✔️ 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, 3.4.20 and 4.10.0.

✔️ We also provide prebuild package for Mac-Catalyst, watchOS, tvOS, visionOS and Apple xcframework.

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

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

	Source
	Android
	iOS
	iOS-Simulator
	macOS
	Mac-Catalyst
	watchOS
	watchOS-Simulator
	tvOS
	tvOS-Simulator
	visionOS
	visionOS-Simulator
	Apple xcframework
	Ubuntu-20.04
	Ubuntu-22.04
	VS2015
	VS2017
	VS2019
	VS2022
	WebAssembly
	ARM-Linux

• Android package build with ndk r26c and android api 21
• iOS / MacOS / Mac-Catalyst / watchOS / tvOS / visionOS package build with Xcode 15.2
• ARM Linux package build with cross-compiler on Ubuntu-22.04
• WebAssembly package build with Emscripten 3.1.28

milkv-duo riscv64-linux-musl ✅ HW JPG decoder ✅ MIPI CSI camera	licheerv-nano riscv64-linux-musl ✅ HW JPG decoder ✅ MIPI CSI camera	luckfox-pico arm-linux-uclibcgnueabihf ✅ HW JPG encoder ✅ MIPI CSI camera
yuzuki-lizard arm-linux-uclibcgnueabihf	tinyvision arm-linux-uclibcgnueabihf ✅ HW JPG decoder ✅ HW JPG encoder ✅ MIPI CSI camera	yuzuki-chameleon arm-openwrt-linux-gnueabi ✅ HW JPG decoder ✅ HW JPG encoder
purple-pi arm-linux-uclibcgnueabihf	myir-t113i arm-linux-gnueabi ✅ HW JPG decoder ✅ HW JPG encoder

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-4.10.0-android/sdk/native/jni)
find_package(OpenCV REQUIRED)

target_link_libraries(your_jni_target ${OpenCV_LIBS})

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

1. Extract archive to <project dir>/
2. Modify <project dir>/CMakeListst.txt to find and link opencv
3. Pass -DOpenCV_STATIC=ON to cmake option for windows build

set(OpenCV_DIR ${CMAKE_SOURCE_DIR}/opencv-mobile-4.10.0-armlinux/arm-linux-gnueabihf/lib/cmake/opencv4)
find_package(OpenCV REQUIRED)

target_link_libraries(your_target ${OpenCV_LIBS})

We reduce the binary size of opencv-mobile in 3 ways

1. Reimplement some modules (such as highgui) and functions (such as putText)
2. Apply patches to disable rtti/exceptions and do not install non-essential files
3. Carefully select cmake options to retain only the modules and functions you want

Steps 1 and 2 are relatively cumbersome and difficult, and require intrusive changes to the opencv source code. If you want to know the details, please refer to the steps in .github/workflows/release.yml

The opencv-mobile source code package is the result of steps 1 and 2. Based on it, we can adjust the cmake option to compile our own package and further delete and add modules and other functions.

step 1. download opencv-mobile source

wget -q https://github.com/nihui/opencv-mobile/releases/latest/download/opencv-mobile-4.10.0.zip
unzip -q opencv-mobile-4.10.0.zip
cd opencv-mobile-4.10.0

step 2. apply your opencv option changes to options.txt

vim options.txt

step 3. build your opencv package with cmake

mkdir -p build
cd build
cmake -DCMAKE_INSTALL_PREFIX=install \
  -DCMAKE_BUILD_TYPE=Release \
  `cat ../options.txt` \
  -DBUILD_opencv_world=OFF ..
make -j4
make install

step 4. make a package

zip -r -9 opencv-mobile-4.10.0-mypackage.zip install

• 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 neural 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