6-sift特征匹配

1-

http://blog.csdn.net/dcrmg/article/details/52578732

2-

#include "highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/legacy/legacy.hpp"

using namespace cv;

int main(int argc, char *argv[])
{
Mat image01 = imread("1.jpg");
Mat image02 = imread("2.jpg");
Mat image1, image2;
GaussianBlur(image01, image1, Size(3, 3), 0.5);
GaussianBlur(image02, image2, Size(3, 3), 0.5);

//提取特征点
SiftFeatureDetector siftDetector(30); //限定提起前15个特征点
vector<KeyPoint> keyPoint1, keyPoint2;
siftDetector.detect(image1, keyPoint1);
siftDetector.detect(image2, keyPoint2);

//绘制特征点
drawKeypoints(image1, keyPoint1, image1, Scalar::all(-1), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
drawKeypoints(image2, keyPoint2, image2, Scalar::all(-1), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
namedWindow("KeyPoints of image1", 0);
namedWindow("KeyPoints of image2", 0);

imshow("KeyPoints of image1", image1);
imshow("KeyPoints of image2", image2);

//特征点描述，为下边的特征点匹配做准备
SiftDescriptorExtractor siftDescriptor;
Mat imageDesc1, imageDesc2;
siftDescriptor.compute(image1, keyPoint1, imageDesc1);
siftDescriptor.compute(image2, keyPoint2, imageDesc2);

//特征点匹配并显示匹配结果
BruteForceMatcher<L2<float>> matcher;
vector<DMatch> matchePoints;
matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
Mat imageOutput;
drawMatches(image01, keyPoint1, image02, keyPoint2, matchePoints, imageOutput);
namedWindow("Mathch Points", 0);
imshow("Mathch Points", imageOutput);
waitKey();
return 0;
}

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6-sift特征匹配

原文：http://www.cnblogs.com/yangyangthss/p/7091902.html