how to do real-time object detection? with yolov5 and Opencv

KhaiHoanNinh commented 8 months ago

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Question

when I do real-time object detection with a camera and OpenCV. I need to deploy a real-time object detection window (screen) to an app GUI that I've made but I can not find the source of it. how can I do it?

Additional

I need to deploy it to the live camera

github-actions[bot] commented 8 months ago

👋 Hello @KhaiHoanNinh, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

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glenn-jocher commented 8 months ago

@KhaiHoanNinh hello! It's great to see your interest in deploying real-time object detection using YOLOv5 and OpenCV. For integrating YOLOv5 with a live camera feed in an application GUI, you'll typically follow these steps:

Capture Video Stream: Use OpenCV to capture video stream from your camera. You can do this with cv2.VideoCapture.
Load YOLOv5 Model: Load your YOLOv5 model using our PyTorch Hub integration or by directly loading the model weights.
Process Frames: For each frame from your camera, convert the frame into the format expected by YOLOv5 (usually resizing and normalization), then pass it through the model to get predictions.
Display Predictions: Process the model's predictions (bounding boxes, classes, and confidence scores) and overlay them on the original frame. Then, display this frame in your GUI.
Loop: Repeat steps 3 and 4 for each new frame from the camera to achieve real-time performance.

For specific code examples and more detailed instructions, please refer to our documentation at https://docs.ultralytics.com/yolov5/. This includes examples on how to use YOLOv5 with PyTorch for real-time detections, which can be adapted for use with OpenCV for capturing and displaying the video feed.

Remember, if you're planning to use YOLOv5 in a commercial application or need to integrate it into a proprietary solution, you'll need an Ultralytics Enterprise License. The AGPL-3.0 license allows for usage as long as the entire project is open-sourced under the same license. For any commercial or internal company usage not intended to be open-sourced, an Enterprise License is required.

If you have further questions or need clarification, feel free to ask. Happy coding! 😊

github-actions[bot] commented 7 months ago

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