OpenCV图像匹配算法之sift
//utils.h #ifndef _UTILS_H #define _UTILS_H #include <opencv2/opencv.hpp> #include <opencv2/features2d/features2d.hpp> #include <opencv2/core/core.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2\nonfree\nonfree.hpp> using namespace cv; // ORB settings const int ORB_MAX_KPTS = 1500; const float ORB_SCALE_FACTOR = 1.5; const int ORB_PYRAMID_LEVELS = 3; const float ORB_EDGE_THRESHOLD = 31.0; const int ORB_FIRST_PYRAMID_LEVEL = 0; const int ORB_WTA_K = 2; const int ORB_PATCH_SIZE = 31; // BRISK settings const float BRISK_HTHRES = 10.0; const int BRISK_NOCTAVES = 6; const float DRATIO = 0.8f; // NNDR Matching value const float MIN_H_ERROR = 2.50f; // Maximum error in pixels to accept an inlier void matches2points_nndr(const std::vector<cv::KeyPoint>& train, const std::vector<cv::KeyPoint>& query, const std::vector<std::vector<cv::DMatch> >& matches, std::vector<cv::Point2f>& pmatches, float nndr); void compute_inliers_ransac(const std::vector<cv::Point2f>& matches, std::vector<cv::Point2f>& inliers, float error, bool use_fund); void draw_inliers(const cv::Mat& img1, const cv::Mat& img2, cv::Mat& img_com, const std::vector<cv::Point2f>& ptpairs, int color); typedef struct info { double t; int n1; int n2; int m; int rm; }INFO; void sift(char* path1, char* path2, INFO& info, bool show); void surf(char* path1, char* path2, INFO& info, bool show); void orb(char* path1, char* path2, INFO& info, bool show); void brisk(char* path1, char* path2, INFO& info, bool show); void freak(char* path1, char* path2, INFO& info, bool show); void showInfo(INFO info); #endif //utils.cpp #include "stdafx.h" #include "utils.h" #include <iostream> using namespace std; /** * @brief This function converts matches to points using nearest neighbor distance * ratio matching strategy * @param train Vector of keypoints from the first image * @param query Vector of keypoints from the second image * @param matches Vector of nearest neighbors for each keypoint * @param pmatches Vector of putative matches * @param nndr Nearest neighbor distance ratio value */ void matches2points_nndr(const std::vector<cv::KeyPoint>& train, const std::vector<cv::KeyPoint>& query, const std::vector<std::vector<cv::DMatch> >& matches, std::vector<cv::Point2f>& pmatches, float nndr) { float dist1 = 0.0, dist2 = 0.0; for (size_t i = 0; i < matches.size(); i++) { DMatch dmatch = matches[i][0]; dist1 = matches[i][0].distance; dist2 = matches[i][1].distance; if (dist1 < nndr*dist2) { pmatches.push_back(train[dmatch.queryIdx].pt); pmatches.push_back(query[dmatch.trainIdx].pt); } } } /** * @brief This function computes the set of inliers estimating the fundamental matrix * or a planar homography in a RANSAC procedure * @param matches Vector of putative matches * @param inliers Vector of inliers * @param error The minimum pixelic error to accept an inlier * @param use_fund Set to true if you want to compute a fundamental matrix */ void compute_inliers_ransac(const std::vector<cv::Point2f>& matches, std::vector<cv::Point2f>& inliers, float error, bool use_fund) { vector<Point2f> points1, points2; Mat H = Mat::zeros(3,3,CV_32F); int npoints = matches.size()/2; Mat status = Mat::zeros(npoints,1,CV_8UC1); for (size_t i = 0; i < matches.size(); i+=2) { points1.push_back(matches[i]); points2.push_back(matches[i+1]); } if (use_fund == true){ H = findFundamentalMat(points1,points2,CV_FM_RANSAC,error,0.99,status); } else { H = findHomography(points1,points2,CV_RANSAC,error,status); } for (int i = 0; i < npoints; i++) { if (status.at<unsigned char>(i) == 1) { inliers.push_back(points1[i]); inliers.push_back(points2[i]); } } } //******************************************************************************* //******************************************************************************* /** * @brief This function draws the set of the inliers between the two images * @param img1 First image * @param img2 Second image * @param img_com Image with the inliers * @param ptpairs Vector of point pairs with the set of inliers * @param color The color for each method */ void draw_inliers(const cv::Mat& img1, const cv::Mat& img2, cv::Mat& img_com, const std::vector<cv::Point2f>& ptpairs, int color) { int x1 = 0, y1 = 0, x2 = 0, y2 = 0; float rows1 = 0.0, cols1 = 0.0; float rows2 = 0.0, cols2 = 0.0; float ufactor = 0.0, vfactor = 0.0; rows1 = img1.rows; cols1 = img1.cols; rows2 = img2.rows; cols2 = img2.cols; ufactor = (float)(cols1)/(float)(cols2); vfactor = (float)(rows1)/(float)(rows2); // This is in case the input images don't have the same resolution Mat img_aux = Mat(Size(img1.cols,img1.rows),CV_8UC3); resize(img2,img_aux,Size(img1.cols,img1.rows),0,0,CV_INTER_LINEAR); for (int i = 0; i < img_com.rows; i++) { for (int j = 0; j < img_com.cols; j++) { if (j < img1.cols) { *(img_com.ptr<unsigned char>(i)+3*j) = *(img1.ptr<unsigned char>(i)+3*j); *(img_com.ptr<unsigned char>(i)+3*j+1) = *(img1.ptr<unsigned char>(i)+3*j+1); *(img_com.ptr<unsigned char>(i)+3*j+2) = *(img1.ptr<unsigned char>(i)+3*j+2); } else { *(img_com.ptr<unsigned char>(i)+3*j) = *(img_aux.ptr<unsigned char>(i)+3*(j-img_aux.cols)); *(img_com.ptr<unsigned char>(i)+3*j+1) = *(img_aux.ptr<unsigned char>(i)+3*(j-img_aux.cols)+1); *(img_com.ptr<unsigned char>(i)+3*j+2) = *(img_aux.ptr<unsigned char>(i)+3*(j-img_aux.cols)+2); } } } for (size_t i = 0; i < ptpairs.size(); i+= 2) { x1 = (int)(ptpairs[i].x+.5); y1 = (int)(ptpairs[i].y+.5); x2 = (int)(ptpairs[i+1].x*ufactor+img1.cols+.5); y2 = (int)(ptpairs[i+1].y*vfactor+.5); if (color == 0) { line(img_com,Point(x1,y1),Point(x2,y2),CV_RGB(255,255,0),1); } else if (color == 1) { line(img_com,Point(x1,y1),Point(x2,y2),CV_RGB(255,0,0),1); } else if (color == 2) { line(img_com,Point(x1,y1),Point(x2,y2),CV_RGB(0,0,255),1); } } } void showInfo(INFO info) { printf("%-40s%d\n","The keypoints number of src image is :", info.n1); printf("%-40s%d\n","The keypoints number of dst image is : ", info.n2); printf("%-40s%d\n","The matching number is : ", info.m); printf("%-40s%d\n","The right result number is : ", info.rm); printf("%-40s%.2fs\n","The total time is : ", info.t); return ; } //sift.cpp #include "stdafx.h" #include <cv.hpp> #include <highgui.h> #include "utils.h" #include <iostream> using namespace std; void sift(char* path1, char* path2, INFO& info, bool show) { double t1,t2; t1=cvGetTickCount(); initModule_nonfree(); Mat img1, img2; img1=imread(path1,0); img2=imread(path2,0); if(img1.data==NULL) { cout<<"The image can not been loaded: "<<path1<<endl; system("pause"); exit(-1); } if(img2.data==NULL) { cout<<"The image can not been loaded: "<<path2<<endl; system("pause"); exit(-1); } Ptr<FeatureDetector> sift_detector = FeatureDetector::create( "SIFT" ); Ptr<DescriptorExtractor> sift_descriptor = DescriptorExtractor::create( "SIFT" ); vector<KeyPoint> kpts1_sift, kpts2_sift; Mat desc1_sift, desc2_sift; Ptr<DescriptorMatcher> matcher_l2 = DescriptorMatcher::create("BruteForce"); //欧氏距离匹配 vector<vector<DMatch> > dmatches_sift; vector<Point2f> matches_sift, inliers_sift; sift_detector->detect(img1,kpts1_sift); sift_detector->detect(img2,kpts2_sift); info.n1=kpts1_sift.size(); info.n2=kpts2_sift.size(); sift_descriptor->compute(img1,kpts1_sift,desc1_sift); sift_descriptor->compute(img2,kpts2_sift,desc2_sift); matcher_l2->knnMatch(desc1_sift,desc2_sift,dmatches_sift,2); //匹配 matches2points_nndr(kpts1_sift,kpts2_sift,dmatches_sift,matches_sift,DRATIO); info.m=matches_sift.size()/2; compute_inliers_ransac(matches_sift,inliers_sift,MIN_H_ERROR,false); info.rm=inliers_sift.size()/2; t2=cvGetTickCount(); info.t=(t2-t1)/1000000.0/cvGetTickFrequency(); Mat img1_rgb_sift = imread(path1,1); Mat img2_rgb_sift = imread(path2,1); Mat img_com_sift = Mat(Size(img1.cols*2,img1.rows),CV_8UC3); if(show == true) { draw_inliers(img1_rgb_sift,img2_rgb_sift,img_com_sift,inliers_sift,2); imshow("sift",img_com_sift); waitKey(0); } return; } 使用
INFO sift_info; sift(path1,path2,sift_info,true); showInfo(sift_info);