SIFT算法的特征提取与匹配

借鉴作者http://licong1018.blog.163.com/blog/static/902697842012111594813944/的程序，我用自己的图片尝试一下果然好用，img1/img2是同一个物体的不同形状图片，算法的步骤很简洁，注释部分是我依据自己的理解添加进去的，可能理解的有些偏差，望指教。

#include "highgui.h"

#include <opencv2/opencv.hpp>

#include <opencv2/features2d/features2d.hpp>

#include<opencv2/nonfree/nonfree.hpp>

#include<opencv2/legacy/legacy.hpp>

#include<vector>

using namespace std;

using namespace cv;

int main()

{
Mat input1 = imread("img1.png", 1);
Mat input2 = imread("img2.png", 1);
SiftFeatureDetector detector;//SIFT的特征探测器；
vector<KeyPoint> keypoint1, keypoint2;
detector.detect(input1, keypoint1);//keypoint存储了检测到特征点的基本信息；

Mat output1;//提取图片特征并显示出来；
drawKeypoints(input1, keypoint1, output1);
imshow("sift_result1.png", output1);
imwrite("sift_result1.png", output1);

Mat output2;
SiftDescriptorExtractor extractor;//存储SIFT提取的特征向量
Mat descriptor1, descriptor2;
BruteForceMatcher<L2<float>> matcher;
vector<DMatch> matches;

Mat img_matches;//提取图片特征函数并显示
detector.detect(input2, keypoint2);
drawKeypoints(input2, keypoint2, output2);
imshow("sift_result2.jpg", output2);
imwrite("sift_result2.jpg", output2);
//计算两幅图像的特征向量
extractor.compute(input1, keypoint1, descriptor1);
extractor.compute(input2, keypoint2, descriptor2);
//两组特征向量匹配；
matcher.match(descriptor1, descriptor2, matches);
//匹配结果的图像存储到img_matches;
drawMatches(input1, keypoint1, input2, keypoint2, matches, img_matches);
imshow("matches", img_matches);
imwrite("matches.jpg", img_matches);

cvWaitKey(100);
return 0;

}