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.
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136 changed files with 14413 additions and 9399 deletions
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@ -1,170 +1,170 @@
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# yolov11
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## 1.Overview
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YOLOv11 was released by Ultralytics on October 2, 2024. It introduces significant architectural refinements and efficiency optimizations, delivering superior accuracy-speed trade-offs compared to previous generations. With enhanced feature extraction capabilities, YOLOv11 is designed for high-performance real-time applications—including object detection, instance segmentation, and pose estimation—to handle demanding 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/v8.3.0
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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==8.3.0
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- **Download weights**
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.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("yolo11n.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 yolov11m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov11s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_covnert.sh yolov11n.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/yolov11/cpp
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AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/yolo11_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/yolo11_demo /data/local/tmp/
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adb push model/yolov11n_int8_A311D2.adla /data/local/tmp/
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adb push imgs /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 yolo11_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./yolo11_demo <model_path> <image_dir>
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./yolo11_demo yolov11n_int8_A311D2.adla ./imgs
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```
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**Note:** Replace `yolov11n_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 yolov11.py \
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--model-path ./yolov11n_int8_A311D2.adla \
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--image-dir ./imgs \
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--run-cycles 1 \
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--loglevel INFO
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```
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Argument Descriptions:
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| Argument | Description |
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| ----------------- | ------------------------------------------------------------ |
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| --board-work-path | Work path on board, default is /data/local/tmp |
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| --model-path | path to .adla model |
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| --image-dir | Directory containing test images |
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| --run-cycles | Number of inference cycles, default is 1 |
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| --loglevel | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
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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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**Performance Feedback**
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By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
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- Hardware Information: System and ADLA library versions.
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- Model Overview: Basic input/output configurations.
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- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
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**Detection Output**
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The program will print the detection count. The result image with bounding boxes will be saved to the specified output path (`{model_name}_result`).
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You can pull the result folder back to view it:
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```bash
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adb pull /data/local/tmp/yolov11n_int8_A311D2_result
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```
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**Profiling Visualization**
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After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
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- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
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- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
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- `pie_charts_distribution.html`: Overall resource allocation.
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You can pull the result folder back to view it:
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```bash
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adb pull /data/local/tmp/yolov11n_int8_A311D2
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```
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Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
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# yolov11
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## 1.Overview
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YOLOv11 was released by Ultralytics on October 2, 2024. It introduces significant architectural refinements and efficiency optimizations, delivering superior accuracy-speed trade-offs compared to previous generations. With enhanced feature extraction capabilities, YOLOv11 is designed for high-performance real-time applications—including object detection, instance segmentation, and pose estimation—to handle demanding 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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|
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- **Open Source projects:** https://github.com/ultralytics/ultralytics/tree/v8.3.0
|
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|
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- **Export Model Step:**
|
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|
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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==8.3.0
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- **Download weights**
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt
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wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.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("yolo11n.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_convert.sh model_path adla_toolkit_path target_platform
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example
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./adla_convert.sh yolov11m.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh yolov11s.onnx /xxxx/adla-toolkit-binary-3.2.9.3 PRODUCT_PID0XA005
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./adla_convert.sh yolov11n.onnx /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/yolov11/cpp
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AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/yolo11_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/yolo11_demo /data/local/tmp/
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adb push model/yolov11n_int8_A311D2.adla /data/local/tmp/
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adb push imgs /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 yolo11_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./yolo11_demo <model_path> <image_dir>
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./yolo11_demo yolov11n_int8_A311D2.adla ./imgs
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```
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**Note:** Replace `yolov11n_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 yolov11.py \
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--model-path ./yolov11n_int8_A311D2.adla \
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--image-dir ./imgs \
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--run-cycles 1 \
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--loglevel INFO
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```
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Argument Descriptions:
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| Argument | Description |
|
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| ----------------- | ------------------------------------------------------------ |
|
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| --board-work-path | Work path on board, default is /data/local/tmp |
|
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| --model-path | path to .adla model |
|
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| --image-dir | Directory containing test images |
|
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| --run-cycles | Number of inference cycles, default is 1 |
|
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| --loglevel | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
|
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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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**Performance Feedback**
|
||||
|
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By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
|
||||
- Hardware Information: System and ADLA library versions.
|
||||
- Model Overview: Basic input/output configurations.
|
||||
- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
|
||||
|
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**Detection Output**
|
||||
|
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The program will print the detection count. The result image with bounding boxes will be saved to the specified output path (`{model_name}_result`).
|
||||
|
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You can pull the result folder back to view it:
|
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```bash
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adb pull /data/local/tmp/yolov11n_int8_A311D2_result
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```
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|
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|
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**Profiling Visualization**
|
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|
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After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
|
||||
- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
|
||||
- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
|
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- `pie_charts_distribution.html`: Overall resource allocation.
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|
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You can pull the result folder back to view it:
|
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```bash
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adb pull /data/local/tmp/yolov11n_int8_A311D2
|
||||
```
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|
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Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
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@ -1,35 +1,35 @@
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cmake_minimum_required(VERSION 3.10...3.27)
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project(yolo11_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 dependency 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(yolo11_demo
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main.cpp
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postprocess.cpp
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${CMAKE_SOURCE_DIR}/../../../../common/model_loader.cpp
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)
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target_link_libraries(yolo11_demo
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${OpenCV_LIBS}
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${AMLNN_LIBRARY}
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cmake_minimum_required(VERSION 3.10...3.27)
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project(yolo11_demo)
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set(CMAKE_CXX_STANDARD 17)
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list(APPEND CMAKE_MODULE_PATH "${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(${CMAKE_SOURCE_DIR}/../../../../common)
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# Set dependency path
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set(3RDPARTY_DIR "${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(yolo11_demo
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main.cpp
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postprocess.cpp
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${CMAKE_SOURCE_DIR}/../../../../common/model_loader.cpp
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)
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target_link_libraries(yolo11_demo
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${OpenCV_LIBS}
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${AMLNN_LIBRARY}
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)
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@ -58,9 +58,9 @@ int main(int argc, char** argv) {
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int nw = img.cols * scale, nh = img.rows * scale;
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int px = (kInputW - nw) / 2, py = (kInputH - nh) / 2;
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cv::Mat res, canvas = cv::Mat::zeros(kInputH, kInputW, CV_32FC3);
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canvas.setTo(cv::Scalar(114.0/255.0, 114.0/255.0, 114.0/255.0));
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canvas.setTo(cv::Scalar(114.0/255.0, 114.0/255.0, 114.0/255.0));
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cv::resize(img, res, {nw, nh});
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res.convertTo(res, CV_32FC3, 1.0 / 255.0);
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res.convertTo(res, CV_32FC3, 1.0 / 255.0);
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res.copyTo(canvas(cv::Rect(px, py, nw, nh)));
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hwc_to_chw(canvas, chw_buffer.data());
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@ -108,7 +108,7 @@ int main(int argc, char** argv) {
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for (size_t i = 0; i < indices.size(); i++) {
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int idx = indices[i];
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printf(" %zu. %s (%.2f)\n", i + 1, kClassNames[class_ids[idx]].c_str(), confs[idx]);
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cv::rectangle(img, bboxes[idx], {0, 255, 0}, 2);
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char text[256]; std::sprintf(text, "%s %.2f", kClassNames[class_ids[idx]].c_str(), confs[idx]);
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cv::putText(img, text, {bboxes[idx].x, bboxes[idx].y - 5}, cv::FONT_HERSHEY_SIMPLEX, 0.5, {0, 255, 0}, 1);
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|
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@ -1,82 +1,82 @@
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/*
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* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
|
||||
*
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* http://www.apache.org/licenses/LICENSE-2.0
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||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
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*/
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#include "postprocess.h"
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include <algorithm>
|
||||
#include <float.h>
|
||||
|
||||
const std::vector<std::string> kClassNames = {
|
||||
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
|
||||
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
|
||||
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
|
||||
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
|
||||
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
|
||||
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||||
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
|
||||
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
|
||||
"scissors", "teddy bear", "hair drier", "toothbrush"
|
||||
};
|
||||
|
||||
float decode_dfl(const float* dfl_ptr) {
|
||||
float max_val = -FLT_MAX;
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
if (dfl_ptr[i] > max_val) max_val = dfl_ptr[i];
|
||||
}
|
||||
float exp_sum = 0;
|
||||
float exp_vals[kDflChannels];
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
exp_vals[i] = std::exp(dfl_ptr[i] - max_val);
|
||||
exp_sum += exp_vals[i];
|
||||
}
|
||||
float res = 0;
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
res += (exp_vals[i] / exp_sum) * i;
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
float calculate_iou(const cv::Rect& a, const cv::Rect& b) {
|
||||
int xx1 = std::max(a.x, b.x);
|
||||
int yy1 = std::max(a.y, b.y);
|
||||
int xx2 = std::min(a.x + a.width, b.x + b.width);
|
||||
int yy2 = std::min(a.y + a.height, b.y + b.height);
|
||||
int w = std::max(0, xx2 - xx1);
|
||||
int h = std::max(0, yy2 - yy1);
|
||||
float inter = (float)w * h;
|
||||
float areaA = (float)a.width * a.height;
|
||||
float areaB = (float)b.width * b.height;
|
||||
return inter / (areaA + areaB - inter);
|
||||
}
|
||||
|
||||
std::vector<int> manual_nms(const std::vector<cv::Rect>& boxes, const std::vector<float>& scores, float thresh) {
|
||||
std::vector<int> order(boxes.size());
|
||||
std::iota(order.begin(), order.end(), 0);
|
||||
std::stable_sort(order.begin(), order.end(), [&](int a, int b) {
|
||||
return scores[a] > scores[b];
|
||||
});
|
||||
std::vector<int> keep;
|
||||
while (!order.empty()) {
|
||||
int i = order[0];
|
||||
keep.push_back(i);
|
||||
order.erase(order.begin());
|
||||
order.erase(std::remove_if(order.begin(), order.end(), [&](int j) {
|
||||
return calculate_iou(boxes[i], boxes[j]) > thresh;
|
||||
}), order.end());
|
||||
}
|
||||
return keep;
|
||||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#include "postprocess.h"
|
||||
#include <cmath>
|
||||
#include <numeric>
|
||||
#include <algorithm>
|
||||
#include <float.h>
|
||||
|
||||
const std::vector<std::string> kClassNames = {
|
||||
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
|
||||
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
|
||||
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
|
||||
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
|
||||
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
|
||||
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||||
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
|
||||
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
|
||||
"scissors", "teddy bear", "hair drier", "toothbrush"
|
||||
};
|
||||
|
||||
float decode_dfl(const float* dfl_ptr) {
|
||||
float max_val = -FLT_MAX;
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
if (dfl_ptr[i] > max_val) max_val = dfl_ptr[i];
|
||||
}
|
||||
float exp_sum = 0;
|
||||
float exp_vals[kDflChannels];
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
exp_vals[i] = std::exp(dfl_ptr[i] - max_val);
|
||||
exp_sum += exp_vals[i];
|
||||
}
|
||||
float res = 0;
|
||||
for (int i = 0; i < kDflChannels; i++) {
|
||||
res += (exp_vals[i] / exp_sum) * i;
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
float calculate_iou(const cv::Rect& a, const cv::Rect& b) {
|
||||
int xx1 = std::max(a.x, b.x);
|
||||
int yy1 = std::max(a.y, b.y);
|
||||
int xx2 = std::min(a.x + a.width, b.x + b.width);
|
||||
int yy2 = std::min(a.y + a.height, b.y + b.height);
|
||||
int w = std::max(0, xx2 - xx1);
|
||||
int h = std::max(0, yy2 - yy1);
|
||||
float inter = (float)w * h;
|
||||
float areaA = (float)a.width * a.height;
|
||||
float areaB = (float)b.width * b.height;
|
||||
return inter / (areaA + areaB - inter);
|
||||
}
|
||||
|
||||
std::vector<int> manual_nms(const std::vector<cv::Rect>& boxes, const std::vector<float>& scores, float thresh) {
|
||||
std::vector<int> order(boxes.size());
|
||||
std::iota(order.begin(), order.end(), 0);
|
||||
std::stable_sort(order.begin(), order.end(), [&](int a, int b) {
|
||||
return scores[a] > scores[b];
|
||||
});
|
||||
std::vector<int> keep;
|
||||
while (!order.empty()) {
|
||||
int i = order[0];
|
||||
keep.push_back(i);
|
||||
order.erase(order.begin());
|
||||
order.erase(std::remove_if(order.begin(), order.end(), [&](int j) {
|
||||
return calculate_iou(boxes[i], boxes[j]) > thresh;
|
||||
}), order.end());
|
||||
}
|
||||
return keep;
|
||||
}
|
||||
|
|
@ -1,42 +1,42 @@
|
|||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef YOLO11_POSTPROCESS_H
|
||||
#define YOLO11_POSTPROCESS_H
|
||||
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <opencv2/opencv.hpp>
|
||||
|
||||
static constexpr int kInputW = 640;
|
||||
static constexpr int kInputH = 640;
|
||||
static constexpr int kNumClasses = 80;
|
||||
static constexpr int kDflChannels = 16;
|
||||
static constexpr int kTotalChannels = 144;
|
||||
|
||||
extern const std::vector<std::string> kClassNames;
|
||||
|
||||
struct Detection {
|
||||
cv::Rect box;
|
||||
float confidence;
|
||||
int class_id;
|
||||
};
|
||||
|
||||
float decode_dfl(const float* dfl_ptr);
|
||||
float calculate_iou(const cv::Rect& a, const cv::Rect& b);
|
||||
std::vector<int> manual_nms(const std::vector<cv::Rect>& boxes, const std::vector<float>& scores, float thresh);
|
||||
|
||||
/*
|
||||
* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#ifndef YOLO11_POSTPROCESS_H
|
||||
#define YOLO11_POSTPROCESS_H
|
||||
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <opencv2/opencv.hpp>
|
||||
|
||||
static constexpr int kInputW = 640;
|
||||
static constexpr int kInputH = 640;
|
||||
static constexpr int kNumClasses = 80;
|
||||
static constexpr int kDflChannels = 16;
|
||||
static constexpr int kTotalChannels = 144;
|
||||
|
||||
extern const std::vector<std::string> kClassNames;
|
||||
|
||||
struct Detection {
|
||||
cv::Rect box;
|
||||
float confidence;
|
||||
int class_id;
|
||||
};
|
||||
|
||||
float decode_dfl(const float* dfl_ptr);
|
||||
float calculate_iou(const cv::Rect& a, const cv::Rect& b);
|
||||
std::vector<int> manual_nms(const std::vector<cv::Rect>& boxes, const std::vector<float>& scores, float thresh);
|
||||
|
||||
#endif
|
||||
|
|
@ -23,7 +23,7 @@ import cv2
|
|||
from pathlib import Path
|
||||
from amlnnlite.api import AMLNNLite
|
||||
|
||||
class_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
|
||||
class_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'doughnut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
|
||||
|
||||
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)):
|
||||
shape = img.shape[:2]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue