OpenCV  4.5.2-dev
Open Source Computer Vision
OpenCV configuration options reference

Table of Contents

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Introduction

Note
We assume you have read OpenCV installation overview tutorial or have experience with CMake.

Configuration options can be set in several different ways:

In this reference we will use regular command line.

Most of the options can be found in the root cmake script of OpenCV: opencv/CMakeLists.txt. Some options can be defined in specific modules.

It is possible to use CMake tool to print all available options:

# initial configuration
cmake ../opencv
# print all options
cmake -L
# print all options with help message
cmake -LH
# print all options including advanced
cmake -LA

Most popular and useful are options starting with WITH_, ENABLE_, BUILD_, OPENCV_.

Default values vary depending on platform and other options values.

General options

Build with extra modules

OPENCV_EXTRA_MODULES_PATH option contains a semicolon-separated list of directories containing extra modules which will be added to the build. Module directory must have compatible layout and CMakeLists.txt, brief description can be found in the Coding Style Guide.

Examples:

# build with all modules in opencv_contrib
cmake -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ../opencv
# build with one of opencv_contrib modules
cmake -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules/bgsegm ../opencv
# build with two custom modules (semicolon must be escaped in bash)
cmake -DOPENCV_EXTRA_MODULES_PATH=../my_mod1\;../my_mod2 ../opencv
Note
Only 0- and 1-level deep module locations are supported, following command will raise an error:
cmake -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib ../opencv

Debug build

CMAKE_BUILD_TYPE option can be used to enable debug build; resulting binaries will contain debug symbols and most of compiler optimizations will be turned off. To enable debug symbols in Release build turn the BUILD_WITH_DEBUG_INFO option on.

On some platforms (e.g. Linux) build type must be set at configuration stage:

cmake -DCMAKE_BUILD_TYPE=Debug ../opencv
cmake --build .

On other platforms different types of build can be produced in the same build directory (e.g. Visual Studio, XCode):

cmake <options> ../opencv
cmake --build . --config Debug

If you use GNU libstdc++ (default for GCC) you can turn on the ENABLE_GNU_STL_DEBUG option, then C++ library will be used in Debug mode, e.g. indexes will be bound-checked during vector element access.

Many kinds of optimizations can be disabled with CV_DISABLE_OPTIMIZATION option:

See also
https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html
https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_macros.html
https://github.com/opencv/opencv/wiki/CPU-optimizations-build-options

Static build

BUILD_SHARED_LIBS option control whether to produce dynamic (.dll, .so, .dylib) or static (.a, .lib) libraries. Default value depends on target platform, in most cases it is ON.

Example:

cmake -DBUILD_SHARED_LIBS=OFF ../opencv
See also
https://en.wikipedia.org/wiki/Static_library

ENABLE_PIC sets the CMAKE_POSITION_INDEPENDENT_CODE option. It enables or disable generation of "position-independent code". This option must be enabled when building dynamic libraries or static libraries intended to be linked into dynamic libraries. Default value is ON.

See also
https://en.wikipedia.org/wiki/Position-independent_code

Generate pkg-config info

OPENCV_GENERATE_PKGCONFIG option enables .pc file generation along with standard CMake package. This file can be useful for projects which do not use CMake for build.

Example:

cmake -DOPENCV_GENERATE_PKGCONFIG=ON ../opencv
Note
Due to complexity of configuration process resulting .pc file can contain incomplete list of third-party dependencies and may not work in some configurations, especially for static builds. This feature is not officially supported since 4.x version and is disabled by default.

Build tests, samples and applications

There are two kinds of tests: accuracy (opencv_test_*) and performance (opencv_perf_*). Tests and applications are enabled by default. Examples are not being built by default and should be enabled explicitly.

Corresponding cmake options:

cmake \
-DBUILD_TESTS=ON \
-DBUILD_PERF_TESTS=ON \
-DBUILD_EXAMPLES=ON \
-DBUILD_opencv_apps=ON \
../opencv

Build limited set of modules

Each module is a subdirectory of the modules directory. It is possible to disable one module:

cmake -DBUILD_opencv_calib3d=OFF ../opencv

The opposite option is to build only specified modules and all modules they depend on:

cmake -DBUILD_LIST=calib3d,videoio,ts ../opencv

In this example we requested 3 modules and configuration script has determined all dependencies automatically:

-- OpenCV modules:
-- To be built: calib3d core features2d flann highgui imgcodecs imgproc ts videoio

Downloaded dependencies

Configuration script can try to download additional libraries and files from the internet, if it fails to do it corresponding features will be turned off. In some cases configuration error can occur. By default all files are first downloaded to the <source>/.cache directory and then unpacked or copied to the build directory. It is possible to change download cache location by setting environment variable or configuration option:

export OPENCV_DOWNLOAD_PATH=/tmp/opencv-cache
cmake ../opencv
# or
cmake -DOPENCV_DOWNLOAD_PATH=/tmp/opencv-cache ../opencv

In case of access via proxy, corresponding environment variables should be set before running cmake:

export http_proxy=<proxy-host>:<port>
export https_proxy=<proxy-host>:<port>

Full log of download process can be found in build directory - CMakeDownloadLog.txt. In addition, for each failed download a command will be added to helper scripts in the build directory, e.g. download_with_wget.sh. Users can run these scripts as is or modify according to their needs.

CPU optimization level

On x86_64 machines the library will be compiled for SSE3 instruction set level by default. This level can be changed by configuration option:

cmake -DCPU_BASELINE=AVX2 ../opencv
Note
Other platforms have their own instruction set levels: VFPV3 and NEON on ARM, VSX on PowerPC.

Some functions support dispatch mechanism allowing to compile them for several instruction sets and to choose one during runtime. List of enabled instruction sets can be changed during configuration:

cmake -DCPU_DISPATCH=AVX,AVX2 ../opencv

To disable dispatch mechanism this option should be set to an empty value:

cmake -DCPU_DISPATCH= ../opencv

It is possible to disable optimized parts of code for troubleshooting and debugging:

# disable universal intrinsics
cmake -DCV_ENABLE_INTRINSICS=OFF ../opencv
# disable all possible built-in optimizations
cmake -DCV_DISABLE_OPTIMIZATION=ON ../opencv
Note
More details on CPU optimization options can be found in wiki: https://github.com/opencv/opencv/wiki/CPU-optimizations-build-options

Profiling, coverage, sanitize, hardening, size optimization

Following options can be used to produce special builds with instrumentation or improved security. All options are disabled by default.

| Option | Compiler | Description | | ENABLE_PROFILING | GCC or Clang | Enable profiling compiler and linker options. | | ENABLE_COVERAGE | GCC or Clang | Enable code coverage support. | | OPENCV_ENABLE_MEMORY_SANITIZER | N/A | Enable several quirks in code to assist memory sanitizer. | | ENABLE_BUILD_HARDENING | GCC, Clang, MSVC | Enable compiler options which reduce possibility of code exploitation. | | ENABLE_LTO | GCC, Clang, MSVC | Enable Link Time Optimization (LTO). | | ENABLE_THIN_LTO | Clang | Enable thin LTO which incorporates intermediate bitcode to binaries allowing consumers optimize their applications later. |

See also
GCC instrumentation
Build hardening
Interprocedural optimization
Link time optimization
ThinLTO

Functional features and dependencies

There are many optional dependencies and features that can be turned on or off. cmake has special option allowing to print all available configuration parameters:

cmake -LH ../opencv

Options naming conventions

There are three kinds of options used to control dependencies of the library, they have different prefixes:

When WITH_ option is enabled:

Heterogeneous computation

CUDA support

WITH_CUDA (default: OFF)

Many algorithms have been implemented using CUDA acceleration, these functions are located in separate modules. CUDA toolkit must be installed from the official NVIDIA site as a prerequisite. For cmake versions older than 3.9 OpenCV uses own cmake/FindCUDA.cmake script, for newer versions - the one packaged with CMake. Additional options can be used to control build process, e.g. CUDA_GENERATION or CUDA_ARCH_BIN. These parameters are not documented yet, please consult with the cmake/OpenCVDetectCUDA.cmake script for details.

Note
Since OpenCV version 4.0 all CUDA-accelerated algorithm implementations have been moved to the opencv_contrib repository. To build opencv and opencv_contrib together check Build with extra modules.
Some tutorials can be found in the corresponding section: GPU-Accelerated Computer Vision (cuda module)
See also
CUDA-accelerated Computer Vision
https://en.wikipedia.org/wiki/CUDA

TODO: other options: WITH_CUFFT, WITH_CUBLAS, WITH_NVCUVID?

OpenCL support

WITH_OPENCL (default: ON)

Multiple OpenCL-accelerated algorithms are available via so-called "Transparent API (T-API)". This integration uses same functions at the user level as regular CPU implementations. Switch to the OpenCL execution branch happens if input and output image arguments are passed as opaque cv::UMat objects. More information can be found in the brief introduction and OpenCL support

At the build time this feature does not have any prerequisites. During runtime a working OpenCL runtime is required, to check it run clinfo and/or opencv_version --opencl command. Some parameters of OpenCL integration can be modified using environment variables, e.g. OPENCV_OPENCL_DEVICE. 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.

See also
https://en.wikipedia.org/wiki/OpenCL

TODO: other options: WITH_OPENCL_SVM, WITH_OPENCLAMDFFT, WITH_OPENCLAMDBLAS, WITH_OPENCL_D3D11_NV, WITH_VA_INTEL

Image reading and writing (imgcodecs module)

Built-in formats

Following formats can be read by OpenCV without help of any third-party library:

PNG, JPEG, TIFF, WEBP support

Formats Option Default Force build own
PNG WITH_PNG ON BUILD_PNG
JPEG WITH_JPEG ON BUILD_JPEG
TIFF WITH_TIFF ON BUILD_TIFF
WEBP WITH_WEBP ON BUILD_WEBP
JPEG2000 with OpenJPEG WITH_OPENJPEG ON BUILD_OPENJPEG
JPEG2000 with JasPer WITH_JASPER ON (see note) BUILD_JASPER
EXR WITH_OPENEXR ON BUILD_OPENEXR

All libraries required to read images in these formats are included into OpenCV and will be built automatically if not found at the configuration stage. Corresponding BUILD_* options will force building and using own libraries, they are enabled by default on some platforms, e.g. Windows.

Note
OpenJPEG have higher priority than JasPer which is deprecated. In order to use JasPer, OpenJPEG must be disabled.

GDAL integration

WITH_GDAL (default: OFF)

GDAL is a higher level library which supports reading multiple file formats including PNG, JPEG and TIFF. It will have higher priority when opening files and can override other backends. This library will be searched using cmake package mechanism, make sure it is installed correctly or manually set GDAL_DIR environment or cmake variable.

GDCM integration

WITH_GDCM (default: OFF)

Enables DICOM medical image format support through GDCM library. This library will be searched using cmake package mechanism, make sure it is installed correctly or manually set GDCM_DIR environment or cmake variable.

Video reading and writing (videoio module)

TODO: how videoio works, registry, priorities

Video4Linux

WITH_V4L (Linux; default: ON )

Capture images from camera using Video4Linux API. Linux kernel headers must be installed.

FFmpeg

WITH_FFMPEG (default: ON)

Integration with FFmpeg library for decoding and encoding video files and network streams. This library can read and write many popular video formats. It consists of several components which must be installed as prerequisites for the build:

Exception is Windows platform where a prebuilt plugin library containing FFmpeg will be downloaded during a configuration stage and copied to the bin folder with all produced libraries.

Note
Libav library can be used instead of FFmpeg, but this combination is not actively supported.

GStreamer

WITH_GSTREAMER (default: ON)

Enable integration with GStreamer library for decoding and encoding video files, capturing frames from cameras and network streams. Numerous plugins can be installed to extend supported formats list. OpenCV allows running arbitrary GStreamer pipelines passed as strings to cv::VideoCapture and cv::VideoWriter objects.

Various GStreamer plugins offer HW-accelerated video processing on different platforms.

Microsoft Media Foundation

WITH_MSMF (Windows; default: ON)

Enables MSMF backend which uses Windows' built-in Media Foundation framework. Can be used to capture frames from camera, decode and encode video files. This backend have HW-accelerated processing support (WITH_MSMF_DXVA option, default is ON).

Note
Older versions of Windows (prior to 10) can have incompatible versions of Media Foundation and are known to have problems when used from OpenCV.

DirectShow

WITH_DSHOW (Windows; default: ON)

This backend uses older DirectShow framework. It can be used only to capture frames from camera. It is now deprecated in favor of MSMF backend, although both can be enabled in the same build.

AVFoundation

WITH_AVFOUNDATION (Apple; default: ON)

AVFoundation framework is part of Apple platforms and can be used to capture frames from camera, encode and decode video files.

Other backends

There are multiple less popular frameworks which can be used to read and write videos. Each requires corresponding library or SDK installed.

Option Default Description
WITH_1394 ON IIDC IEEE1394 support using DC1394 library
WITH_OPENNI OFF OpenNI can be used to capture data from depth-sensing cameras. Deprecated.
WITH_OPENNI2 OFF OpenNI2 can be used to capture data from depth-sensing cameras.
WITH_PVAPI OFF PVAPI is legacy SDK for Prosilica GigE cameras. Deprecated.
WITH_ARAVIS OFF Aravis library is used for video acquisition using Genicam cameras.
WITH_XIMEA OFF XIMEA cameras support.
WITH_XINE OFF XINE library support.
WITH_LIBREALSENSE OFF RealSense cameras support.
WITH_MFX OFF MediaSDK library can be used for HW-accelerated decoding and encoding of raw video streams.
WITH_GPHOTO2 OFF GPhoto library can be used to capure frames from cameras.
WITH_ANDROID_MEDIANDK ON MediaNDK library is available on Android since API level 21.

videoio plugins

Some videoio backends can be built as plugins thus breaking strict dependency on third-party libraries and making them optional at runtime. Following options can be used to control this mechanism:

Option Default Description
VIDEOIO_ENABLE_PLUGINS ON Enable or disable plugins completely.
VIDEOIO_PLUGIN_LIST empty Comma- or semicolon-separated list of backend names to be compiled as plugins. Supported names are ffmpeg, gstreamer, msmf, mfx and all.
VIDEOIO_ENABLE_STRICT_PLUGIN_CHECK ON Enable strict runtime version check to only allow plugins built with the same version of OpenCV.

Parallel processing

Some of OpenCV algorithms can use multithreading to accelerate processing. OpenCV can be built with one of threading backends.

Backend Option Default Platform Description
pthreads WITH_PTHREADS_PF ON Unix-like Default backend based on pthreads library is available on Linux, Android and other Unix-like platforms. Thread pool is implemented in OpenCV and can be controlled with environment variables OPENCV_THREAD_POOL_*. Please check sources in modules/core/src/parallel_impl.cpp file for details.
Concurrency N/A ON Windows Concurrency runtime is available on Windows and will be turned ON on supported platforms unless other backend is enabled.
GCD N/A ON Apple Grand Central Dispatch is available on Apple platforms and will be turned ON automatically unless other backend is enabled. Uses global system thread pool.
TBB WITH_TBB OFF Multiple Threading Building Blocks is a cross-platform library for parallel programming.
OpenMP WITH_OPENMP OFF Multiple OpenMP API relies on compiler support.
HPX WITH_HPX OFF Multiple High Performance ParallelX is an experimental backend which is more suitable for multiprocessor environments.
Note
OpenCV can download and build TBB library from GitHub, this functionality can be enabled with the BUILD_TBB option.

GUI backends (highgui module)

OpenCV relies on various GUI libraries for window drawing.

Option Default Platform Description
WITH_GTK ON Linux GTK is a common toolkit in Linux and Unix-like OS-es. By default version 3 will be used if found, version 2 can be forced with the WITH_GTK_2_X option.
WITH_WIN32UI ON Windows WinAPI is a standard GUI API in Windows.
N/A ON macOS Cocoa is a framework used in macOS.
WITH_QT OFF Cross-platform Qt is a cross-platform GUI framework.
Note
OpenCV compiled with Qt support enables advanced highgui interface, see Qt New Functions for details.

OpenGL

WITH_OPENGL (default: OFF)

OpenGL integration can be used to draw HW-accelerated windows with following backends: GTK, WIN32 and Qt. And enables basic interoperability with OpenGL, see OpenGL interoperability and OpenGL support for details.

Deep learning neural networks inference backends and options (dnn module)

OpenCV have own DNN inference module which have own build-in engine, but can also use other libraries for optimized processing. Multiple backends can be enabled in single build. Selection happens at runtime automatically or manually.

Option Default Description
WITH_PROTOBUF ON Enables protobuf library search. OpenCV can either build own copy of the library or use external one. This dependency is required by the dnn module, if it can't be found module will be disabled.
BUILD_PROTOBUF ON Build own copy of protobuf. Must be disabled if you want to use external library.
PROTOBUF_UPDATE_FILES OFF Re-generate all .proto files. protoc compiler compatible with used version of protobuf must be installed.
OPENCV_DNN_OPENCL ON Enable built-in OpenCL inference backend.
WITH_INF_ENGINE OFF Enables Intel Inference Engine (IE) backend. Allows to execute networks in IE format (.xml + .bin). Inference Engine must be installed either as part of OpenVINO toolkit, either as a standalone library built from sources.
INF_ENGINE_RELEASE 2020040000 Defines version of Inference Engine library which is tied to OpenVINO toolkit version. Must be a 10-digit string, e.g. 2020040000 for OpenVINO 2020.4.
WITH_NGRAPH OFF Enables Intel NGraph library support. This library is part of Inference Engine backend which allows executing arbitrary networks read from files in multiple formats supported by OpenCV: Caffe, TensorFlow, PyTorch, Darknet, etc.. NGraph library must be installed, it is included into Inference Engine.
OPENCV_DNN_CUDA OFF Enable CUDA backend. CUDA, CUBLAS and CUDNN must be installed.
WITH_HALIDE OFF Use experimental Halide backend which can generate optimized code for dnn-layers at runtime. Halide must be installed.
WITH_VULKAN OFF Enable experimental Vulkan backend. Does not require additional dependencies, but can use external Vulkan headers (VULKAN_INCLUDE_DIRS).
WITH_TENGINE OFF Enable experimental Tengine backend for ARM CPUs. Tengine library must be installed.

Installation layout

Installation root

To install produced binaries root location should be configured. Default value depends on distribution, in Ubuntu it is usually set to /usr/local. It can be changed during configuration:

cmake -DCMAKE_INSTALL_PREFIX=/opt/opencv ../opencv

This path can be relative to current working directory, in the following example it will be set to <absolute-path-to-build>/install:

cmake -DCMAKE_INSTALL_PREFIX=install ../opencv

After building the library, all files can be copied to the configured install location using the following command:

cmake --build . --target install

To install binaries to the system location (e.g. /usr/local) as a regular user it is necessary to run the previous command with elevated privileges:

sudo cmake --build . --target install
Note
On some platforms (Linux) it is possible to remove symbol information during install. Binaries will become 10-15% smaller but debugging will be limited:
cmake --build . --target install/strip

Components and locations

Options cane be used to control whether or not a part of the library will be installed:

Option Default Description
INSTALL_C_EXAMPLES OFF Install C++ sample sources from the samples/cpp directory.
INSTALL_PYTHON_EXAMPLES OFF Install Python sample sources from the samples/python directory.
INSTALL_ANDROID_EXAMPLES OFF Install Android sample sources from the samples/android directory.
INSTALL_BIN_EXAMPLES OFF Install prebuilt sample applications (BUILD_EXAMPLES must be enabled).
INSTALL_TESTS OFF Install tests (BUILD_TESTS must be enabled).
OPENCV_INSTALL_APPS_LIST all Comma- or semicolon-separated list of prebuilt applications to install (from apps directory)

Following options allow to modify components' installation locations relatively to install prefix. Default values of these options depend on platform and other options, please check the cmake/OpenCVInstallLayout.cmake file for details.

Option Components
OPENCV_BIN_INSTALL_PATH applications, dynamic libraries (win)
OPENCV_TEST_INSTALL_PATH test applications
OPENCV_SAMPLES_BIN_INSTALL_PATH sample applications
OPENCV_LIB_INSTALL_PATH dynamic libraries, import libraries (win)
OPENCV_LIB_ARCHIVE_INSTALL_PATH static libraries
OPENCV_3P_LIB_INSTALL_PATH 3rdparty libraries
OPENCV_CONFIG_INSTALL_PATH cmake config package
OPENCV_INCLUDE_INSTALL_PATH header files
OPENCV_OTHER_INSTALL_PATH extra data files
OPENCV_SAMPLES_SRC_INSTALL_PATH sample sources
OPENCV_LICENSES_INSTALL_PATH licenses for included 3rdparty components
OPENCV_TEST_DATA_INSTALL_PATH test data
OPENCV_DOC_INSTALL_PATH documentation
OPENCV_JAR_INSTALL_PATH JAR file with Java bindings
OPENCV_JNI_INSTALL_PATH JNI part of Java bindings
OPENCV_JNI_BIN_INSTALL_PATH Dynamic libraries from the JNI part of Java bindings

Following options can be used to change installation layout for common scenarios:

Option Default Description
INSTALL_CREATE_DISTRIB OFF Tune multiple things to produce Windows and Android distributions.
INSTALL_TO_MANGLED_PATHS OFF Adds one level to several installation locations to allow side-by-side installations. For example, headers will be installed to _/usr/include/opencv-4.4.0_ instead of _/usr/include/opencv4_ with this option enabled.

Miscellaneous features

Option Default Description
OPENCV_ENABLE_NONFREE OFF Some algorithms included in the library are known to be protected by patents and are disabled by default.
OPENCV_FORCE_3RDPARTY_BUILDOFF Enable all BUILD_ options at once.
ENABLE_CCACHE ON (on Unix-like platforms) Enable ccache auto-detection. This tool wraps compiler calls and caches results, can significantly improve re-compilation time.
ENABLE_PRECOMPILED_HEADERS ON (for MSVC) Enable precompiled headers support. Improves build time.
BUILD_DOCS OFF Enable documentation build (doxygen, doxygen_cpp, doxygen_python, doxygen_javadoc targets). Doxygen must be installed for C++ documentation build. Python and BeautifulSoup4 must be installed for Python documentation build. Javadoc and Ant must be installed for Java documentation build (part of Java SDK).
ENABLE_PYLINT ON (when docs or examples are enabled) Enable python scripts check with Pylint (check_pylint target). Pylint must be installed.
ENABLE_FLAKE8 ON (when docs or examples are enabled) Enable python scripts check with Flake8 (check_flake8 target). Flake8 must be installed.
BUILD_JAVA ON Enable Java wrappers build. Java SDK and Ant must be installed.
BUILD_FAT_JAVA_LIB ON (for static Android builds) Build single opencv_java dynamic library containing all library functionality bundled with Java bindings.
BUILD_opencv_python2 ON Build python2 bindings (deprecated). Python with development files and numpy must be installed.
BUILD_opencv_python3 ON Build python3 bindings. Python with development files and numpy must be installed.

TODO: need separate tutorials covering bindings builds

Automated builds

Some features have been added specifically for automated build environments, like continuous integration and packaging systems.

Option Default Description
ENABLE_NOISY_WARNINGS OFF Enables several compiler warnings considered noisy, i.e. having less importance than others. These warnings are usually ignored but in some cases can be worth being checked for.
OPENCV_WARNINGS_ARE_ERRORS OFF Treat compiler warnings as errors. Build will be halted.
ENABLE_CONFIG_VERIFICATION OFF For each enabled dependency (WITH_ option) verify that it has been found and enabled (HAVE_ variable). By default feature will be silently turned off if dependency was not found, but with this option enabled cmake configuration will fail. Convenient for packaging systems which require stable library configuration not depending on environment fluctuations.
OPENCV_CMAKE_HOOKS_DIR empty OpenCV allows to customize configuration process by adding custom hook scripts at each stage and substage. cmake scripts with predefined names located in the directory set by this variable will be included before and after various configuration stages. Examples of file names: CMAKE_INIT.cmake, PRE_CMAKE_BOOTSTRAP.cmake, POST_CMAKE_BOOTSTRAP.cmake, etc.. Other names are not documented and can be found in the project cmake files by searching for the ocv_cmake_hook macro calls.
OPENCV_DUMP_HOOKS_FLOW OFF Enables a debug message print on each cmake hook script call.

Other non-documented options

BUILD_ANDROID_PROJECTS BUILD_ANDROID_EXAMPLES ANDROID_HOME ANDROID_SDK ANDROID_NDK ANDROID_SDK_ROOT

CMAKE_TOOLCHAIN_FILE

WITH_CAROTENE WITH_CPUFEATURES WITH_EIGEN WITH_OPENVX WITH_CLP WITH_DIRECTX WITH_VA WITH_LAPACK WITH_QUIRC BUILD_ZLIB BUILD_ITT WITH_IPP BUILD_IPP_IW