1.概述

在深度学习出现之前，图像中的特征匹配方法主要有

2.理论对比

3.代码实现

#include <iostream>
#include <opencv2/opencv.hpp>int main(int argc, char** argv) {if(argc != 3) {std::cerr << "Usage: " << argv[0] << " <image1_path> <image2_path>" << std::endl;return -1;}// Load the imagescv::Mat img1 = cv::imread(argv[1], cv::IMREAD_GRAYSCALE);cv::Mat img2 = cv::imread(argv[2], cv::IMREAD_GRAYSCALE);if(img1.empty() || img2.empty()) {std::cerr << "Error: Couldn't read one or both images. Check the paths and try again." << std::endl;return -1;}// Detect ORB keypoints and descriptorscv::Ptr<cv::ORB> orb = cv::ORB::create();std::vector<cv::KeyPoint> keypoints1, keypoints2;cv::Mat descriptors1, descriptors2;orb->detectAndCompute(img1, cv::noArray(), keypoints1, descriptors1);orb->detectAndCompute(img2, cv::noArray(), keypoints2, descriptors2);// Use BFMatcher to match the ORB descriptorscv::BFMatcher matcher(cv::NORM_HAMMING);std::vector<cv::DMatch> matches;matcher.match(descriptors1, descriptors2, matches);// Draw the matchescv::Mat imgMatches;cv::drawMatches(img1, keypoints1, img2, keypoints2, matches, imgMatches);cv::imshow("ORB Feature Matches", imgMatches);// Wait for a key press and then closecv::waitKey(0);return 0;
}