models/handpose_estimation_mediapipe/README.md

# Hand pose estimation from MediaPipe Handpose

This model estimates 21 hand keypoints per detected hand from [palm detector](../palm_detection_mediapipe). (The image below is referenced from [MediaPipe Hands Keypoints](https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection#mediapipe-hands-keypoints-used-in-mediapipe-hands))
 
![MediaPipe Hands Keypoints](./example_outputs/hand_keypoints.png)

Hand gesture classification demo (0-9)
![hand gestures](./example_outputs/gesture_classification.png)

This model is converted from TFlite to ONNX using following tools:
- TFLite model to ONNX: https://github.com/onnx/tensorflow-onnx
- simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)

**Note**:
- The int8-quantized model may produce invalid results due to a significant drop of accuracy.
- Visit https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#hands for models of larger scale.
- `handpose_estimation_mediapipe_2023feb_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.

## Demo

Run the following commands to try the demo:
```bash
# detect on camera input
python demo.py
# detect on an image
python demo.py -i /path/to/image -v
```

### Example outputs

![webcam demo](./example_outputs/mphandpose_demo.webp)

## License

All files in this directory are licensed under [Apache 2.0 License](./LICENSE).

## Reference

- MediaPipe Handpose: https://developers.google.com/mediapipe/solutions/vision/hand_landmarker
- MediaPipe hands model and model card: https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#hands
- Handpose TFJS:https://github.com/tensorflow/tfjs-models/tree/master/handpose
- Int8 model quantized with rgb evaluation set of FreiHAND: https://lmb.informatik.uni-freiburg.de/resources/datasets/FreihandDataset.en.html