In order for OpenCV to get access to CUDA acceleration on the NVIDIA Jetson TX2 running L4T 28.2 (JetPack 3.2), you need to build the library from source. Looky here:
Background
With the latest release of L4T, 28.2, OpenCV version 3.3 may be installed through the JetPack installer. At the time of the L4T release, OpenCV did not provide support for CUDA 9.0 with which L4T 28.2 ships. Over the next couple of months, version OpenCV 3.4 added CUDA 9.0 support.
So what does that mean? Well, if you want OpenCV CUDA support under L4T 28.2 you need to compile it from source. Fortunately we have some convenience scripts to help with that task in the JetsonHacks repository buildOpenCVTX2 on Github.
Installation
You should note that OpenCV is a rich environment, and can be custom tailored to your needs. As such, some of the more common options are in the build command, but are not comprehensive. Modify the options to suit your needs.
Library location
With this script release, the script now installs OpenCV in /usr/local. Earlier versions of this script installed in /usr. You may have to set your include and libraries and/or PYTHONPATH to point to the new version. See the Examples folder. Alternatively, you may want to change the script to install into the /usr directory.
All of this may lead to a conflict. You may consider removing OpenCV installed by JetPack before performing this script installation:
$ sudo apt-get purge libopencv*
Options
Make sure to read through the install script. In the script, here are some of the options that were included:
- CUDA
- Fast Math (cuBLAS)
- OpenGL
- GStreamer 1.0
- Video 4 Linux (V4L)
- Python 2.7 and Python 3.5 support
Build and Install
To download the source, build and install OpenCV:
$ git clone https://github.com/jetsonhacks/buildOpenCVTX2.git
$ cd buildOpenCVTX2
$ ./buildOpenCV.sh
You can remove the sources and build files after you are done:
$ ./removeOpenCVSources.sh
This will remove the OpenCV source, as well as the opencv_extras directories.
Examples
There are a couple of demos in the Examples folder.
There are two example programs here. Both programs require OpenCV to be installed with GStreamer support enabled. Both of these examples were last tested with L4T 28.2, OpenCV 3.4.1
The first is a simple C++ program to view the onboard camera feed from the Jetson Dev Kit.
To compile gstreamer_view.cpp:
$ gcc -std=c++11 ‘pkg-config –cflags opencv’ ‘pkg-config –libs opencv’ gstreamer_view.cpp -o gstreamer_view -lstdc++ -lopencv_core -lopencv_highgui -lopencv_videoio
to run the program:
$ ./gstreamer_view
The second is a Python program that reads the onboard camera feed from the Jetson Dev Kit and does Canny Edge Detection.
To run the Canny detection demo (Python 2.7):
$ python cannyDetection.py
With Python 3.3:
$ python3 cannyDetection.py
With the Canny detection demo, use the less than (<) and greater than (>) to adjust the edge detection parameters. You can pass the command line flags —video_device=<videoDeviceNumber> to use a USB camera instead of the built in camera.
Notes
- This is meant to be a template for building your own custom version of OpenCV, pick and choose your own modules and options
- Most people do NOT have both the JetPack installed and the source built OpenCV on their system. Some people have noted success using both however, check the forums.
- Different modules and setting may require different dependencies, make sure to look for error messages when building.
- OpenCV.org – Tutorial Building Tegra CUDA
- Jetson Developers Forum
- JK Jung How to Install OpenCV (3.4.0) on Jetson TX2 which is a comprehensive walk through of a customized installation with emphasis on Python and Matplotlib.
The information for this script was gathered from several places:
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