引入极线约束的surf特征匹配

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 #include
 #include "opencv2/core/core.hpp"
 #include "opencv2/features2d/features2d.hpp"
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/nonfree/nonfree.hpp"
 #include
 #include
 using namespace std;

 using namespace cv;

 static void help()
 {
     printf("\nThis program demonstrates using features2d detector, descriptor extractor and simple matcher\n"
             "Using the SURF desriptor:\n"
             "\n"
             "Usage:\n matcher_simple  \n");
 }

 int main(int argc, char** argv)
 {
     //if(argc != 3)
     //{
     //    help();
     //    return -1;
     //}

     Mat img1 = imread("D:\\faceData\\stereopairs\\cones\\imL.jpg", CV_LOAD_IMAGE_GRAYSCALE);
     Mat img2 = imread("D:\\faceData\\stereopairs\\cones\\imR.jpg", CV_LOAD_IMAGE_GRAYSCALE);
     if(img1.empty() || img2.empty())
     {
         printf("Can't read one of the images\n");
         return -1;
     }

     // detecting keypoints
     SurfFeatureDetector detector(400);
     vector keypoints1, keypoints2;
     detector.detect(img1, keypoints1);
     detector.detect(img2, keypoints2);

     // computing descriptors
     SurfDescriptorExtractor extractor;
     Mat descriptors1, descriptors2;
     extractor.compute(img1, keypoints1, descriptors1);
     extractor.compute(img2, keypoints2, descriptors2);

     // matching descriptors
     BFMatcher matcher(NORM_L2);
     vector matches;
     matcher.match(descriptors1, descriptors2, matches);

     // drawing the results
     namedWindow("matches", 1);
     Mat img_matches0,img_matches1;
     drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches0);
     //for(int i=0;i
     //{
     //  //if(abs((keypoints1[matches[i].queryIdx].pt.y-keypoints2[matches[i].trainIdx].pt.y)>5)
     //  //{

     //  //}
     //  //cout<
     //  Point2f pt1,pt2;
     //  pt1=keypoints1[matches[i].queryIdx].pt;
     //  pt2=keypoints2[matches[i].trainIdx].pt;
     //  if(abs(pt1.y-pt2.y)>5)
     //  {
     //      matches[i].distance=100;
     //  }


     //}
  //   drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches1);
     // 分配空间
     int ptCount = (int)matches.size();
     Mat p1(ptCount, 2, CV_32F);
     Mat p2(ptCount, 2, CV_32F);

     // 把Keypoint转换为Mat
     Point2f pt;
     for (int i=0; i
     {
         pt = keypoints1[matches[i].queryIdx].pt;
         p1.at<float>(i, 0) = pt.x;
         p1.at<float>(i, 1) = pt.y;

         pt = keypoints2[matches[i].trainIdx].pt;
         p2.at<float>(i, 0) = pt.x;
         p2.at<float>(i, 1) = pt.y;
     }
     // 用RANSAC方法计算F
     // Mat m_Fundamental;
     // 上面这个变量是基本矩阵
      vector m_RANSACStatus;
     // 上面这个变量已经定义过，用于存储RANSAC后每个点的状态
     //m_Fundamental = findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);
      Mat m_Fundamental=findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);
      cout<
     // 计算野点个数
     int OutlinerCount = 0;
     for (int i=0; i
     {
         if (m_RANSACStatus[i] == 0) // 状态为0表示野点
         {
             OutlinerCount++;
         }
     }

     // 计算内点
     vector m_LeftInlier;
     vector m_RightInlier;
     vector m_InlierMatches;
     // 上面三个变量用于保存内点和匹配关系
     int InlinerCount = ptCount - OutlinerCount;
     m_InlierMatches.resize(InlinerCount);
     m_LeftInlier.resize(InlinerCount);
     m_RightInlier.resize(InlinerCount);
     InlinerCount = 0;
     for (int i=0; i
     {
         if (m_RANSACStatus[i] != 0)
         {
             m_LeftInlier[InlinerCount].x = p1.at<float>(i, 0);
             m_LeftInlier[InlinerCount].y = p1.at<float>(i, 1);
             m_RightInlier[InlinerCount].x = p2.at<float>(i, 0);
             m_RightInlier[InlinerCount].y = p2.at<float>(i, 1);
             m_InlierMatches[InlinerCount].queryIdx = InlinerCount;
             m_InlierMatches[InlinerCount].trainIdx = InlinerCount;
             InlinerCount++;
         }
     }

     // 把内点转换为drawMatches可以使用的格式
     vector key1(InlinerCount);
     vector key2(InlinerCount);
     KeyPoint::convert(m_LeftInlier, key1);
     KeyPoint::convert(m_RightInlier, key2);
     for(int i=0;i<10;i++)
     cout<" "<" "<

     drawMatches(img1, key1, img2, key2, m_InlierMatches, img_matches1);
     imshow("matches0", img_matches0);
      imshow("matches1", img_matches1);
      Mat test(img1.rows,img1.cols,CV_32FC1);
      float a=0.0105591872,b=0.1024243227,c=10.106085341;
      for(int i=0;i
      {
          float* pt=test.ptr<float>(i);
          for(int j=0;j
          {
              float val=j*a+i*b+c;
              pt[j]=val;
          }
      }
      //cout<
      cv::normalize(test,test,0,1,CV_MINMAX);
      cv::convertScaleAbs(test,test,255);
      test.convertTo(test,CV_8UC1);
      imshow("test",test);
     waitKey(0);

     return 0;
 }