yolov5 Ip camera

[FratCan]

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YoloV5 has a latency problem with ip camera. When I use the webcam, it works normally via CPU with default yoloV5 or custom trained model, but when I switch to Ip camera the video comes very late. i connected Ip camera using openCv and RTSP. how can i solve this delay?

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[glenn-jocher]

@FratCan hi there!

Thank you for reaching out and for providing detailed information about your issue. Latency problems with IP cameras can often be attributed to several factors, including network delays, buffering, and the processing power required for decoding RTSP streams. Here are a few suggestions to help you mitigate the delay:

1. Buffer Size: OpenCV's default buffer size might be causing the delay. You can try reducing the buffer size by setting the CAP_PROP_BUFFERSIZE property:

   cap = cv2.VideoCapture("rtsp://your_ip_camera_stream")
   cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

2. Threading: Implementing threading can help in reducing latency by processing frames in parallel. Here’s a simple example:

   import cv2
   import threading
   
   def get_frame(cap):
       while True:
           ret, frame = cap.read()
           if ret:
               # Process frame
               pass
   
   cap = cv2.VideoCapture("rtsp://your_ip_camera_stream")
   threading.Thread(target=get_frame, args=(cap,)).start()

3. FFmpeg: Sometimes using FFmpeg can provide better performance for handling RTSP streams. You can use FFmpeg with OpenCV as follows:

   cap = cv2.VideoCapture("ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -", cv2.CAP_FFMPEG)

4. Network Optimization: Ensure that your network connection is stable and has sufficient bandwidth to handle the video stream from the IP camera.

5. Frame Skipping: If real-time processing is more critical than processing every frame, you can skip frames to reduce latency:

   frame_skip = 5
   frame_count = 0
   
   while True:
       ret, frame = cap.read()
       if not ret:
           break
       if frame_count % frame_skip == 0:
           # Process frame
           pass
       frame_count += 1

Please ensure you are using the latest version of YOLOv5 and OpenCV to benefit from the latest improvements and bug fixes. If the issue persists, feel free to share more details, and we can further investigate.

Best of luck with your project! 😊

[FratCan]

@FratCan hi there!

Thank you for reaching out and for providing detailed information about your issue. Latency problems with IP cameras can often be attributed to several factors, including network delays, buffering, and the processing power required for decoding RTSP streams. Here are a few suggestions to help you mitigate the delay:

1. Buffer Size: OpenCV's default buffer size might be causing the delay. You can try reducing the buffer size by setting the CAP_PROP_BUFFERSIZE property:

   cap = cv2.VideoCapture("rtsp://your_ip_camera_stream")
   cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

2. Threading: Implementing threading can help in reducing latency by processing frames in parallel. Here’s a simple example:

   import cv2
   import threading
   
   def get_frame(cap):
       while True:
           ret, frame = cap.read()
           if ret:
               # Process frame
               pass
   
   cap = cv2.VideoCapture("rtsp://your_ip_camera_stream")
   threading.Thread(target=get_frame, args=(cap,)).start()

3. FFmpeg: Sometimes using FFmpeg can provide better performance for handling RTSP streams. You can use FFmpeg with OpenCV as follows:

   cap = cv2.VideoCapture("ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -", cv2.CAP_FFMPEG)

4. Network Optimization: Ensure that your network connection is stable and has sufficient bandwidth to handle the video stream from the IP camera.
5. Frame Skipping: If real-time processing is more critical than processing every frame, you can skip frames to reduce latency:

   frame_skip = 5
   frame_count = 0
   
   while True:
       ret, frame = cap.read()
       if not ret:
           break
       if frame_count % frame_skip == 0:
           # Process frame
           pass
       frame_count += 1

Please ensure you are using the latest version of YOLOv5 and OpenCV to benefit from the latest improvements and bug fixes. If the issue persists, feel free to share more details, and we can further investigate.

Best of luck with your project! 😊

First of all, thank you very much for your reply.
Multithread almost solved the problem but there is no video when we tried to use ffmpeg with cv2.VideoCapture
cap = cv2.VideoCapture(ip_camera_url) --> works,

cap = cv2.VideoCapture(‘ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -’, cv2.CAP_FFMPEG) -->doesn't work

[glenn-jocher]

Hi @FratCan,

Thank you for your feedback! I'm glad to hear that multithreading has helped reduce the latency. Regarding the issue with FFmpeg, it seems like there might be a problem with the FFmpeg command or its integration with OpenCV. Let's troubleshoot this further:

1. Verify FFmpeg Installation: Ensure that FFmpeg is correctly installed and accessible from your command line. You can check this by running:

   ffmpeg -version

2. Correct FFmpeg Command: The FFmpeg command needs to be correctly formatted. Here’s a refined version:

   cap = cv2.VideoCapture("ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -", cv2.CAP_FFMPEG)

3. Alternative FFmpeg Command: Sometimes, specifying the input format can help:

   cap = cv2.VideoCapture("ffmpeg -rtsp_transport tcp -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -", cv2.CAP_FFMPEG)

4. Check for Errors: If the above commands do not work, try running the FFmpeg command directly in your terminal to see if there are any errors:

   ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -

5. Debugging Output: You can also enable logging in OpenCV to get more information about what might be going wrong:

   cv2.utils.logging.setLogLevel(cv2.utils.logging.LOG_LEVEL_DEBUG)
   cap = cv2.VideoCapture("ffmpeg -i rtsp://your_ip_camera_stream -f rawvideo -pix_fmt bgr24 -", cv2.CAP_FFMPEG)

If none of these solutions work, it might be helpful to share any error messages you receive when attempting to use FFmpeg with OpenCV. This can provide more context for further troubleshooting.

Thank you for your patience, and I appreciate your collaboration in resolving this issue. The YOLO community and the Ultralytics team are always here to help! 😊