基于SIFT的快速图像匹配，可适用于于目标追踪python.zip

///SIFT特征点匹配 #include "opencv2/opencv.hpp" using namespace cv; #include "opencv2/nonfree/nonfree.hpp"//SIFT相关 #include "opencv2/legacy/legacy.hpp"//匹配器相关 #include <iostream> using namespace std; int main() { //1.SIFT特征点提取——detect()方法 Mat srcImg1 = imread("3.jpg", CV_LOAD_IMAGE_COLOR); Mat srcImg2 = imread("4.jpg", CV_LOAD_IMAGE_COLOR); Mat dstImg1, dstImg2; SiftFeatureDetector siftDetector;//SiftFeatureDetector是SIFT类的别名 vector<KeyPoint> keyPoints1; vector<KeyPoint> keyPoints2; siftDetector.detect(srcImg1, keyPoints1); siftDetector.detect(srcImg2, keyPoints2); drawKeypoints(srcImg1, keyPoints1, dstImg1); drawKeypoints(srcImg2, keyPoints2, dstImg2); imshow("dstImg1", dstImg1); imshow("dstImg2", dstImg2); //2.特征点描述符(特征向量)提取——compute()方法 SiftDescriptorExtractor descriptor;//SiftDescriptorExtractor是SIFT类的别名 Mat description1; Mat description2; descriptor.compute(srcImg1, keyPoints1, description1);//注意原图和特征点要对应，不要写错 descriptor.compute(srcImg2, keyPoints2, description2); imshow("description1", description1); imshow("description2", description2); //3.使用暴力匹配器进行暴力匹配——BruteForceMatcher类的match()方法 BruteForceMatcher<L2<float>> matcher;//实例化暴力匹配器 vector<DMatch> matches; //定义匹配结果变量 matcher.match(description1, description2, matches);//实现描述符之间的匹配 //4.对匹配结果进行筛选（依据DMatch结构体中的float类型变量distance进行筛选） float minDistance = 100; float maxDistance = 0; for (int i = 0; i < matches.size(); i++) { if (matches[i].distance < minDistance) minDistance = matches[i].distance; if (matches[i].distance > maxDistance) maxDistance = matches[i].distance; } cout << "minDistance: " << minDistance << endl; cout << "maxDistance: " << maxDistance << endl; vector<DMatch> goodMatches; for (int i = 0; i < matches.size(); i++) { if (matches[i].distance < 2 * minDistance) { goodMatches.push_back(matches[i]); } } //5.绘制匹配结果——drawMatches() Mat dstImg3; drawMatches(srcImg1, keyPoints1, srcImg2, keyPoints2, goodMatches, dstImg3); imshow("dstImg3", dstImg3); waitKey(0); return 0; }