opencv3/C++ FLANN特征匹配方式
人气:0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; }
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