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1BFSIFT

2级联SIFT

基于级联结构的多光谱图像精确特征点匹配	荆晶	北京邮电大学	2016-01-10	硕士应用于多源SAR图像匹配的级联SIFT算法	王峰; 尤红建; 傅兴玉; 许宁	电子学报	2016-03-15	期刊

最近自己写了一个orb级联的，这种方式局限性是两幅图一样大且视角偏移不太大

void runImagePair() {  Mat img1_ori = imread("Texture_8Bit.png");  Mat img2_ori = imread("Texture_8Bit.png");  Mat img1,img2;  cv::resize(img1_ori, img1, Size(800,600));  cv::resize(img2_ori, img2, Size(800,600));  GmsMatch(img1, img2, img1_ori, img2_ori);}int GmsMatch(Mat &img1, Mat &img2, Mat &img1_ori, Mat &img2_ori) {  float ratio_x = img1_ori.cols / (1.0 * img1.cols);  float ratio_y = img1_ori.rows / (1.0 * img1.rows);  int oriH = img1.rows;  int oriW = img2.cols;  vector
   
     rectVector;  rectVector.push_back(Rect(0,0, oriW/2, oriH/2));  rectVector.push_back(Rect(oriW/2,0, oriW/2, oriH/2));  rectVector.push_back(Rect(0,oriH/2, oriW/2, oriH/2));  rectVector.push_back(Rect(oriW/2,oriH/2, oriW/2, oriH/2));  std::vector
    
      queryPoints;  std::vector
     
       trainPoints;  vector
      
        goodMatches;  //https://blog.csdn.net/linxihe123/article/details/70173476  vector
       
        
          > kp_pairs_temp; for (int re = 0; re < rectVector.size(); ++re) { Mat img1ROI = img1(rectVector[re]); cv::Mat img1Temp = cv::Mat::zeros(cv::Size(oriW,oriH),CV_8UC3); Mat zero1ROI = img1Temp(rectVector[re]); Mat temp1 = img1ROI.clone(); temp1.copyTo(zero1ROI); Mat img2ROI = img2(rectVector[re]); cv::Mat img2Temp = cv::Mat::zeros(cv::Size(oriW,oriH),CV_8UC3); Mat zero2ROI = img2Temp(rectVector[re]); Mat temp2 = img2ROI.clone(); temp2.copyTo(zero2ROI); vector
         
           kp1, kp2; Mat d1, d2; vector
          
            matches_all, matches_gms; Ptr
           
             orb = ORB::create(100000); orb->setFastThreshold(0); orb->detectAndCompute(img1Temp, Mat(), kp1, d1); orb->detectAndCompute(img2Temp, Mat(), kp2, d2); cout << "Get total " << kp1.size() << " kp1." << endl; cout << "Get total " << kp2.size() << " kp2." << endl;#ifdef USE_GPU GpuMat gd1(d1), gd2(d2); Ptr
            
              matcher = cv::cuda::DescriptorMatcher::createBFMatcher(NORM_HAMMING); matcher->match(gd1, gd2, matches_all);#else BFMatcher matcher(NORM_HAMMING); matcher.match(d1, d2, matches_all);#endif // GMS filter std::vector
             
               vbInliers; gms_matcher gms(kp1, img1.size(), kp2, img2.size(), matches_all); int num_inliers = gms.GetInlierMask(vbInliers, false, false); cout << "Get total " << num_inliers << " gms-matches." << endl; // collect matches for (size_t i = 0; i < vbInliers.size(); ++i) { if (vbInliers[i] == true) { matches_gms.push_back(matches_all[i]); } } for (size_t i = 0; i < matches_gms.size(); i++) { KeyPoint temp1 = kp1[matches_gms[i].queryIdx]; KeyPoint temp2 = kp2[matches_gms[i].trainIdx]; goodMatches.push_back(matches_gms[i]); queryPoints.push_back(temp1.pt); trainPoints.push_back(temp2.pt); kp_pairs_temp.push_back(make_pair(temp1, temp2)); } } cout << "Get total " << goodMatches.size() << " gms-matches." << endl;}
             
            
           
          
         
        
       
      
     
    
   

修改版：

void find_homography_orbgms(const cv::Mat &img1_src, const cv::Mat &img2_src, const cv::Mat &img1_dep, const cv::Mat &img2_dep, cv::Mat &homography){  float ratio_x = img1_src.cols / 800.0;  float ratio_y = img1_src.rows / 600.0;  cv::Mat img1;  cv::Mat img2;  cv::resize(img1_src, img1, Size(800, 600));  cv::resize(img2_src, img2, Size(800, 600));  int oriH = img1.rows;  int oriW = img2.cols;  vector
   
     rectVector;  rectVector.push_back(Rect(0,0, oriW/2, oriH/2));  rectVector.push_back(Rect(oriW/2,0, oriW/2, oriH/2));  rectVector.push_back(Rect(0,oriH/2, oriW/2, oriH/2));  rectVector.push_back(Rect(oriW/2,oriH/2, oriW/2, oriH/2));  std::vector
    
      queryPoints;  std::vector
     
       trainPoints;  vector
      
        goodMatches;  //https://blog.csdn.net/linxihe123/article/details/70173476  vector
       
        
          > kp_pairs_temp; for (int re = 0; re < rectVector.size(); ++re) { Mat img1ROI = img1(rectVector[re]); cv::Mat img1Temp = cv::Mat::zeros(cv::Size(oriW,oriH),CV_8UC3); Mat zero1ROI = img1Temp(rectVector[re]); Mat temp1 = img1ROI.clone(); temp1.copyTo(zero1ROI); Mat img2ROI = img2(rectVector[re]); cv::Mat img2Temp = cv::Mat::zeros(cv::Size(oriW,oriH),CV_8UC3); Mat zero2ROI = img2Temp(rectVector[re]); Mat temp2 = img2ROI.clone(); temp2.copyTo(zero2ROI); vector
         
           kp1, kp2; Mat d1, d2; vector
          
            matches_all, matches_gms; Ptr
           
             orb = ORB::create(100000); orb->setFastThreshold(0); orb->detectAndCompute(img1Temp, Mat(), kp1, d1); orb->detectAndCompute(img2Temp, Mat(), kp2, d2); cout << "Get total " << kp1.size() << " kp1." << endl; cout << "Get total " << kp2.size() << " kp2." << endl;#ifdef USE_GPU GpuMat gd1(d1), gd2(d2); Ptr
            
              matcher = cv::cuda::DescriptorMatcher::createBFMatcher(NORM_HAMMING); matcher->match(gd1, gd2, matches_all);#else BFMatcher matcher(NORM_HAMMING); matcher.match(d1, d2, matches_all);#endif // GMS filter std::vector
             
               vbInliers; gms_matcher gms(kp1, img1.size(), kp2, img2.size(), matches_all); int num_inliers = gms.GetInlierMask(vbInliers, false, false); cout << "Get total " << num_inliers << " gms-matches." << endl; // collect matches for (size_t i = 0; i < vbInliers.size(); ++i) { if (vbInliers[i] == true) { matches_gms.push_back(matches_all[i]); } } for (size_t i = 0; i < matches_gms.size(); i++) { KeyPoint temp1 = kp1[matches_gms[i].queryIdx]; KeyPoint temp2 = kp2[matches_gms[i].trainIdx]; goodMatches.push_back(matches_gms[i]); queryPoints.push_back(temp1.pt); trainPoints.push_back(temp2.pt); kp_pairs_temp.push_back(make_pair(temp1, temp2)); } } cout << "Get total " << goodMatches.size() << " gms-matches." << endl; // 4点条件判断 const int minNumberMatchesAllowed = 4; if (queryPoints.size() < minNumberMatchesAllowed) return -1; double reprojectionThreshold = 10.0; std::vector
              
                inliersMask(goodMatches.size()); Mat homography_temp = findHomography(queryPoints, trainPoints, FM_RANSAC, reprojectionThreshold, inliersMask, 2000, 0.995); vector
               
                
                  > kp_pairs; std::vector
                 
                   newp1; std::vector
                  
                    newp2; std::vector
                   
                     newp1c; std::vector
                    
                      newp2c; vector
                     
                       goodgoodMatches; int hh = img1_src.rows; int ww = img1_src.cols; for (size_t i = 0; i < inliersMask.size(); i++) { if (inliersMask[i]) { goodgoodMatches.push_back(goodMatches[i]); kp_pairs.push_back(kp_pairs_temp[i]); float x1 = kp_pairs_temp[i].first.pt.x * ratio_x; float y1 = kp_pairs_temp[i].first.pt.y * ratio_y; float x2 = kp_pairs_temp[i].second.pt.x * ratio_x; float y2 = kp_pairs_temp[i].second.pt.y * ratio_y; x1 = x1 > ww ? ww : x1; x1 = x1 < 0 ? 0 : x1; x2 = x2 > ww ? ww : x2; x2 = x2 < 0 ? 0 : x2; y1 = y1 > hh ? hh : y1; y1 = y1 < 0 ? 0 : y1; y2 = y2 > hh ? hh : y2; y2 = y2 < 0 ? 0 : y2; newp1.push_back(cv::Point2f(x1, y1)); newp2.push_back(cv::Point2f(x2, y2)); newp1c.push_back(kp_pairs_temp[i].first.pt); newp2c.push_back(kp_pairs_temp[i].second.pt); } } std::vector
                      
                        EssentialMask(newp1.size()); cv::Mat intrinsics = (cv::Mat_
                       
                        (3, 3) << 2269.16, 0, 1065.54, 0, 2268.4, 799.032, 0, 0, 1); Mat Essential = findEssentialMat(newp1, newp2, intrinsics, cv::RANSAC, 0.999, 1, EssentialMask); std::vector
                        
                          Essential_p1; std::vector
                         
                           Essential_p2; for (size_t key = 0; key < newp1.size(); key++) { if (EssentialMask[key]) { Essential_p1.push_back(newp1[key]); Essential_p2.push_back(newp2[key]); } } std::vector
                          
                            FundamentalMask(Essential_p1.size()); Mat Fundamental = findFundamentalMat(Essential_p1, Essential_p2, cv::RANSAC, 3, 0.999, FundamentalMask); std::vector
                           
                             Fundamental_p1; std::vector
                            
                              Fundamental_p2; for (size_t key = 0; key < Essential_p1.size(); key++) { if (FundamentalMask[key]) { Fundamental_p1.push_back(Essential_p1[key]); Fundamental_p2.push_back(Essential_p2[key]); } } std::vector
                             
                               trp1_temp; std::vector
                              
                                trp2_temp; for (size_t key = 0; key < Fundamental_p1.size(); key++) { float x1 = Fundamental_p1[key].x; float y1 = Fundamental_p1[key].y; float x2 = Fundamental_p2[key].x; float y2 = Fundamental_p2[key].y; float d1 = img1_dep.at
                               
                                (int(y1+0.5), int(x1+0.5)); cv::Point3f p1; p1.z = float(d1) / intrinsics.ptr
                                
                                 (2)[2]; p1.x = (x1 - intrinsics.ptr
                                 
                                  (0)[2]) * p1.z / intrinsics.ptr
                                  
                                   (0)[0]; p1.y = (y1 - intrinsics.ptr
                                   
                                    (1)[2]) * p1.z / intrinsics.ptr
                                    
                                     (1)[1]; float d2 = img2_dep.at
                                     
                                      (int(y2+0.5), int(x2+0.5)); cv::Point3f p2; p2.z = float(d2) / intrinsics.ptr
                                      
                                       (2)[2]; p2.x = (x2 - intrinsics.ptr
                                       
                                        (0)[2]) * p2.z / intrinsics.ptr
                                        
                                         (0)[0]; p2.y = (y2 - intrinsics.ptr
                                         
                                          (1)[2]) * p2.z / intrinsics.ptr
                                          
                                           (1)[1]; //>0.001是为了去除深度为0的特征点 if (((abs(p1.x) + abs(p1.y) + abs(p1.z)) > 0.001) and ((abs(p2.x) + abs(p2.y) + abs(p2.z)) > 0.001)) { trp1_temp.push_back(Fundamental_p1[key]); trp2_temp.push_back(Fundamental_p2[key]); } } double reprojectionThreshold_ = 5.0; std::vector
                                           
                                             inliersMask_(goodMatches.size()); homography = findHomography(trp1_temp, trp2_temp, FM_RANSAC, reprojectionThreshold_, inliersMask_, 20, 0.995);}