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260 lines
8.4 KiB
C++
260 lines
8.4 KiB
C++
#include <cv.h>
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#include <cvaux.h>
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#include <highgui.h>
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#include <iostream>
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using namespace cv;
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using namespace std;
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inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
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{
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double w = 1./(H(2,0)*pt.x + H(2,1)*pt.y + H(2,2));
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return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w), (float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
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}
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void drawCorrespondences( const Mat& img1, const Mat& img2, const Mat& transfMtr,
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const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
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const vector<int>& matches, float maxDist, Mat& drawImg )
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{
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Scalar RED = CV_RGB(255, 0, 0);
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Scalar PINK = CV_RGB(255,130,230);
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Scalar GREEN = CV_RGB(0, 255, 0);
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Scalar BLUE = CV_RGB(0, 0, 255);
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/* Output:
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red point - point without corresponding point;
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grean point - point having correct corresponding point;
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pink point - point having incorrect corresponding point, but excised by threshold of distance;
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blue point - point having incorrect corresponding point;
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*/
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Size size(img1.cols + img2.cols, MAX(img1.rows, img2.rows));
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drawImg.create(size, CV_MAKETYPE(img1.depth(), 3));
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Mat drawImg1 = drawImg(Rect(0, 0, img1.cols, img1.rows));
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cvtColor(img1, drawImg1, CV_GRAY2RGB);
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Mat drawImg2 = drawImg(Rect(img1.cols, 0, img2.cols, img2.rows));
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cvtColor(img2, drawImg2, CV_GRAY2RGB);
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for(vector<KeyPoint>::const_iterator it = keypoints1.begin(); it < keypoints1.end(); ++it )
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{
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circle(drawImg, it->pt, 3, RED);
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}
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for(vector<KeyPoint>::const_iterator it = keypoints2.begin(); it < keypoints2.end(); ++it )
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{
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Point p = it->pt;
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circle(drawImg, Point2f(p.x+img1.cols, p.y), 3, RED);
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}
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Mat vec1(3, 1, CV_32FC1), vec2;
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float err = 3;
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vector<int>::const_iterator mit = matches.begin();
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assert( matches.size() == keypoints1.size() );
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for( int i1 = 0; mit < matches.end(); ++mit, i1++ )
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{
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Point2f pt1 = keypoints1[i1].pt, pt2 = keypoints2[*mit].pt;
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Point2f diff = applyHomography(transfMtr, pt1) - pt2;
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if( norm(diff) < err )
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{
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circle(drawImg, pt1, 3, GREEN);
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circle(drawImg, Point2f(pt2.x+img1.cols, pt2.y), 3, GREEN);
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line(drawImg, pt1, Point2f(pt2.x+img1.cols, pt2.y), GREEN);
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}
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else
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{
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/*if( *dit > maxDist )
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{
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circle(drawImg, pt1, 3, PINK);
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circle(drawImg, Point2f(pt2.x+img1.cols, pt2.y), 3, PINK);
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}
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// TODO add key point filter
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else*/
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{
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circle(drawImg, pt1, 3, BLUE);
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circle(drawImg, Point2f(pt2.x+img1.cols, pt2.y), 3, BLUE);
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line(drawImg, pt1, Point2f(pt2.x+img1.cols, pt2.y), BLUE);
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}
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}
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}
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}
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FeatureDetector* createDetector( const string& detectorType )
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{
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FeatureDetector* fd = 0;
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if( !detectorType.compare( "FAST" ) )
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{
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fd = new FastFeatureDetector( 1/*threshold*/, true/*nonmax_suppression*/ );
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}
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else if( !detectorType.compare( "STAR" ) )
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{
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fd = new StarFeatureDetector( 16/*max_size*/, 30/*response_threshold*/, 10/*line_threshold_projected*/,
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8/*line_threshold_binarized*/, 5/*suppress_nonmax_size*/ );
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}
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else if( !detectorType.compare( "SIFT" ) )
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{
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fd = new SiftFeatureDetector(SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
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SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD());
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}
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else if( !detectorType.compare( "SURF" ) )
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{
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fd = new SurfFeatureDetector( 400./*hessian_threshold*/, 3 /*octaves*/, 4/*octave_layers*/ );
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}
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else if( !detectorType.compare( "MSER" ) )
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{
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fd = new MserFeatureDetector( 5/*delta*/, 60/*min_area*/, 14400/*_max_area*/, 0.25f/*max_variation*/,
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0.2/*min_diversity*/, 200/*max_evolution*/, 1.01/*area_threshold*/, 0.003/*min_margin*/,
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5/*edge_blur_size*/ );
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}
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else if( !detectorType.compare( "GFTT" ) )
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{
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fd = new GoodFeaturesToTrackDetector( 1000/*maxCorners*/, 0.01/*qualityLevel*/, 1./*minDistance*/,
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3/*int _blockSize*/, true/*useHarrisDetector*/, 0.04/*k*/ );
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}
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else
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fd = 0;
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return fd;
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}
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DescriptorExtractor* createDescExtractor( const string& descriptorType )
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{
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DescriptorExtractor* de = 0;
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if( !descriptorType.compare( "CALONDER" ) )
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{
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assert(0);
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//de = new CalonderDescriptorExtractor<float>("");
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}
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else if( !descriptorType.compare( "SURF" ) )
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{
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de = new SurfDescriptorExtractor( 3/*octaves*/, 4/*octave_layers*/, false/*extended*/ );
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}
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else
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de = 0;
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return de;
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}
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DescriptorMatcher* createDescMatcher( const string& matherType = string() )
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{
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return new BruteForceMatcher<L2<float> >();
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}
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const string DETECTOR_TYPE_STR = "detector_type";
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const string DESCRIPTOR_TYPE_STR = "descriptor_type";
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const string winName = "correspondences";
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void iter( Ptr<FeatureDetector> detector, Ptr<DescriptorExtractor> descriptor,
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const Mat& img1, float maxDist, Mat& transfMtr, RNG* rng = 0 )
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{
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if( transfMtr.empty() )
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transfMtr = Mat::eye(3, 3, CV_32FC1);
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if( rng )
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{
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transfMtr.at<float>(0,0) = rng->uniform( 0.7f, 1.3f);
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transfMtr.at<float>(0,1) = rng->uniform(-0.2f, 0.2f);
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transfMtr.at<float>(0,2) = rng->uniform(-0.1f, 0.1f)*img1.cols;
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transfMtr.at<float>(1,0) = rng->uniform(-0.2f, 0.2f);
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transfMtr.at<float>(1,1) = rng->uniform( 0.7f, 1.3f);
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transfMtr.at<float>(1,2) = rng->uniform(-0.1f, 0.3f)*img1.rows;
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transfMtr.at<float>(2,0) = rng->uniform( -1e-4f, 1e-4f);
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transfMtr.at<float>(2,1) = rng->uniform( -1e-4f, 1e-4f);
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transfMtr.at<float>(2,2) = rng->uniform( 0.7f, 1.3f);
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}
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Mat img2; warpPerspective( img1, img2, transfMtr, img1.size() );
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cout << endl << "< Extracting keypoints... ";
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vector<KeyPoint> keypoints1, keypoints2;
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detector->detect( img1, keypoints1 );
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detector->detect( img2, keypoints2 );
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cout << keypoints1.size() << " from first image and " << keypoints2.size() << " from second image >" << endl;
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if( keypoints1.empty() || keypoints2.empty() )
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cout << "end" << endl;
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cout << "< Computing descriptors... ";
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Mat descs1, descs2;
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if( keypoints1.size()>0 && keypoints2.size()>0 )
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{
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descriptor->compute( img1, keypoints1, descs1 );
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descriptor->compute( img2, keypoints2, descs2 );
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}
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cout << ">" << endl;
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cout << "< Matching keypoints by descriptors... ";
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vector<int> matches;
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Ptr<DescriptorMatcher> matcher = createDescMatcher();
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matcher->add( descs2 );
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matcher->match( descs1, matches );
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cout << ">" << endl;
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// TODO time
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Mat drawImg;
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drawCorrespondences( img1, img2, transfMtr, keypoints1, keypoints2,
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matches, maxDist, drawImg );
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imshow( winName, drawImg);
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}
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Ptr<FeatureDetector> detector;
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Ptr<DescriptorExtractor> descriptor;
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Mat img1;
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Mat transfMtr;
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RNG rng;
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const float maxDistScale = 0.01f;
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int maxDist;
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void onMaxDistChange( int maxDist, void* )
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{
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float realMaxDist = maxDist*maxDistScale;
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cout << "maxDist " << realMaxDist << endl;
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iter( detector, descriptor, img1, realMaxDist, transfMtr );
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}
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int main(int argc, char** argv)
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{
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if( argc != 4 )
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{
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cout << "Format:" << endl;
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cout << "./" << argv[0] << " [detector_type] [descriptor_type] [image]" << endl;
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return 0;
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}
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cout << "< Creating detector, descriptor and matcher... ";
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detector = createDetector(argv[1]);
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descriptor = createDescExtractor(argv[2]);
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//Ptr<DescriptorMatcher> matcher = createDescMatcher(argv[3]);
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cout << ">" << endl;
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if( detector.empty() || descriptor.empty()/* || matcher.empty() */ )
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{
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cout << "Can not create detector or descriptor or matcher of given types" << endl;
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return 0;
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}
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cout << "< Reading the image... ";
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img1 = imread( argv[3], CV_LOAD_IMAGE_GRAYSCALE);
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cout << ">" << endl;
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if( img1.empty() )
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{
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cout << "Can not read image" << endl;
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return 0;
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}
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namedWindow(winName, 1);
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maxDist = 12;
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createTrackbar( "maxDist", winName, &maxDist, 100, onMaxDistChange );
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onMaxDistChange(maxDist, 0);
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for(;;)
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{
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char c = (char)cvWaitKey(0);
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if( c == '\x1b' ) // esc
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{
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cout << "Exiting ..." << endl;
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return 0;
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}
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else if( c == 'n' )
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iter(detector, descriptor, img1, maxDist*maxDistScale, transfMtr, &rng);
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}
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waitKey(0);
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}
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