openvino-pose-estimation.md

openVINO Pose Estimation

Pose Estimation in ~100ms on a cpu!

Warning! You must be using a 6, 7, or 8th generation intel cpu with ubuntu see here for more info

• This model leverages hardware acceleration for extremely fast inference times. Make sure to read the warning above to ensure you get the most speed out of this!

Pose demo!

Speed

Hardware	Inference Time (Milliseconds)
CPU	100 (intel i-7)

Accuracy

Detection	Accuracy
Person	42.8% AP

Detector

Model	Description
Pose	multi-person 2d pose estimation (based on open pose) with MobileNetv1 as a feature extractor.

Run

#cpu
docker run -ti \\
-p 9090:9090 \\
sugarkubes/openvino-pose-estimation:cpu

ENV Variables

Variable	Default
PORT	8080
HOST	0.0.0.0
GRPC_PORT	9001
GRPC_HOST	0.0.0.0
BASIC_AUTH_USERNAME	""
BASIC_AUTH_PASSWORD	""

Routes

GET / GET /health GET /healthz

• Responds with a 200 for healthcheck

POST /predict

• Example:

curl -X POST \\
http://0.0.0.0:9090/predict \\
-H 'Content-Type: application/json' \\
  -d '{ "url": "https://s3.us-west-1.wasabisys.com/public.sugarkubes/repos/sugar-cv/object-detection/friends.jpg" }'

Example Output

"connections": [[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[1,0],[0,14],[14,16],[0,15],[15,17],[2,16],[5,17]],
  "image_size": [ 1200, 800 ],
  "parts_list": {
    "0": "Nose",
    "1": "Neck",
    "2": "RShoulder",
    "3": "RElbow",
    "4": "RWrist",
    "5": "LShoulder",
    "6": "LElbow",
    "7": "LWrist",
    "8": "RHip",
    "9": "RKnee",
    "10": "RAnkle",
    "11": "LHip",
    "12": "LKnee",
    "13": "LAnkle",
    "14": "REye",
    "15": "LEye",
    "16": "REar",
    "17": "LEar",
    "18": "Background"
  },
  "points": [
    {
      "LAnkle": {
        "confidence": "0.74461085", point confidence
        "part_index": 13, you can look up the parts_list for text
        "x": "0.22807017543859648", x as percent. multiply times image_size[0] for pixel value
        "y": "0.8125" y as percent. multiply by image_size[1]for pixel value
      },
      "LEar": {
        "confidence": "0.93777394",
        "part_index": 17,
        "x": "0.2631578947368421",
        "y": "0.25"
      },
      "LElbow": {
        "confidence": "0.7759637",
        "part_index": 6,
        "x": "0.2982456140350877",
        "y": "0.46875"
      },
      "LEye": {
        "confidence": "0.89811933",
        "part_index": 15,
        "x": "0.24561403508771928",
        "y": "0.25"
      },
      "LHip": {
        "confidence": "0.80005884",
        "part_index": 11,
        "x": "0.2631578947368421",
        "y": "0.53125"
      },
      "LKnee": {
        "confidence": "0.82161593",
        "part_index": 12,
        "x": "0.22807017543859648",
        "y": "0.65625"
      },
      "LShoulder": {
        "confidence": "0.92546237",
        "part_index": 5,
        "x": "0.2807017543859649",
        "y": "0.34375"
      },
      "LWrist": {
        "confidence": "0.91839874",
        "part_index": 7,
        "x": "0.2982456140350877",
        "y": "0.5625"
      },
      "Neck": {
        "confidence": "0.8682677",
        "part_index": 1,
        "x": "0.24561403508771928",
        "y": "0.34375"
      },
      "Nose": {
        "confidence": "1.0116411",
        "part_index": 0,
        "x": "0.22807017543859648",
        "y": "0.25"
      },
      "RAnkle": {
        "confidence": "0.7825638",
        "part_index": 10,
        "x": "0.2807017543859649",
        "y": "0.75"
      },
      "RElbow": {
        "confidence": "0.8665149",
        "part_index": 3,
        "x": "0.19298245614035087",
        "y": "0.4375"
      },
      "RHip": {
        "confidence": "0.86423767",
        "part_index": 8,
        "x": "0.21052631578947367",
        "y": "0.53125"
      },
      "RKnee": {
        "confidence": "0.69661057",
        "part_index": 9,
        "x": "0.24561403508771928",
        "y": "0.65625"
      },
      "RShoulder": {
        "confidence": "0.8426995",
        "part_index": 2,
        "x": "0.21052631578947367",
        "y": "0.34375"
      },
      "RWrist": {
        "confidence": "0.9170083",
        "part_index": 4,
        "x": "0.17543859649122806",
        "y": "0.53125"
      }
    },
    ... + 6 more
    ],
  "speed": "123 ms"

Validated Hosts

• intel i-3, i-5, i-7

Webcam demo!

• you can set up the base inference container on a compatible device (see warning), and change the config so if you have the container running on an intel compatible device, you can change the grpc_address to point to the intel device and run the api on any x86 machine that runs docker!