feat: Update NNSDK path and library linking in CMake configurations and Android build script, and add a new script to build all Android examples.
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dian.yuan
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35 changed files with 1438 additions and 1222 deletions
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## 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/yoloworld/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/yolo_world_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/yolo_world_demo /data/local/tmp/
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adb push model/yoloworld_int8_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 yolo_world_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./yolo_world_demo <model_path> <image_path>
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./yolo_world_demo yoloworld_int8_A311D2.adla test_image.jpg
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```
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**Note:** Replace `yoloworld_int8_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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# Basic usage (process current directory)
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python yoloworld.py --model-path ./yoloworld_int8_A311D2.adla
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# Specify image directory
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python yoloworld.py --model-path ./yoloworld_int8_A311D2.adla --image-dir ./
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```
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the specified directory and save results to a `{model_name}_result` folder.
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## Results
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The program will print the detection count and detected objects for each processed image. The result image with bounding boxes will be saved to the specified output directory.
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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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The program detects objects from predefined classes (handbag, backpack, wallet, watch, necklace, bracelet, earrings, finger ring, sunglass, hat, shoes, belt, makeup palette, lipstick tube, car, truck, bicycle, motorcycle, phone, laptop, camera, wine bottle, stuffed toy) and draws bounding boxes with class labels on the result images.
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## Demo Run
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### CPP
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#### 1. Compile
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#### AMLNN SDK Setup
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Resolve the AMLNN nnsdk dependency using one of the following methods:
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- **Priority 1 – Environment variable (recommended)**
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```bash
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export AMLNN_HOME=/path/to/amlnn-toolkit
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```
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- **Priority 3 – Sibling directory fallback** *(automatic)*
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Place `amlnn-toolkit` as a sibling to `amlnn-model-playground`:
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```bash
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git clone git@github.com:Amlogic-NN/amlnn-toolkit.git ../amlnn-toolkit
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```
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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/yoloworld/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/yolo_world_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/yolo_world_demo /data/local/tmp/
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adb push model/yoloworld_int8_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 yolo_world_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./yolo_world_demo <model_path> <image_path>
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./yolo_world_demo yoloworld_int8_A311D2.adla test_image.jpg
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```
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**Note:** Replace `yoloworld_int8_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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# Basic usage (process current directory)
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python yoloworld.py --model-path ./yoloworld_int8_A311D2.adla
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# Specify image directory
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python yoloworld.py --model-path ./yoloworld_int8_A311D2.adla --image-dir ./
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```
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the specified directory and save results to a `{model_name}_result` folder.
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## Results
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The program will print the detection count and detected objects for each processed image. The result image with bounding boxes will be saved to the specified output directory.
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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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The program detects objects from predefined classes (handbag, backpack, wallet, watch, necklace, bracelet, earrings, finger ring, sunglass, hat, shoes, belt, makeup palette, lipstick tube, car, truck, bicycle, motorcycle, phone, laptop, camera, wine bottle, stuffed toy) and draws bounding boxes with class labels on the result images.
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