How to display real-time MP4 processing results

[hnulmz3]

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Question

Using decect.py to test MP4 video has only one result output and an intermediate 'Video 1/1 (469/486).......... 640x384 (no detections), text display of 80.6ms', what should I do if I want to display the video in real time?

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Sorry, I'm a green hand

[github-actions]

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[hnulmz3]

谢谢，已收到

[hnulmz3]

OK，i got it by myself

[glenn-jocher]

@hnulmz3 that's great to hear! Don't hesitate to reach out if you have any further questions or if there's anything we can assist you with in the future. Have a great day!

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

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• Docs: https://docs.ultralytics.com
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• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 太好了，很高兴您已经解决了问题！如果将来还有任何问题或需要我们的帮助，请随时告诉我们。祝您拥有愉快的一天！

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 感谢您的反馈！如果将来还有任何问题或需要我们的帮助，请随时告诉我们。祝您拥有愉快的一天！

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 you're welcome! If you have any other questions or need further assistance, feel free to ask.

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 您好！非常感谢您的反馈！如果您有任何其他问题或需要进一步的帮助，请随时告知我们。祝您使用 YOLOv5 的愉快！

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 你好！

感谢你的反馈！如果你还有其他问题或需要进一步的帮助，请随时告诉我们。我们团队一直致力于提供高质量的支持和解决方案。祝你使用 YOLOv5 愉快！

最好的祝愿！

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 不客气！如果将来需要帮助，随时欢迎回来。祝您一切顺利！ 😊👍

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 很好！如果您有其他问题或需要进一步的帮助，请随时告知。祝您使用YOLOv5愉快！🌟

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

@hnulmz3 you're welcome! If you have more questions or need further assistance in the future, feel free to reach out. Happy coding with YOLOv5! 😊🚀

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

You're welcome! If you have any more questions or need assistance, just let us know. Happy to help! 😊

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

You're welcome! If there's anything else you need help with, just let me know. Happy coding! 😊

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

[hnulmz3]

谢谢，已收到

[glenn-jocher]

Hello @hnulmz3,

Thank you for reaching out! If you want to display real-time processing results while testing an MP4 video using detect.py, you can modify the script to show the video frames with detections as they are processed.

Here's a step-by-step guide to help you achieve this:

1. Ensure you have the latest version: Make sure you are using the most recent versions of torch and YOLOv5. You can update YOLOv5 by running:

   git pull
   pip install -U -r requirements.txt

2. Modify detect.py: You can add code to display each frame with OpenCV. Below is an example modification to detect.py:

   import cv2
   from pathlib import Path
   import torch
   from models.common import DetectMultiBackend
   from utils.datasets import LoadStreams, LoadImages
   from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
   from utils.plots import plot_one_box
   from utils.torch_utils import select_device, load_classifier, time_synchronized
   
   # Initialize
   set_logging()
   device = select_device('')
   half = device.type != 'cpu'  # half precision only supported on CUDA
   
   # Load model
   model = DetectMultiBackend(weights, device=device, dnn=dnn)
   stride, names, pt = model.stride, model.names, model.pt
   imgsz = check_img_size(imgsz, s=stride)  # check image size
   
   # Dataloader
   dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
   bs = 1  # batch_size
   
   # Run inference
   model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
   seen, dt = 0, [0.0, 0.0, 0.0]
   for path, img, im0s, vid_cap in dataset:
       img = torch.from_numpy(img).to(device)
       img = img.half() if half else img.float()  # uint8 to fp16/32
       img /= 255.0  # 0 - 255 to 0.0 - 1.0
       if img.ndimension() == 3:
           img = img.unsqueeze(0)
   
       # Inference
       t1 = time_synchronized()
       pred = model(img, augment=augment, visualize=visualize)
       t2 = time_synchronized()
   
       # NMS
       pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms)
       t3 = time_synchronized()
   
       # Process detections
       for i, det in enumerate(pred):  # detections per image
           seen += 1
           p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
   
           p = Path(p)  # to Path
           save_path = str(save_dir / p.name)  # img.jpg
           txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
           s += '%gx%g ' % img.shape[2:]  # print string
           gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
           imc = im0.copy() if save_crop else im0  # for save_crop
           annotator = Annotator(im0, line_width=line_thickness, example=str(names))
           if len(det):
               # Rescale boxes from img_size to im0 size
               det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
   
               # Print results
               for c in det[:, -1].unique():
                   n = (det[:, -1] == c).sum()  # detections per class
                   s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
   
               # Write results
               for *xyxy, conf, cls in reversed(det):
                   if save_txt:  # Write to file
                       xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                       line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                       with open(txt_path + '.txt', 'a') as f:
                           f.write(('%g ' * len(line)).rstrip() % line + '\n')
   
                   if save_img or view_img:  # Add bbox to image
                       label = f'{names[int(cls)]} {conf:.2f}'
                       plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=line_thickness)
   
           # Stream results
           if view_img:
               cv2.imshow(str(p), im0)
               if cv2.waitKey(1) == ord('q'):  # q to quit
                   raise StopIteration
   
           # Save results (image with detections)
           if save_img:
               if dataset.mode == 'image':
                   cv2.imwrite(save_path, im0)
               else:  # 'video' or 'stream'
                   if vid_path != save_path:  # new video
                       vid_path = save_path
                       if isinstance(vid_writer, cv2.VideoWriter):
                           vid_writer.release()  # release previous video writer
                       if vid_cap:  # video
                           fps = vid_cap.get(cv2.CAP_PROP_FPS)
                           w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                           h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                       else:  # stream
                           fps, w, h = 30, im0.shape[1], im0.shape[0]
                       save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                       vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                   vid_writer.write(im0)
   
   if save_txt or save_img:
       s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
       print(f"Results saved to {save_dir}{s}")
   print(f'Done. ({t3 - t0:.3f}s)')

3. Run the modified script: Execute the modified detect.py script with your video file as the source:

   python detect.py --source path/to/your/video.mp4 --view-img

This modification will display the video frames with detections in real-time. If you encounter any issues or have further questions, feel free to ask!