Multithreading Issues with GStreamer VideoCapture in OpenCV on Nvidia Jetson Orin Nano Dev Board

Issue Overview

Users are experiencing crashes when attempting to utilize a multithreaded approach for capturing video streams using GStreamer in OpenCV on the Nvidia Jetson Orin Nano Dev Board. The issue arises when two video capture threads are initiated—one connected through the Camera Serial Interface (CSI) and another streaming over TCP. Symptoms include the script functioning for approximately five seconds before crashing, while a single-threaded approach works without issues but at a slower pace. The specific hardware involved is the Nvidia Jetson Orin Nano, and the software environment includes OpenCV with GStreamer support. The problem appears consistently across attempts, significantly impacting user experience by limiting the ability to process multiple video streams simultaneously.

Possible Causes

• Global Interpreter Lock (GIL): In Python, the GIL can cause conflicts in multithreaded applications, leading to crashes when multiple threads attempt to access shared resources like camera feeds simultaneously.
• Thread Safety of OpenCV: OpenCV’s VideoCapture may not be thread-safe, which could result in undefined behavior or crashes when accessed from multiple threads.
• Resource Contention: Both threads may contend for system resources, leading to performance degradation or crashes due to insufficient memory or processing power when handling multiple streams.
• Driver Issues: Potential incompatibilities or bugs in the GStreamer drivers used for video capture could also contribute to instability during multithreaded operations.
• Configuration Errors: Incorrect GStreamer pipeline configurations may lead to failures in establishing video streams, especially under concurrent access scenarios.

Troubleshooting Steps, Solutions & Fixes

1. Use Multiprocessing Instead of Threading:

   • Since threading can lead to issues due to the GIL, consider using Python’s multiprocessing module instead. This allows each camera feed to run in its own process, avoiding shared memory conflicts.

   import cv2
   import multiprocessing
   
   def open_cam(command):
       camera = cv2.VideoCapture(command)
       while True:
           ret, frame = camera.read()
           if not ret:
               break
           cv2.imshow('Camera', frame)
           if cv2.waitKey(1) & 0xFF == ord('q'):
               break
       camera.release()
       cv2.destroyAllWindows()
   
   if __name__ == '__main__':
       vc = 'v4l2src device=/dev/video0 ! ...'
       raspicap = 'tcpclientsrc host=192.168.0.248 port=5000 ! ...'
       p1 = multiprocessing.Process(target=open_cam, args=(vc,))
       p2 = multiprocessing.Process(target=open_cam, args=(raspicap,))
       p1.start()
       p2.start()
       p1.join()
       p2.join()
   

2. Optimize GStreamer Pipelines:

   • Ensure that the GStreamer pipelines are correctly configured and optimized for performance. Validate each pipeline independently before integrating them into a multithreaded context.

3. Check for Resource Limits:

   • Monitor system resources such as CPU and memory usage during execution. If resource limits are being hit, consider optimizing code or upgrading hardware.

4. Single-threaded Testing:

   • Test each camera feed in isolation using a single-threaded approach to confirm that both streams work independently without issues.

5. Error Handling:

   • Implement robust error handling within the camPreview function to catch exceptions and prevent crashes from unhandled errors.

   try:
       # existing code...
   except Exception as e:
       print(f"Error occurred: {e}")
   

6. Update Drivers and Libraries:

   • Ensure that all relevant drivers (especially for GStreamer) and libraries (OpenCV) are up-to-date to benefit from bug fixes and performance improvements.

7. Explore Buffer Mapping with CUDA:

   • As suggested by forum users, consider using CUDA with OpenCV for better performance when dealing with high-throughput video feeds.

8. Documentation and Community Resources:

   • Reference relevant documentation for OpenCV and GStreamer for additional insights on best practices for multithreading and resource management.

9. Testing Different Configurations:

   • Experiment with different configurations of threads and processes to find a combination that works reliably without crashing.

By following these steps, users should be able to diagnose the underlying issues with their multithreaded implementation and apply effective solutions to improve stability and performance while capturing video streams on the Nvidia Jetson Orin Nano Dev Board.