使用函数detectAndCompute(）检测关键点并计算描述符

函数detectAndCompute(）参数说明：

void detectAndCompute( InputArray image, //图像InputArray mask, //掩模CV_OUT std::vector<KeyPoint>& keypoints,//输出关键点的集合OutputArray descriptors,//计算描述符（descriptors[i]是为keypoints[i]的计算描述符）bool useProvidedKeypoints=false //使用提供的关键点);

match(）从查询集中查找每个描述符的最佳匹配。

参数说明：

void match( InputArray queryDescriptors, //查询描述符集InputArray trainDescriptors, //训练描述符集合CV_OUT std::vector<DMatch>& matches, //匹配InputArray mask=noArray() //指定输入查询和描述符的列表矩阵之间的允许匹配的掩码) const;

FLANN特征匹配示例：

#include<opencv2/opencv.hpp>#include<opencv2/xfeatures2d.hpp>using namespace cv;using namespace cv::xfeatures2d;//FLANN对高维数据较快int main(){ Mat src1,src2; src1 = imread("E:/image/image/card2.jpg"); src2 = imread("E:/image/image/cards.jpg"); if (src1.empty() || src2.empty()) { printf("can ont load images....\n"); return -1; } imshow("image1", src1); imshow("image2", src2); int minHessian = 400; //选择SURF特征 Ptr<SURF>detector = SURF::create(minHessian); std::vector<KeyPoint>keypoints1; std::vector<KeyPoint>keypoints2; Mat descriptor1, descriptor2; //检测关键点并计算描述符 detector->detectAndCompute(src1, Mat(), keypoints1, descriptor1); detector->detectAndCompute(src2, Mat(), keypoints2, descriptor2); //基于Flann的描述符匹配器 FlannBasedMatcher matcher; std::vector<DMatch>matches; //从查询集中查找每个描述符的最佳匹配 matcher.match(descriptor1, descriptor2, matches); double minDist = 1000; double maxDist = 0; for (int i = 0; i < descriptor1.rows; i++) { double dist = matches[i].distance; printf("%f \n", dist); if (dist > maxDist) { maxDist = dist; } if (dist < minDist) { minDist = dist; } } //DMatch类用于匹配关键点描述符的 std::vector<DMatch>goodMatches; for (int i = 0; i < descriptor1.rows; i++) { double dist = matches[i].distance; if (dist < max(2.5*minDist, 0.02)) { goodMatches.push_back(matches[i]); } } Mat matchesImg; drawMatches(src1, keypoints1, src2, keypoints2, goodMatches, matchesImg, Scalar::all(-1), Scalar::all(-1), std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); imshow("output", matchesImg); waitKey(); return 0;}

以上这篇opencv3/C++ FLANN特征匹配方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。