#安装依赖 cmake >= 3.10.0 opencv >= 3.0.0 openmp

x86平台Linux安装指令

git clone https://github.com/lqian/light-LPR
cd light-LPR && mkdir build && cd build
cmake ../
make

ARM平台Linux安装指令

git clone https://github.com/lqian/light-LPR
cd light-LPR && mkdir build && cd build
cmake ../ -DLIGHT_LPR_ARCH=arm
make

Windows平台上安装指令

• 下载cmake 3.10以上版本并安装
• 首先下载Visual Studio 2017或者 Native Builder Tool for Visual Studio 2017，安装c++编译工具
• 如果编译64位系统，下载64位opencv-3.4.2-install-win64.zip, 32位系统则下载opencv-3.4.2-install-win32.zip，解压到任意目录
• 克隆MNN的源码
• 下载flatc_windows_exe.zip，把flatc.exe可执行文件复制到{MNN}/3rd_party/flatbuffers/tmp目录下
• 以管理员权限打开powershell.exe，然后执行set-executionpolicy -executionpolicy unrestricted，提示选Y
• 注释掉MNN的源码目录中的CMakelist.txt中的COMMAND powershell ${CMAKE_CURRENT_SOURCE_DIR}/schema/generate.ps1 -lazy这行，大约在461行

> cd MNN
> schema\enerate.ps1
> mkdir build 
> cd build
按win键，根据需要，搜索x86 native tools command prompt for VS 2017 或者x64 native tools command prompt for VS 2017
> cmake -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=Release ../
> nmake 
把编译成功的MNN.dll、MNN.lib文件复制到light-LPR项目的lib目录下
> cd light-LPR && mkdir build && cd build
> set OpenCV_DIR=/path/to/opencv-install/directory
> cmake -G "NMake Makefiles" ..
> nmake

运行测试

./examples/demo ../models/ [/path/to/a/image] 本项目在Fedora 29，CentOS 7.6, Windows 10 64位家庭版，Ubuntu 18.04 mate for ARM平台测试通过

参考和引用

• Alibaba MNN
• License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
• Caffe_OCR
• MNN MTCNN CPU OPENCL