amlnn-model-playground/examples/blazepose_landmark

blazepose_landmark

1.Overview

​ BlazePose Landmark builds upon the detected ROI to predict 33 precise body keypoints, enabling full-body pose estimation with high accuracy and temporal stability. Its efficient design makes it suitable for applications such as fitness tracking, motion analysis, augmented reality, and real-time human–computer interaction.

2.Model Download

• Open Source model

  • Open Source projects: https://github.com/google-ai-edge/mediapipe/tree/master

  • Download weights

    wget https://storage.googleapis.com/mediapipe-assets/pose_landmark_full.tflite

3. Model Conversion

cd model
Usage:   ./adla_convert.sh model_path adla_toolkit_path target_platform

example
 ./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
 ./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
 ./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005

Parameter	Description
model_path	onnx model path
adla_toolkit_path	path to adla_toolkit
target_platform	Specify target platform. for A311D2: PRODUCT_PID0XA003. for S905X5: PRODUCT_PID0XA005

4. Demo Run

CPP

1. Compile

Prerequisites:

• Android NDK (r25e recommended)
• ANDROID_NDK_PATH environment variable set

Build:

# Build for arm64-v8a
cd examples/blazepose_landmark/cpp
./build-android.sh -a arm64-v8a

The executable will be generated at build/android/blazepose_landmark_demo (Note: executable name may vary, verify in build folder).

2. Run

# Push executable to device
adb push build/android/blazepose_landmark_demo /data/local/tmp/
adb push model/blazepose_landmark_full_int16_A311D2.adla /data/local/tmp/
adb push test_image.jpg /data/local/tmp/

# Run on device
adb shell
cd /data/local/tmp
chmod +x blazepose_landmark_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./blazepose_landmark_demo <model_path> <image_path>
./blazepose_landmark_demo blazepose_landmark_full_int16_A311D2.adla test_image.jpg"

Note: Replace blazepose_landmark_full_int16_A311D2.adla with your actual model file path.

Python

Prerequisites:

• Python 3.10
• Required packages: numpy, opencv-python, amlnnlite

Install dependencies:

pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl

Run on device:

python blazepose_landmark.py --model-path ./blazepose_landmark_full_int16_A311D2.adla

The script will automatically process all image files (.jpg, .jpeg, .png, .bmp) in the current directory and save results to a {model_name}_result folder.

5.Results

The program will print the detection count and inference time. The result image with bounding boxes will be saved to the specified output path (result.jpg by default).

You can pull the result image back to view it:

adb pull result.jpg.