基于ORB算法的特征提取和匹配(VS2015+OpenCV2.4.9)

#include <iostream> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/legacy/legacy.hpp> #include <iostream> #include <vector> using namespace cv; using namespace std; int main() { Mat img_1 = imread(".\\imagedata\\beaver.png"); Mat img_2 = imread(".\\imagedata\\beaver_xform.png"); /*Mat img_1 = imread(".\\imagedata\\beaver.png"); Mat img_2 = imread(".\\imagedata\\beaver_xform.png");*/ if (!img_1.data || !img_2.data) { cout << "error reading images " << endl; return -1; } ORB orb; vector<KeyPoint> keyPoints_1, keyPoints_2; Mat descriptors_1, descriptors_2; orb(img_1, Mat(), keyPoints_1, descriptors_1); orb(img_2, Mat(), keyPoints_2, descriptors_2); BruteForceMatcher<HammingLUT> matcher; vector<DMatch> matches; matcher.match(descriptors_1, descriptors_2, matches); double max_dist = 0; double min_dist = 100; //-- Quick calculation of max and min distances between keypoints for (int i = 0; i < descriptors_1.rows; i++) { double dist = matches[i].distance; if (dist < min_dist) min_dist = dist; if (dist > max_dist) max_dist = dist; } printf("-- Max dist : %f \n", max_dist); printf("-- Min dist : %f \n", min_dist); //-- Draw only "good" matches (i.e. whose distance is less than 0.6*max_dist ) //-- PS.- radiusMatch can also be used here. std::vector< DMatch > good_matches; for (int i = 0; i < descriptors_1.rows; i++) { if (matches[i].distance < 0.6*max_dist) { good_matches.push_back(matches[i]); } } Mat img_matches; drawMatches(img_1, keyPoints_1, img_2, keyPoints_2, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); imshow("Match", img_matches); cvWaitKey(); return 0; }