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Added YOLOv5 serverless function for auto labeling #4178

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Jan 17, 2022
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2 changes: 1 addition & 1 deletion CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Added information about OpenVINO toolkit to login page (<https://github.com/openvinotoolkit/cvat/pull/4077>)
- Support for working with ellipses (<https://github.com/openvinotoolkit/cvat/pull/4062>)
- Add several flags to task creation CLI (<https://github.com/openvinotoolkit/cvat/pull/4119>)

- Add YOLOv5 serverless function for automatic annotation (<https://github.com/openvinotoolkit/cvat/pull/4178>)
### Changed
- Users don't have access to a task object anymore if they are assigneed only on some jobs of the task (<https://github.com/openvinotoolkit/cvat/pull/3788>)
- Different resources (tasks, projects) are not visible anymore for all CVAT instance users by default (<https://github.com/openvinotoolkit/cvat/pull/3788>)
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -90,6 +90,7 @@ For more information about supported formats look at the
| [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-300/nuclio) | reid | OpenVINO | X | |
| [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector | OpenVINO | X | |
| [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio) | detector | OpenVINO | X | |
| [YOLO v5](/serverless/pytorch/ultralytics/yolov5/nuclio) | detector | PyTorch | X | |
| [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio) | tracker | PyTorch | X | X |
| [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio) | interactor | PyTorch | X | |
| [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio) | interactor | PyTorch | | X |
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121 changes: 121 additions & 0 deletions serverless/pytorch/ultralytics/yolov5/nuclio/function.yaml
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metadata:
name: ultralytics-yolov5
namespace: cvat
annotations:
name: YOLO v5
type: detector
framework: pytorch
spec: |
[
{ "id": 0, "name": "person" },
{ "id": 1, "name": "bicycle" },
{ "id": 2, "name": "car" },
{ "id": 3, "name": "motorbike" },
{ "id": 4, "name": "aeroplane" },
{ "id": 5, "name": "bus" },
{ "id": 6, "name": "train" },
{ "id": 7, "name": "truck" },
{ "id": 8, "name": "boat" },
{ "id": 9, "name": "traffic light" },
{ "id": 10, "name": "fire hydrant" },
{ "id": 11, "name": "stop sign" },
{ "id": 12, "name": "parking meter" },
{ "id": 13, "name": "bench" },
{ "id": 14, "name": "bird" },
{ "id": 15, "name": "cat" },
{ "id": 16, "name": "dog" },
{ "id": 17, "name": "horse" },
{ "id": 18, "name": "sheep" },
{ "id": 19, "name": "cow" },
{ "id": 20, "name": "elephant" },
{ "id": 21, "name": "bear" },
{ "id": 22, "name": "zebra" },
{ "id": 23, "name": "giraffe" },
{ "id": 24, "name": "backpack" },
{ "id": 25, "name": "umbrella" },
{ "id": 26, "name": "handbag" },
{ "id": 27, "name": "tie" },
{ "id": 28, "name": "suitcase" },
{ "id": 29, "name": "frisbee" },
{ "id": 30, "name": "skis" },
{ "id": 31, "name": "snowboard" },
{ "id": 32, "name": "sports ball" },
{ "id": 33, "name": "kite" },
{ "id": 34, "name": "baseball bat" },
{ "id": 35, "name": "baseball glove" },
{ "id": 36, "name": "skateboard" },
{ "id": 37, "name": "surfboard" },
{ "id": 38, "name": "tennis racket" },
{ "id": 39, "name": "bottle" },
{ "id": 40, "name": "wine glass" },
{ "id": 41, "name": "cup" },
{ "id": 42, "name": "fork" },
{ "id": 43, "name": "knife" },
{ "id": 44, "name": "spoon" },
{ "id": 45, "name": "bowl" },
{ "id": 46, "name": "banana" },
{ "id": 47, "name": "apple" },
{ "id": 48, "name": "sandwich" },
{ "id": 49, "name": "orange" },
{ "id": 50, "name": "broccoli" },
{ "id": 51, "name": "carrot" },
{ "id": 52, "name": "hot dog" },
{ "id": 53, "name": "pizza" },
{ "id": 54, "name": "donut" },
{ "id": 55, "name": "cake" },
{ "id": 56, "name": "chair" },
{ "id": 57, "name": "sofa" },
{ "id": 58, "name": "pottedplant" },
{ "id": 59, "name": "bed" },
{ "id": 60, "name": "diningtable" },
{ "id": 61, "name": "toilet" },
{ "id": 62, "name": "tvmonitor" },
{ "id": 63, "name": "laptop" },
{ "id": 64, "name": "mouse" },
{ "id": 65, "name": "remote" },
{ "id": 66, "name": "keyboard" },
{ "id": 67, "name": "cell phone" },
{ "id": 68, "name": "microwave" },
{ "id": 69, "name": "oven" },
{ "id": 70, "name": "toaster" },
{ "id": 71, "name": "sink" },
{ "id": 72, "name": "refrigerator" },
{ "id": 73, "name": "book" },
{ "id": 74, "name": "clock" },
{ "id": 75, "name": "vase" },
{ "id": 76, "name": "scissors" },
{ "id": 77, "name": "teddy bear" },
{ "id": 78, "name": "hair drier" },
{ "id": 79, "name": "toothbrush" }
]
spec:
description: YOLO v5 via pytorch hub
runtime: 'python:3.6'
handler: main:handler
eventTimeout: 30s
build:
image: cvat/ultralytics-yolov5
baseImage: ultralytics/yolov5:latest

directives:
preCopy:
- kind: USER
value: root
- kind: WORKDIR
value: /opt/nuclio

triggers:
myHttpTrigger:
maxWorkers: 2
kind: 'http'
workerAvailabilityTimeoutMilliseconds: 10000
attributes:
maxRequestBodySize: 33554432 # 32MB

platform:
attributes:
restartPolicy:
name: always
maximumRetryCount: 3
mountMode: volume
40 changes: 40 additions & 0 deletions serverless/pytorch/ultralytics/yolov5/nuclio/main.py
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import json
import base64
from PIL import Image
import io
import torch

def init_context(context):
context.logger.info("Init context... 0%")

# Read the DL model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
context.user_data.model = model

context.logger.info("Init context...100%")

def handler(context, event):
context.logger.info("Run yolo-v5 model")
data = event.body
buf = io.BytesIO(base64.b64decode(data["image"]))
threshold = float(data.get("threshold", 0.5))
context.user_data.model.conf = threshold
image = Image.open(buf)
yolo_results_json = context.user_data.model(image).pandas().xyxy[0].to_dict(orient='records')

encoded_results = []
for result in yolo_results_json:
encoded_results.append({
'confidence': result['confidence'],
'label': result['name'],
'points': [
result['xmin'],
result['ymin'],
result['xmax'],
result['ymax']
],
'type': 'rectangle'
})

return context.Response(body=json.dumps(encoded_results), headers={},
content_type='application/json', status_code=200)