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

SSD Anchors are generated from GenMediaPipePalmDectionSSDAnchors

Note:

• Visit https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#hands for models of larger scale.
• palm_detection_mediapipe_2023feb_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Run the following commands to try the demo:

# 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

Install latest OpenCV (with opencv_contrib) and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/demo
# detect on an image
./build/demo -i=/path/to/image -v
# get help messages
./build/demo -h

All files in this directory are licensed under Apache 2.0 License.

• 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