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305 lines
12 KiB
C++
305 lines
12 KiB
C++
#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/calib3d/calib3d.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/nonfree/nonfree.hpp"
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#include <iostream>
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using namespace cv;
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using namespace std;
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static void help(char** argv)
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{
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cout << "\nThis program demonstrats keypoint finding and matching between 2 images using features2d framework.\n"
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<< " In one case, the 2nd image is synthesized by homography from the first, in the second case, there are 2 images\n"
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<< "\n"
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<< "Case1: second image is obtained from the first (given) image using random generated homography matrix\n"
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<< argv[0] << " [detectorType] [descriptorType] [matcherType] [matcherFilterType] [image] [evaluate(0 or 1)]\n"
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<< "Example of case1:\n"
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<< "./descriptor_extractor_matcher SURF SURF FlannBased NoneFilter cola.jpg 0\n"
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<< "\n"
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<< "Case2: both images are given. If ransacReprojThreshold>=0 then homography matrix are calculated\n"
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<< argv[0] << " [detectorType] [descriptorType] [matcherType] [matcherFilterType] [image1] [image2] [ransacReprojThreshold]\n"
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<< "\n"
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<< "Matches are filtered using homography matrix in case1 and case2 (if ransacReprojThreshold>=0)\n"
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<< "Example of case2:\n"
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<< "./descriptor_extractor_matcher SURF SURF BruteForce CrossCheckFilter cola1.jpg cola2.jpg 3\n"
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<< "\n"
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<< "Possible detectorType values: see in documentation on createFeatureDetector().\n"
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<< "Possible descriptorType values: see in documentation on createDescriptorExtractor().\n"
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<< "Possible matcherType values: see in documentation on createDescriptorMatcher().\n"
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<< "Possible matcherFilterType values: NoneFilter, CrossCheckFilter." << endl;
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}
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#define DRAW_RICH_KEYPOINTS_MODE 0
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#define DRAW_OUTLIERS_MODE 0
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const string winName = "correspondences";
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enum { NONE_FILTER = 0, CROSS_CHECK_FILTER = 1 };
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static int getMatcherFilterType( const string& str )
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{
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if( str == "NoneFilter" )
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return NONE_FILTER;
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if( str == "CrossCheckFilter" )
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return CROSS_CHECK_FILTER;
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CV_Error(CV_StsBadArg, "Invalid filter name");
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return -1;
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}
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static void simpleMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
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const Mat& descriptors1, const Mat& descriptors2,
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vector<DMatch>& matches12 )
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{
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vector<DMatch> matches;
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descriptorMatcher->match( descriptors1, descriptors2, matches12 );
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}
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static void crossCheckMatching( Ptr<DescriptorMatcher>& descriptorMatcher,
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const Mat& descriptors1, const Mat& descriptors2,
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vector<DMatch>& filteredMatches12, int knn=1 )
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{
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filteredMatches12.clear();
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vector<vector<DMatch> > matches12, matches21;
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descriptorMatcher->knnMatch( descriptors1, descriptors2, matches12, knn );
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descriptorMatcher->knnMatch( descriptors2, descriptors1, matches21, knn );
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for( size_t m = 0; m < matches12.size(); m++ )
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{
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bool findCrossCheck = false;
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for( size_t fk = 0; fk < matches12[m].size(); fk++ )
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{
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DMatch forward = matches12[m][fk];
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for( size_t bk = 0; bk < matches21[forward.trainIdx].size(); bk++ )
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{
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DMatch backward = matches21[forward.trainIdx][bk];
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if( backward.trainIdx == forward.queryIdx )
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{
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filteredMatches12.push_back(forward);
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findCrossCheck = true;
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break;
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}
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}
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if( findCrossCheck ) break;
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}
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}
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}
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static void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
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{
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H.create(3, 3, CV_32FC1);
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H.at<float>(0,0) = rng.uniform( 0.8f, 1.2f);
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H.at<float>(0,1) = rng.uniform(-0.1f, 0.1f);
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H.at<float>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
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H.at<float>(1,0) = rng.uniform(-0.1f, 0.1f);
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H.at<float>(1,1) = rng.uniform( 0.8f, 1.2f);
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H.at<float>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
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H.at<float>(2,0) = rng.uniform( -1e-4f, 1e-4f);
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H.at<float>(2,1) = rng.uniform( -1e-4f, 1e-4f);
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H.at<float>(2,2) = rng.uniform( 0.8f, 1.2f);
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warpPerspective( src, dst, H, src.size() );
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}
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static void doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
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vector<KeyPoint>& keypoints1, const Mat& descriptors1,
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Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
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Ptr<DescriptorMatcher>& descriptorMatcher, int matcherFilter, bool eval,
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double ransacReprojThreshold, RNG& rng )
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{
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assert( !img1.empty() );
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Mat H12;
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if( isWarpPerspective )
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warpPerspectiveRand(img1, img2, H12, rng );
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else
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assert( !img2.empty()/* && img2.cols==img1.cols && img2.rows==img1.rows*/ );
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cout << endl << "< Extracting keypoints from second image..." << endl;
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vector<KeyPoint> keypoints2;
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detector->detect( img2, keypoints2 );
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cout << keypoints2.size() << " points" << endl << ">" << endl;
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if( !H12.empty() && eval )
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{
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cout << "< Evaluate feature detector..." << endl;
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float repeatability;
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int correspCount;
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evaluateFeatureDetector( img1, img2, H12, &keypoints1, &keypoints2, repeatability, correspCount );
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cout << "repeatability = " << repeatability << endl;
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cout << "correspCount = " << correspCount << endl;
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cout << ">" << endl;
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}
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cout << "< Computing descriptors for keypoints from second image..." << endl;
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Mat descriptors2;
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descriptorExtractor->compute( img2, keypoints2, descriptors2 );
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cout << ">" << endl;
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cout << "< Matching descriptors..." << endl;
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vector<DMatch> filteredMatches;
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switch( matcherFilter )
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{
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case CROSS_CHECK_FILTER :
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crossCheckMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches, 1 );
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break;
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default :
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simpleMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches );
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}
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cout << ">" << endl;
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if( !H12.empty() && eval )
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{
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cout << "< Evaluate descriptor matcher..." << endl;
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vector<Point2f> curve;
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Ptr<GenericDescriptorMatcher> gdm = new VectorDescriptorMatcher( descriptorExtractor, descriptorMatcher );
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evaluateGenericDescriptorMatcher( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
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Point2f firstPoint = *curve.begin();
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Point2f lastPoint = *curve.rbegin();
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int prevPointIndex = -1;
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cout << "1-precision = " << firstPoint.x << "; recall = " << firstPoint.y << endl;
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for( float l_p = 0; l_p <= 1 + FLT_EPSILON; l_p+=0.05f )
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{
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int nearest = getNearestPoint( curve, l_p );
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if( nearest >= 0 )
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{
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Point2f curPoint = curve[nearest];
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if( curPoint.x > firstPoint.x && curPoint.x < lastPoint.x && nearest != prevPointIndex )
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{
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cout << "1-precision = " << curPoint.x << "; recall = " << curPoint.y << endl;
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prevPointIndex = nearest;
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}
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}
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}
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cout << "1-precision = " << lastPoint.x << "; recall = " << lastPoint.y << endl;
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cout << ">" << endl;
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}
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vector<int> queryIdxs( filteredMatches.size() ), trainIdxs( filteredMatches.size() );
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for( size_t i = 0; i < filteredMatches.size(); i++ )
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{
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queryIdxs[i] = filteredMatches[i].queryIdx;
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trainIdxs[i] = filteredMatches[i].trainIdx;
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}
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if( !isWarpPerspective && ransacReprojThreshold >= 0 )
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{
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cout << "< Computing homography (RANSAC)..." << endl;
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vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
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vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
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H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
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cout << ">" << endl;
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}
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Mat drawImg;
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if( !H12.empty() ) // filter outliers
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{
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vector<char> matchesMask( filteredMatches.size(), 0 );
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vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
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vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
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Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
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double maxInlierDist = ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;
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for( size_t i1 = 0; i1 < points1.size(); i1++ )
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{
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if( norm(points2[i1] - points1t.at<Point2f>((int)i1,0)) <= maxInlierDist ) // inlier
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matchesMask[i1] = 1;
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}
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// draw inliers
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drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
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#if DRAW_RICH_KEYPOINTS_MODE
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, DrawMatchesFlags::DRAW_RICH_KEYPOINTS
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#endif
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);
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#if DRAW_OUTLIERS_MODE
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// draw outliers
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for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
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matchesMask[i1] = !matchesMask[i1];
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drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg, CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
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DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
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#endif
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cout << "Number of inliers: " << countNonZero(matchesMask) << endl;
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}
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else
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drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg );
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imshow( winName, drawImg );
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}
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int main(int argc, char** argv)
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{
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if( argc != 7 && argc != 8 )
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{
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help(argv);
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return -1;
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}
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cv::initModule_nonfree();
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bool isWarpPerspective = argc == 7;
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double ransacReprojThreshold = -1;
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if( !isWarpPerspective )
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ransacReprojThreshold = atof(argv[7]);
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cout << "< Creating detector, descriptor extractor and descriptor matcher ..." << endl;
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Ptr<FeatureDetector> detector = FeatureDetector::create( argv[1] );
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Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create( argv[2] );
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Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( argv[3] );
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int mactherFilterType = getMatcherFilterType( argv[4] );
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bool eval = !isWarpPerspective ? false : (atoi(argv[6]) == 0 ? false : true);
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cout << ">" << endl;
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if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() )
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{
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cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;
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return -1;
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}
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cout << "< Reading the images..." << endl;
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Mat img1 = imread( argv[5] ), img2;
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if( !isWarpPerspective )
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img2 = imread( argv[6] );
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cout << ">" << endl;
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if( img1.empty() || (!isWarpPerspective && img2.empty()) )
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{
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cout << "Can not read images" << endl;
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return -1;
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}
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cout << endl << "< Extracting keypoints from first image..." << endl;
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vector<KeyPoint> keypoints1;
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detector->detect( img1, keypoints1 );
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cout << keypoints1.size() << " points" << endl << ">" << endl;
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cout << "< Computing descriptors for keypoints from first image..." << endl;
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Mat descriptors1;
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descriptorExtractor->compute( img1, keypoints1, descriptors1 );
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cout << ">" << endl;
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namedWindow(winName, 1);
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RNG rng = theRNG();
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doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
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detector, descriptorExtractor, descriptorMatcher, mactherFilterType, eval,
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ransacReprojThreshold, rng );
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for(;;)
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{
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char c = (char)waitKey(0);
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if( c == '\x1b' ) // esc
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{
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cout << "Exiting ..." << endl;
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break;
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}
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else if( isWarpPerspective )
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{
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doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
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detector, descriptorExtractor, descriptorMatcher, mactherFilterType, eval,
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ransacReprojThreshold, rng );
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}
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}
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return 0;
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}
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