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nihui/opencv-mobile: The minimal opencv for Android, iOS, ARM Linux, Windows, Li ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称（OpenSource Name）：

nihui/opencv-mobile

开源软件地址(OpenSource Url)：

https://github.com/nihui/opencv-mobile

开源编程语言(OpenSource Language)：

C 84.2%

开源软件介绍(OpenSource Introduction)：

opencv-mobile

License release download

✔️ This project provides the minimal build of opencv library for the Android, iOS and ARM Linux platforms.

✔️ Packages for Windows, Linux, MacOS and WebAssembly are available now.

✔️ We provide prebuild binary packages for opencv 2.4.13.7, 3.4.16 and 4.5.4.

✔️ We also provide prebuild binary package for iOS with bitcode enabled, that the official package lacks.

✔️ All the binaries are compiled from source on github action, no virus, no backdoor, no secret code.

opencv 4.5.4 android package size
The official opencv 235MB
opencv-mobile 16.1MB
opencv 4.5.4 ios package size
The official opencv 183MB
opencv-mobile 15.4MB
opencv 4.5.4 ios with bitcode package size
The official opencv missing :(
opencv-mobile 53.7MB

Download

Android

(armeabi-v7a, arm64-v8a, x86, x86_64) build with ndk r21d and android api 24.

iOS

(armv7, arm64, arm64e, i386, x86_64) build with Xcode 12.4.

iOS with bitcode

(armv7, arm64, arm64e, i386, x86_64) build with Xcode 12.4.

ARM Linux

(arm-linux-gnueabi, arm-linux-gnueabihf, aarch64-linux-gnu) build with ubuntu cross compiler.

Windows

(x86, x64) build with VS2015, VS2017 and VS2019.

Linux

(x86_64) build on Ubuntu-18.04 and 20.04.

MacOS

(x86_64, arm64) build with Xcode 12.4.

WebAssembly

(basic, simd, threads, simd+threads) build with Emscripten 2.0.8.

Usage Android

  1. Extract archive to <project dir>/app/src/main/jni/
  2. Modify <project dir>/app/src/main/jni/CMakeListst.txt to find and link opencv
set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/opencv-mobile-4.5.4-android/sdk/native/jni)
find_package(OpenCV REQUIRED)

target_link_libraries(your_jni_target ${OpenCV_LIBS})

Usage iOS and MacOS

  1. Extract archive, and drag opencv2.framework into your project

Usage ARM Linux, Windows, Linux, WebAssembly

  1. Extract archive to <project dir>/
  2. Modify <project dir>/CMakeListst.txt to find and link opencv
set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/opencv-mobile-4.5.4-armlinux/arm-linux-gnueabihf/lib/cmake/opencv4)
find_package(OpenCV REQUIRED)

target_link_libraries(your_target ${OpenCV_LIBS})

How-to-build your custom package

step 1. download opencv source

wget -q https://github.com/opencv/opencv/archive/4.5.4.zip -O opencv-4.5.4.zip
unzip -q opencv-4.5.4.zip
cd opencv-4.5.4

step 2. strip zlib dependency and use stb-based highgui implementation (optional)

patch -p1 -i ../opencv-4.5.4-no-zlib.patch
truncate -s 0 cmake/OpenCVFindLibsGrfmt.cmake
rm -rf modules/gapi
rm -rf modules/highgui
cp -r ../highgui modules/

step 3. patch opencv source for no-rtti build (optional)

patch -p1 -i ../opencv-4.5.4-no-rtti.patch

step 4. apply your opencv options to opencv4_cmake_options.txt

step 5. build your opencv package with cmake

mkdir -p build
cd build
cmake -DCMAKE_INSTALL_PREFIX=install \
  -DCMAKE_BUILD_TYPE=Release \
  `cat ../../opencv4_cmake_options.txt` \
  -DBUILD_opencv_world=OFF ..

step 6. make a package

zip -r -9 opencv-mobile-4.5.4.zip install

Some notes

  • The minimal opencv build contains most basic opencv operators and common image processing functions, with some handy additions like keypoint feature extraction and matching, image inpainting and opticalflow estimation.

  • Many computer vision algorithms that reside in dedicated modules are discarded, such as face detection etc. You could try deep-learning based algorithms with nerual network inference library optimized for mobile.

  • Image IO functions in highgui module, like cv::imread and cv::imwrite, are re-implemented using stb for smaller code size. GUI functions, like cv::imshow, are discarded.

  • cuda and opencl are disabled because there is no cuda on mobile, no opencl on ios, and opencl on android is slow. opencv on gpu is not suitable for real productions. Write metal on ios and opengles/vulkan on android if you need good gpu acceleration.

  • C++ RTTI and exceptions are disabled for minimal build on mobile platforms and webassembly build. Be careful when you write cv::Mat roi = image(roirect); :P

opencv modules included

module comment
opencv_core Mat, matrix operations, etc
opencv_imgproc resize, cvtColor, warpAffine, etc
opencv_highgui imread, imwrite
opencv_features2d keypoint feature and matcher, etc (not included in opencv 2.x package)
opencv_photo inpaint, etc
opencv_video opticalflow, etc

opencv modules discarded

module comment
opencv_androidcamera use android Camera api instead
opencv_calib3d camera calibration, rare uses on mobile
opencv_contrib experimental functions, build part of the source externally if you need
opencv_dnn very slow on mobile, try ncnn for nerual network inference on mobile
opencv_dynamicuda no cuda on mobile
opencv_flann feature matching, rare uses on mobile, build the source externally if you need
opencv_gapi graph based image processing, little gain on mobile
opencv_gpu no cuda/opencl on mobile
opencv_imgcodecs link with opencv_highgui instead
opencv_java wrap your c++ code with jni
opencv_js write native code on mobile
opencv_lagacy various good-old cv routines, build part of the source externally if you need
opencv_ml train your ML algorithm on powerful pc or server
opencv_nonfree the SIFT and SURF, use ORB which is faster and better
opencv_objdetect HOG, cascade detector, use deep learning detector which is faster and better
opencv_ocl no opencl on mobile
opencv_python no python on mobile
opencv_shape shape matching, rare uses on mobile, build the source externally if you need
opencv_stitching image stitching, rare uses on mobile, build the source externally if you need
opencv_superres do video super-resolution on powerful pc or server
opencv_ts test modules, useless in production anyway
opencv_videoio use android MediaCodec or ios AVFoundation api instead
opencv_videostab do video stablization on powerful pc or server
opencv_viz vtk is not available on mobile, write your own data visualization routines



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