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| # [How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python) | |
| To run this: | |
| - `pip3 install -r requirements.txt` | |
| - Download the [model weights](https://pjreddie.com/media/files/yolov3.weights) and put them in `weights` folder. | |
| - To generate a object detection image on `images/dog.jpg`: | |
| ``` | |
| python yolo_opencv.py images/dog.jpg | |
| ``` | |
| A new image `dog_yolo3.jpg` will appear which has the bounding boxes of different objects in the image. | |
| - For live object detection: | |
| ``` | |
| python live_yolo_opencv.py | |
| ``` | |
| - If you want to read from a video file and make predictions: | |
| ``` | |
| python read_video.py video.avi | |
| ``` | |
| This will start detecting objects in that video, in the end, it'll save the resulting video to `output.avi` | |
| - If you wish to use PyTorch for GPU acceleration, please install PyTorch CUDA [here](https://pytorch.org/get-started) and use `yolo.py` file. | |
| - Feel free to edit the codes for your needs! | |