OpenMP并行程式設計應用—加速OpenCV圖像拼接算法

OpenMP是一種應用于多處理器程式設計的并行程式設計處理方案，它提供了對于并行程式設計的高層抽象。僅僅須要在程式中加入簡單的指令，就能夠編寫高效的并行程式，而不用關心詳細的并行實作細節。減少了并行程式設計的難度和複雜度。也正由于OpenMP的簡單易用性，它并不适合于須要複雜的線程間同步和互相排斥的場合。

OpenCV中使用Sift或者Surf特征進行圖像拼接的算法。須要分别對兩幅或多幅圖像進行特征提取和特征描寫叙述，之後再進行圖像特征點的配對。圖像變換等操作。不同圖像的特征提取和描寫叙述的工作是整個過程中最耗費時間的，也是獨立執行的，能夠使用OpenMP進行加速。

下面是不使用OpenMP加速的Sift圖像拼接原程式：

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

using namespace cv;

//計算原始圖像點位在經過矩陣變換後在目标圖像上相應位置  
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("拼接圖像1", image01);
  imshow("拼接圖像2", image02);

  //灰階圖轉換  
  Mat image1, image2;
  cvtColor(image01, image1, CV_RGB2GRAY);
  cvtColor(image02, image2, CV_RGB2GRAY);

  //提取特征點    
  SiftFeatureDetector siftDetector(800);  // 海塞矩陣門檻值  
  vector<KeyPoint> keyPoint1, keyPoint2;
  siftDetector.detect(image1, keyPoint1);
  siftDetector.detect(image2, keyPoint2);

  //特征點描寫叙述，為下邊的特征點比對做準備    
  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加速消耗時間： " << endTime - startTime << std::endl;
  //獲得比對特征點。并提取最優配對     
  FlannBasedMatcher matcher;
  vector<DMatch> matchePoints;
  matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
  sort(matchePoints.begin(), matchePoints.end()); //特征點排序    
                          //擷取排在前N個的最優比對特征點  
  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);
  }

  //擷取圖像1到圖像2的投影映射矩陣，尺寸為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;

  //擷取最強配對點在原始圖像和矩陣變換後圖像上的相應位置，用于圖像拼接點的定位  
  Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;
  originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;
  targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
  basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

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

  //在最強比對點左側的重疊區域進行累加。是銜接穩定過渡。消除突變  
  Mat image1Overlap, image2Overlap; //圖1和圖2的重疊部分     
  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();  //複制一份圖1的重疊部分  
  for (int i = 0; i < image1Overlap.rows; i++)
  {
    for (int j = 0; j < image1Overlap.cols; j++)
    {
      double weight;
      weight = (double)j / image1Overlap.cols;  //随距離改變而改變的疊加系數  
      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)));  //圖2中不重合的部分  
  ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接銜接上去  
  namedWindow("拼接結果", 0);
  imshow("拼接結果", imageTransform1);
  imwrite("D:\\拼接結果.jpg", imageTransform1);
  waitKey();
  return 0;
}

//計算原始圖像點位在經過矩陣變換後在目标圖像上相應位置  
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);
}

圖像一：

圖像二：

拼接結果：

在我的機器上不使用OpenMP平均耗時 4.7S。

使用OpenMP也非常easy。VS 内置了對OpenMP的支援。在項目上右鍵->屬性->配置屬性->C/C++->語言->OpenMP支援裡選擇是：

之後在程式中增加OpenMP的頭檔案“omp.h”就能夠了：

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

using namespace cv;

//計算原始圖像點位在經過矩陣變換後在目标圖像上相應位置  
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);  // 海塞矩陣門檻值  
  SiftDescriptorExtractor siftDescriptor;
  //使用OpenMP的sections制導指令開啟多線程
#pragma omp parallel sections  
  {
#pragma omp section  
    {
      image01 = imread("Test01.jpg");
      imshow("拼接圖像1", image01);
      //灰階圖轉換 
      cvtColor(image01, image1, CV_RGB2GRAY);
      //提取特征點  
      siftDetector.detect(image1, keyPoint1);
      //特征點描寫叙述。為下邊的特征點比對做準備    
      siftDescriptor.compute(image1, keyPoint1, imageDesc1);
    }
#pragma omp section  
    {
      image02 = imread("Test02.jpg");
      imshow("拼接圖像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加速消耗時間： " << endTime - startTime << std::endl;

  //獲得比對特征點。并提取最優配對     
  FlannBasedMatcher matcher;
  vector<DMatch> matchePoints;
  matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
  sort(matchePoints.begin(), matchePoints.end()); //特征點排序    
  //擷取排在前N個的最優比對特征點  
  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);
  }

  //擷取圖像1到圖像2的投影映射矩陣。尺寸為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;

  //擷取最強配對點在原始圖像和矩陣變換後圖像上的相應位置。用于圖像拼接點的定位  
  Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;
  originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;
  targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
  basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

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

  //在最強比對點左側的重疊區域進行累加，是銜接穩定過渡，消除突變  
  Mat image1Overlap, image2Overlap; //圖1和圖2的重疊部分     
  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();  //複制一份圖1的重疊部分 
  for (int i = 0; i < image1Overlap.rows; i++)
  {
    for (int j = 0; j < image1Overlap.cols; j++)
    {
      double weight;
      weight = (double)j / image1Overlap.cols;  //随距離改變而改變的疊加系數  
      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)));  //圖2中不重合的部分  
  ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接銜接上去  
  namedWindow("拼接結果", 0);
  imshow("拼接結果", imageTransform1);
  imwrite("D:\\拼接結果.jpg", imageTransform1);
  waitKey();
  return 0;
}

//計算原始圖像點位在經過矩陣變換後在目标圖像上相應位置  
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制導指令用于疊代計算的任務配置設定，sections制導指令用于非疊代計算的任務配置設定，每一個#pragma omp section 語句會引導一個線程。

OpenMP并行程式設計應用—加速OpenCV圖像拼接算法

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