OpenCV 特征点匹配及 优化下载

#include "FeatureMatchTest.h" #include <opencv2\nonfree\features2d.hpp> using namespace std; using namespace cv; FeatureMatchTest::FeatureMatchTest(std::shared_ptr<Pattern> left, std::shared_ptr<Pattern> right, std::shared_ptr<cv::DescriptorMatcher> matcher) : leftPattern(left), rightPattern(right), matcher(matcher) { //step1:Create detector int minHessian = 400; SurfFeatureDetector detector(minHessian); //step2:Detecte keypoint detector.detect(leftPattern->image, leftPattern->keypoints); detector.detect(rightPattern->image, rightPattern->keypoints); //step3:Compute descriptor detector.compute(leftPattern->image, leftPattern->keypoints, leftPattern->descriptors); detector.compute(rightPattern->image, rightPattern->keypoints, rightPattern->descriptors); } void FeatureMatchTest::match(vector<DMatch>& matches) { matcher->match(leftPattern->descriptors, rightPattern->descriptors, matches); } void FeatureMatchTest::knnMatch(vector<DMatch>& matches) { const float minRatio = 1.f / 1.5f; const int k = 2; vector<vector<DMatch>> knnMatches; matcher->knnMatch(leftPattern->descriptors, rightPattern->descriptors, knnMatches, k); for (size_t i = 0; i < knnMatches.size(); i++) { const DMatch& bestMatch = knnMatches[i][0]; const DMatch& betterMatch = knnMatches[i][1]; float distanceRatio = bestMatch.distance / betterMatch.distance; if (distanceRatio < minRatio) matches.push_back(bestMatch); } } void FeatureMatchTest::refineMatcheswithHomography(vector<DMatch>& matches, double reprojectionThreshold, Mat& homography){ const int minNumbermatchesAllowed = 8; if (matches.size() < minNumbermatchesAllowed) return; //Prepare data for findHomography vector<Point2f> srcPoints(matches.size()); vector<Point2f> dstPoints(matches.size()); for (size_t i = 0; i < matches.size(); i++) { srcPoints[i] = rightPattern->keypoints[matches[i].trainIdx].pt; dstPoints[i] = leftPattern->keypoints[matches[i].queryIdx].pt; } //find homography matrix and get inliers mask vector<uchar> inliersMask(srcPoints.size()); homography = findHomography(srcPoints, dstPoints, CV_FM_RANSAC, reprojectionThreshold, inliersMask); vector<DMatch> inliers; for (size_t i = 0; i < inliersMask.size(); i++){ if (inliersMask[i]) inliers.push_back(matches[i]); } matches.swap(inliers); } void FeatureMatchTest::refineMatchesWithFundmentalMatrix(vector<DMatch>& matches, Mat& F) { //Align all points vector<KeyPoint> alignedKps1, alignedKps2; for (size_t i = 0; i < matches.size(); i++) { alignedKps1.push_back(leftPattern->keypoints[matches[i].queryIdx]); alignedKps2.push_back(rightPattern->keypoints[matches[i].trainIdx]); } //Keypoints to points vector<Point2f> ps1, ps2; for (unsigned i = 0; i < alignedKps1.size(); i++) ps1.push_back(alignedKps1[i].pt); for (unsigned i = 0; i < alignedKps2.size(); i++) ps2.push_back(alignedKps2[i].pt); //Compute fundmental matrix vector<uchar> status; F = findFundamentalMat(ps1, ps2, status, FM_RANSAC); cout << "Fundmental Matric:" << endl; cout << F << endl; //优化匹配结果 vector<KeyPoint> leftInlier; vector<KeyPoint> rightInlier; vector<DMatch> inlierMatch; int index = 0; for (unsigned i = 0; i < matches.size(); i++) { if (status[i] != 0){ leftInlier.push_back(alignedKps1[i]); rightInlier.push_back(alignedKps2[i]); matches[i].trainIdx = index; matches[i].queryIdx = index; inlierMatch.push_back(matches[i]); index++; } } leftPattern->keypoints = leftInlier; rightPattern->keypoints = rightInlier; matches = inlierMatch; } /*void FeatureMatchTest::showMatches(const vector<DMatch>& matches,Mat& matchesImg, const string& windowName) { drawMatches(leftPattern->image, leftPattern->keypoints, rightPattern->image, rightPattern->keypoints, matches, matchesImg); namedWindow(windowName); imshow(windowName, matchesImg); waitKey(); destroyWindow(windowName); }*/