OpenCL運行測試

由于雙目的分辨率會非常大计雌，因此特來測試下OpenCL的運算性能（奈何手中無英偉達）
環(huán)境：MacOS x86_64 Darwin 17.7.0
CPU：Intel Core i5-5350U
GPU：Intel HD Graphics 6000
OpenCV：OpenCV 4.2
編譯器：clang++

以下是選用一張4480x6720和一張640x480圖像做測試的結果郎汪，原始數(shù)據(jù)輸出：

測試結果（僅供參考）

1. 像素：4480x6720，循環(huán)次數(shù)：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移袍，循環(huán)次數(shù)：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，循環(huán)次數(shù)：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钳榨，循環(huán)次數(shù)：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，循環(huán)次數(shù)：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州藕，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------------

表格總結

表1：

表2：

圖表分析

為此我專門做了如下圖表：

處理一張圖

循環(huán)處理

因為這個數(shù)據(jù)差距有些大酝陈，不好直觀看出差距床玻，那么我們不妨采用log10為底的坐標刻度：

對數(shù)1

對數(shù)2

初步分析

1. 在分辨率較小的情況下，使用CPU處理會快于GPU(OpenCL),分辨率較大則GPU占優(yōu)如圖表中顯示沉帮。
2. 在分辨率大(>1080P)而且循環(huán)次數(shù)多（>1000）的情況下锈死，使用GPU(OpenCL)明顯會快出CPU,參見表2最后一組數(shù)據(jù)，1000次循環(huán)整整快出了81.58s穆壕。
   當然待牵，由于平臺不同，這個結論不一定具有普適性

結尾放上測試代碼喇勋，簡單的canny邊緣檢測

edge_test.cpp

#include <opencv2/opencv.hpp>
using namespace cv;
void opencl_process(std::string &filename); //處理一張圖（OpenCL）
void cpu_process(std::string &filename);  //處理一張圖（CPU）
void loops_opencl(std::string &filename,int &times); //循環(huán)處理（OpenCL）
void loops_cpu(std::string &filename,int &times); //循環(huán)處理（CPU）
int main(int argc, char** argv)
{
    std::string filename = 文件名;
    int times = 循環(huán)次數(shù);
    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;
    // 復制缨该，從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;
}

更改文件名和循環(huán)次數(shù)即可。
編譯命令參考：

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

注意：opencv 4茄蚯，開啟c++11編譯選項压彭。