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amlnn-model-playground/examples/blazepose_detect/README.md

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blazepose_detect

1.Overview

​ BlazePose Detection was introduced by Google as part of the MediaPipe framework, providing fast and lightweight person detection optimized for real-time performance on mobile and edge devices. The detector identifies the human region of interest (ROI) in an image, ensuring stable and efficient pose tracking in subsequent stages.

2.Model Download

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_detect/cpp
./build-android.sh -a arm64-v8a

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

2. Run

# Push executable to device
adb push build/android/blazepose_detect_demo /data/local/tmp/
adb push model/blazepose_detect_int8_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_detect_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./blazepose_detect_demo <model_path> <image_path>
./blazepose_detect_demo blazepose_detect_int8_A311D2.adla test_image.jpg"

Note: Replace blazepose_detect_int8_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_detect.py --model-path ./blazepose_detect_int8_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.

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