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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35 changed files with 1438 additions and 1222 deletions
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@ -1,133 +1,147 @@
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# yolov8
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## 1.Overview
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YOLOv8 was released by Ultralytics on January 10, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications.
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.2.0
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- **Export Model Step:**
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- **Install ultralytics**
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pip install torch==2.4.1
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pip install torchvision==0.19.1
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pip install ultralytics==8.2.0
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- **Download weights**
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
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- **Export Model**
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```
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from ultralytics import YOLO
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model = YOLO("yolov8m.pt")
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model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
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```
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- **Exported Model**
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link to amlogic server( **onnx model or quantized tflite**)
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_covnert.sh model_path adla_tookkit_path target_platform
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example
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./adla_covnert.sh yolov8m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov8s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov8n.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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```
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| Parameter | Discription |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_tookkit_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/yolov8/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/yolov8_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/yolov8_demo /data/local/tmp/
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adb push model/yolov8s_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 yolov8_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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./yolov8_demo yolov8s_int8_A311D2.adla test_image.jpg"
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```
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**Note:** Replace `yolov8s_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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python yolov8.py --model-path ./yolov8s_int8_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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# yolov8
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## 1.Overview
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YOLOv8 was released by Ultralytics on January 10, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications.
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.2.0
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- **Export Model Step:**
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- **Install ultralytics**
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pip install torch==2.4.1
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pip install torchvision==0.19.1
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pip install ultralytics==8.2.0
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- **Download weights**
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt
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- **Export Model**
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```
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from ultralytics import YOLO
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model = YOLO("yolov8m.pt")
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model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
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```
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- **Exported Model**
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link to amlogic server( **onnx model or quantized tflite**)
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_covnert.sh model_path adla_tookkit_path target_platform
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example
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./adla_covnert.sh yolov8m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov8s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov8n.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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```
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| Parameter | Discription |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_tookkit_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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#### 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/yolov8/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/yolov8_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/yolov8_demo /data/local/tmp/
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adb push model/yolov8s_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 yolov8_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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./yolov8_demo yolov8s_int8_A311D2.adla test_image.jpg"
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```
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**Note:** Replace `yolov8s_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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python yolov8.py --model-path ./yolov8s_int8_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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@ -65,13 +65,13 @@ echo "BUILD_DIR: ${BUILD_DIR}"
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mkdir -p ${BUILD_DIR}
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cd ${BUILD_DIR}
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cmake ../../src \
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cmake -Wno-dev ../../src \
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-DAMLNN_HOME=${AMLNN_HOME:-} \
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-DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_PATH}/build/cmake/android.toolchain.cmake \
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-DANDROID_ABI=${TARGET_ABI} \
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-DANDROID_PLATFORM=android-24 \
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-DCMAKE_BUILD_TYPE=Release \
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-DOpenCV_DIR=${ROOT_PWD}/../../../dependency/opencv/opencv-android-sdk-build/sdk/native/jni/abi-${TARGET_ABI} \
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-DNNSDK_DIR=${ROOT_PWD}/../../../../amlnn-toolkit/nn_runtime/nnsdk
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make -j4
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@ -1,46 +1,36 @@
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cmake_minimum_required(VERSION 3.5)
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project(yolo_world_demo)
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set(CMAKE_CXX_STANDARD 17)
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# Set NNSDK path
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if(NOT DEFINED NNSDK_DIR)
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set(NNSDK_DIR "${CMAKE_SOURCE_DIR}/../../../../../amlnn-toolkit/nn_runtime/nnsdk")
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endif()
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set(NNSDK_ROOT "${NNSDK_DIR}")
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message(STATUS "NNSDK_ROOT: ${NNSDK_ROOT}")
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include_directories(${NNSDK_ROOT}/include)
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include_directories(${CMAKE_SOURCE_DIR}/../../../../common)
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# Set 3rdparty path
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set(3RDPARTY_DIR "${CMAKE_SOURCE_DIR}/../../../../dependency")
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if(CMAKE_SYSTEM_NAME STREQUAL "Android")
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if (ANDROID_ABI STREQUAL "arm64-v8a")
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link_directories(${NNSDK_ROOT}/android/arm64-v8a)
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else()
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link_directories(${NNSDK_ROOT}/android/armeabi-v7a)
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endif()
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# Android needs log
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link_libraries(log)
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elseif(CMAKE_SYSTEM_NAME STREQUAL "Linux")
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link_directories(${NNSDK_ROOT}/linux/yocto/aarch64-poky-linux)
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endif()
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# Find OpenCV
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message(STATUS "OpenCV_DIR: ${OpenCV_DIR}")
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find_package(OpenCV REQUIRED)
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include_directories(${OpenCV_INCLUDE_DIRS})
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add_executable(yolov8_demo
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main.cpp
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postprocess.cpp
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postprocess.h
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${CMAKE_SOURCE_DIR}/../../../../common/model_loader.cpp
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)
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target_link_libraries(yolov8_demo
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${OpenCV_LIBS}
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nnsdk
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)
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cmake_minimum_required(VERSION 3.10...3.27)
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project(yolo_world_demo)
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set(CMAKE_CXX_STANDARD 17)
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list(APPEND CMAKE_MODULE_PATH "${CMAKE_SOURCE_DIR}/../../../../cmake")
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find_package(AMLNN REQUIRED)
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include_directories(${AMLNN_INCLUDE_DIR})
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link_directories(${AMLNN_LIBRARY_DIR})
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include_directories(${CMAKE_SOURCE_DIR}/../../../../common)
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# Set 3rdparty path
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set(3RDPARTY_DIR "${CMAKE_SOURCE_DIR}/../../../../dependency")
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if(CMAKE_SYSTEM_NAME STREQUAL "Android")
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# Android needs log
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link_libraries(log)
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endif()
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# Find OpenCV
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message(STATUS "OpenCV_DIR: ${OpenCV_DIR}")
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find_package(OpenCV REQUIRED)
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include_directories(${OpenCV_INCLUDE_DIRS})
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add_executable(yolov8_demo
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main.cpp
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postprocess.cpp
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postprocess.h
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${CMAKE_SOURCE_DIR}/../../../../common/model_loader.cpp
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)
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target_link_libraries(yolov8_demo
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${OpenCV_LIBS}
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${AMLNN_LIBRARY}
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)
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