Open Source Computer Vision Library
https://opencv.org/
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129 lines
4.0 KiB
129 lines
4.0 KiB
/** |
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* @file SURF_Homography |
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* @brief SURF detector + descriptor + FLANN Matcher + FindHomography |
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* @author A. Huaman |
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*/ |
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#include <stdio.h> |
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#include <iostream> |
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#include "opencv2/core/core.hpp" |
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#include "opencv2/features2d/features2d.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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#include "opencv2/calib3d/calib3d.hpp" |
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using namespace cv; |
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void readme(); |
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/** |
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* @function main |
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* @brief Main function |
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*/ |
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int main( int argc, char** argv ) |
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{ |
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if( argc != 3 ) |
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{ readme(); return -1; } |
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Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE ); |
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Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE ); |
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if( !img_1.data || !img_2.data ) |
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{ std::cout<< " --(!) Error reading images " << std::endl; return -1; } |
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//-- Step 1: Detect the keypoints using SURF Detector |
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int minHessian = 400; |
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SurfFeatureDetector detector( minHessian ); |
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std::vector<KeyPoint> keypoints_1, keypoints_2; |
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detector.detect( img_1, keypoints_1 ); |
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detector.detect( img_2, keypoints_2 ); |
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//-- Step 2: Calculate descriptors (feature vectors) |
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SurfDescriptorExtractor extractor; |
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Mat descriptors_1, descriptors_2; |
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extractor.compute( img_1, keypoints_1, descriptors_1 ); |
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extractor.compute( img_2, keypoints_2, descriptors_2 ); |
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//-- Step 3: Matching descriptor vectors using FLANN matcher |
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FlannBasedMatcher matcher; |
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std::vector< DMatch > matches; |
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matcher.match( descriptors_1, descriptors_2, matches ); |
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double max_dist = 0; double min_dist = 100; |
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//-- Quick calculation of max and min distances between keypoints |
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for( int i = 0; i < descriptors_1.rows; i++ ) |
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{ double dist = matches[i].distance; |
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if( dist < min_dist ) min_dist = dist; |
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if( dist > max_dist ) max_dist = dist; |
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} |
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printf("-- Max dist : %f \n", max_dist ); |
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printf("-- Min dist : %f \n", min_dist ); |
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//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist ) |
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std::vector< DMatch > good_matches; |
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for( int i = 0; i < descriptors_1.rows; i++ ) |
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{ if( matches[i].distance < 3*min_dist ) |
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{ good_matches.push_back( matches[i]); } |
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} |
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Mat img_matches; |
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drawMatches( img_1, keypoints_1, img_2, keypoints_2, |
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good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), |
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vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); |
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//-- Localize the object from img_1 in img_2 |
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std::vector<Point2f> obj; |
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std::vector<Point2f> scene; |
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for( int i = 0; i < good_matches.size(); i++ ) |
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{ |
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//-- Get the keypoints from the good matches |
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obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt ); |
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scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt ); |
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} |
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Mat H = findHomography( obj, scene, CV_RANSAC ); |
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//-- Get the corners from the image_1 ( the object to be "detected" ) |
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Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) }; |
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Point scene_corners[4]; |
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//-- Map these corners in the scene ( image_2) |
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for( int i = 0; i < 4; i++ ) |
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{ |
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double x = obj_corners[i].x; |
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double y = obj_corners[i].y; |
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double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) ); |
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double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z; |
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double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z; |
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scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) ); |
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} |
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//-- Draw lines between the corners (the mapped object in the scene - image_2 ) |
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line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 ); |
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line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 ); |
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line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 ); |
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line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 ); |
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//-- Show detected matches |
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imshow( "Good Matches & Object detection", img_matches ); |
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waitKey(0); |
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return 0; |
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} |
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/** |
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* @function readme |
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*/ |
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void readme() |
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{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
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