:36:07 STATE: test-backend-node.js passed: monkey patch +2022-11-22 10:36:08 STATE: test-backend-node.js passed: segmentation [262144] +2022-11-22 10:36:08 STATE: test-backend-node.js passeed: equal usage +2022-11-22 10:36:08 INFO:  test-backend-node.js test: input compare +2022-11-22 10:36:08 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} +2022-11-22 10:36:08 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 +2022-11-22 10:36:08 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} +2022-11-22 10:36:08 INFO:  test-backend-node.js tensors 4441 +2022-11-22 10:36:08 INFO:  test-backend-node.js test complete: 20130 ms +2022-11-22 10:36:08 INFO:  +2022-11-22 10:36:08 INFO:  test-backend-node-gpu.js start +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js test: configuration validation +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: configuration default validation [] +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js test: model load +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"file://models/blazeface.json"},{"name":"emotion","loaded":true,"size":820516,"url":"file://models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"file://models/antispoof.json"},{"name":"liveness","loaded":true,"size":592976,"url":"file://models/liveness.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"file://models/facemesh.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"file://models/handlandmark-lite.json"},{"name":"iris","loaded":true,"size":2599092,"url":"file://models/iris.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"file://models/handtrack.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"file://models/movenet-lightning.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"file://models/centernet.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"file://models/faceres.json"}] +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js test: warmup +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: create human +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js human version: 3.0.1 +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v19.1.0 +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js tfjs version: 4.1.0 +2022-11-22 10:36:09 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.9.1","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: set backend: tensorflow +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js tensors 1785 +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js result: defined models: 11 loaded models: 11 +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: load models 11 +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: warmup: none default +2022-11-22 10:36:09 DATA:  test-backend-node-gpu.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2022-11-22 10:36:09 DATA:  test-backend-node-gpu.js result: performance: load: null total: null +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js passed: warmup none result match +2022-11-22 10:36:09 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:11 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:11 STATE: test-backend-node-gpu.js event: warmup +2022-11-22 10:36:11 STATE: test-backend-node-gpu.js passed: warmup: face default +2022-11-22 10:36:11 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} +2022-11-22 10:36:11 DATA:  test-backend-node-gpu.js result: performance: load: null total: 1901 +2022-11-22 10:36:11 STATE: test-backend-node-gpu.js passed: warmup face result match +2022-11-22 10:36:11 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js event: warmup +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: warmup: body default +2022-11-22 10:36:12 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:12 DATA:  test-backend-node-gpu.js result: performance: load: null total: 199 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: warmup body result match +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2022-11-22 10:36:12 INFO:  test-backend-node-gpu.js test: details verification +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js start default +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default +2022-11-22 10:36:12 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:12 DATA:  test-backend-node-gpu.js result: performance: load: null total: 179 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face length 1 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 2.34 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details body length 1 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details hand length 1 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details gesture length 7 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details object length 1 +2022-11-22 10:36:12 STATE: test-backend-node-gpu.js passed: details object 0.72 person +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:13 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2022-11-22 10:36:14 INFO:  test-backend-node-gpu.js test default +2022-11-22 10:36:14 STATE: test-backend-node-gpu.js start async +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: performance: load: null total: 171 +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 +2022-11-22 10:36:15 INFO:  test-backend-node-gpu.js test sync +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js start sync +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: performance: load: null total: 146 +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 +2022-11-22 10:36:15 INFO:  test-backend-node-gpu.js test: image process +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] +2022-11-22 10:36:15 INFO:  test-backend-node-gpu.js test: image null +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor +2022-11-22 10:36:15 INFO:  test-backend-node-gpu.js test face similarity +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js start face similarity +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2022-11-22 10:36:15 DATA:  test-backend-node-gpu.js result: performance: load: null total: 158 +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js start face similarity +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:15 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity +2022-11-22 10:36:16 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:16 DATA:  test-backend-node-gpu.js result: performance: load: null total: 138 +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js start face similarity +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity +2022-11-22 10:36:16 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2022-11-22 10:36:16 DATA:  test-backend-node-gpu.js result: performance: load: null total: 160 +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: face descriptor +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.4475002983522097,0.5570879556505012],"descriptors":[1024,1024,1024]} +2022-11-22 10:36:16 INFO:  test-backend-node-gpu.js test object +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js start object +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:16 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:17 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:17 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:36:17 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:17 DATA:  test-backend-node-gpu.js result: performance: load: null total: 167 +2022-11-22 10:36:17 STATE: test-backend-node-gpu.js passed: centernet +2022-11-22 10:36:17 STATE: test-backend-node-gpu.js start object +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:36:18 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:18 DATA:  test-backend-node-gpu.js result: performance: load: null total: 703 +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js passed: nanodet +2022-11-22 10:36:18 INFO:  test-backend-node-gpu.js test sensitive +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js start sensitive +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:18 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive +2022-11-22 10:36:19 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:19 DATA:  test-backend-node-gpu.js result: performance: load: null total: 136 +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: sensitive result match +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: sensitive face result match +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: sensitive body result match +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js passed: sensitive hand result match +2022-11-22 10:36:19 INFO:  test-backend-node-gpu.js test body +2022-11-22 10:36:19 STATE: test-backend-node-gpu.js start blazepose +2022-11-22 10:36:20 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:20 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose +2022-11-22 10:36:21 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} +2022-11-22 10:36:21 DATA:  test-backend-node-gpu.js result: performance: load: null total: 464 +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js passed: blazepose +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js start efficientpose +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:21 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:22 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:22 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose +2022-11-22 10:36:22 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} +2022-11-22 10:36:22 DATA:  test-backend-node-gpu.js result: performance: load: null total: 921 +2022-11-22 10:36:22 STATE: test-backend-node-gpu.js passed: efficientpose +2022-11-22 10:36:22 STATE: test-backend-node-gpu.js start posenet +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet +2022-11-22 10:36:23 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} +2022-11-22 10:36:23 DATA:  test-backend-node-gpu.js result: performance: load: null total: 127 +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js passed: posenet +2022-11-22 10:36:23 STATE: test-backend-node-gpu.js start movenet +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet +2022-11-22 10:36:24 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:24 DATA:  test-backend-node-gpu.js result: performance: load: null total: 138 +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js passed: movenet +2022-11-22 10:36:24 INFO:  test-backend-node-gpu.js test face matching +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js passed: face database 40 +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7829338043932047}} {"second":{"index":4,"similarity":0.5002928781584631}} {"third":{"index":4,"similarity":0.5402934771672516}} +2022-11-22 10:36:24 INFO:  test-backend-node-gpu.js test face similarity alternative +2022-11-22 10:36:24 STATE: test-backend-node-gpu.js start face embeddings +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:25 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:25 DATA:  test-backend-node-gpu.js result: performance: load: null total: 204 +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js start face embeddings +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:25 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 233 +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} +2022-11-22 10:36:26 INFO:  test-backend-node-gpu.js test face attention +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js start face attention +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 234 +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: face attention +2022-11-22 10:36:26 INFO:  test-backend-node-gpu.js test detectors +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js start detectors +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:26 DATA:  test-backend-node-gpu.js result: performance: load: null total: 109 +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: detector result face match +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js passed: detector result hand match +2022-11-22 10:36:26 INFO:  test-backend-node-gpu.js test: multi-instance +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js start multi instance +2022-11-22 10:36:26 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: detect: random multi instance +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: performance: load: null total: 67 +2022-11-22 10:36:27 INFO:  test-backend-node-gpu.js test: first instance +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start multi instance +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: performance: load: null total: 82 +2022-11-22 10:36:27 INFO:  test-backend-node-gpu.js test: second instance +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start multi instance +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:27 DATA:  test-backend-node-gpu.js result: performance: load: null total: 86 +2022-11-22 10:36:27 INFO:  test-backend-node-gpu.js test: concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js start concurrent +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:28 DATA:  test-backend-node-gpu.js result: performance: load: null total: 745 +2022-11-22 10:36:28 INFO:  test-backend-node-gpu.js test: monkey-patch +2022-11-22 10:36:28 STATE: test-backend-node-gpu.js event: image +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js event: detect +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passed: monkey patch +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passed: segmentation [262144] +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passeed: equal usage +2022-11-22 10:36:29 INFO:  test-backend-node-gpu.js test: input compare +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} +2022-11-22 10:36:29 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 +2022-11-22 10:36:29 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} +2022-11-22 10:36:29 INFO:  test-backend-node-gpu.js tensors 4441 +2022-11-22 10:36:29 INFO:  test-backend-node-gpu.js test complete: 20448 ms +2022-11-22 10:36:30 INFO:  +2022-11-22 10:36:30 INFO:  test-backend-node-wasm.js start +2022-11-22 10:36:30 DATA:  test-backend-node-wasm.js stdout: 2022-11-22 10:36:30 INFO:  { supported: true, backend: true, simd: undefined, multithread: undefined } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@4.1.0/dist/ +2022-11-22 10:36:30 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ +2022-11-22 10:36:30 INFO:  test-backend-node-wasm.js test: configuration validation +2022-11-22 10:36:30 STATE: test-backend-node-wasm.js passed: configuration default validation [] +2022-11-22 10:36:30 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] +2022-11-22 10:36:30 INFO:  test-backend-node-wasm.js test: model load +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: models loaded 11 11 [{"name":"blazeface","loaded":true,"size":538928,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"liveness","loaded":true,"size":592976,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"facemesh","loaded":true,"size":1477958,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"emotion","loaded":true,"size":820516,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"antispoof","loaded":true,"size":853098,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"},{"name":"centernet","loaded":true,"size":4030290,"url":"https://vladmandic.github.io/human-models/models/centernet.json"},{"name":"iris","loaded":true,"size":2599092,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"handskeleton","loaded":true,"size":0,"url":"https://vladmandic.github.io/human-models/models/handlandmark-lite.json"},{"name":"movenet","loaded":true,"size":4650216,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"faceres","loaded":true,"size":6978814,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"handtrack","loaded":true,"size":2964837,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"}] +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":56431460}} +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":56431460,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js test: warmup +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: create human +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js human version: 3.0.1 +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v19.1.0 +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js tfjs version: 4.1.0 +2022-11-22 10:36:32 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v19.1.0","backends":["wasm"],"initial":false,"tfjs":{"version":"4.1.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":128} +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: set backend: wasm +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js tensors 1785 +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js result: defined models: 11 loaded models: 11 +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: load models 11 +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: warmup: none default +2022-11-22 10:36:32 DATA:  test-backend-node-wasm.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} +2022-11-22 10:36:32 DATA:  test-backend-node-wasm.js result: performance: load: null total: null +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js passed: warmup none result match +2022-11-22 10:36:32 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js event: warmup +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js passed: warmup: face default +2022-11-22 10:36:33 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2022-11-22 10:36:33 DATA:  test-backend-node-wasm.js result: performance: load: null total: 459 +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js passed: warmup face result match +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js event: warmup +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js passed: warmup: body default +2022-11-22 10:36:33 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:33 DATA:  test-backend-node-wasm.js result: performance: load: null total: 327 +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js passed: warmup body result match +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"thumb up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} +2022-11-22 10:36:33 INFO:  test-backend-node-wasm.js test: details verification +2022-11-22 10:36:33 STATE: test-backend-node-wasm.js start default +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default +2022-11-22 10:36:34 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:34 DATA:  test-backend-node-wasm.js result: performance: load: null total: 320 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face length 1 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 2.34 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details body length 1 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details hand length 1 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details gesture length 7 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details object length 1 +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: details object 0.72 person +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:34 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:35 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} +2022-11-22 10:36:36 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 +2022-11-22 10:36:37 INFO:  test-backend-node-wasm.js test default +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js start async +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async +2022-11-22 10:36:37 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:37 DATA:  test-backend-node-wasm.js result: performance: load: null total: 316 +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 +2022-11-22 10:36:37 INFO:  test-backend-node-wasm.js test sync +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js start sync +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:37 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: performance: load: null total: 310 +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 +2022-11-22 10:36:38 INFO:  test-backend-node-wasm.js test: image process +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] +2022-11-22 10:36:38 INFO:  test-backend-node-wasm.js test: image null +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor +2022-11-22 10:36:38 INFO:  test-backend-node-wasm.js test face similarity +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js start face similarity +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: performance: load: null total: 292 +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js start face similarity +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:38 DATA:  test-backend-node-wasm.js result: performance: load: null total: 308 +2022-11-22 10:36:38 STATE: test-backend-node-wasm.js start face similarity +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity +2022-11-22 10:36:39 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} +2022-11-22 10:36:39 DATA:  test-backend-node-wasm.js result: performance: load: null total: 284 +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: face descriptor +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} +2022-11-22 10:36:39 INFO:  test-backend-node-wasm.js test object +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js start object +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:36:39 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} +2022-11-22 10:36:39 DATA:  test-backend-node-wasm.js result: performance: load: null total: 305 +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js passed: centernet +2022-11-22 10:36:39 STATE: test-backend-node-wasm.js start object +2022-11-22 10:36:40 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object +2022-11-22 10:36:41 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 8 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:41 DATA:  test-backend-node-wasm.js result: performance: load: null total: 198 +2022-11-22 10:36:41 ERROR: test-backend-node-wasm.js failed: nanodet [] +2022-11-22 10:36:41 INFO:  test-backend-node-wasm.js test sensitive +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js start sensitive +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive +2022-11-22 10:36:41 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:41 DATA:  test-backend-node-wasm.js result: performance: load: null total: 217 +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: sensitive result match +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: sensitive face result match +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: sensitive body result match +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js passed: sensitive hand result match +2022-11-22 10:36:41 INFO:  test-backend-node-wasm.js test body +2022-11-22 10:36:41 STATE: test-backend-node-wasm.js start blazepose +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose +2022-11-22 10:36:44 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.99,"keypoints":39} +2022-11-22 10:36:44 DATA:  test-backend-node-wasm.js result: performance: load: null total: 387 +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js passed: blazepose +2022-11-22 10:36:44 STATE: test-backend-node-wasm.js start efficientpose +2022-11-22 10:36:45 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:45 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:46 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:46 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose +2022-11-22 10:36:46 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.75,"keypoints":13} +2022-11-22 10:36:46 DATA:  test-backend-node-wasm.js result: performance: load: null total: 644 +2022-11-22 10:36:46 STATE: test-backend-node-wasm.js passed: efficientpose +2022-11-22 10:36:46 STATE: test-backend-node-wasm.js start posenet +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet +2022-11-22 10:36:47 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.96,"keypoints":16} +2022-11-22 10:36:47 DATA:  test-backend-node-wasm.js result: performance: load: null total: 268 +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: posenet +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js start movenet +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet +2022-11-22 10:36:47 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:47 DATA:  test-backend-node-wasm.js result: performance: load: null total: 221 +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: movenet +2022-11-22 10:36:47 INFO:  test-backend-node-wasm.js test face matching +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: face database 40 +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} +2022-11-22 10:36:47 INFO:  test-backend-node-wasm.js test face similarity alternative +2022-11-22 10:36:47 STATE: test-backend-node-wasm.js start face embeddings +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:48 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:48 DATA:  test-backend-node-wasm.js result: performance: load: null total: 217 +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} +2022-11-22 10:36:48 STATE: test-backend-node-wasm.js start face embeddings +2022-11-22 10:36:49 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:49 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: performance: load: null total: 259 +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} +2022-11-22 10:36:50 INFO:  test-backend-node-wasm.js test face attention +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js start face attention +2022-11-22 10:36:50 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: performance: load: null total: 104 +2022-11-22 10:36:50 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} +2022-11-22 10:36:50 INFO:  test-backend-node-wasm.js test detectors +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js start detectors +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:50 DATA:  test-backend-node-wasm.js result: performance: load: null total: 124 +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: detector result face match +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js passed: detector result hand match +2022-11-22 10:36:50 INFO:  test-backend-node-wasm.js test: multi-instance +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js start multi instance +2022-11-22 10:36:50 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: detect: random multi instance +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 100 +2022-11-22 10:36:51 INFO:  test-backend-node-wasm.js test: first instance +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start multi instance +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 107 +2022-11-22 10:36:51 INFO:  test-backend-node-wasm.js test: second instance +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start multi instance +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:51 DATA:  test-backend-node-wasm.js result: performance: load: null total: 107 +2022-11-22 10:36:51 INFO:  test-backend-node-wasm.js test: concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js start concurrent +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:51 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:52 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 953 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 953 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 953 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} +2022-11-22 10:36:53 DATA:  test-backend-node-wasm.js result: performance: load: null total: 954 +2022-11-22 10:36:53 INFO:  test-backend-node-wasm.js test: monkey-patch +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js event: image +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js event: detect +2022-11-22 10:36:53 STATE: test-backend-node-wasm.js passed: monkey patch +2022-11-22 10:36:54 STATE: test-backend-node-wasm.js passed: segmentation [262144] +2022-11-22 10:36:54 STATE: test-backend-node-wasm.js passeed: equal usage +2022-11-22 10:36:54 INFO:  test-backend-node-wasm.js test: input compare +2022-11-22 10:36:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} +2022-11-22 10:36:54 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} +2022-11-22 10:36:54 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 +2022-11-22 10:36:54 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} +2022-11-22 10:36:54 INFO:  test-backend-node-wasm.js tensors 4443 +2022-11-22 10:36:54 INFO:  test-backend-node-wasm.js test complete: 23460 ms +2022-11-22 10:36:54 STATE: all tests complete +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"../demo/nodejs/node-bench.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"test-node-load.js","passed":1,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"test-node-gear.js","passed":3,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"test-backend-node.js","passed":125,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} +2022-11-22 10:36:54 INFO:  status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} +2022-11-22 10:36:54 INFO:  failures {"count":2} +2022-11-22 10:36:54 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} +2022-11-22 10:36:54 WARN:  failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} diff --git a/typedoc/assets/search.js b/typedoc/assets/search.js index a3971f5c..cfe60e8e 100644 --- a/typedoc/assets/search.js +++ b/typedoc/assets/search.js @@ -1 +1 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,\"comment\":{}}],[\"validatemodel\",{\"_index\":51,\"name\":{\"58\":{}},\"comment\":{}}],[\"validatemodels\",{\"_index\":425,\"name\":{\"566\":{}},\"comment\":{}}],[\"values\",{\"_index\":222,\"name\":{\"240\":{},\"245\":{}},\"comment\":{}}],[\"variable\",{\"_index\":91,\"name\":{\"107\":{}},\"comment\":{}}],[\"vendor\",{\"_index\":306,\"name\":{\"347\":{}},\"comment\":{}}],[\"version\",{\"_index\":2,\"name\":{\"2\":{},\"327\":{},\"332\":{},\"344\":{}},\"comment\":{}}],[\"video\",{\"_index\":34,\"name\":{\"34\":{}},\"comment\":{}}],[\"vintage\",{\"_index\":414,\"name\":{\"550\":{}},\"comment\":{}}],[\"warmup\",{\"_index\":30,\"name\":{\"30\":{},\"563\":{}},\"comment\":{}}],[\"warmupenum\",{\"_index\":363,\"name\":{\"420\":{}},\"comment\":{}}],[\"wasm\",{\"_index\":298,\"name\":{\"334\":{}},\"comment\":{}}],[\"wasmpath\",{\"_index\":420,\"name\":{\"559\":{}},\"comment\":{}}],[\"wasmplatformfetch\",{\"_index\":421,\"name\":{\"560\":{}},\"comment\":{}}],[\"webcam\",{\"_index\":26,\"name\":{\"26\":{},\"378\":{}},\"comment\":{}}],[\"webcamconfig\",{\"_index\":347,\"name\":{\"397\":{}},\"comment\":{}}],[\"webgl\",{\"_index\":303,\"name\":{\"340\":{}},\"comment\":{}}],[\"webgpu\",{\"_index\":307,\"name\":{\"348\":{}},\"comment\":{}}],[\"weights\",{\"_index\":237,\"name\":{\"260\":{}},\"comment\":{}}],[\"where\",{\"_index\":228,\"name\":{\"249\":{}},\"comment\":{}}],[\"width\",{\"_index\":340,\"name\":{\"390\":{},\"402\":{},\"537\":{},\"661\":{}},\"comment\":{}}],[\"worker\",{\"_index\":287,\"name\":{\"319\":{}},\"comment\":{}}],[\"zeroslike\",{\"_index\":229,\"name\":{\"250\":{}},\"comment\":{}}]],\"pipeline\":[]}}"); \ No newline at end of file diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html index 1db0171c..1d84d6d4 100644 --- a/typedoc/classes/Env.html +++ b/typedoc/classes/Env.html @@ -1,4 +1,4 @@ -Env | @vladmandic/human - v3.0.0
+Env | @vladmandic/human - v3.0.1
  • Preparing search index...
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+
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    • @vladmandic/human - v3.0.1

    Class Env

    @@ -35,10 +35,7 @@

    Constructors

    Properties

    -
    Canvas -Image -ImageData -agent +
    agent backends browser cpu @@ -57,6 +54,12 @@

    Properties

    worker
    +

    Accessors

    +
    Canvas +Image +ImageData +
    +

    Methods

    updateBackend updateCPU @@ -70,30 +73,9 @@

    constructor

    Returns Env

    +
  • Defined in src/util/env.ts:101

    • Properties

    -
    -

    Canvas

    -
    Canvas: undefined
    -

    MonkeyPatch for Canvas

    -
    -
    -

    Image

    -
    Image: undefined
    -

    MonkeyPatch for Image

    -
    -
    -

    ImageData

    -
    ImageData: undefined
    -

    MonkeyPatch for ImageData

    -

    agent

    agent: string = ''
    @@ -268,6 +250,62 @@

    workersrc/util/env.ts:11

    +

    Accessors

    +
    +

    Canvas

    +
      +
    • get Canvas(): undefined
      • +
    • +

      Returns undefined

      • +
    • set Canvas(val: undefined): void
      • +
    • +
      +

      Parameters

      +
        +
      • +
        val: undefined
      +

      Returns void

    +
    +

    Image

    +
      +
    • get Image(): undefined
      • +
    • +

      Returns undefined

      • +
    • set Image(val: undefined): void
      • +
    • +
      +

      Parameters

      +
        +
      • +
        val: undefined
      +

      Returns void

    +
    +

    ImageData

    +
      +
    • get ImageData(): undefined
      • +
    • +

      Returns undefined

      • +
    • set ImageData(val: undefined): void
      • +
    • +
      +

      Parameters

      +
        +
      • +
        val: undefined
      +

      Returns void

    +

    Methods

    updateBackend

    @@ -278,7 +316,7 @@

    updateBackendReturns Promise<void>

    +
  • Defined in src/util/env.ts:126

    • updateCPU

    +
  • Defined in src/util/env.ts:167
    • @@ -305,7 +343,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    @vladmandic/human - v3.0.0 +
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    • @vladmandic/human - v3.0.1

    Class GraphModel<ModelURL>

    @@ -449,7 +449,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    • @vladmandic/human - v3.0.1

    Class Human

    @@ -558,7 +558,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    • @vladmandic/human - v3.0.1

    Class Tensor<R>

    @@ -3378,7 +3378,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Class WebCam

    @@ -249,7 +249,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    • @vladmandic/human - v3.0.1

    Class Models

    @@ -148,7 +148,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    • @vladmandic/human - v3.0.1

    Enumeration Rank

    @@ -6,13 +6,13 @@
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    Function all

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    @vladmandic/human - v3.0.0 +
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    • @vladmandic/human - v3.0.1

    Function body

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    @vladmandic/human - v3.0.0 +
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    • @vladmandic/human - v3.0.1

    Function canvas

    @@ -48,7 +48,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    @vladmandic/human - v3.0.0 +
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    • @vladmandic/human - v3.0.1

    Function face

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Function gesture

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Function hand

    @@ -50,7 +50,7 @@

    Theme

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    Function init

    @@ -41,7 +41,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Function object

    @@ -50,7 +50,7 @@

    Theme

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    Function person

    @@ -50,7 +50,7 @@

    Theme

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    Function empty

    @@ -43,7 +43,7 @@

    Theme

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    Function distance

    @@ -58,7 +58,7 @@

    Theme

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    Function find

    @@ -69,7 +69,7 @@

    Theme

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    Function similarity

    @@ -62,7 +62,7 @@

    Theme

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    Function validateModel

    @@ -48,7 +48,7 @@

    Theme

    @@ -6,12 +6,12 @@
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    • @vladmandic/human - v3.0.1
    -

    @vladmandic/human - v3.0.0

    +

    @vladmandic/human - v3.0.1

    @@ -138,7 +138,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/human - v3.0.0 +
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    • @vladmandic/human - v3.0.1

    Interface BodyConfig

    @@ -106,7 +106,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Interface BodyKeypoint

    @@ -90,7 +90,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Interface BodyResult

    @@ -98,7 +98,7 @@

    Theme

    @@ -6,13 +6,13 @@
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    Interface Config

    @@ -248,7 +248,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    • @vladmandic/human - v3.0.1

    Interface DrawOptions

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    Interface FaceAntiSpoofConfig

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    Interface FaceAttentionConfig

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    Interface FaceConfig

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    Interface FaceDescriptionConfig

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    Interface FaceDetectorConfig

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    Interface FaceEmotionConfig

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    Interface FaceGearConfig

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    Interface FaceIrisConfig

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    Interface FaceLivenessConfig

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    Interface FaceMeshConfig

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    Interface FaceResult

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    Interface FilterConfig

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    Interface GenericConfig

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    Interface GestureConfig

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    Interface HandConfig

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    Interface HandResult

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    Interface ModelInfo

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    Interface ObjectConfig

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    Interface ObjectResult

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    Interface PersonResult

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    Interface Result

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    Interface SegmentationConfig

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    Interface WebCamConfig

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    Interface KernelOps

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    Interface ModelStats

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    Namespace Tensor

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    Namespace draw

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    Namespace match

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    Namespace models

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    Type alias AnyCanvas

    AnyCanvas: HTMLCanvasElement | OffscreenCanvas
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    Type alias AnyImage

    AnyImage: HTMLImageElement | typeof Image
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    Type alias AnyVideo

    AnyVideo: HTMLMediaElement | HTMLVideoElement
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    Type alias BackendEnum

    BackendEnum: "" | "cpu" | "wasm" | "webgl" | "humangl" | "tensorflow" | "webgpu"
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    Type alias BodyAnnotation

    BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose
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    Type alias BodyAnnotationBlazePose

    BodyAnnotationBlazePose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "leftEye" | "rightEye" | "mouth"
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    Type alias BodyAnnotationEfficientPose

    BodyAnnotationEfficientPose: "leftLeg" | "rightLeg" | "torso" | "leftArm" | "rightArm" | "head"
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    Type alias BodyGesture

    BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"
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    Type alias BodyLandmark

    BodyLandmark: BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose
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    Type alias BodyLandmarkBlazePose

    BodyLandmarkBlazePose: "nose" | "leftEyeInside" | "leftEye" | "leftEyeOutside" | "rightEyeInside" | "rightEye" | "rightEyeOutside" | "leftEar" | "rightEar" | "leftMouth" | "rightMouth" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftPinky" | "rightPinky" | "leftIndex" | "rightIndex" | "leftThumb" | "rightThumb" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle" | "leftHeel" | "rightHeel" | "leftFoot" | "rightFoot" | "bodyCenter" | "bodyTop" | "leftPalm" | "leftHand" | "rightPalm" | "rightHand"
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    Type alias BodyLandmarkEfficientNet

    BodyLandmarkEfficientNet: "head" | "neck" | "rightShoulder" | "rightElbow" | "rightWrist" | "chest" | "leftShoulder" | "leftElbow" | "leftWrist" | "bodyCenter" | "rightHip" | "rightKnee" | "rightAnkle" | "leftHip" | "leftKnee" | "leftAnkle"
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    Type alias BodyLandmarkMoveNet

    BodyLandmarkMoveNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"
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    Type alias BodyLandmarkPoseNet

    BodyLandmarkPoseNet: "nose" | "leftEye" | "rightEye" | "leftEar" | "rightEar" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"
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    Type alias Box

    Box: [number, number, number, number]
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    Type alias Emotion

    Emotion: "angry" | "disgust" | "fear" | "happy" | "sad" | "surprise" | "neutral"
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    Type alias Events

    Events: "create" | "load" | "image" | "result" | "warmup" | "error"
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    Theme

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    Type alias ExternalCanvas

    -
    ExternalCanvas: typeof Canvas
    +
    ExternalCanvas: typeof Canvas

    Defines possible externally defined canvas

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    Type alias FaceGesture

    FaceGesture: `facing ${"left" | "center" | "right"}` | `blink ${"left" | "right"} eye` | `mouth ${number}% open` | `head ${"up" | "down"}`
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    Type alias FaceLandmark

    FaceLandmark: "leftEye" | "rightEye" | "nose" | "mouth" | "leftEar" | "rightEar" | "symmetryLine" | "silhouette" | "lipsUpperOuter" | "lipsLowerOuter" | "lipsUpperInner" | "lipsLowerInner" | "rightEyeUpper0" | "rightEyeLower0" | "rightEyeUpper1" | "rightEyeLower1" | "rightEyeUpper2" | "rightEyeLower2" | "rightEyeLower3" | "rightEyebrowUpper" | "rightEyebrowLower" | "rightEyeIris" | "leftEyeUpper0" | "leftEyeLower0" | "leftEyeUpper1" | "leftEyeLower1" | "leftEyeUpper2" | "leftEyeLower2" | "leftEyeLower3" | "leftEyebrowUpper" | "leftEyebrowLower" | "leftEyeIris" | "midwayBetweenEyes" | "noseTip" | "noseBottom" | "noseRightCorner" | "noseLeftCorner" | "rightCheek" | "leftCheek"
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    Type alias Finger

    Finger: "index" | "middle" | "pinky" | "ring" | "thumb" | "palm"
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    Type alias FingerCurl

    FingerCurl: "none" | "half" | "full"
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    Type alias FingerDirection

    FingerDirection: "verticalUp" | "verticalDown" | "horizontalLeft" | "horizontalRight" | "diagonalUpRight" | "diagonalUpLeft" | "diagonalDownRight" | "diagonalDownLeft"
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    Type alias Gender

    Gender: "male" | "female" | "unknown"
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    Type alias GestureResult

    GestureResult: {
        face: number;
        gesture: FaceGesture;
    } | {
        gesture: IrisGesture;
        iris: number;
    } | {
        body: number;
        gesture: BodyGesture;
    } | {
        gesture: HandGesture;
        hand: number;
    }
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    Theme

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    Type alias HandGesture

    HandGesture: `${"thumb" | "index" | "middle" | "ring" | "pinky"} forward` | `${"thumb" | "index" | "middle" | "ring" | "pinky"} up` | "victory" | "thumbs up"
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    Type alias HandType

    HandType: "hand" | "fist" | "pinch" | "point" | "face" | "tip" | "pinchtip"
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    Type alias ImageObjects

    ImageObjects: ImageData | ImageBitmap
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    Theme

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    Type alias Input

    Input: Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas
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    Type alias IrisGesture

    IrisGesture: "facing center" | `looking ${"left" | "right" | "up" | "down"}` | "looking center"
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    Type alias ObjectType

    ObjectType: "person" | "bicycle" | "car" | "motorcycle" | "airplane" | "bus" | "train" | "truck" | "boat" | "traffic light" | "fire hydrant" | "stop sign" | "parking meter" | "bench" | "bird" | "cat" | "dog" | "horse" | "sheep" | "cow" | "elephant" | "bear" | "zebra" | "giraffe" | "backpack" | "umbrella" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "kite" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "bottle" | "wine glass" | "cup" | "fork" | "knife" | "spoon" | "bowl" | "banana" | "apple" | "sandwich" | "orange" | "broccoli" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "chair" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "toaster" | "sink" | "refrigerator" | "book" | "clock" | "vase" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"
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    Type alias Point

    Point: [number, number, number?]
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    Theme

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    Type alias Race

    Race: "white" | "black" | "asian" | "indian" | "other"
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    Type alias SegmentationEnum

    SegmentationEnum: "default" | "alpha" | "foreground" | "state"
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    Theme

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    Type alias Tensor4D

    Tensor4D: Tensor<R4>
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    Theme

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    Type alias WarmupEnum

    WarmupEnum: "" | "none" | "face" | "full" | "body"
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    Type alias Descriptor

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    Type alias MatchOptions

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    Theme

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    Variable defaultsConst

    defaults: Config = ...
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    Variable optionsConst

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    Theme

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    Variable envConst

    env: Env = ...
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  • Defined in src/util/env.ts:186
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    Theme