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Open Source Computer Vision Library
https://opencv.org/
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79 lines
2.7 KiB
79 lines
2.7 KiB
#include <opencv2/features2d.hpp> |
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#include <opencv2/imgcodecs.hpp> |
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#include <opencv2/opencv.hpp> |
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#include <vector> |
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#include <iostream> |
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using namespace std; |
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using namespace cv; |
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const float inlier_threshold = 2.5f; // Distance threshold to identify inliers |
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const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio |
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int main(void) |
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{ |
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Mat img1 = imread("../data/graf1.png", IMREAD_GRAYSCALE); |
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Mat img2 = imread("../data/graf3.png", IMREAD_GRAYSCALE); |
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Mat homography; |
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FileStorage fs("../data/H1to3p.xml", FileStorage::READ); |
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fs.getFirstTopLevelNode() >> homography; |
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vector<KeyPoint> kpts1, kpts2; |
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Mat desc1, desc2; |
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Ptr<AKAZE> akaze = AKAZE::create(); |
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akaze->detectAndCompute(img1, noArray(), kpts1, desc1); |
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akaze->detectAndCompute(img2, noArray(), kpts2, desc2); |
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BFMatcher matcher(NORM_HAMMING); |
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vector< vector<DMatch> > nn_matches; |
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matcher.knnMatch(desc1, desc2, nn_matches, 2); |
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vector<KeyPoint> matched1, matched2, inliers1, inliers2; |
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vector<DMatch> good_matches; |
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for(size_t i = 0; i < nn_matches.size(); i++) { |
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DMatch first = nn_matches[i][0]; |
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float dist1 = nn_matches[i][0].distance; |
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float dist2 = nn_matches[i][1].distance; |
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if(dist1 < nn_match_ratio * dist2) { |
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matched1.push_back(kpts1[first.queryIdx]); |
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matched2.push_back(kpts2[first.trainIdx]); |
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} |
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} |
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for(unsigned i = 0; i < matched1.size(); i++) { |
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Mat col = Mat::ones(3, 1, CV_64F); |
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col.at<double>(0) = matched1[i].pt.x; |
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col.at<double>(1) = matched1[i].pt.y; |
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col = homography * col; |
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col /= col.at<double>(2); |
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double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) + |
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pow(col.at<double>(1) - matched2[i].pt.y, 2)); |
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if(dist < inlier_threshold) { |
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int new_i = static_cast<int>(inliers1.size()); |
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inliers1.push_back(matched1[i]); |
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inliers2.push_back(matched2[i]); |
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good_matches.push_back(DMatch(new_i, new_i, 0)); |
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} |
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} |
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Mat res; |
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drawMatches(img1, inliers1, img2, inliers2, good_matches, res); |
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imwrite("res.png", res); |
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double inlier_ratio = inliers1.size() * 1.0 / matched1.size(); |
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cout << "A-KAZE Matching Results" << endl; |
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cout << "*******************************" << endl; |
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cout << "# Keypoints 1: \t" << kpts1.size() << endl; |
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cout << "# Keypoints 2: \t" << kpts2.size() << endl; |
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cout << "# Matches: \t" << matched1.size() << endl; |
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cout << "# Inliers: \t" << inliers1.size() << endl; |
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cout << "# Inliers Ratio: \t" << inlier_ratio << endl; |
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cout << endl; |
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return 0; |
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}
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