#include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include <opencv2/nonfree/nonfree.hpp> #include<opencv2/legacy/legacy.hpp> #include <iostream> using namespace cv; using namespace std; int main( ) { //【0】改变console字体颜色 system("color 4F"); //【1】载入源图片 Mat img_1 = imread("1.jpg", 1 ); Mat img_2 = imread( "2.jpg", 1 );//【2】利用SURF检测器检测的关键点 int minHessian = 300; SURF detector( minHessian ); std::vector<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 ); //【3】计算描述符(特征向量) SURF extractor; Mat descriptors_1, descriptors_2; extractor.compute( img_1, keypoints_1, descriptors_1 ); extractor.compute( img_2, keypoints_2, descriptors_2 ); //【4】采用FLANN算法匹配描述符向量 FlannBasedMatcher matcher; std::vector< DMatch > matches; matcher.match( descriptors_1, descriptors_2, matches ); double max_dist = 0; double min_dist = 100; //【5】快速计算关键点之间的最大和最小距离 for( int i = 0; i < descriptors_1.rows; i++ ) { double dist = matches[i].distance; if( dist < min_dist ) min_dist = dist; if( dist > max_dist ) max_dist = dist; } //输出距离信息 printf("> 最大距离(Max dist) : %f ", max_dist ); printf("> 最小距离(Min dist) : %f ", min_dist ); //【6】存下符合条件的匹配结果(即其距离小于2* min_dist的),使用radiusMatch同样可行 std::vector< DMatch > good_matches; for( int i = 0; i < descriptors_1.rows; i++ ) { if( matches[i].distance < 2*min_dist ) { good_matches.push_back( matches[i]); } } //【7】绘制出符合条件的匹配点 Mat img_matches; drawMatches( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //【8】输出相关匹配点信息 for( int i = 0; i < good_matches.size(); i++ ) { printf( ">符合条件的匹配点 [%d] 特征点1: %d -- 特征点2: %d ", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); } //【9】显示效果图 imshow( "匹配效果图", img_matches ); //按任意键退出程序 waitKey(0); return 0; }