86 lines
4.0 KiB
Markdown
86 lines
4.0 KiB
Markdown
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# YOLOv8/YOLOv5 C++ Inference with OpenCV DNN
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This example demonstrates how to perform inference using Ultralytics YOLOv8 and YOLOv5 models in C++ leveraging the [OpenCV DNN module](https://docs.opencv.org/4.x/d6/d0f/group__dnn.html).
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## 🛠️ Usage
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Follow these steps to set up and run the C++ inference example:
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```bash
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# 1. Clone the Ultralytics repository
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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# 2. Install Ultralytics Python package (needed for exporting models)
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pip install .
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# 3. Navigate to the C++ example directory
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cd examples/YOLOv8-CPP-Inference
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# 4. Export Models: Add yolov8*.onnx and/or yolov5*.onnx models (see export instructions below)
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# Place the exported ONNX models in the current directory (YOLOv8-CPP-Inference).
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# 5. Update Source Code: Edit main.cpp and set the 'projectBasePath' variable
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# to the absolute path of the 'YOLOv8-CPP-Inference' directory on your system.
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# Example: std::string projectBasePath = "/path/to/your/ultralytics/examples/YOLOv8-CPP-Inference";
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# 6. Configure OpenCV DNN Backend (Optional - CUDA):
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# - The default CMakeLists.txt attempts to use CUDA for GPU acceleration with OpenCV DNN.
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# - If your OpenCV build doesn't support CUDA/cuDNN, or you want CPU inference,
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# remove the CUDA-related lines from CMakeLists.txt.
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# 7. Build the project
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mkdir build
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cd build
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cmake ..
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make
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# 8. Run the inference executable
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./Yolov8CPPInference
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```
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## ✨ Exporting YOLOv8 and YOLOv5 Models
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You need to export your trained PyTorch models to the [ONNX](https://onnx.ai/) format to use them with OpenCV DNN.
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**Exporting Ultralytics YOLOv8 Models:**
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Use the Ultralytics CLI to export. Ensure you specify the desired `imgsz` and `opset`. For compatibility with this example, `opset=12` is recommended.
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```bash
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yolo export model=yolov8s.pt imgsz=640,480 format=onnx opset=12 # Example: 640x480 resolution
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```
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**Exporting YOLOv5 Models:**
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Use the `export.py` script from the YOLOv5 repository structure (included within the cloned `ultralytics` repo).
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```bash
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# Assuming you are in the 'ultralytics' base directory after cloning
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python export.py --weights yolov5s.pt --imgsz 640 480 --include onnx --opset 12 # Example: 640x480 resolution
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```
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Place the generated `.onnx` files (e.g., `yolov8s.onnx`, `yolov5s.onnx`) into the `ultralytics/examples/YOLOv8-CPP-Inference/` directory.
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**Example Output:**
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_yolov8s.onnx:_
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_yolov5s.onnx:_
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## 📝 Notes
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- This repository utilizes the [OpenCV DNN API](https://docs.opencv.org/4.x/d6/d0f/group__dnn.html) to run [ONNX](https://onnx.ai/) exported models of YOLOv5 and Ultralytics YOLOv8.
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- While not explicitly tested, it might theoretically work for other YOLO architectures like YOLOv6 and YOLOv7 if their ONNX export formats are compatible.
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- The example models are exported with a rectangular resolution (640x480), but the code should handle models exported with different resolutions. Consider using techniques like [letterboxing](https://docs.ultralytics.com/modes/predict/#letterbox) if your input images have different aspect ratios than the model's training resolution, especially for square `imgsz` exports.
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- The `main` branch version includes a simple GUI wrapper using [Qt](https://www.qt.io/). However, the core logic resides in the `Inference` class (`inference.h`, `inference.cpp`).
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- A key part of the `Inference` class demonstrates how to handle the output differences between YOLOv5 and YOLOv8 models, effectively transposing YOLOv8's output format to match the structure expected from YOLOv5 for consistent post-processing.
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## 🤝 Contributing
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Contributions are welcome! If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request. See our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for more details.
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