This model detects upper body and full body keypoints of a person, and is downloaded from https://github.com/PINTO0309/PINTO_model_zoo/blob/main/053_BlazePose/20_densify_pose_detection/download.sh or converted from TFLite to ONNX using following tools:

• TFLite model to ONNX with MediaPipe custom densify op: https://github.com/PINTO0309/tflite2tensorflow
• simplified by onnx-simplifier

SSD Anchors are generated from GenMediaPipePalmDectionSSDAnchors

Note:

• person_detection_mediapipe_2023mar_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 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/opencv_zoo_person_detection_mediapipe
# detect on an image
./build/opencv_zoo_person_detection_mediapipe -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_person_detection_mediapipe -h

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

• MediaPipe Pose: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker
• MediaPipe pose model and model card: https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#pose
• BlazePose TFJS: https://github.com/tensorflow/tfjs-models/tree/master/pose-detection/src/blazepose_tfjs