OpenCV算法加速（1）基础知识

#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#pragma comment(lib,"opencv_core2410d.lib")
#pragma comment(lib,"opencv_highgui2410d.lib")
#pragma comment(lib,"opencv_imgproc2410d.lib")
void EdgeOpenMP(IplImage *src,IplImage *dst,int thresh)
{
    int height    = src->height;
    int width     = src->width;
    int step      = src->widthStep;
    uchar *data1  = (uchar *)src->imageData;
    uchar *data2  = (uchar *)dst->imageData;
    int i = step;
    #pragma omp parallel for
    for(i=step+1;i<height*width;i++)
    {
         if(abs(data1[i]-data1[i-1])>thresh || abs(data1[i]-data1[i-step])>thresh)
            data2[i]=255;/* 对于单通道，前后两帧差分大于门限
            或者对于多通道前后两帧的一个指标差分大于门限，则视为边缘*/
         else
            data2[i]=0;
    }
}
void Edge(IplImage *src,IplImage *dst,int thresh)
{
    int height    = src->height;
    int width     = src->width;
    int step      = src->widthStep;
    uchar *data1      = (uchar *)src->imageData;
    uchar *data2      = (uchar *)dst->imageData;
    int i = step;
    for(i=step+1;i<height*width;i++)
    {
         if(abs(data1[i]-data1[i-1])>thresh || abs(data1[i]-data1[i-step])>thresh)
            data2[i]=255;
         else
            data2[i]=0;
    }
}
int main()
{
  char filename[512];
  IplImage *src,*edge1,*edge2;
  puts("File name:");
  gets(filename);
  src = cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE );
  edge1=cvCloneImage(src);
  edge2=cvCloneImage(src);
  cvNamedWindow("src", CV_WINDOW_AUTOSIZE);
  cvMoveWindow("src", 100, 100);
  cvShowImage( "src", src);
  cvNamedWindow("Edge", CV_WINDOW_AUTOSIZE);
  cvMoveWindow("Edge", 200, 100);
  cvNamedWindow("EdgeOpenMP", CV_WINDOW_AUTOSIZE);
  cvMoveWindow("EdgeOpenMP", 300, 100);
  /* 以上都是准备一些窗口和图形基本数据 */
  int tekrar=100;//运行次数
  int thresh=30;
  double start, end,t1, t2;
  /* 计算没有使用OpenMP优化的时间 */
  start= (double)cvGetTickCount();//记下开始的时钟计数，以便计算函数或用户代码执行时间
  for(int i=0;i<tekrar;i++)
    Edge(src,edge1,thresh);
  end= (double)cvGetTickCount();//记下结束的时钟计数
  t1= (end-start)/((double)cvGetTickFrequency()*1000.);//计算运行时间，以毫秒为单位
  printf( "Run time without OpenMP = %g ms\n", t1 );
  /* 计算使用了OpenMP优化的时间 */
  start= (double)cvGetTickCount();
  for(int i=0;i<tekrar;i++)
    EdgeOpenMP(src,edge2,thresh);
  end= (double)cvGetTickCount();
  t2= (end-start)/((double)cvGetTickFrequency()*1000.);
  printf( "Run time with OpenMP = %g ms\n", t2 );
  printf( "Performance ratio (%%) = %% %.1f \n", 100*(t1/t2-1) );
  cvShowImage( "Edge", edge1);
  cvShowImage( "EdgeOpenMP", edge2);
  cvWaitKey();
  cvDestroyWindow("Edge");
  cvDestroyWindow("EdgeOpenMP");
  cvReleaseImage(&src);
  cvReleaseImage(&edge1);
  cvReleaseImage(&edge2);
}

// A very raw example of using tbb's thread
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/video/video.hpp>
#include <opencv2/highgui.hpp>
#include <tbb/tbb.h>
#include <iostream>
using namespace std;
using namespace cv;
using namespace tbb;
int main(int argc, char* argv[])
{
    int TBB_THREADS = 3;
    task_scheduler_init init(TBB_THREADS);
    Mat im1 = imread(argv[1]);
    Mat imGray;
    if (im1.data == nullptr)
    {
        cout << "Error while reading file " << argv[1];
        return 1;
    }
    imshow("Input image", im1);
    cvtColor(im1, imGray, CV_RGB2GRAY);
    Mat im3, im4;
    tbb_thread th1([&imGray, &im3]() // in fact, you can do this with C++ thread
    {
        int windowSize = 5; // starting threshold value
        int constant = 5; // starting constant value
        adaptiveThreshold(imGray, im3, 255,
            CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY,
            windowSize, constant);
    });
    tbb_thread th2([&imGray, &im4]()
    {
        cv::GaussianBlur(imGray, im4, cv::Size(3, 3), 5.0f);
    });
    th1.join();
    th2.join();
    imshow("Grayscale image", imGray);
    imshow("Adaptive thresholding", im3);
    imshow("Gaussian blur", im4);
    cvWaitKey(0);
    return 0;
}