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✔️ This project provides the minimal build of opencv library for theAndroid,iOS andARM Linux platforms.

✔️ Packages forWindows,Linux,MacOS,HarmonyOS andWebAssembly are available now.

✔️ We provide prebuild binary packages for opencv2.4.13.7,3.4.20 and4.10.0.

✔️ We also provide prebuild package forMac-Catalyst,watchOS,tvOS,visionOS andApple xcframework.

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

✔️NEW FEATUREcv::putText supports full-width CJK characters

✔️NEW FEATUREcv::imshow supports Linux framebuffer and Windows

✔️NEW FEATUREcv::VideoWriter supports jpg streaming over http

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

	Source
	Android
	HarmonyOS
	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 ✅ DPI LCD screen
yuzuki-lizard arm-linux-uclibcgnueabihf	tinyvision arm-linux-uclibcgnueabihf ✅ HW JPG decoder ✅ HW JPG encoder ✅ MIPI CSI camera ✅ SPI LCD screen	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	2k0300-fengniao loongarch64-linux-gnu
lockzhiner-vision-module arm-linux-uclibcgnueabihf ✅ HW JPG encoder ✅ MIPI CSI camera ✅ DPI LCD screen

1. Extract archive to<project dir>/app/src/main/jni/
2. Modify<project dir>/app/src/main/jni/CMakeLists.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 dragopencv2.framework oropencv2.xcframework into your project

1. Extract archive to<project dir>/
2. Modify<project dir>/CMakeLists.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.zipunzip -q opencv-mobile-4.10.0.zipcd 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 buildcd buildcmake -DCMAKE_INSTALL_PREFIX=install \  -DCMAKE_BUILD_TYPE=Release \`cat ../options.txt` \  -DBUILD_opencv_world=OFF ..make -j4make 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, likecv::imread andcv::imwrite, are re-implemented usingstb for smaller code size. GUI functions, likecv::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 writecv::Mat roi = image(roirect); :P

1. Openhttps://nihui.github.io/opencv-mobile/patches/fontface.html oropencv-mobile-X.Y.Z/fontface.html in your browser.
2. In the opened page, enter all the text to be drawn, select the TTF font file (optional), click theConvert to Font Header button to download the fontface header. This step is completely local operation, without connecting to a remote server, your data is private and safe.
3. Include the generated fontface header, initialize a fontface instance, and pass it as the argument tocv::putText. The source file must be encoded in UTF-8.

Since all characters have been converted to embedded bitmap, the drawing routine does not depend on freetype library or any font files at runtime.

#include<opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>#include<opencv2/imgproc/imgproc.hpp>#include"myfontface.h"intmain(){    cv::Mat bgr =cv::imread("atari.jpg",1);// use this font    MyFontFace myfont;// draw full-width text with myfontconstchar* zhtext ="称呼机器人为破铜烂铁，\n违反了禁止歧视机器人法！";cv::putText(bgr, zhtext,cv::Point(30,250),cv::Scalar(127,0,127), myfont,20);// get bounding rect    cv::Rect rr =cv::getTextSize(bgr.size(), zhtext,cv::Point(30,250), myfont,20);cv::imwrite("out.jpg", bgr);return0;}

In Linux,cv::imshow can display images on the screen (/dev/fb0) via theLinux Framebuffer API.cv::imshow can work without desktop environment (gnome, KDE Plasma, xfce, etc.) or window manager (X or wayland), making it suitable for embedded scenarios. The first argument tocv::imshow must befb.

In Windows,cv::imshow will use the Windows API to create a simple window for displaying.

#include<opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>intmain(){    cv::Mat bgr =cv::imread("im.jpg",1);cv::imshow("fb", bgr);return0;}

#include<opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>intmain(){    cv::VideoCapture cap;    cap.set(cv::CAP_PROP_FRAME_WIDTH,320);    cap.set(cv::CAP_PROP_FRAME_HEIGHT,240);    cap.open(0);    cv::Mat bgr;while (1)    {        cap >> bgr;cv::imshow("fb", bgr);    }return0;}

In Linux,cv::VideoWriter could be used for streaming images as jpg over http, while it is noop on other platforms. Initialize acv::VideoWriter instance,open the writer with namehttpjpg and a port number, then it will setup a simple http server. You can open the streaming url with a web browser, and image will be shown in the browser as soon as it is sent to the writer. The image size and content can be dynamic, which is useful for streaming frames from a live camera.

#include<opencv2/core/core.hpp>#include<opencv2/highgui/highgui.hpp>intmain(){    cv::VideoCapture cap;    cap.open(0);    cv::VideoWriter http;    http.open("httpjpg",7766);// open streaming url http://<server ip>:7766 in web browser    cv::Mat bgr;while (1)    {        cap >> bgr;        http << bgr;    }return0;}