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# Palm detector from MediaPipe Handpose
This model detects palm bounding boxes and palm landmarks, and 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)
SSD Anchors are generated from [GenMediaPipePalmDectionSSDAnchors](https://github.com/VimalMollyn/GenMediaPipePalmDectionSSDAnchors)
**Note**:
- Visit https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#hands for models of larger scale.
## 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
# get help regarding various parameters
python demo.py --help
```
### Example outputs
## 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 |