【CS】尺度不变特征变换匹配算法SIFT（2）

#include <opencv2/opencv.hpp>  
#include <istream>  
  
using namespace std;  
using namespace cv;  
int main()  
{  
    //read the two input images  
    Mat image1 = imread("image1.jpg");  
    Mat image2 = imread("image2.jpg");  
    //if failed  
    if(image1.empty()||image2.empty())  
    {  
        cout<<"error,the image is not exist"<<endl;  
        return -1;  
    }  
    //difine a sift detector  
    SiftFeatureDetector siftDetector;  
    //store key points  
    vector<KeyPoint> keypoint1,keypoint2;  
    //detect image with SIFT,get key points  
    siftDetector.detect(image1,keypoint1);  
    Mat outImage1;  
    //draw key points at the out image and show to the user  
    drawKeypoints(image1,keypoint1,outImage1,Scalar(255,0,0));  
  
    imshow("original_image1",image1);  
    imshow("sift_image1",outImage1);  
      
    Mat outImage2;  
      
    siftDetector.detect(image2,keypoint2);  
    drawKeypoints(image2,keypoint2,outImage2,Scalar(255,0,0));  
  
    imshow("sift_image2.jpg",outImage2);  
    //imwrite("sift_result2.jpg",outImage2);  
    //store 10 keypoints in order to watch the effect clearly  
    vector<KeyPoint> keypoint3,keypoint4;  
    for(int i=0;i<10;i++)  
    {  
        keypoint3.push_back(keypoint1[i]);  
        keypoint4.push_back(keypoint2[i]);  
    }  
    // difine a sift descriptor extractor  
    SiftDescriptorExtractor extractor;  
    //store the descriptor of each image  
    Mat descriptor1,descriptor2;  
    BruteForceMatcher<L2<float>> matcher;  
  
    vector<DMatch> matches;  
    Mat img_matches;  
    //compute the descriptor of each image  
    extractor.compute(image1,keypoint3,descriptor1);  
    extractor.compute(image2,keypoint4,descriptor2);  
    //match  
    matcher.match(descriptor1,descriptor2,matches);  
    //show the result  
    drawMatches(image1,keypoint3,image2,keypoint4,matches,img_matches,Scalar(255,0,0));  
    imshow("matches",img_matches);  
    //store the match_image  
    //imwrite("matches.jpg",img_matches);  
  
    waitKey(0);  
    return 0;  
}  

img1 – First source image. keypoints1 – Keypoints from the first source image. img2 – Second source image. keypoints2 – Keypoints from the second source image. matches1to2 – Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] . outImg – Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below. matchColor – Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly. singlePointColor – Color of single keypoints (circles), which means that keypoints do not have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. matchesMask – Mask determining which matches are drawn. If the mask is empty, all matches are drawn. flags – Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags.

<span style="font-family:Microsoft YaHei;">       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;  //此匹配对应的查询图像的特征描述子索引
           inttrainIdx;   //此匹配对应的训练(模板)图像的特征描述子索引
           intimgIdx;    //训练图像的索引(若有多个)
           float distance;  //两个特征向量之间的欧氏距离，越小表明匹配度越高。
           booloperator < (const DMatch &m) const;
       };</span>

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