This library will be searched using cmake package mechanism, make sure it is installed correctly or manually set GDAL_DIR environment or cmake variable. It will have higher priority when opening files and can override other backends. GDAL is a higher level library which supports reading multiple file formats including PNG, JPEG and TIFF. In order to use JasPer, OpenJPEG must be disabled. Note OpenJPEG have higher priority than JasPer which is deprecated. Corresponding BUILD_* options will force building and using own libraries, they are enabled by default on some platforms, e.g.
#CMAKE INSTALL PREFIX OPENCV CODE#
However there is no thorough documentation for this feature yet, so please check the source code in modules/core/src/ocl.cpp file for details. Some parameters of OpenCL integration can be modified using environment variables, e.g. During runtime a working OpenCL runtime is required, to check it run clinfo and/or opencv_version -opencl command. More information can be found in the brief introduction and OpenCL supportĪt the build time this feature does not have any prerequisites. Switch to the OpenCL execution branch happens if input and output image arguments are passed as opaque cv::UMat objects. This integration uses same functions at the user level as regular CPU implementations. Multiple OpenCL-accelerated algorithms are available via so-called "Transparent API (T-API)". TODO: other options: WITH_CUFFT, WITH_CUBLAS, WITH_NVCUVID? OpenCL support Some tutorials can be found in the corresponding section: GPU-Accelerated Computer Vision (cuda module) See also CUDA-accelerated Computer Vision To build opencv and opencv_contrib together check Build with extra modules. Note Since OpenCV version 4.0 all CUDA-accelerated algorithm implementations have been moved to the opencv_contrib repository. These parameters are not documented yet, please consult with the cmake/OpenCVDetectCUDA.cmake script for details. Additional options can be used to control build process, e.g. For cmake versions older than 3.9 OpenCV uses own cmake/FindCUDA.cmake script, for newer versions - the one packaged with CMake. CUDA toolkit must be installed from the official NVIDIA site as a prerequisite. Many algorithms have been implemented using CUDA acceleration, these functions are located in separate modules.