104 lines
3.3 KiB
Markdown
104 lines
3.3 KiB
Markdown
# blazepose_landmark
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## 1.Overview
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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.
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:** https://github.com/google-ai-edge/mediapipe/tree/master
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- **Download weights**
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wget https://storage.googleapis.com/mediapipe-assets/pose_landmark_full.tflite
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_convert.sh model_path adla_toolkit_path target_platform
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example
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./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh pose_detection.tflite /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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```
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| Parameter | Description |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_toolkit_path | path to adla_toolkit |
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| target_platform | Specify target platform. for A311D2: PRODUCT_PID0XA003. for S905X5: PRODUCT_PID0XA005 |
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## 4. Demo Run
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### CPP
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#### 1. Compile
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**Prerequisites:**
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- Android NDK (r25e recommended)
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- `ANDROID_NDK_PATH` environment variable set
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**Build:**
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```bash
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# Build for arm64-v8a
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cd examples/blazepose_landmark/cpp
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./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/blazepose_landmark_demo` (Note: executable name may vary, verify in build folder).
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#### 2. Run
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```bash
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# Push executable to device
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adb push build/android/blazepose_landmark_demo /data/local/tmp/
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adb push model/blazepose_landmark_full_int16_A311D2.adla /data/local/tmp/
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adb push test_image.jpg /data/local/tmp/
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# Run on device
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adb shell
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cd /data/local/tmp
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chmod +x blazepose_landmark_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./blazepose_landmark_demo <model_path> <image_path>
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./blazepose_landmark_demo blazepose_landmark_full_int16_A311D2.adla test_image.jpg"
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```
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**Note:** Replace `blazepose_landmark_full_int16_A311D2.adla` with your actual model file path.
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### Python
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**Prerequisites:**
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- Python 3.10
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- Required packages: `numpy`, `opencv-python`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
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```
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**Run on device:**
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```bash
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python blazepose_landmark.py --model-path ./blazepose_landmark_full_int16_A311D2.adla
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```
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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.
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## 5.Results
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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).
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You can pull the result image back to view it:
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```bash
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adb pull result.jpg.
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```
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