docs: Update README and compilation guides for clarity and consistency, including path corrections and improved formatting. Add copyright notices to source files and adjust file permissions for several scripts and directories.

2026-02-28 11:06:26 +08:00 · 2026-02-28 11:06:26 +08:00 · bd891a96dd
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+# 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:**
+```bash
+# 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
+
+```bash
+# 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:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+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:
+```bash
+adb pull result.jpg.
+```
+![alt text](result.jpg)
+