OpenCV2.4.4中调用SIFT特征检测器进行图像匹配

OpenCV中一些相关结构说明：

特征点类：

 class KeyPoint
 {
        Point2f  pt;  //坐标
        float  size; //<strong>特征</strong>点邻域直径
        float  angle; //<strong>特征</strong>点的方向，值为[0,360)，负值表示不使用
        float  response; //
        int  octave; //<strong>特征</strong>点所在的<strong>图像</strong>金字塔的组
        int  class_id; //用于聚类的id
 }

存放匹配结果的结构：

 struct DMatch
 {
        //三个构造函数
     DMatch(): queryIdx(-1), trainIdx(-1),imgIdx(-1),distance(std::numeric_limits<float>::max()) {}
     DMatch(int  _queryIdx, int  _trainIdx, float  _distance ) :
                      queryIdx( _queryIdx),trainIdx( _trainIdx), imgIdx(-1),distance( _distance) {}
     DMatch(int  _queryIdx, int  _trainIdx, int  _imgIdx, float  _distance ) :
             queryIdx(_queryIdx), trainIdx( _trainIdx), imgIdx( _imgIdx),distance( _distance) {}

     intqueryIdx;  //此<strong>匹配</strong>对应的查询<strong>图像</strong>的<strong>特征</strong>描述子索引
     inttrainIdx;   //此<strong>匹配</strong>对应的训练(模板)<strong>图像</strong>的<strong>特征</strong>描述子索引
     intimgIdx;    //训练<strong>图像</strong>的索引(若有多个)
     float distance;  //两个<strong>特征</strong>向量之间的欧氏距离，越小表明<strong>匹配</strong>度越高。
     booloperator < (const DMatch &m) const;
 };

       说明：以两个特征点描述子(特征向量)之间的欧氏距离作为特征点匹配的相似度准则，假设特征点对p和q的

               特征描述子分别为Des_p和Des_q，则其欧氏距离定义为：

                所以每个匹配分别对应训练图像(train)和查询图像(query)中的一个特征描述子(特征向量)。

 #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<strong>特征</strong><strong>检测器</strong>
     Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建<strong>特征</strong>向量生成器
     Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建<strong>特征</strong><strong>匹配</strong>器
     if( detector.empty() || descriptor_extractor.empty() )
         cout<<"fail to create detector!";

     //读入<strong>图像</strong>
     Mat img1 = imread("desk.jpg");
     Mat img2 = imread("desk_glue.jpg");

     //<strong>特征</strong>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<strong>特征</strong>点，存储到keypoints1中
     detector->detect( img2, keypoints2 );
     cout<<"<strong>图像</strong>1<strong>特征</strong>点个数:"<<keypoints1.size()<<endl;
     cout<<"<strong>图像</strong>2<strong>特征</strong>点个数:"<<keypoints2.size()<<endl;

     //根据<strong>特征</strong>点计算<strong>特征</strong>描述子矩阵，即<strong>特征</strong>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<<"<strong>图像</strong>1<strong>特征</strong>描述矩阵大小："<<descriptors1.size()
         <<"，<strong>特征</strong>向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;
     cout<<"<strong>图像</strong>2<strong>特征</strong>描述矩阵大小："<<descriptors2.size()
         <<"，<strong>特征</strong>向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;

     //画出<strong>特征</strong>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);

     //<strong>特征</strong><strong>匹配</strong>
     vector<DMatch> matches;//<strong>匹配</strong>结果
     descriptor_matcher->match( descriptors1, descriptors2, matches );//<strong>匹配</strong>两个<strong>图像</strong>的<strong>特征</strong>矩阵
     cout<<"Match个数："<<matches.size()<<endl;

     //计算<strong>匹配</strong>结果中距离的最大和最小值
     //距离是指两个<strong>特征</strong>向量间的欧式距离，表明两个<strong>特征</strong>的差异，值越小表明两个<strong>特征</strong>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<strong>匹配</strong>点
     vector<DMatch> goodMatches;
     for(int i=0; i<matches.size(); i++)
     {
         if(matches[i].distance < 0.31 * max_dist)
         {
             goodMatches.push_back(matches[i]);
         }
     }
     cout<<"goodMatch个数："<<goodMatches.size()<<endl;

     //画出<strong>匹配</strong>结果
     Mat img_matches;
     //红色连接的是<strong>匹配</strong>的<strong>特征</strong>点对，绿色是未<strong>匹配</strong>的<strong>特征</strong>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;
 }