OpenCV Build with OpenGL Support Failed on Jetson Orin Nano

Issue Overview

Users are experiencing difficulties building OpenCV 4.9.0 with OpenGL support on the Nvidia Jetson Orin Nano 8GB module running Jetpack 6.1. Despite specifying the -D WITH_OPENGL=ON flag in the CMake command and providing specific paths to OpenGL libraries, the build process consistently disables OpenGL support. This issue prevents users from leveraging OpenGL capabilities in their OpenCV applications, potentially affecting performance and display quality, particularly for fullscreen applications experiencing significant tearing.

Possible Causes

1. Incompatible OpenGL libraries: The Jetson Orin Nano may have incompatible or outdated OpenGL libraries that are not recognized by the OpenCV build process.

2. Incorrect library paths: The specified paths to OpenGL libraries in the CMake command might be incorrect or not accessible during the build process.

3. Missing dependencies: Some required dependencies for OpenGL support in OpenCV might be missing from the system.

4. CMake configuration issues: There could be conflicts or overrides in the CMake configuration that are disabling OpenGL support despite the explicit flag.

5. Jetpack 6.1 compatibility: The specific version of Jetpack might have compatibility issues with OpenCV 4.9.0 and OpenGL integration.

6. Hardware limitations: The Jetson Orin Nano’s GPU capabilities might not fully support the required OpenGL features.

Troubleshooting Steps, Solutions & Fixes

1. Verify OpenGL installation:

   • Check if OpenGL libraries are properly installed:

     ls /usr/lib/aarch64-linux-gnu/libGL.so
     ls /usr/lib/aarch64-linux-gnu/libGLU.so
     ls /usr/lib/aarch64-linux-gnu/libOpenGL.so
     ls /usr/lib/aarch64-linux-gnu/libGLX.so
     

   • If any of these files are missing, install the necessary packages:

     sudo apt-get update
     sudo apt-get install libgl1-mesa-dev libglu1-mesa-dev
     

2. Update CMake command:

   • Modify the CMake command to use the correct library paths:

     cmake -D CMAKE_BUILD_TYPE=Release \
           -D CMAKE_INSTALL_PREFIX=/usr/local \
           -D WITH_OPENGL=ON \
           -D OPENGL_gl_LIBRARY=/usr/lib/aarch64-linux-gnu/libGL.so \
           -D OPENGL_glu_LIBRARY=/usr/lib/aarch64-linux-gnu/libGLU.so \
           -D OPENGL_opengl_LIBRARY=/usr/lib/aarch64-linux-gnu/libOpenGL.so \
           -D OPENGL_glx_LIBRARY=/usr/lib/aarch64-linux-gnu/libGLX.so \
           -D OPENGL_INCLUDE_DIR=/usr/include \
           ... (other flags)
     

3. Check for conflicting flags:

   • Review the CMake command for any flags that might be conflicting with OpenGL support, such as -D WITH_QT=OFF. Consider removing or adjusting these flags.

4. Use a known working script as a base:

   • Adapt the CUDA-enabled OpenCV installation script from the JEP repository:

     wget https://raw.githubusercontent.com/AastaNV/JEP/master/script/install_opencv4.6.0_Jetson.sh
     

   • Modify the script to include OpenGL support and update the OpenCV version to 4.9.0.

5. Verify system dependencies:

   • Install additional dependencies that might be required for OpenGL support:

     sudo apt-get install libgtk2.0-dev pkg-config
     

6. Check OpenCV build logs:

   • After running CMake, check the OpenCVDetectOpenGL.cmake log file in the build directory for any error messages or warnings related to OpenGL detection.

7. Try an older OpenCV version:

   • If the issue persists, attempt to build OpenCV 4.6.0 or 4.5.0 with OpenGL support to determine if it’s a version-specific problem.

8. Update Jetpack:

   • Check for any available updates to Jetpack 6.1 that might address compatibility issues:

     sudo apt-get update
     sudo apt-get upgrade
     

9. Consult Nvidia Developer Forums:

   • If the issue remains unresolved, consider posting a detailed description of the problem, including build logs and system information, on the Nvidia Developer Forums for Jetson-specific support.

10. Alternative display methods:

    • If OpenGL support cannot be enabled, explore alternative methods to improve display performance, such as using CUDA-accelerated rendering or optimizing the existing OpenCV window management.