图像匹配算法之brisk

最新推荐文章于 2022-12-05 12:00:00 发布

jessie Fei

最新推荐文章于 2022-12-05 12:00:00 发布

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CC 4.0 BY-SA版权

分类专栏： OPENCV图像处理文章标签： opencv opencv源码分析图像处理

本文链接：https://blog.youkuaiyun.com/qq_19833853/article/details/72935366

本文通过一个实例展示了如何使用BRISK算法进行特征检测和匹配，并探讨了OpenCV中的BFMatcher和FlannBasedMatcher的区别。BFMatcher寻找最佳匹配，而FlannBasedMatcher则采用近似匹配，适用于速度要求较高的场景。同时，文中提到了不同特征描述符类型对应的匹配方式，如ORB和BRIEF只能使用BruteForce匹配。

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单目标匹配

#include <cv.h>
#include<opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <Windows.h>
using namespace cv;
using namespace std;
int main()
{
//Load Image
Mat c_src1 = imread("1-301.bmp");
Mat c_src2 = imread("1-200(1).bmp");
Mat src1 = imread("1-301.bmp", CV_LOAD_IMAGE_GRAYSCALE);
Mat src2 = imread("1-200(1).bmp", CV_LOAD_IMAGE_GRAYSCALE);
if (!src1.data || !src2.data)
{
cout << "Error reading images " << std::endl;
return -1;
}
//feature detect
BRISK detector;
vector<KeyPoint> kp1, kp2;
double start = GetTickCount();
detector.detect(src1, kp1);
detector.detect(src2, kp2);
//cv::BRISK extractor;
Mat des1, des2;//descriptor
detector.compute(src1, kp1, des1);
detector.compute(src2, kp2, des2);
Mat res1, res2;
int drawmode = DrawMatchesFlags::DRAW_RICH_KEYPOINTS;
drawKeypoints(c_src1, kp1, res1, Scalar::all(-1), drawmode);//画出特征点
drawKeypoints(c_src2, kp2, res2, Scalar::all(-1), drawmode);
cout << "size of description of Img1: " << kp1.size() << endl;
cout << "size of description of Img2: " << kp2.size() << endl;
BFMatcher matcher(NORM_HAMMING);
vector<DMatch> matches;
matcher.match(des1, des2, matches);
double end = GetTickCount();
cout << "耗时：" << (end - start) << "ms" << endl;
Mat img_match;
drawMatches(src1, kp1, src2, kp2, matches, img_match);
cout << "number of matched points: " << matches.size() << endl;
namedWindow("m