IMX519 Camera Focuser Example Not Running on Jetson Orin Nano Dev Kit

Issue Overview

Users are experiencing difficulties running the IMX519 camera focuser example on the Jetson Orin Nano Developer Kit outside of a Docker container. The issue occurs when attempting to execute the Python script FocuserExample.py directly on the local operating system. While the script runs successfully within the jetson-inference Docker container, it fails when executed on the host OS, resulting in a "Failed to open camera!" error.

The problem appears to be related to camera initialization and access, specifically when using OpenCV’s VideoCapture with GStreamer pipeline. This issue prevents users from integrating the camera focuser functionality with other components that require a modified ROS installation outside the Docker environment.

Possible Causes

1. Incorrect camera driver loading: The camera sensor driver may not be properly loaded or recognized by the host OS.

2. Permission issues: The user may lack the necessary permissions to access the camera device outside the Docker container.

3. Library incompatibilities: There might be version mismatches or missing dependencies between the Docker environment and the host OS.

4. GStreamer pipeline configuration: The GStreamer pipeline used in the script may not be compatible with the host OS setup.

5. Hardware initialization problems: The camera hardware might not be properly initialized or detected by the system.

6. Resource conflicts: Other processes or applications might be interfering with camera access.

Troubleshooting Steps, Solutions & Fixes

1. Verify camera driver loading:
   Run the following command to check if the camera is detected:

   v4l2-ctl --list-devices
   

   Ensure that the IMX519 camera is listed in the output.

2. Check camera formats and capabilities:
   Execute the following command to view supported formats:

   v4l2-ctl --list-formats-ext
   

   Verify that the expected resolutions and pixel formats are available.

3. Test basic camera functionality:
   Try capturing a single frame using v4l2-ctl:

   v4l2-ctl -d /dev/video0 --set-fmt-video=width=4656,height=3496,pixelformat=RG10 --set-ctrl bypass_mode=0 --stream-mmap --stream-count=1 --stream-to=test.raw
   

   Check if the resulting test.raw file contains data.

4. Enable debug logging:
   To gather more information about the camera initialization process, enable trace logging:

   echo 1 > /sys/kernel/debug/tracing/tracing_on
   echo 30720 > /sys/kernel/debug/tracing/buffer_size_kb
   echo 1 > /sys/kernel/debug/tracing/events/tegra_rtcpu/enable
   echo 1 > /sys/kernel/debug/tracing/events/freertos/enable
   echo 2 > /sys/kernel/debug/camrtc/log-level
   echo 1 > /sys/kernel/debug/tracing/events/camera_common/enable
   echo > /sys/kernel/debug/tracing/trace
   

   After running the camera command, capture the trace log:

   cat /sys/kernel/debug/tracing/trace > camera_trace.log
   

   Analyze the log for any error messages or warnings related to camera initialization.

5. Check for CHANSEL_SHORT_FRAME and CHANSEL_FAULT errors:
   Review the trace log for these specific errors, which may indicate issues with camera sensor communication or power supply.

6. Boost system clocks:
   Temporarily increase system clock speeds to rule out performance-related issues:

   sudo jetson_clocks
   

7. Verify GStreamer pipeline:
   Test the camera using a simple GStreamer pipeline:

   FRAMERATE=9
   gst-launch-1.0 nvarguscamerasrc sensor-id=0 ! "video/x-raw(memory:NVMM),width=4656,height=3496,framerate=$FRAMERATE/1" ! nvvidconv ! "video/x-raw(memory:NVMM),width=640,height=480,framerate=$FRAMERATE/1" ! nvdrmvideosink
   

   If this works, the issue may be specific to the Python script or OpenCV integration.

8. Check Python environment:
   Ensure that the correct versions of OpenCV and other dependencies are installed in the host OS Python environment:

   pip list | grep -E "opencv-python|numpy"
   

   Compare these versions with those in the Docker container.

9. Rebuild jetson-inference from source:
   If library incompatibilities are suspected, try rebuilding the jetson-inference package on the host OS:

   git clone --recursive https://github.com/dusty-nv/jetson-inference
   cd jetson-inference
   mkdir build
   cd build
   cmake ../
   make -j$(nproc)
   sudo make install
   sudo ldconfig
   

10. Investigate Docker container differences:
    Compare the environment variables and installed packages between the Docker container and the host OS. Use the following commands inside and outside the container:

    env
    dpkg -l | grep -E "libopencv|libgstreamer"
    

    Identify any missing packages or environment variables on the host OS and install or set them accordingly.

11. Check system logs:
    Review system logs for any camera-related errors:

    sudo journalctl -b | grep -i camera
    

12. Update Jetson OS and packages:
    Ensure that the Jetson OS and all relevant packages are up to date:

    sudo apt update
    sudo apt upgrade
    

If the issue persists after trying these steps, consider reaching out to NVIDIA developer support or the Jetson community forums with the detailed logs and information gathered during troubleshooting.