Since pre-built OpenCV binaries do not include CUDA modules, this post is a tutorial for building OpenCV with CUDA on Windows 11. The obvious dvantage of OpenCV CUDA is boosting performance of most functions, you can find evidence here.
1. Prerequisite
There are a couples of softwares or libraries having been downloaded and installed before getting started:
Install the Visual Studio Community 2022 and select Desktop development with C++ workload.
Download the sources for OpenCV from GitHub by cloning the repositories (opencv and opencv_contrib).
After downloading, you can indicate the OpenCV’s version you want to. For the current version, you can run the following command in Command Prompt at OpenCV’s repositories:
git checkout tags/4.8.0
Install the latest stable version (not release candidate -rc) of CMake.
Install the latest version of NVIDIA CUDA Toolkit and add PATH. You can follow this tutorial.
The latest CUDA Toolkit version
At the time writting this post, the latest NVIDIA CUDA Toolkit version 12.2 still makes somes error when building with OpenCV 4.8.0, like error C2666: 'operator !=': overloaded functions have similar conversions. Therefore, if meeting such problem, try installing older version.
Register an account and download the latest verson of NVIDIA DNN CUDA backend for the version of CUDA. Extract the downloaded .zip file and copy bin, include and lib directories to your CUDA installation directory, i.e.,
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vxx.x.Register an account and download the latest version of NVIDIA VIdeo Codec SDK. Extract the downloaded .zip file and copy the contents inside Interface and Lib to include and lib directories inside the CUDA installation directory.
Optional - Download and install the latest version of Gstreamer.
Download and install the lastest version of mambaforge to call OpenCV CUDA routines from python.
2. Create virtual environtment from Mambaforge
To bind OpenCV for python3 without any conflit package installation, you should create an new virtual environment for the installation.
mamba create -n opencv-cuda python=3.10
Activate the created environment and install numpy package.
mamba activate opencv-cuda
mamaba install numpy
3. Build OpenCV with CMake
Preparation
Create a build folder with your OpenCV extracted folders.
Edit the prioeiry of Python3 installation in OpenCVDetectPython.cmake file inside opencv-x.x.x/cmake folder.
Build GUI Build Configuration
Open Cmake GUI and provide the paths to the OpenCV and target build folders.
Hit Configure and select x64 for the Optional platform for generator, then hit finish to start the configuration.
Once the configuration is done, edit the following parameters:
| Parameter | Value |
|---|---|
| CMAKE_INSTALL_PREFIX | path of opencv installation |
| ENABLE_FAST_MATH | ✅ |
| WITH_CUDA | ✅ |
| BUILD_opencv_world | ✅ |
| BUILD_opencv_python3 | ✅ |
| OPENCV_DNN_CUDA | ✅ |
| OPENCV_EXTRA_MODULES_PATH | path of modules directory in opencv_contrib-x.x.x |
| OPENCV_PYTHON3_VERSION | ✅ |
| PYTHON3_EXECUTABLE | path of python3 executable in virtual env, i.e., C:/Users/ntthi/mambaforge/envs/opencv-cuda/python.exe |
| PYTHON3_INCLUDE_DIR | path of include folder in the virtual env, i.e., C:/Users/ntthi/mambaforge/envs/opencv-cuda/include |
| PYTHON3_LIBRARY | path of .lib file in the virtual env, i.e., C:/Users/ntthi/mambaforge/envs/opencv-cuda/libs/python310.lib |
| PYTHON3_NUMPY_INCLUDE_DIRS | path of numpy in the virtual env, i.e., C:/Users/ntthi/mambaforge/envs/opencv-cuda/Lib/site-pakages/numpy/core/include |
| PYTHON3_PACKAGES_PATH | path of site-packages in the virtual env, i.e., C:/Users/ntthi/mambaforge/envs/opencv-cuda/Lib/site-pakages |
Note that the path separator hase to be “/” , not “".
Hit Configure again again and check edit more parameters:
| Parameter | Value |
|---|---|
| CUDA_FAST_MATH | ✅ |
| CUDA_ARCH_BIN | version of computing capability, i.e., 8.6 |
| WITH_CUBLAS | ✅ |
| WITH_CUDNN | ✅ |
| WITH_CUFFT | ✅ |
The CUDA_ARCH_BIN corresponding to your GPU is the value found in the left column of the GPU support table. For instance, “8.6” fir the RTX 3070 Ti.
If you do not want to create shared lib and make sure the opencv python libraries is installed, edit the following parameters:
| Parameter | Value |
|---|---|
| BUILD_SHARED_LIBS | 🔳 |
| OPENCV_FORCE_PYTHON_LIBS | ✅ |
Hit Configure at the last time and then hit Generate.
Build the project with Visual Studio
Open project OpenCV.sln created in the build folder. Go to Tools > Options…, then uncheck the last parameter in Projects and Solutions > Web Projects.
This setting may help to prevent the ImportError: DLL load failed while importing cv2: The specified module could not be found. error.
To build the OpenCV project, change Debug mode to Release. In the solution explorer expand CMakeTargets, right-click ALL_BUILD and select Build. This will take about an hour.
Then repeat the step for INSTALL (below ALL_BUILD). Check for error in the two building steps. If everything is fine, you are done.
Check Installation and Troubleshooting
To verify the Python installation, activate the virtual environment for OpenCV install and try this code:
import cv2
print(cv2.__version__)
print(cv2.cuda.getCudaEnabledDeviceCount())
If it works, congratulations you are good to go!
If you meets the problem ImportError: DLL load failed while importing cv2: The specified module could not be found., it may lack the library’s binaries. One solution is to edit config.py in C:/Users/ntthi/mambaforge/envs/opencv-cuda/Lib/site-packages/cv2.
import os
BINARIES_PATHS = [
os.path.join('C:/opencv-cuda-4.8.0', 'x64/vc17/bin'),
os.path.join(os.getenv('CUDA_PATH', 'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.1'), 'bin'),
os.path.join('C:/gstreamer/1.0/msvc_x86_64', 'bin'),
] + BINARIES_PATHS
These binary paths are from installed OpenCV, CUDA and Gstreamer (if installed).
For other bugs and problems, I refer you to the chrismeunier and Bowley’s troubleshooting tutorial.