c69312ea0d
modified SIFT to 1) double image before finding keypoints, 2) use floating-point internally instead of 16-bit integers, 3) set the keypoint response to the abs(interpolated_DoG_value). step 1) increases the number of detected keypoints significantly and together with 2) and 3) it improves some detection benchmarks. On the other hand, the stability of the small keypoints is lower, so the rotation and scale invariance tests now struggle a bit. In 2.5 need to make this feature optional and add some more intelligence to the algorithm. added test that finds a planar object using SIFT.
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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printf("Number of inliers: %d\n", countNonZero(matchesMask));
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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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