windows 源码编译 opencv-4.5.4 + tbb + ipp + openmp

Posted 2021-12-28 Arabican

1. 下载opencv源码

• 官网下载需要版本的opencv源码
  

• 解压到本地文件夹，进入解压后的opencv-4.5.4文件夹，新建source文件夹，将解压的内容放入source文件夹中，新建build文件夹
  

• 下载opencv_contrib，并解压
  

2. 安装cmake

3. 安装Intel oneAPI Base Toolkit

• Intel将TBB、IPP等加速库集成到了oneAPI库中，所以可以直接安装oneAPI
  

*按提示安装，勾选TBB，IPP等库

4. 安装Visual Studio 2019

• 省略

5. 编译opencv

• 打开cmake，设置好opencv源码路径和build路径

• 点击configure，设置generator
  

• configure完成后，设置with tbb
  

• 设置with ipp
  IPP的cmake路径一般为C:/Program Files (x86)/Intel/oneAPI/ipp/2021.4.0/lib/cmake/ipp
  

• 设置with openmp
  

• 设置 BUILD_opencv_world
  

• 点击Generate，等待完成
  

• 在build文件夹中找到Opencv.sln，使用VS打开
  

• 选择Debug模式，右键点击CMakeTargets中的ALL_BUILD，点击"生成"，完成后右键点击INSTALL，然后点击"生成";
  

• 选择Release模式，重复以上操作

• 完成后可在文件夹中看到编译好的opencv
  

• 设置环境变量（省略）

6. 速度测试

测试的CPU为8核16线程, 在开满16个线程后对比测试有加速的OpenCV和没有加速库的OpenCV。
测试了以下算子：

cv::cvtColor()
cv::erode()
cv::dilate()
cv::findContours()

代码如下：

void testSpeed()

    cv::Mat image = cv::imread("C:\\\\Workbench\\\\Data\\\\lenna.png");
    cv::Mat cvtImage;
    cv::Mat kernel_1 = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
    cv::Mat kernel_2 = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
    float t = 0.0;
    for (int i = 0; i < 100; ++i)
       
        auto start_time = std::chrono::high_resolution_clock::now();
        cv::cvtColor(image, cvtImage, cv::COLOR_BGR2GRAY);
        cv::erode(cvtImage, cvtImage, kernel_1, cv::Point(-1, -1), 1);
        cv::dilate(cvtImage, cvtImage, kernel_2, cv::Point(-1, -1), 3);
        std::vector<std::vector<cv::Point>> _contours;
        std::vector<cv::Vec4i> hierarchy;
        cv::findContours(cvtImage, _contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_NONE);
        auto end_time = std::chrono::high_resolution_clock::now();
        auto ms = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count();
        t = t + (ms * 1.0 / 1e6);
    
    std::cout << "time: " << t << std::endl;


int main()

    std::thread threads[16];
    for (int i = 0; i < 16; ++i)
    
        threads[i] = std::thread(testSpeed);
    
    for (auto &t : threads)
    
        t.join();
    
    return 0;

测试结果：

• 有TBB加速的OpenCV

[ INFO:0] global C:\\Softwares\\opencv-4.5.4\\source\\modules\\core\\src\\parallel\\registry_parallel.impl.hpp (96) cv::parallel::ParallelBackendRegistry::ParallelBackendRegistry core(parallel): Enabled backends(2, sorted by priority): TBB(1000); OPENMP(990)
[ INFO:0] global C:\\Softwares\\opencv-4.5.4\\source\\modules\\core\\include\\opencv2/core/parallel/backend/parallel_for.tbb.hpp (54) cv::parallel::tbb::ParallelForBackend::ParallelForBackend Initializing TBB parallel backend: TBB_INTERFACE_VERSION=12040
[ INFO:0] global C:\\Softwares\\opencv-4.5.4\\source\\modules\\core\\src\\parallel\\parallel.cpp (77) cv::parallel::createParallelForAPI core(parallel): using backend: TBB (priority=1000)
time: 0.990235
time: 0.990842
time: 0.994129
time: 0.992949
time: 1.00523
time: 0.995566
time: 1.01266
time: 1.00874
time: 1.02145
time: 1.02373
time: 1.0271
time: 1.03198
time: 1.02038
time: 1.03185
time: 1.03868
time: 1.05034

• 没有TBB加速的OpenCV

[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\parallel\\registry_parallel.impl.hpp (96) cv::parallel::ParallelBackendRegistry::ParallelBackendRegistry core(parallel): Enabled backends(3, sorted by priority): ONETBB(1000); TBB(990); OPENMP(980)
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load C:\\Softwares\\opencv\\build\\x64\\vc15\\bin\\opencv_core_parallel_onetbb454_64d.dll => FAILED
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_onetbb454_64d.dll => FAILED
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load C:\\Softwares\\opencv\\build\\x64\\vc15\\bin\\opencv_core_parallel_tbb454_64d.dll => FAILED
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_tbb454_64d.dll => FAILED
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load C:\\Softwares\\opencv\\build\\x64\\vc15\\bin\\opencv_core_parallel_openmp454_64d.dll => FAILED
[ INFO:0] global c:\\build\\master_winpack-build-win64-vc15\\opencv\\modules\\core\\src\\utils\\plugin_loader.impl.hpp (67) cv::plugin::impl::DynamicLib::libraryLoad load opencv_core_parallel_openmp454_64d.dll => FAILED
time: 2.38442
time: 2.43082
time: 2.43363
time: 2.42781
time: 2.46706
time: 2.47458
time: 2.48732
time: 2.49592
time: 2.50646
time: 2.51118
time: 2.50738
time: 2.51676
time: 2.50897
time: 2.52977
time: 2.50874
time: 2.55397

• 结论：在线程资源占满的情况下，以上算子测得有TBB加速的Opencv比没有加速的Opencv要快出约2.5倍。