标签：img start tv opencl times opencv linux include cv

主要的步骤其他人已经写过，请参考这篇：https://www.cnblogs.com/hrlnw/p/4720977.html

操作的细节请参考附件的pdf:  https://files.cnblogs.com/files/ahfuzhang/opencvwithopencl4androidndk-141129030940-conversion-gate02.pdf.zip

用于测试的代码如下：

//jpg2gary.cpp
#include <inttypes.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/time.h>
#include <opencv2/core.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/core/ocl.hpp>
#include <fstream>

#ifndef P
#define P(format, ...)                                                    \
    do {                                                                  \
        printf("%s %s %d " format "\n", __FILE__, __FUNCTION__, __LINE__, \
               ##__VA_ARGS__);                                            \
        fflush(stdout);                                                   \
    } while (0);
#endif

void cpu(const char* img, int times) {
    cv::Mat image = cv::imread(img, cv::IMREAD_UNCHANGED);
    cv::Mat out;
    struct timeval start, end;
    gettimeofday(&start, NULL);
    for (int i = 0; i < times; i++) {
        cv::cvtColor(image, out, cv::COLOR_BGR2GRAY);
    }
    gettimeofday(&end, NULL);
    P("run times:%d, spend:%d ms", times, (end.tv_sec - start.tv_sec) * 1000 +
                                       (end.tv_usec - start.tv_usec) / 1000);
}

void opencl(const char* img, int times) {
    cv::Mat image = cv::imread(img, cv::IMREAD_UNCHANGED);
    //cv::UMat u_img = image.getUMat(cv::ACCESS_READ);
    cv::UMat u_img;
    image.copyTo(u_img);
    cv::UMat out;
    struct timeval start, end;
    gettimeofday(&start, NULL);
    for (int i = 0; i < times; i++) {
        cv::cvtColor(u_img, out, cv::COLOR_BGR2GRAY);
    }
    gettimeofday(&end, NULL);
    P("run times:%d, spend:%d ms", times, (end.tv_sec - start.tv_sec) * 1000 +
                                       (end.tv_usec - start.tv_usec) / 1000);
}

int init_col(){
    cv::ocl::setUseOpenCL(true);
  if (!cv::ocl::haveOpenCL()) {
    P("OpenCL is not available...");
    return -1;
  }
  cv::ocl::Context context;
  if (!context.create(cv::ocl::Device::TYPE_GPU)) {
    P("Failed creating the context...");
    return -1;
  }
  std::vector<cv::ocl::PlatformInfo> platform_info;
  cv::ocl::getPlatfomsInfo(platform_info);
  for (int i = 0; i < platform_info.size(); i++) {
    cv::ocl::PlatformInfo sdk = platform_info.at(i);
    for (int j = 0; j < sdk.deviceNumber(); j++) {
      cv::ocl::Device device;
      sdk.getDevice(device, j);

      std::cout << "\n\n*********************\n Device " << i + 1 << std::endl;
      std::cout << "Vendor ID: " << device.vendorID() << std::endl;
      std::cout << "Vendor name: " << device.vendorName() << std::endl;
      std::cout << "Name: " << device.name() << std::endl;
      std::cout << "Driver version: " << device.driverVersion() << std::endl;
      std::cout << "available: " << device.available() << std::endl;

      if (device.isAMD()) std::cout << "Is an AMD device" << std::endl;
      if (device.isIntel()) std::cout << "Is a Intel device" << std::endl;

      std::cout << "Global Memory size: " << device.globalMemSize()
                << std::endl;
      std::cout << "Memory cache size: " << device.globalMemCacheSize()
                << std::endl;
      std::cout << "Memory cache type: " << device.globalMemCacheType()
                << std::endl;
      std::cout << "Local Memory size: " << device.localMemSize() << std::endl;
      std::cout << "Local Memory type: " << device.localMemType() << std::endl;
      std::cout << "Max Clock frequency: " << device.maxClockFrequency()
                << std::endl;
    }
  }
  if (!cv::ocl::haveOpenCL()) {
    P("OpenCL is not available, again...");
    return -1;
  }  
  cv::ocl::Device(context.device(0));   
  return 0; 
}

int main(int argc, char* argv[]) {
    if (argc < 3) {
        printf("usage:%s <from> <cpu/opencl> [times=1]\n", argv[0]);
        return 0;
    }
    int times = 1;
    if (argc >= 4) {
        times = atoi(argv[3]);
    }
    if (strcmp(argv[2], "cpu") == 0) {
        cpu(argv[1], times);
    } else if (strcmp(argv[2], "opencl") == 0) {
        if (0!=init_col()){
            return 1;
        }
        opencl(argv[1], times);
    } else {
        P("unknow cpu/opencl");
        return 0;
    }

    return 1;
}

使用xiaomi mix 2s, 高通骁龙 845， GPU Adreno 630, 对一张1080*1443尺寸的图片使用cvtColor转换RGB到灰度。
连续执行1000次：
   CPU  595ms
   OpenCL  96ms

加速6.2倍！

标签：img,start,tv,opencl,times,opencv,linux,include,cv
来源： https://www.cnblogs.com/ahfuzhang/p/11071865.html

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