opencv/pose_estimation_mediapipe

Pose estimation from MediaPipe Pose

This model estimates 33 pose keypoints and person segmentation mask per detected person from person detector. (The image below is referenced from MediaPipe Pose Keypoints)

[image]

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

Note:

• —Visit https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#pose for models of larger scale.
• —pose_estimation_mediapipe_2023mar_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.

Demo

python

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

C++

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

Example outputs

[image]

License

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

Reference

• —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