Stopping and Restarting Image Acquisition with libargus on Nvidia Jetson Orin Nano

Issue Overview

Users are experiencing difficulties in implementing a feature to start and stop image acquisition for individual cameras in a multi-camera setup using libargus on the Nvidia Jetson Orin Nano development board. The main challenge is to stop the image acquisition (i.e., halt traffic on the MIPI channel) without deconstructing the entire Argus/EGL pipeline. This functionality is desired to allow users to control cameras individually within a larger application.

The current implementation, derived from Nvidia samples, does not provide a straightforward method to stop and restart camera streams without closing and reconstructing the entire pipeline. Attempts to use methods like iCaptureSession->stopRepeat() result in buffer overflows or segmentation faults.

Possible Causes

1. Limited libargus functionality: The libargus API may not have built-in support for temporarily stopping and restarting image acquisition without full pipeline deconstruction.

2. Incorrect usage of existing methods: The stopRepeat() and waitForIdle() methods may not be sufficient or properly implemented for the desired use case.

3. Buffer management issues: Improper handling of buffers when attempting to stop acquisition could lead to overflows or segmentation faults.

4. MIPI signaling complexities: The direct control of MIPI channel traffic may not be exposed through the libargus API, making it challenging to implement V4L2-like functionality.

5. Lack of documentation or examples: Nvidia samples and documentation may not cover this specific use case, leading to uncertainty in implementation.

Troubleshooting Steps, Solutions & Fixes

1. Implement a proper shutdown sequence:
   Instead of only calling stopRepeat(), ensure you’re following the complete shutdown sequence:

   iCaptureSession->stopRepeat();
   iCaptureSession->waitForIdle();
   

   This may help prevent buffer overflows and segmentation faults.

2. Explore Argus API for stream control:
   Investigate if there are any Argus API calls specifically designed for temporary stream control. Look for methods similar to V4L2’s VIDIOC_STREAMOFF command in the Argus documentation.

3. Implement a lightweight reconstruction:
   If stopping the stream isn’t possible without deconstructing the pipeline, optimize the reconstruction process:

   • Keep configuration parameters in memory
   • Minimize object creation/destruction
   • Use a pool of pre-allocated resources

4. Error handling and recovery:
   Implement robust error handling to recover from issues like intermittent MIPI signaling:

   • Detect error conditions (e.g., buffer overflows, signaling issues)
   • Implement a recovery mechanism that can restart the affected stream without restarting the entire application

5. Consult Nvidia Developer Forums:
   Post a detailed description of your use case and current implementation on the Nvidia Developer Forums. Include code snippets and specific error messages to get targeted assistance from Nvidia experts or community members.

6. Consider alternative approaches:
   If libargus doesn’t support the desired functionality, explore alternative methods:

   • Investigate if using the V4L2 API directly is feasible for your use case
   • Look into using NVIDIA’s lower-level camera APIs that might offer more granular control

7. Buffer management optimization:
   Review and optimize your buffer management strategy:

   • Ensure proper allocation and deallocation of buffers
   • Implement a buffer recycling mechanism to avoid memory leaks and reduce allocation overhead

8. Implement a software-level stream control:
   If hardware-level stream control isn’t possible, consider implementing a software-level solution:

   • Continue acquiring frames but discard them when the stream is "stopped"
   • Implement a queue system to manage frame processing based on the desired state of each camera

9. Explore Jetson Linux source code:
   Analyze the Jetson Linux source code, particularly the camera and MIPI drivers, to gain insights into low-level stream control mechanisms that might be adaptable to your use case.

10. Performance profiling:
    Use Nvidia’s profiling tools to analyze the performance impact of your current implementation and any proposed solutions. This can help identify bottlenecks and optimize your approach.

It is worth noting that as of the last update in this discussion, there was no confirmed solution for dynamically stopping and restarting individual streams using libargus without restarting the entire application. Further investigation and potentially reaching out to Nvidia support may be necessary to find a definitive solution for this specific use case.