【CS】尺度不变特征变换匹配算法SIFT（2）

尺度不变特征变换匹配算法SIFT（2）

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SIFT算法

在10月初，草草学习了一下SIFT（可以戳这里查看），主要是调用opencv函数库了的函数进行了实践，而并没有深入了解SIFT描述子的原理以及opencv中相关函数的用法和参数说明。本篇blog作为LZ的小笔记，记录一下opencv中相关函数的说明，对于SIFT特征的原理后续将花时间继续了解。

C++代码

环境：vs2010+opencv2.3.1+win7
×64

这部分代码还是使用上一篇SIFT的代码，本篇重在了解一些函数和数据结构。

#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;
}  

opencv相关函数和数据结构说明

1.drawMatcher():Draws the found matches of keypoints from two images.

参考：http://docs.opencv.org/2.4/modules/features2d/doc/drawing_function_of_keypoints_and_matches.html

C++: void drawMatches(const Mat& img1,
const vector<KeyPoint>& keypoints1, const Mat& img2, const  vector<KeyPoint>& keypoints2,
const vector<vector<DMatch>>& matches1to2, 
  Mat& outImg, const Scalar& matchColor=Scalar::all(-1),  const
Scalar& singlePointColor=Scalar::all(-1), 
 const vector<vector<char>>& matchesMask=vector<vector<char>
>(), int flags=DrawMatchesFlags::DEFAULT )

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.

2.DMatch:Class for matching keypoint descriptors: query descriptor index, train descriptor index, train image index, and distance between descriptors.

可参考：http://docs.opencv.org/master/d4/de0/classcv_1_1DMatch.html

<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>

一般使用Brute-force descriptor matcher进行匹配，结果并不具有可读性（戳这里看图），那么这里请留意匹配的结果保存在了vector<DMatch>定义的动态数组matches中，这就意味着我们可以对匹配结果进行一系列操作，比如再drawMatches()函数前添加一句：matches.erase(matches.begin()+25,matches.end());
既可以选择最新的25个匹配结果。

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