# Palm detector from MediaPipe Handpose

This model detects palm bounding boxes and palm landmarks, and is converted from Tensorflow-JS to ONNX using following tools:
- tfjs to tf_saved_model:  https://github.com/patlevin/tfjs-to-tf/
- tf_saved_model to ONNX: https://github.com/onnx/tensorflow-onnx
- simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)

Also note that the model is quantized in per-channel mode with [Intel's neural compressor](https://github.com/intel/neural-compressor), which gives better accuracy but may lose some speed.

## 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
```

NOTE: For the quantized model, you will need to install OpenCV 4.6.0 to have asymmetric paddings support for quantized convolution layer in OpenCV. Score threshold needs to be adjusted as well for the quantized model, which is empirically 0.49.

## License

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

## Reference

- MediaPipe Handpose: https://github.com/tensorflow/tfjs-models/tree/master/handpose