OpenCL运行测试

由于双目的分辨率会非常大，因此特来测试下OpenCL的运算性能（奈何手中无英伟达）
环境：MacOS x86_64 Darwin 17.7.0
CPU：Intel Core i5-5350U
GPU：Intel HD Graphics 6000
OpenCV：OpenCV 4.2
编译器：clang++

以下是选用一张4480x6720和一张640x480图像做测试的结果，原始数据输出：

测试结果（仅供参考）

1. 像素：4480x6720，循环次数：100次
   Size:4480x6720
   OpenCL time consume = 85.6954ms
   Size:4480x6720
   CPU time consume = 120.533ms
   Size:4480 x 6720
   OpenCL loops time consume = 1050.15 ms
   Size:4480x6720
   CPU loop time consume = 1058.4ms
   -------------Process Complete------------

2. 像素：4480x6720，循环次数：100次
   Size:4480x6720
   OpenCL time consume = 86.6929 ms
   Size:4480x6720
   CPU time consume = 118.128 ms
   Size:4480 x 6720
   OpenCL loops time consume = 1055.27 ms
   Size:4480x6720
   CPU loops time consume = 1065.48 ms
   -------------Process Complete------------

3. 像素4480x6720，循环次数：1000
   Size:4480x6720
   OpenCL time consume = 86.6043 ms
   Size:4480x6720
   CPU time consume = 119.366 ms
   Size:4480 x 6720
   OpenCL loops time consume = 97815.7 ms
   Size:4480x6720
   CPU loops time consume = 105973 ms
   -------------Process Complete------------

4. 像素：640x480，循环次数：100次
   Size:640x480
   OpenCL time consume = 3.01333 ms
   Size:640x480
   CPU time consume = 1.55183 ms
   Size:640 x 480
   OpenCL loops time consume = 109.044 ms
   Size:640x480
   CPU loops time consume = 84.6049 ms
   -------------Process Complete------------

5. 像素：640x480，循环次数：100次
   Size:640x480
   OpenCL time consume = 3.2602 ms
   Size:640x480
   CPU time consume = 1.29072 ms
   Size:640 x 480
   OpenCL loops time consume = 107.968 ms
   Size:640x480
   CPU loops time consume = 86.4707 ms
   -------------Process Complete------------

6. 像素：640x480，1000次
   Size:640x480
   OpenCL time consume = 3.23095 ms
   Size:640x480
   CPU time consume = 1.17594 ms
   Size:640 x 480
   OpenCL loops time consume = 1164.68 ms
   Size:640x480
   CPU loops time consume = 940.939 ms
   -------------Process Complete------------

7. 像素：640x480，10000次
   Size:640x480
   OpenCL time consume = 3.64011 ms
   Size:640x480
   CPU time consume = 1.2437 ms
   Size:640 x 480
   OpenCL loops time consume = 10745.1 ms
   Size:640x480
   CPU loops time consume = 8566.75 ms
   -------------Process Complete------------

表格总结

表1：

表2：

图表分析

为此我专门做了如下图表：

处理一张图

循环处理

因为这个数据差距有些大，不好直观看出差距，那么我们不妨采用log10为底的坐标刻度：

对数1

对数2

初步分析

1. 在分辨率较小的情况下，使用CPU处理会快于GPU(OpenCL),分辨率较大则GPU占优如图表中显示。
2. 在分辨率大(>1080P)而且循环次数多（>1000）的情况下，使用GPU(OpenCL)明显会快出CPU,参见表2最后一组数据，1000次循环整整快出了81.58s。
   当然，由于平台不同，这个结论不一定具有普适性

结尾放上测试代码，简单的canny边缘检测

edge_test.cpp

#include <opencv2/opencv.hpp>
using namespace cv;
void opencl_process(std::string &filename); //处理一张图（OpenCL）
void cpu_process(std::string &filename);  //处理一张图（CPU）
void loops_opencl(std::string &filename,int &times); //循环处理（OpenCL）
void loops_cpu(std::string &filename,int &times); //循环处理（CPU）
int main(int argc, char** argv)
{
    std::string filename = 文件名;
    int times = 循环次数;
    opencl_process(filename);
    cpu_process(filename);
    loops_opencl(filename,times);
    loops_cpu(filename,times);
    std::cout <<"-------------Process Complete------------"<<std::endl;
    return 0;
}

void opencl_process(std::string &filename){
    double start = (double)getTickCount();
    UMat img, gray;
    // 复制，从Mat->UMat
    imread(filename, IMREAD_COLOR).copyTo(img);
    cvtColor(img, gray, COLOR_BGR2GRAY);
    GaussianBlur(gray, gray,Size(7, 7), 1.5);
    Canny(gray, gray, 0, 50);
    double time_consume = ((double)getTickCount() - start) / getTickFrequency();
    std::cout << "Size:" << gray.cols << "x" << gray.rows << std::endl;
    std::cout << "OpenCL time consume = " << time_consume * 100<< " ms" << std::endl;
}

void cpu_process(std::string &filename){
    double start = (double)getTickCount();
    Mat img, gray;
    imread(filename, IMREAD_COLOR).copyTo(img);
    cvtColor(img, gray, COLOR_BGR2GRAY);
    GaussianBlur(gray, gray,Size(7, 7), 1.5);
    Canny(gray, gray, 0, 50);
    double time_consume = ((double)getTickCount() - start) / getTickFrequency();
    std::cout << "Size:" << gray.cols << "x" << gray.rows << std::endl;
    std::cout << "CPU time consume = " << time_consume * 100<< " ms" << std::endl;
}

void loops_opencl(std::string &filename,int &times){
    double start = (double)getTickCount();
    UMat img, gray;
    for(int i=0;i<=times;i++){
        imread(filename, IMREAD_COLOR).copyTo(img);
        cvtColor(img, gray, COLOR_BGR2GRAY);
        GaussianBlur(gray, gray,Size(7, 7), 1.5);
        Canny(gray, gray, 0, 50);
    }
    double time_consume = ((double)getTickCount() - start) / getTickFrequency();
    std::cout << "Size:" << gray.cols << " x " << gray.rows << std::endl;
    std::cout << "OpenCL loops time consume = " << time_consume * 100<< " ms" << std::endl;

}

void loops_cpu(std::string &filename,int &times){
    double start = (double)getTickCount();
    Mat img, gray;
    for (int i =0; i < times; i++){
        imread(filename, IMREAD_COLOR).copyTo(img);
        cvtColor(img, gray, COLOR_BGR2GRAY);
        GaussianBlur(gray, gray,Size(7, 7), 1.5);
        Canny(gray, gray, 0, 50);
    }
    double time_consume = ((double)getTickCount() - start) / getTickFrequency();
    std::cout << "Size:" << gray.cols << "x" << gray.rows << std::endl;
    std::cout << "CPU loops time consume = " << time_consume * 100<< " ms" << std::endl;
}

更改文件名和循环次数即可。
编译命令参考：

clang++ -std=c++11 edge_test.cpp -o edge_test `pkg-config --cflags --libs opencv4`

注意：opencv 4，开启c++11编译选项。