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2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
126 lines
3.9 KiB
C++
126 lines
3.9 KiB
C++
/**
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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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#include "opencv2/nonfree/features2d.hpp"
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using namespace std;
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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_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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if( !img_object.data || !img_scene.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_object, keypoints_scene;
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detector.detect( img_object, keypoints_object );
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detector.detect( img_scene, keypoints_scene );
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//-- Step 2: Calculate descriptors (feature vectors)
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SurfDescriptorExtractor extractor;
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Mat descriptors_object, descriptors_scene;
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extractor.compute( img_object, keypoints_object, descriptors_object );
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extractor.compute( img_scene, keypoints_scene, descriptors_scene );
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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_object, descriptors_scene, 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_object.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_object.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_object, keypoints_object, img_scene, keypoints_scene,
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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( size_t 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_object[ good_matches[i].queryIdx ].pt );
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scene.push_back( keypoints_scene[ 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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std::vector<Point2f> obj_corners(4);
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obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
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obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
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std::vector<Point2f> scene_corners(4);
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perspectiveTransform( obj_corners, scene_corners, H);
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//-- Draw lines between the corners (the mapped object in the scene - image_2 )
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Point2f offset( (float)img_object.cols, 0);
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line( img_matches, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4 );
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line( img_matches, scene_corners[1] + offset, scene_corners[2] + offset, Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[2] + offset, scene_corners[3] + offset, Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[3] + offset, scene_corners[0] + offset, Scalar( 0, 255, 0), 4 );
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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_Homography <img1> <img2>" << std::endl; }
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