52 lines
2.5 KiB
Markdown
52 lines
2.5 KiB
Markdown
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# Ultralytics YOLOv8 Object Detection with OpenCV and ONNX
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This example demonstrates how to implement [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) object detection using [OpenCV](https://opencv.org/) in [Python](https://www.python.org/), leveraging the [ONNX (Open Neural Network Exchange)](https://onnx.ai/) model format for efficient inference.
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## 🚀 Getting Started
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Follow these simple steps to get the example running on your local machine.
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1. **Clone the Repository:**
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If you haven't already, clone the Ultralytics repository to access the example code:
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```bash
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git clone https://github.com/ultralytics/ultralytics.git
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cd ultralytics/examples/YOLOv8-OpenCV-ONNX-Python/
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```
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2. **Install Requirements:**
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Install the necessary Python packages listed in the `requirements.txt` file. We recommend using a virtual environment.
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```bash
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pip install -r requirements.txt
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```
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3. **Run the Detection Script:**
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Execute the main Python script, specifying the ONNX model path and the input image. A pre-exported `yolov8n.onnx` model is included for convenience.
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```bash
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python main.py --model yolov8n.onnx --img image.jpg
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```
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The script will perform object detection on `image.jpg` using the `yolov8n.onnx` model and display the results.
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## 🛠️ Exporting Your Model
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If you want to use a different Ultralytics YOLOv8 model or one you've trained yourself, you need to export it to the ONNX format first.
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1. **Install Ultralytics:**
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If you don't have it installed, get the latest `ultralytics` package:
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```bash
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pip install ultralytics
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```
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2. **Export the Model:**
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Use the `yolo export` command to convert your desired model (e.g., `yolov8n.pt`) to ONNX. Ensure you specify `opset=12` or higher for compatibility with OpenCV's DNN module. You can find more details in the Ultralytics [Export documentation](https://docs.ultralytics.com/modes/export/).
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```bash
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yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12
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```
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This command will generate a `yolov8n.onnx` file (or the corresponding name for your model) in your working directory. You can then use this `.onnx` file with the `main.py` script.
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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 to the main [Ultralytics repository](https://github.com/ultralytics/ultralytics). Thank you for helping us make Ultralytics YOLO even better!
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