OpenCV2.4.4中调用SIFT特征检测器进行图像匹配

#include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/nonfree/nonfree.hpp" #include "opencv2/nonfree/features2d.hpp" #include <iostream> #include <stdio.h> #include <stdlib.h> using namespace cv; using namespace std; int main() { initModule_nonfree();//初始化模块，使用SIFT或SURF时用到 Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器 Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器 Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器 if( detector.empty() || descriptor_extractor.empty() ) cout<<"fail to create detector!"; //读入图像 Mat img1 = imread("phone2.jpg"); Mat img2 = imread("phone3.jpg"); //特征点检测 double t = getTickCount();//当前滴答数 vector<KeyPoint> keypoints1,keypoints2; detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中 detector->detect( img2, keypoints2 ); cout<<"图像1特征点个数:"<<keypoints1.size()<<endl; cout<<"图像2特征点个数:"<<keypoints2.size()<<endl; //输出特征点 cout<<"图像1的特征点："<<endl; for(int i=0;i<keypoints1.size();i++) { cout<<"坐标："<<keypoints1[i].pt; cout<<"，邻域直径："<<keypoints1[i].size; cout<<"，方向："<<keypoints1[i].angle; cout<<"，octave："<<keypoints1[i].octave; cout<<"，id："<<keypoints1[i].class_id<<endl; } //根据特征点计算特征描述子矩阵，即特征向量矩阵 Mat descriptors1,descriptors2; descriptor_extractor->compute( img1, keypoints1, descriptors1 ); descriptor_extractor->compute( img2, keypoints2, descriptors2 ); t = ((double)getTickCount() - t)/getTickFrequency(); cout<<"SIFT算法用时："<<t<<"秒"<<endl; cout<<"图像1特征描述矩阵大小："<<descriptors1.size() <<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl; cout<<"图像2特征描述矩阵大小："<<descriptors2.size() <<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl; //输出特征描述 for(int i=0;i<128;i++) cout<<descriptors1.at<long>(0,i)<<","; //画出特征点 Mat img_keypoints1,img_keypoints2; drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0); drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0); //imshow("Src1",img_keypoints1); //imshow("Src2",img_keypoints2); //特征匹配 vector<DMatch> matches;//匹配结果 descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵 cout<<"Match个数："<<matches.size()<<endl; //计算匹配结果中距离的最大和最小值 //距离是指两个特征向量间的欧式距离，表明两个特征的差异，值越小表明两个特征点越接近 double max_dist = 0; double min_dist = 100; for(int i=0; i<matches.size(); i++) { double dist = matches[i].distance; if(dist < min_dist) min_dist = dist; if(dist > max_dist) max_dist = dist; } cout<<"最大距离："<<max_dist<<endl; cout<<"最小距离："<<min_dist<<endl; //筛选出较好的匹配 vector<DMatch> goodMatches; for(int i=0; i<matches.size(); i++) { if(matches[i].distance < 0.5 * max_dist) { goodMatches.push_back(matches[i]); } } cout<<"goodMatch个数："<<goodMatches.size()<<endl; //输出匹配结果 for(int i=0; i<goodMatches.size(); i++) { cout<<"queryIdx："<<matches[i].queryIdx; cout<<"，trainIdx："<<matches[i].trainIdx; cout<<"，imgIdx："<<matches[i].imgIdx; cout<<"，欧氏距离："<<matches[i].distance<<endl; } //画出匹配结果 Mat img_matches; //红色连接的是匹配的特征点对，绿色是未匹配的特征点 drawMatches(img1,keypoints1,img2,keypoints2,goodMatches,img_matches, Scalar::all(-1)/*CV_RGB(255,0,0)*/,CV_RGB(0,255,0),Mat(),2); imshow("MatchSIFT",img_matches); waitKey(0); return 0; }