【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.
<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。