tequ-jetson-basler-nodered

This repository is developed in Fish-IoT project

https://www.tequ.fi/en/project-bank/fish-iot/

tequ-jetson-basler-nodered

This repository is developed with fresh Jetpack 5.0.2 and Jetson AGX Orin Developer Kit. Example flows should work with any Basler camera and can easily be modified to work with multiple cameras. Basic support for v4l2 video sources is also available. This is developed and tested with Logitech C920 USB camera.

Other tested machines:

Neousys NRU-120S, Jetson AGX Xavier with Jetpack 4.6.1
Dell Precision 7560, Windows 10

Please first configure your Jetson setup using following repositiories:

Basic configuration for Jetson: https://github.com/Lapland-UAS-Tequ/tequ-jetson-setup
Triton Inference Server configuration: https://github.com/Lapland-UAS-Tequ/tequ-setup-triton-inference-server
Basler Camera configuration: https://github.com/Lapland-UAS-Tequ/tequ-jetson-basler
Windows 10 configuration: https://github.com/Lapland-UAS-Tequ/win10-nodered-tensorflow

Node-RED functionality (example 1)

Starts local RTSP-server to serve encoded camera stream
Starts multiple GStreamer instances that connects to Basler cameras using its serial number and predefined configuration files
Starts GStreamer instances that connects to Logitech USB camera using v4l2_device video source (/dev/video0)
GStreamer reads camera video stream and publishes encoded H264/H265 video stream to RTSP server and JPEG stream to local TCP port
Node-RED application is used to
- Manage GStreamer instances
- Manage Triton Inference Server Dockers
- Receive and process JPEG image streams from GStreamer
- Serve MJPEG streams in local network
- Convert image to tensor
- Send tensor to local or remote Triton Inference Server for object detection using "ssd-example-model" that detects common things
- Post-process Triton Inference Server response
- Annotate detected objects to images
- Formats and sends annotated images to Tequ-API based on object detection threshold

Real time video streams will be available in following addresses:

rtsp://IP:8554/CAMERA_SERIAL_NUMBER
http://IP:1880/camera/id/CAMERA_SERIAL_NUMBER/stream/1
http://IP:1880/camera/id/CAMERA_SERIAL_NUMBER/annotated/1

You can disable functionality

Disable "Inference" flow if you dont want to run inference
Disable "Triton" flow if you dont need Triton docker on this machine
Disable "Tequ-API" flow if you need to send images to Tequ-API

Install Node-RED nodes

cd ~/.node-red

npm install node-red-contrib-image-info &&
npm install node-red-contrib-image-output &&
npm install node-red-contrib-moment &&
npm install node-red-contrib-multipart-stream-encoder &&
npm install node-red-contrib-multipart-stream-decoder &&
npm install node-red-node-daemon && 
npm install node-red-node-smooth && 
npm install node-red-node-exif &&
npm install node-red-contrib-msg-speed &&
npm install node-red-contrib-random-output &&
npm install canvas &&
npm install numjs &&
npm install sharp &&
npm install piscina &&
npm install uuid

Install tfjs-node-gpu on Jetson

https://github.com/Lapland-UAS-Tequ/tequ-jetson-nodered-tensorflow

Note: If you are using Jetpack 4.6.1 you have to install numjs and canvas more or less manually:

Install Node-RED flow

Example flow 1

Copy and import this example flow to your Node-RED. Configure nodes to match your setup, this means that you should corrent paths to point right files in your system and set Basler camera serial numbers according to your setup etc..

This flow will add following subflows in your Node-RED:

[IMG] Annotate
gst - jetson
Parse JPEG
gst WD
Triton request
Pre-process
Post-process

This example flow starts 5 Gstreamer pipelines and uses 5 different cameras:

Basler daA3840-45uc
Basler daA1920-160uc
Basler a2A1920-51gcBAS
Basler acA2500-14gc
Logitech C920

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unix_timestamp,\n        \"cos\": {\n            \"bucket\": \"\",\n            \"region\": \"\",\n            \"service_id\": \"\"\n        },\n        \"object\": {\n            \"type\": image_type,\n            \"data\": {\n                \"width\": msg.width,\n                \"height\": msg.height,\n                \"size\": (msg.payload).length,\n                \"exif\": msg.exif,\n                \"parameters\": parameters,\n                \"original\": {\n                    \"image\": (msg.payload).toString('base64'),\n                    \"objectname\": datasource + \"-\" + random_name + file_extension,\n                    \"thumbnail\": (msg.thumbnail).toString('base64'),\n                    \"thumbnail_ms\": msg.thumbnail_ms,\n                    \"mjpeg_process_ms\":0\n                },\n                \"annotated\": {\n                    \"image\": \"\",\n                    \"objectname\": datasource + \"-\" + random_name+ \"_annotated\"+file_extension,\n        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msg.payload;\nlet piscina = context.get(\"piscina\")\nlet pre_process_time;\n\ntry{\n    let result = await piscina.run({jpeg:image,gzip:false});\n       \n    msg.tensor = {\n        \"image\":image,\n        \"data\":Buffer.from(result.tensorBuffer),\n        \"tensor_shape\":result.tensorShape,\n        \"inference_header_length\":result.inference_header_length,\n        \"image_tensor_buffer_length\":result.image_tensor_buffer_length\n    }\n    \n    pre_process_time =  Date.now() - start;\n    msg.data.properties.computer_vision.pre_process_ms = pre_process_time\n    node.status({fill:\"green\",shape:\"dot\",text:pre_process_time+\" ms\"});    \n    return msg;\n}\ncatch(e){\n    node.status({fill:\"red\",shape:\"dot\",text:\"Convert failed...\"});    \n    node.error(\"Image to tensor conversion failed. Probably input is not an image buffer.\", msg);\n}","outputs":1,"noerr":0,"initialize":"let nr_folder;\nconst scriptName = 'numjs_piscina_worker.js';\nvar worker_path = \"\"\nconst platform = os.platform()\nvar worker_script;\nlet sep;\nlet numberOfWorkers;\n\nnode.warn(\"Platform: \" + platform)\n\nif(platform == \"win32\"){\n   sep = \"\\\\\"\n   nr_folder = path.resolve()\n   worker_path = path.resolve(scriptName)\n   worker_script = \"console.log(\\\"Worker starting...\\\");\\r\\nlet nj;\\r\\nlet zlib;\\r\\n\\r\\n\\r\\ntry{\\r\\n\\tnj = require(\\\"./node_modules\\//numjs\\\")\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(\\\"Loading numjs failed\\\")\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\ntry{\\r\\n\\tzlib = require(\\'zlib\\');\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\nmodule.exports = async ({jpeg,gzip}) => {\\r\\n\\ttry{\\r\\n\\t\\tconst start = Date.now();\\t\\r\\n\\t\\tconst img = nj.images.read(jpeg);\\r\\n\\t\\tconst dataBuffer = 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false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_classes\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ]\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tconst inference_header_buffer = Buffer.from(JSON.stringify(inference_header))\\r\\n\\t\\tconst inference_header_length = inference_header_buffer.length\\r\\n\\t\\tlet payload = Buffer.concat([inference_header_buffer,dataBuffer])\\r\\n\\t\\ttime_ms =  Date.now() - start;\\r\\n\\t\\t\\r\\n\\t\\tif(gzip){\\r\\n\\t\\t\\tpayload = zlib.gzipSync(payload)\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tresult = {\\r\\n\\t\\t\\t\\\"tensorBuffer\\\" : payload,\\r\\n\\t\\t\\t\\\"Content-Type\\\" : \\\"application\\/octet-stream\\\",\\r\\n\\t\\t\\t\\\"inference_header_length\\\": 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start = Date.now();\\t\\r\\n\\t\\tconst img = nj.images.read(jpeg);\\r\\n\\t\\tconst dataBuffer = Buffer.from(img.selection.data);\\r\\n\\t\\tconst shape = [1].concat(img.shape);\\r\\n\\t\\tconst image_tensor_buffer_length = dataBuffer.length\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\tconst inference_header = {\\r\\n\\t\\t  \\\"model_name\\\" : \\\"test\\\",\\r\\n\\t\\t  \\\"inputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"input_tensor\\\",\\r\\n\\t\\t\\t  \\\"shape\\\" : shape,\\r\\n\\t\\t\\t  \\\"datatype\\\" : \\\"UINT8\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t  \\\"binary_data_size\\\":image_tensor_buffer_length\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ],\\r\\n\\t\\t  \\\"outputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_scores\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : 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node.warn(error)\n    node.status({fill:\"red\", shape:\"dot\", text:\"Initializing piscina failed...\"});\n}","finalize":"// Code added here will be run when the\n// node is being stopped or re-deployed.\ncontext.set(\"piscina\",{})","libs":[{"var":"pis","module":"piscina"},{"var":"fs","module":"fs"},{"var":"os","module":"os"},{"var":"process","module":"process"},{"var":"path","module":"path"}],"x":210,"y":60,"wires":[[]]},{"id":"752a76d98d97af39","type":"status","z":"fde3bd1da88bc03c","name":"","scope":null,"x":240,"y":140,"wires":[[]]},{"id":"1e80d9526ad0c4ee","type":"function","z":"9215deec580c5391","name":"Post-process","func":"function chunk(arr, chunkSize) {\n    if (chunkSize <= 0) throw \"Invalid chunk size\";\n    var R = [];\n    for (var i = 0, len = arr.length; i < len; i += chunkSize)\n        R.push(arr.slice(i, i + chunkSize));\n    return R;\n}\n\ntry{\n    let ready = flow.get(\"ready\") || false;\n\n    if (ready){\n        \n        let start = Date.now()\n        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Example flow 2

Connects to MJPEG streams
Parses JPEG streams
Converts Images to Tensor
Makes inference using Triton Inference Server
Post-process image files
Annotates image files
Formats and sends annotated images to Tequ-API

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0\nmsg.request_ms = request_ms\n\ntry{\n    rate_limit_ms = parseInt(env.get(\"rate_limit_ms\"))\n}\ncatch(e){\n    //\n}\n\nif (statuscode == 200){\n    color = \"blue\"\n    delete msg.request_start;\n    delete msg.headers;\n    delete msg.statusCode;\n    delete msg.method;\n    delete msg.retry;\n    delete msg.url;\n    delete msg.responseUrl;\n    delete msg.requestTimeout;\n    delete msg.redirectList;\n    \n    if(rate_limit_ms === 0){\n        color = \"blue\"\n        node.status({\n            fill: color, shape: \"dot\", text: statuscode + \" | \" + request_ms + \" ms\"\n        }); \n    }\n    else{\n        color = \"yellow\"\n        const rate = Math.round(1000 / rate_limit_ms*100)/100\n        node.status({\n            fill: color, shape: \"dot\", text: statuscode + \" | \" + request_ms + \" ms | \" + rate+\" msg/s\"\n        }); \n    }\n    \n    \n    return msg;\n}\n\nelse{\n    color = \"red\"\n    node.status({ fill: color, shape: \"dot\", text: statuscode + \" | \"+msg.payload.error});\n    node.error(\"Processing request failed\", msg);\n    return null;\n}   ","outputs":1,"noerr":0,"initialize":"","finalize":"","libs":[],"x":850,"y":100,"wires":[[]]},{"id":"db3449811636781b","type":"http request","z":"fd51dfdc3367d25c","name":"status request","method":"use","ret":"obj","paytoqs":"ignore","url":"","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":600,"y":420,"wires":[["c9d10c33a635199d"]]},{"id":"7276cd4d5b2ed247","type":"function","z":"fd51dfdc3367d25c","name":"request","func":"let modelName = env.get(\"model_name\")\nlet serverUrl = env.get(\"server_url\")\n\nmsg.requestTimeout = 1000;\nmsg.method = \"GET\";\nmsg.url = serverUrl+\"/v2/models/\"+modelName+\"/config\";\nreturn 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limit?","property":"rate_limit_ms","propertyType":"env","rules":[{"t":"eq","v":"0","vt":"num"},{"t":"else"}],"checkall":"true","repair":false,"outputs":2,"x":220,"y":160,"wires":[["b63e9a83f3001f21"],["2cc1bd4e5a083146"]]},{"id":"8c7c992ba1c6145c","type":"delay","z":"fd51dfdc3367d25c","name":"","pauseType":"rate","timeout":"5","timeoutUnits":"seconds","rate":"1","nbRateUnits":"1","rateUnits":"second","randomFirst":"1","randomLast":"5","randomUnits":"seconds","drop":true,"allowrate":true,"outputs":1,"x":410,"y":220,"wires":[["b63e9a83f3001f21"]]},{"id":"2cc1bd4e5a083146","type":"change","z":"fd51dfdc3367d25c","name":"","rules":[{"t":"set","p":"rate","pt":"msg","to":"rate_limit_ms","tot":"env"}],"action":"","property":"","from":"","to":"","reg":false,"x":230,"y":220,"wires":[["8c7c992ba1c6145c"]]},{"id":"557647c8b195bdea","type":"function","z":"fde3bd1da88bc03c","name":"Image to tensor","func":"//Get a worker from the pool and execute the task\nconst start = Date.now();\nconst image = msg.payload;\nlet piscina = context.get(\"piscina\")\nlet pre_process_time;\n\ntry{\n    let result = await piscina.run({jpeg:image,gzip:false});\n       \n    msg.tensor = {\n        \"image\":image,\n        \"data\":Buffer.from(result.tensorBuffer),\n        \"tensor_shape\":result.tensorShape,\n        \"inference_header_length\":result.inference_header_length,\n        \"image_tensor_buffer_length\":result.image_tensor_buffer_length\n    }\n    \n    pre_process_time =  Date.now() - start;\n    msg.data.properties.computer_vision.pre_process_ms = pre_process_time\n    node.status({fill:\"green\",shape:\"dot\",text:pre_process_time+\" ms\"});    \n    return msg;\n}\ncatch(e){\n    node.status({fill:\"red\",shape:\"dot\",text:\"Convert failed...\"});    \n    node.error(\"Image to tensor conversion failed. Probably input is not an image buffer.\", msg);\n}","outputs":1,"noerr":0,"initialize":"let nr_folder;\nconst scriptName = 'numjs_piscina_worker.js';\nvar worker_path = \"\"\nconst platform = os.platform()\nvar worker_script;\nlet sep;\nlet numberOfWorkers;\n\nnode.warn(\"Platform: \" + platform)\n\nif(platform == \"win32\"){\n   sep = \"\\\\\"\n   nr_folder = path.resolve()\n   worker_path = path.resolve(scriptName)\n   worker_script = \"console.log(\\\"Worker starting...\\\");\\r\\nlet nj;\\r\\nlet zlib;\\r\\n\\r\\n\\r\\ntry{\\r\\n\\tnj = require(\\\"./node_modules\\//numjs\\\")\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(\\\"Loading numjs failed\\\")\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\ntry{\\r\\n\\tzlib = require(\\'zlib\\');\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\nmodule.exports = async ({jpeg,gzip}) => {\\r\\n\\ttry{\\r\\n\\t\\tconst start = Date.now();\\t\\r\\n\\t\\tconst img = nj.images.read(jpeg);\\r\\n\\t\\tconst dataBuffer = Buffer.from(img.selection.data);\\r\\n\\t\\tconst shape = [1].concat(img.shape);\\r\\n\\t\\tconst image_tensor_buffer_length = dataBuffer.length\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\tconst inference_header = {\\r\\n\\t\\t  \\\"model_name\\\" : \\\"test\\\",\\r\\n\\t\\t  \\\"inputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"input_tensor\\\",\\r\\n\\t\\t\\t  \\\"shape\\\" : shape,\\r\\n\\t\\t\\t  \\\"datatype\\\" : \\\"UINT8\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t  \\\"binary_data_size\\\":image_tensor_buffer_length\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ],\\r\\n\\t\\t  \\\"outputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_scores\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_boxes\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_classes\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ]\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tconst inference_header_buffer = Buffer.from(JSON.stringify(inference_header))\\r\\n\\t\\tconst inference_header_length = inference_header_buffer.length\\r\\n\\t\\tlet payload = Buffer.concat([inference_header_buffer,dataBuffer])\\r\\n\\t\\ttime_ms =  Date.now() - start;\\r\\n\\t\\t\\r\\n\\t\\tif(gzip){\\r\\n\\t\\t\\tpayload = zlib.gzipSync(payload)\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tresult = {\\r\\n\\t\\t\\t\\\"tensorBuffer\\\" : payload,\\r\\n\\t\\t\\t\\\"Content-Type\\\" : \\\"application\\/octet-stream\\\",\\r\\n\\t\\t\\t\\\"inference_header_length\\\": inference_header_length,\\r\\n\\t\\t\\t\\\"image_tensor_buffer_length\\\":image_tensor_buffer_length,\\r\\n\\t\\t\\t\\\"tensorShape\\\":shape,\\r\\n\\t\\t\\t\\\"processing_time\\\":time_ms,\\r\\n\\t\\t\\t\\\"payload\\\":jpeg\\r\\n\\t\\t}\\t\\t\\r\\n\\t\\treturn result\\t\\t\\r\\n\\t}\\r\\n\\tcatch(e){\\r\\n\\t\\tconsole.log(e)\\r\\n\\t}\\r\\n\\t\\t\\r\\n}\"\n}\nelse{\n   nr_folder = path.resolve(\".node-red\")\n   node.warn(nr_folder)\n   worker_path = path.resolve(\".node-red\",scriptName)\n   node.warn(worker_path)\n   sep = \"/\" \n   worker_script = \"console.log(\\\"Worker starting...\\\");\\r\\nlet nj;\\r\\nlet zlib;\\r\\n\\r\\n\\r\\ntry{\\r\\n\\tnj = require(\\\"./node_modules\\//numjs\\\")\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(\\\"Loading numjs failed\\\")\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\ntry{\\r\\n\\tzlib = require(\\'zlib\\');\\r\\n}\\r\\ncatch(e){\\r\\n\\tconsole.log(e)\\r\\n}\\r\\n\\r\\nmodule.exports = async ({jpeg,gzip}) => {\\r\\n\\ttry{\\r\\n\\t\\tconst start = Date.now();\\t\\r\\n\\t\\tconst img = nj.images.read(jpeg);\\r\\n\\t\\tconst dataBuffer = Buffer.from(img.selection.data);\\r\\n\\t\\tconst shape = [1].concat(img.shape);\\r\\n\\t\\tconst image_tensor_buffer_length = dataBuffer.length\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\tconst inference_header = {\\r\\n\\t\\t  \\\"model_name\\\" : \\\"test\\\",\\r\\n\\t\\t  \\\"inputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"input_tensor\\\",\\r\\n\\t\\t\\t  \\\"shape\\\" : shape,\\r\\n\\t\\t\\t  \\\"datatype\\\" : \\\"UINT8\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t  \\\"binary_data_size\\\":image_tensor_buffer_length\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ],\\r\\n\\t\\t  \\\"outputs\\\" : [\\r\\n\\t\\t\\t{\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_scores\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_boxes\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t},\\r\\n\\t\\t\\t {\\r\\n\\t\\t\\t  \\\"name\\\" : \\\"detection_classes\\\",\\r\\n\\t\\t\\t  \\\"parameters\\\" : {\\r\\n\\t\\t\\t\\t\\\"binary_data\\\" : false\\r\\n\\t\\t\\t  }\\r\\n\\t\\t\\t}\\r\\n\\t\\t  ]\\r\\n\\t\\t}\\r\\n\\r\\n\\t\\tconst inference_header_buffer = Buffer.from(JSON.stringify(inference_header))\\r\\n\\t\\tconst inference_header_length = inference_header_buffer.length\\r\\n\\t\\tlet payload = Buffer.concat([inference_header_buffer,dataBuffer])\\r\\n\\t\\ttime_ms =  Date.now() - start;\\r\\n\\t\\t\\r\\n\\t\\tif(gzip){\\r\\n\\t\\t\\tpayload = zlib.gzipSync(payload)\\r\\n\\t\\t}\\r\\n\\t\\t\\r\\n\\t\\tresult = {\\r\\n\\t\\t\\t\\\"tensorBuffer\\\" : payload,\\r\\n\\t\\t\\t\\\"Content-Type\\\" : \\\"application\\/octet-stream\\\",\\r\\n\\t\\t\\t\\\"inference_header_length\\\": 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node.warn(error)\n    node.status({fill:\"red\", shape:\"dot\", text:\"Initializing piscina failed...\"});\n}","finalize":"// Code added here will be run when the\n// node is being stopped or re-deployed.\ncontext.set(\"piscina\",{})","libs":[{"var":"pis","module":"piscina"},{"var":"fs","module":"fs"},{"var":"os","module":"os"},{"var":"process","module":"process"},{"var":"path","module":"path"}],"x":220,"y":60,"wires":[[]]},{"id":"752a76d98d97af39","type":"status","z":"fde3bd1da88bc03c","name":"","scope":null,"x":240,"y":140,"wires":[[]]},{"id":"1e80d9526ad0c4ee","type":"function","z":"9215deec580c5391","name":"Post-process","func":"function chunk(arr, chunkSize) {\n    if (chunkSize <= 0) throw \"Invalid chunk size\";\n    var R = [];\n    for (var i = 0, len = arr.length; i < len; i += chunkSize)\n        R.push(arr.slice(i, i + chunkSize));\n    return R;\n}\n\ntry{\n    let ready = flow.get(\"ready\") || false;\n\n    if (ready){\n        \n        let start = Date.now()\n        let inference_result = msg.payload;\n        let results = [];\n        const modelName = env.get(\"model_name\")\n        const model = flow.get(modelName);\n        const labels = model.parameters.labels;\n        const metadata = model.parameters.metadata;\n        const ts = msg.ts;\n        let post_process_time = 0;\n        const threshold = env.get(\"threshold\")\n        msg.threshold = threshold;\n        const image_width = msg.data.properties.object.data.width;\n        const image_height = msg.data.properties.object.data.height;\n\n        let scores;\n        let boxes;\n        let names;\n        let newObject;\n\n        for(let i=0;i<(inference_result.outputs).length;i++){  \n            if(inference_result.outputs[i][\"name\"] == \"detection_scores\"){\n                scores = inference_result.outputs[i].data\n            }\n            else if(inference_result.outputs[i][\"name\"] == \"detection_boxes\"){\n                boxes = inference_result.outputs[i].data\n                boxes = chunk(boxes,4)       \n            }\n            else if(inference_result.outputs[i][\"name\"] == \"detection_classes\"){\n                names = inference_result.outputs[i].data \n            }\n        } \n\n        for (let i = 0; i < scores.length; i++) {\n            if (scores[i] > threshold) {\n                newObject = {\n                    \"bbox\":[\n                            boxes[i][1] * image_width,\n                            boxes[i][0] * image_height,\n                            (boxes[i][3] - boxes[i][1]) * image_width,\n                            (boxes[i][2] - boxes[i][0]) * image_height\n                    ],\n                    \"class\":labels[names[i]-1],\n                    \"label\":labels[names[i]-1],\n                    \"score\":scores[i],\n                    \"length_cm\":NaN\n                    }\n                results.push(newObject)\n            }\n        }\n\n        post_process_time = Date.now() - start;     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msg;\n        }\n        else{\n            node.status({fill: \"red\", shape: \"dot\", text: \" No objects over threshold: \" +threshold});\n            return msg;\n        }\n        \n    }\n    else{\n        node.status({ fill: \"red\", shape: \"dot\", text: \"Modeldata is not loaded\"});\n        return null;\n    }\n}\ncatch(e){\n    node.error(\"Post process failed.\", msg);\n    return null;\n}\n","outputs":1,"noerr":0,"initialize":"","finalize":"","libs":[],"x":330,"y":120,"wires":[[]]},{"id":"bc716e2cbc7c0995","type":"http request","z":"9215deec580c5391","name":"status request","method":"use","ret":"obj","paytoqs":"ignore","url":"","tls":"","persist":false,"proxy":"","insecureHTTPParser":false,"authType":"","senderr":false,"headers":[],"x":500,"y":220,"wires":[["c42a4bd0916459d6"]]},{"id":"e070e6af761a0137","type":"function","z":"9215deec580c5391","name":"request","func":"let modelName = env.get(\"model_name\")\nlet serverUrl = env.get(\"server_url\")\n\nmsg.requestTimeout 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Example flow 3

Connects to single Basler camera using GStreamer
Parses JPEG stream from camera
Opens RTSP server
Publishes stream to RTSP
Published MJPEG stream

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unix_timestamp,\n        \"cos\": {\n            \"bucket\": \"\",\n            \"region\": \"\",\n            \"service_id\": \"\"\n        },\n        \"object\": {\n            \"type\": image_type,\n            \"data\": {\n                \"width\": msg.width,\n                \"height\": msg.height,\n                \"size\": (msg.payload).length,\n                \"exif\": msg.exif,\n                \"parameters\": parameters,\n                \"original\": {\n                    \"image\": (msg.payload).toString('base64'),\n                    \"objectname\": datasource + \"-\" + random_name + file_extension,\n                    \"thumbnail\": (msg.thumbnail).toString('base64'),\n                    \"thumbnail_ms\": msg.thumbnail_ms,\n                    \"mjpeg_process_ms\":0\n                },\n                \"annotated\": {\n                    \"image\": \"\",\n                    \"objectname\": datasource + \"-\" + random_name+ \"_annotated\"+file_extension,\n        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tequ-jetson-basler-nodered

Node-RED functionality (example 1)

Install Node-RED nodes

Install Node-RED flow

Example flow 1

Example flow 2

Example flow 3

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tequ-jetson-basler-nodered

Node-RED functionality (example 1)

Install Node-RED nodes

Install Node-RED flow

Example flow 1

Example flow 2

Example flow 3

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