OpenMP并行编程应用—加速OpenCV图像拼接算法

OpenMP是一种应用于多处理器程序设计的并行编程处理方案，它提供了对于并行编程的高层抽象。仅仅须要在程序中加入简单的指令，就能够编写高效的并行程序，而不用关心详细的并行实现细节。减少了并行编程的难度和复杂度。也正由于OpenMP的简单易用性，它并不适合于须要复杂的线程间同步和相互排斥的场合。

OpenCV中使用Sift或者Surf特征进行图像拼接的算法。须要分别对两幅或多幅图像进行特征提取和特征描写叙述，之后再进行图像特征点的配对。图像变换等操作。不同图像的特征提取和描写叙述的工作是整个过程中最耗费时间的，也是独立执行的，能够使用OpenMP进行加速。

下面是不使用OpenMP加速的Sift图像拼接原程序：

#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include "omp.h"

using namespace cv;

//计算原始图像点位在经过矩阵变换后在目标图像上相应位置  
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri);

int main(int argc, char *argv[])
{
  float startTime = omp_get_wtime();

  Mat image01 = imread("Test01.jpg");
  Mat image02 = imread("Test02.jpg");
  imshow("拼接图像1", image01);
  imshow("拼接图像2", image02);

  //灰度图转换  
  Mat image1, image2;
  cvtColor(image01, image1, CV_RGB2GRAY);
  cvtColor(image02, image2, CV_RGB2GRAY);

  //提取特征点    
  SiftFeatureDetector siftDetector(800);  // 海塞矩阵阈值  
  vector<KeyPoint> keyPoint1, keyPoint2;
  siftDetector.detect(image1, keyPoint1);
  siftDetector.detect(image2, keyPoint2);

  //特征点描写叙述，为下边的特征点匹配做准备    
  SiftDescriptorExtractor siftDescriptor;
  Mat imageDesc1, imageDesc2;
  siftDescriptor.compute(image1, keyPoint1, imageDesc1);
  siftDescriptor.compute(image2, keyPoint2, imageDesc2);

  float endTime = omp_get_wtime();
  std::cout << "不使用OpenMP加速消耗时间： " << endTime - startTime << std::endl;
  //获得匹配特征点。并提取最优配对     
  FlannBasedMatcher matcher;
  vector<DMatch> matchePoints;
  matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
  sort(matchePoints.begin(), matchePoints.end()); //特征点排序    
                          //获取排在前N个的最优匹配特征点  
  vector<Point2f> imagePoints1, imagePoints2;
  for (int i = 0; i < 10; i++)
  {
    imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);
    imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);
  }

  //获取图像1到图像2的投影映射矩阵，尺寸为3*3  
  Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
  Mat adjustMat = (Mat_<double>(3, 3) << 1.0, 0, image01.cols, 0, 1.0, 0, 0, 0, 1.0);
  Mat adjustHomo = adjustMat*homo;

  //获取最强配对点在原始图像和矩阵变换后图像上的相应位置，用于图像拼接点的定位  
  Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;
  originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;
  targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
  basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

  //图像配准  
  Mat imageTransform1;
  warpPerspective(image01, imageTransform1, adjustMat*homo, Size(image02.cols + image01.cols + 110, image02.rows));

  //在最强匹配点左側的重叠区域进行累加。是衔接稳定过渡。消除突变  
  Mat image1Overlap, image2Overlap; //图1和图2的重叠部分     
  image1Overlap = imageTransform1(Rect(Point(targetLinkPoint.x - basedImagePoint.x, 0), Point(targetLinkPoint.x, image02.rows)));
  image2Overlap = image02(Rect(0, 0, image1Overlap.cols, image1Overlap.rows));
  Mat image1ROICopy = image1Overlap.clone();  //复制一份图1的重叠部分  
  for (int i = 0; i < image1Overlap.rows; i++)
  {
    for (int j = 0; j < image1Overlap.cols; j++)
    {
      double weight;
      weight = (double)j / image1Overlap.cols;  //随距离改变而改变的叠加系数  
      image1Overlap.at<Vec3b>(i, j)[0] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[0] + weight*image2Overlap.at<Vec3b>(i, j)[0];
      image1Overlap.at<Vec3b>(i, j)[1] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[1] + weight*image2Overlap.at<Vec3b>(i, j)[1];
      image1Overlap.at<Vec3b>(i, j)[2] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[2] + weight*image2Overlap.at<Vec3b>(i, j)[2];
    }
  }
  Mat ROIMat = image02(Rect(Point(image1Overlap.cols, 0), Point(image02.cols, image02.rows)));  //图2中不重合的部分  
  ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接衔接上去  
  namedWindow("拼接结果", 0);
  imshow("拼接结果", imageTransform1);
  imwrite("D:\\拼接结果.jpg", imageTransform1);
  waitKey();
  return 0;
}

//计算原始图像点位在经过矩阵变换后在目标图像上相应位置  
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri)
{
  Mat originelP, targetP;
  originelP = (Mat_<double>(3, 1) << originalPoint.x, originalPoint.y, 1.0);
  targetP = transformMaxtri*originelP;
  float x = targetP.at<double>(0, 0) / targetP.at<double>(2, 0);
  float y = targetP.at<double>(1, 0) / targetP.at<double>(2, 0);
  return Point2f(x, y);
}

图像一：

图像二：

拼接结果：

在我的机器上不使用OpenMP平均耗时 4.7S。

使用OpenMP也非常easy。VS 内置了对OpenMP的支持。在项目上右键->属性->配置属性->C/C++->语言->OpenMP支持里选择是：

之后在程序中增加OpenMP的头文件“omp.h”就能够了：

#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include "omp.h"

using namespace cv;

//计算原始图像点位在经过矩阵变换后在目标图像上相应位置  
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri);

int main(int argc, char *argv[])
{
  float startTime = omp_get_wtime();

  Mat image01, image02;
  Mat image1, image2;
  vector<KeyPoint> keyPoint1, keyPoint2;
  Mat imageDesc1, imageDesc2;
  SiftFeatureDetector siftDetector(800);  // 海塞矩阵阈值  
  SiftDescriptorExtractor siftDescriptor;
  //使用OpenMP的sections制导指令开启多线程
#pragma omp parallel sections  
  {
#pragma omp section  
    {
      image01 = imread("Test01.jpg");
      imshow("拼接图像1", image01);
      //灰度图转换 
      cvtColor(image01, image1, CV_RGB2GRAY);
      //提取特征点  
      siftDetector.detect(image1, keyPoint1);
      //特征点描写叙述。为下边的特征点匹配做准备    
      siftDescriptor.compute(image1, keyPoint1, imageDesc1);
    }
#pragma omp section  
    {
      image02 = imread("Test02.jpg");
      imshow("拼接图像2", image02);
      cvtColor(image02, image2, CV_RGB2GRAY);
      siftDetector.detect(image2, keyPoint2);
      siftDescriptor.compute(image2, keyPoint2, imageDesc2);
    }
  }
  float endTime = omp_get_wtime();
  std::cout << "使用OpenMP加速消耗时间： " << endTime - startTime << std::endl;

  //获得匹配特征点。并提取最优配对     
  FlannBasedMatcher matcher;
  vector<DMatch> matchePoints;
  matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
  sort(matchePoints.begin(), matchePoints.end()); //特征点排序    
  //获取排在前N个的最优匹配特征点  
  vector<Point2f> imagePoints1, imagePoints2;
  for (int i = 0; i < 10; i++)
  {
    imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);
    imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);
  }

  //获取图像1到图像2的投影映射矩阵。尺寸为3*3  
  Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
  Mat adjustMat = (Mat_<double>(3, 3) << 1.0, 0, image01.cols, 0, 1.0, 0, 0, 0, 1.0);
  Mat adjustHomo = adjustMat*homo;

  //获取最强配对点在原始图像和矩阵变换后图像上的相应位置。用于图像拼接点的定位  
  Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;
  originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;
  targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
  basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

  //图像配准  
  Mat imageTransform1;
  warpPerspective(image01, imageTransform1, adjustMat*homo, Size(image02.cols + image01.cols + 110, image02.rows));

  //在最强匹配点左側的重叠区域进行累加，是衔接稳定过渡，消除突变  
  Mat image1Overlap, image2Overlap; //图1和图2的重叠部分     
  image1Overlap = imageTransform1(Rect(Point(targetLinkPoint.x - basedImagePoint.x, 0), Point(targetLinkPoint.x, image02.rows)));
  image2Overlap = image02(Rect(0, 0, image1Overlap.cols, image1Overlap.rows));
  Mat image1ROICopy = image1Overlap.clone();  //复制一份图1的重叠部分 
  for (int i = 0; i < image1Overlap.rows; i++)
  {
    for (int j = 0; j < image1Overlap.cols; j++)
    {
      double weight;
      weight = (double)j / image1Overlap.cols;  //随距离改变而改变的叠加系数  
      image1Overlap.at<Vec3b>(i, j)[0] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[0] + weight*image2Overlap.at<Vec3b>(i, j)[0];
      image1Overlap.at<Vec3b>(i, j)[1] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[1] + weight*image2Overlap.at<Vec3b>(i, j)[1];
      image1Overlap.at<Vec3b>(i, j)[2] = (1 - weight)*image1ROICopy.at<Vec3b>(i, j)[2] + weight*image2Overlap.at<Vec3b>(i, j)[2];
    }
  }
  Mat ROIMat = image02(Rect(Point(image1Overlap.cols, 0), Point(image02.cols, image02.rows)));  //图2中不重合的部分  
  ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接衔接上去  
  namedWindow("拼接结果", 0);
  imshow("拼接结果", imageTransform1);
  imwrite("D:\\拼接结果.jpg", imageTransform1);
  waitKey();
  return 0;
}

//计算原始图像点位在经过矩阵变换后在目标图像上相应位置  
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri)
{
  Mat originelP, targetP;
  originelP = (Mat_<double>(3, 1) << originalPoint.x, originalPoint.y, 1.0);
  targetP = transformMaxtri*originelP;
  float x = targetP.at<double>(0, 0) / targetP.at<double>(2, 0);
  float y = targetP.at<double>(1, 0) / targetP.at<double>(2, 0);
  return Point2f(x, y);
}

OpenMP中for制导指令用于迭代计算的任务分配，sections制导指令用于非迭代计算的任务分配，每一个#pragma omp section 语句会引导一个线程。

OpenMP并行编程应用—加速OpenCV图像拼接算法

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