2010-05-12 01:44:00 +08:00
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace std;
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2010-06-12 02:44:22 +08:00
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namespace cv
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{
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2010-05-12 01:44:00 +08:00
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/*
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2010-08-04 00:28:52 +08:00
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* FeatureDetector
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*/
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2010-05-12 01:44:00 +08:00
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struct MaskPredicate
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{
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MaskPredicate( const Mat& _mask ) : mask(_mask)
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{}
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2010-07-07 23:25:42 +08:00
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MaskPredicate& operator=(const MaskPredicate&) { return *this; }
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2010-05-12 01:44:00 +08:00
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bool operator() (const KeyPoint& key_pt) const
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{
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2010-07-03 01:36:28 +08:00
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return mask.at<uchar>( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) == 0;
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2010-05-12 01:44:00 +08:00
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}
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const Mat& mask;
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};
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2010-11-23 02:27:08 +08:00
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FeatureDetector::~FeatureDetector()
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{}
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void FeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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{
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keypoints.clear();
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if( image.empty() )
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return;
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CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );
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detectImpl( image, keypoints, mask );
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}
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2010-10-29 16:44:42 +08:00
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void FeatureDetector::detect(const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks ) const
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{
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pointCollection.resize( imageCollection.size() );
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for( size_t i = 0; i < imageCollection.size(); i++ )
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detect( imageCollection[i], pointCollection[i], masks.empty() ? Mat() : masks[i] );
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}
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2010-05-12 01:44:00 +08:00
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void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints )
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{
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if( mask.empty() )
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return;
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keypoints.erase(remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
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};
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2010-11-23 02:27:08 +08:00
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void FeatureDetector::read( const FileNode& )
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{}
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void FeatureDetector::write( FileStorage& ) const
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{}
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2010-05-12 01:44:00 +08:00
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/*
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2010-08-04 00:28:52 +08:00
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* FastFeatureDetector
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*/
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2010-05-12 01:44:00 +08:00
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FastFeatureDetector::FastFeatureDetector( int _threshold, bool _nonmaxSuppression )
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: threshold(_threshold), nonmaxSuppression(_nonmaxSuppression)
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{}
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2010-06-04 13:30:09 +08:00
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void FastFeatureDetector::read (const FileNode& fn)
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{
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threshold = fn["threshold"];
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nonmaxSuppression = (int)fn["nonmaxSuppression"] ? true : false;
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}
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void FastFeatureDetector::write (FileStorage& fs) const
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{
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fs << "threshold" << threshold;
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fs << "nonmaxSuppression" << nonmaxSuppression;
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}
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2010-11-23 02:27:08 +08:00
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void FastFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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2010-05-12 01:44:00 +08:00
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{
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2010-09-23 18:53:36 +08:00
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Mat grayImage = image;
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if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
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FAST( grayImage, keypoints, threshold, nonmaxSuppression );
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2010-05-12 01:44:00 +08:00
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removeInvalidPoints( mask, keypoints );
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}
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/*
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2010-08-04 00:28:52 +08:00
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* GoodFeaturesToTrackDetector
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*/
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2010-11-23 02:27:08 +08:00
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GoodFeaturesToTrackDetector::Params::Params( int _maxCorners, double _qualityLevel, double _minDistance,
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int _blockSize, bool _useHarrisDetector, double _k ) :
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maxCorners(_maxCorners), qualityLevel(_qualityLevel), minDistance(_minDistance),
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blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
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2010-05-12 01:44:00 +08:00
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{}
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2010-11-23 02:27:08 +08:00
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void GoodFeaturesToTrackDetector::Params::read (const FileNode& fn)
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2010-06-04 13:30:09 +08:00
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{
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maxCorners = fn["maxCorners"];
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qualityLevel = fn["qualityLevel"];
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minDistance = fn["minDistance"];
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blockSize = fn["blockSize"];
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2010-07-16 20:54:53 +08:00
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useHarrisDetector = (int)fn["useHarrisDetector"] != 0;
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2010-06-04 13:30:09 +08:00
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k = fn["k"];
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}
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2010-11-23 02:27:08 +08:00
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void GoodFeaturesToTrackDetector::Params::write (FileStorage& fs) const
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2010-06-04 13:30:09 +08:00
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{
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fs << "maxCorners" << maxCorners;
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fs << "qualityLevel" << qualityLevel;
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fs << "minDistance" << minDistance;
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fs << "blockSize" << blockSize;
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fs << "useHarrisDetector" << useHarrisDetector;
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fs << "k" << k;
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}
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2010-11-23 02:27:08 +08:00
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GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( const Params& _params ) : params(_params)
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{}
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GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
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double minDistance, int blockSize,
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bool useHarrisDetector, double k )
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{
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params = Params( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k );
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}
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void GoodFeaturesToTrackDetector::read (const FileNode& fn)
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{
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params.read(fn);
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}
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void GoodFeaturesToTrackDetector::write (FileStorage& fs) const
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{
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params.write(fs);
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}
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void GoodFeaturesToTrackDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
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2010-05-12 01:44:00 +08:00
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{
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2010-09-23 18:53:36 +08:00
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Mat grayImage = image;
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if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
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2010-05-12 01:44:00 +08:00
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vector<Point2f> corners;
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2010-11-23 02:27:08 +08:00
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goodFeaturesToTrack( grayImage, corners, params.maxCorners, params.qualityLevel, params.minDistance, mask,
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params.blockSize, params.useHarrisDetector, params.k );
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2010-05-12 01:44:00 +08:00
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keypoints.resize(corners.size());
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vector<Point2f>::const_iterator corner_it = corners.begin();
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vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
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for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
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{
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2010-11-23 02:27:08 +08:00
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*keypoint_it = KeyPoint( *corner_it, (float)params.blockSize );
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2010-05-12 01:44:00 +08:00
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}
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}
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/*
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2010-08-04 00:28:52 +08:00
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* MserFeatureDetector
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*/
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2010-05-12 01:44:00 +08:00
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MserFeatureDetector::MserFeatureDetector( int delta, int minArea, int maxArea,
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2010-07-16 20:54:53 +08:00
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double maxVariation, double minDiversity,
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2010-05-12 01:44:00 +08:00
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int maxEvolution, double areaThreshold,
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double minMargin, int edgeBlurSize )
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: mser( delta, minArea, maxArea, maxVariation, minDiversity,
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maxEvolution, areaThreshold, minMargin, edgeBlurSize )
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{}
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2010-06-04 13:30:09 +08:00
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MserFeatureDetector::MserFeatureDetector( CvMSERParams params )
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: mser( params.delta, params.minArea, params.maxArea, params.maxVariation, params.minDiversity,
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params.maxEvolution, params.areaThreshold, params.minMargin, params.edgeBlurSize )
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{}
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void MserFeatureDetector::read (const FileNode& fn)
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{
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int delta = fn["delta"];
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int minArea = fn["minArea"];
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int maxArea = fn["maxArea"];
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float maxVariation = fn["maxVariation"];
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float minDiversity = fn["minDiversity"];
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int maxEvolution = fn["maxEvolution"];
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double areaThreshold = fn["areaThreshold"];
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double minMargin = fn["minMargin"];
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int edgeBlurSize = fn["edgeBlurSize"];
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mser = MSER( delta, minArea, maxArea, maxVariation, minDiversity,
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maxEvolution, areaThreshold, minMargin, edgeBlurSize );
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}
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void MserFeatureDetector::write (FileStorage& fs) const
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{
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//fs << "algorithm" << getAlgorithmName ();
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fs << "delta" << mser.delta;
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fs << "minArea" << mser.minArea;
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fs << "maxArea" << mser.maxArea;
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fs << "maxVariation" << mser.maxVariation;
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fs << "minDiversity" << mser.minDiversity;
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fs << "maxEvolution" << mser.maxEvolution;
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fs << "areaThreshold" << mser.areaThreshold;
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fs << "minMargin" << mser.minMargin;
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fs << "edgeBlurSize" << mser.edgeBlurSize;
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}
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2010-11-23 02:27:08 +08:00
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void MserFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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2010-05-12 01:44:00 +08:00
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{
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vector<vector<Point> > msers;
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2010-09-23 18:53:36 +08:00
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2010-11-11 23:45:54 +08:00
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mser(image, msers, mask);
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2010-05-12 01:44:00 +08:00
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vector<vector<Point> >::const_iterator contour_it = msers.begin();
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2010-11-11 23:45:54 +08:00
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for( ; contour_it != msers.end(); ++contour_it )
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2010-05-12 01:44:00 +08:00
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{
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2010-05-22 01:36:36 +08:00
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// TODO check transformation from MSER region to KeyPoint
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2010-05-12 01:44:00 +08:00
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RotatedRect rect = fitEllipse(Mat(*contour_it));
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2010-11-11 23:45:54 +08:00
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float diam = sqrt(rect.size.height*rect.size.width);
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if( diam > std::numeric_limits<float>::epsilon() )
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keypoints.push_back( KeyPoint( rect.center, diam, rect.angle) );
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2010-05-12 01:44:00 +08:00
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}
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}
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/*
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2010-08-04 00:28:52 +08:00
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* StarFeatureDetector
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*/
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2010-11-23 02:27:08 +08:00
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StarFeatureDetector::StarFeatureDetector( const CvStarDetectorParams& params )
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: star( params.maxSize, params.responseThreshold, params.lineThresholdProjected,
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params.lineThresholdBinarized, params.suppressNonmaxSize)
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{}
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2010-05-12 01:44:00 +08:00
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StarFeatureDetector::StarFeatureDetector(int maxSize, int responseThreshold,
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int lineThresholdProjected,
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int lineThresholdBinarized,
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int suppressNonmaxSize)
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: star( maxSize, responseThreshold, lineThresholdProjected,
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lineThresholdBinarized, suppressNonmaxSize)
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{}
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2010-06-04 13:30:09 +08:00
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void StarFeatureDetector::read (const FileNode& fn)
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{
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int maxSize = fn["maxSize"];
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int responseThreshold = fn["responseThreshold"];
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int lineThresholdProjected = fn["lineThresholdProjected"];
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int lineThresholdBinarized = fn["lineThresholdBinarized"];
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int suppressNonmaxSize = fn["suppressNonmaxSize"];
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star = StarDetector( maxSize, responseThreshold, lineThresholdProjected,
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lineThresholdBinarized, suppressNonmaxSize);
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}
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void StarFeatureDetector::write (FileStorage& fs) const
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{
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//fs << "algorithm" << getAlgorithmName ();
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fs << "maxSize" << star.maxSize;
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fs << "responseThreshold" << star.responseThreshold;
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fs << "lineThresholdProjected" << star.lineThresholdProjected;
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fs << "lineThresholdBinarized" << star.lineThresholdBinarized;
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fs << "suppressNonmaxSize" << star.suppressNonmaxSize;
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}
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2010-11-23 02:27:08 +08:00
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void StarFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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2010-05-12 01:44:00 +08:00
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{
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2010-09-23 18:53:36 +08:00
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Mat grayImage = image;
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if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
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star(grayImage, keypoints);
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2010-05-12 01:44:00 +08:00
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removeInvalidPoints(mask, keypoints);
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}
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/*
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2010-08-04 00:28:52 +08:00
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* SiftFeatureDetector
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*/
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2010-11-23 02:27:08 +08:00
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SiftFeatureDetector::SiftFeatureDetector( const SIFT::DetectorParams &detectorParams,
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const SIFT::CommonParams &commonParams )
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: sift(detectorParams.threshold, detectorParams.edgeThreshold,
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commonParams.nOctaves, commonParams.nOctaveLayers, commonParams.firstOctave, commonParams.angleMode)
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{
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}
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SiftFeatureDetector::SiftFeatureDetector( double threshold, double edgeThreshold,
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int nOctaves, int nOctaveLayers, int firstOctave, int angleMode ) :
|
2010-05-20 00:02:30 +08:00
|
|
|
sift(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode)
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
|
|
|
}
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void SiftFeatureDetector::read( const FileNode& fn )
|
2010-06-04 13:30:09 +08:00
|
|
|
{
|
|
|
|
double threshold = fn["threshold"];
|
|
|
|
double edgeThreshold = fn["edgeThreshold"];
|
|
|
|
int nOctaves = fn["nOctaves"];
|
|
|
|
int nOctaveLayers = fn["nOctaveLayers"];
|
|
|
|
int firstOctave = fn["firstOctave"];
|
|
|
|
int angleMode = fn["angleMode"];
|
|
|
|
|
|
|
|
sift = SIFT(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode);
|
|
|
|
}
|
|
|
|
|
|
|
|
void SiftFeatureDetector::write (FileStorage& fs) const
|
|
|
|
{
|
|
|
|
//fs << "algorithm" << getAlgorithmName ();
|
|
|
|
|
|
|
|
SIFT::CommonParams commParams = sift.getCommonParams ();
|
|
|
|
SIFT::DetectorParams detectorParams = sift.getDetectorParams ();
|
|
|
|
fs << "threshold" << detectorParams.threshold;
|
|
|
|
fs << "edgeThreshold" << detectorParams.edgeThreshold;
|
|
|
|
fs << "nOctaves" << commParams.nOctaves;
|
|
|
|
fs << "nOctaveLayers" << commParams.nOctaveLayers;
|
|
|
|
fs << "firstOctave" << commParams.firstOctave;
|
|
|
|
fs << "angleMode" << commParams.angleMode;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void SiftFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
2010-09-23 18:53:36 +08:00
|
|
|
Mat grayImage = image;
|
|
|
|
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
|
|
|
|
|
|
|
|
sift(grayImage, mask, keypoints);
|
2010-05-12 01:44:00 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
2010-08-04 00:28:52 +08:00
|
|
|
* SurfFeatureDetector
|
|
|
|
*/
|
2010-05-12 01:44:00 +08:00
|
|
|
SurfFeatureDetector::SurfFeatureDetector( double hessianThreshold, int octaves, int octaveLayers)
|
|
|
|
: surf(hessianThreshold, octaves, octaveLayers)
|
|
|
|
{}
|
|
|
|
|
2010-06-04 13:30:09 +08:00
|
|
|
void SurfFeatureDetector::read (const FileNode& fn)
|
|
|
|
{
|
|
|
|
double hessianThreshold = fn["hessianThreshold"];
|
|
|
|
int octaves = fn["octaves"];
|
|
|
|
int octaveLayers = fn["octaveLayers"];
|
|
|
|
|
|
|
|
surf = SURF( hessianThreshold, octaves, octaveLayers );
|
|
|
|
}
|
|
|
|
|
|
|
|
void SurfFeatureDetector::write (FileStorage& fs) const
|
|
|
|
{
|
|
|
|
//fs << "algorithm" << getAlgorithmName ();
|
|
|
|
|
|
|
|
fs << "hessianThreshold" << surf.hessianThreshold;
|
|
|
|
fs << "octaves" << surf.nOctaves;
|
|
|
|
fs << "octaveLayers" << surf.nOctaveLayers;
|
|
|
|
}
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void SurfFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
|
2010-05-12 01:44:00 +08:00
|
|
|
{
|
2010-09-23 18:53:36 +08:00
|
|
|
Mat grayImage = image;
|
|
|
|
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
|
|
|
|
|
|
|
|
surf(grayImage, mask, keypoints);
|
2010-05-12 01:44:00 +08:00
|
|
|
}
|
2010-06-12 02:44:22 +08:00
|
|
|
|
2010-09-25 00:55:12 +08:00
|
|
|
/*
|
2010-09-30 22:21:22 +08:00
|
|
|
* DenseFeatureDetector
|
2010-09-25 00:55:12 +08:00
|
|
|
*/
|
2010-11-23 02:27:08 +08:00
|
|
|
DenseFeatureDetector::Params::Params( float _initFeatureScale, int _featureScaleLevels,
|
|
|
|
float _featureScaleMul, int _initXyStep,
|
|
|
|
int _initImgBound, bool _varyXyStepWithScale,
|
|
|
|
bool _varyImgBoundWithScale ) :
|
|
|
|
initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
|
|
|
|
featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
|
|
|
|
varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
|
|
|
|
{}
|
|
|
|
|
|
|
|
DenseFeatureDetector::DenseFeatureDetector(const DenseFeatureDetector::Params &_params) : params(_params)
|
|
|
|
{}
|
2010-09-25 00:55:12 +08:00
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
|
|
|
|
{
|
|
|
|
float curScale = params.initFeatureScale;
|
|
|
|
int curStep = params.initXyStep;
|
|
|
|
int curBound = params.initImgBound;
|
|
|
|
for( int curLevel = 0; curLevel < params.featureScaleLevels; curLevel++ )
|
2010-06-12 02:44:22 +08:00
|
|
|
{
|
2010-09-25 00:55:12 +08:00
|
|
|
for( int x = curBound; x < image.cols - curBound; x += curStep )
|
|
|
|
{
|
|
|
|
for( int y = curBound; y < image.rows - curBound; y += curStep )
|
|
|
|
{
|
|
|
|
keypoints.push_back( KeyPoint(static_cast<float>(x), static_cast<float>(y), curScale) );
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
curScale = curScale * params.featureScaleMul;
|
|
|
|
if( params.varyXyStepWithScale ) curStep = static_cast<int>( curStep * params.featureScaleMul + 0.5f );
|
|
|
|
if( params.varyImgBoundWithScale ) curBound = static_cast<int>( curBound * params.featureScaleMul + 0.5f );
|
2010-06-12 02:44:22 +08:00
|
|
|
}
|
2010-09-25 00:55:12 +08:00
|
|
|
|
|
|
|
removeInvalidPoints( mask, keypoints );
|
2010-06-12 02:44:22 +08:00
|
|
|
}
|
|
|
|
|
2010-08-04 00:28:52 +08:00
|
|
|
/*
|
|
|
|
* GridAdaptedFeatureDetector
|
|
|
|
*/
|
|
|
|
GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector,
|
|
|
|
int _maxTotalKeypoints, int _gridRows, int _gridCols )
|
|
|
|
: detector(_detector), maxTotalKeypoints(_maxTotalKeypoints), gridRows(_gridRows), gridCols(_gridCols)
|
|
|
|
{}
|
|
|
|
|
|
|
|
struct ResponseComparator
|
|
|
|
{
|
|
|
|
bool operator() (const KeyPoint& a, const KeyPoint& b)
|
|
|
|
{
|
|
|
|
return std::abs(a.response) > std::abs(b.response);
|
|
|
|
}
|
|
|
|
};
|
|
|
|
|
|
|
|
void keepStrongest( int N, vector<KeyPoint>& keypoints )
|
|
|
|
{
|
|
|
|
if( (int)keypoints.size() > N )
|
|
|
|
{
|
|
|
|
vector<KeyPoint>::iterator nth = keypoints.begin() + N;
|
|
|
|
std::nth_element( keypoints.begin(), nth, keypoints.end(), ResponseComparator() );
|
|
|
|
keypoints.erase( nth, keypoints.end() );
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
|
2010-08-04 00:28:52 +08:00
|
|
|
{
|
|
|
|
keypoints.reserve(maxTotalKeypoints);
|
|
|
|
|
|
|
|
int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
|
|
|
|
for( int i = 0; i < gridRows; ++i )
|
|
|
|
{
|
|
|
|
Range row_range((i*image.rows)/gridRows, ((i+1)*image.rows)/gridRows);
|
|
|
|
for( int j = 0; j < gridCols; ++j )
|
|
|
|
{
|
|
|
|
Range col_range((j*image.cols)/gridCols, ((j+1)*image.cols)/gridCols);
|
|
|
|
Mat sub_image = image(row_range, col_range);
|
|
|
|
Mat sub_mask;
|
|
|
|
if( !mask.empty() )
|
|
|
|
sub_mask = mask(row_range, col_range);
|
|
|
|
|
|
|
|
vector<KeyPoint> sub_keypoints;
|
|
|
|
detector->detect( sub_image, sub_keypoints, sub_mask );
|
|
|
|
keepStrongest( maxPerCell, sub_keypoints );
|
|
|
|
for( std::vector<cv::KeyPoint>::iterator it = sub_keypoints.begin(), end = sub_keypoints.end();
|
|
|
|
it != end; ++it )
|
|
|
|
{
|
|
|
|
it->pt.x += col_range.start;
|
|
|
|
it->pt.y += row_range.start;
|
|
|
|
}
|
|
|
|
|
|
|
|
keypoints.insert( keypoints.end(), sub_keypoints.begin(), sub_keypoints.end() );
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
2010-11-03 18:00:24 +08:00
|
|
|
* PyramidAdaptedFeatureDetector
|
2010-08-04 00:28:52 +08:00
|
|
|
*/
|
|
|
|
PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _levels )
|
|
|
|
: detector(_detector), levels(_levels)
|
|
|
|
{}
|
|
|
|
|
2010-11-23 02:27:08 +08:00
|
|
|
void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
|
2010-08-04 00:28:52 +08:00
|
|
|
{
|
|
|
|
Mat src = image;
|
|
|
|
for( int l = 0, multiplier = 1; l <= levels; ++l, multiplier *= 2 )
|
|
|
|
{
|
|
|
|
// Detect on current level of the pyramid
|
|
|
|
vector<KeyPoint> new_pts;
|
|
|
|
detector->detect(src, new_pts);
|
|
|
|
for( vector<KeyPoint>::iterator it = new_pts.begin(), end = new_pts.end(); it != end; ++it)
|
|
|
|
{
|
|
|
|
it->pt.x *= multiplier;
|
|
|
|
it->pt.y *= multiplier;
|
|
|
|
it->size *= multiplier;
|
|
|
|
it->octave = l;
|
|
|
|
}
|
|
|
|
removeInvalidPoints( mask, new_pts );
|
|
|
|
keypoints.insert( keypoints.end(), new_pts.begin(), new_pts.end() );
|
|
|
|
|
|
|
|
// Downsample
|
|
|
|
if( l < levels )
|
|
|
|
{
|
|
|
|
Mat dst;
|
|
|
|
pyrDown(src, dst);
|
|
|
|
src = dst;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2010-09-25 00:55:12 +08:00
|
|
|
Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
|
|
|
|
{
|
|
|
|
FeatureDetector* fd = 0;
|
|
|
|
if( !detectorType.compare( "FAST" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
fd = new FastFeatureDetector();
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
2010-11-23 07:59:25 +08:00
|
|
|
else if( !detectorType.compare( "DynamicFAST" ) )
|
|
|
|
{
|
|
|
|
fd = new FASTDynamicDetector(400,500,5);
|
|
|
|
}
|
2010-09-25 00:55:12 +08:00
|
|
|
else if( !detectorType.compare( "STAR" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
fd = new StarFeatureDetector();
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
2010-11-23 07:59:25 +08:00
|
|
|
else if( !detectorType.compare( "DynamicSTAR" ) )
|
|
|
|
{
|
|
|
|
fd = new StarDynamicDetector(400,500,5);
|
|
|
|
}
|
2010-09-25 00:55:12 +08:00
|
|
|
else if( !detectorType.compare( "SIFT" ) )
|
|
|
|
{
|
|
|
|
fd = new SiftFeatureDetector(SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
|
|
|
|
SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD());
|
|
|
|
}
|
|
|
|
else if( !detectorType.compare( "SURF" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
fd = new SurfFeatureDetector();
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
2010-11-23 07:59:25 +08:00
|
|
|
else if( !detectorType.compare( "DynamicSURF" ) )
|
|
|
|
{
|
|
|
|
fd = new SurfDynamicDetector(400,500,5);
|
|
|
|
}
|
2010-09-25 00:55:12 +08:00
|
|
|
else if( !detectorType.compare( "MSER" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
fd = new MserFeatureDetector();
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
|
|
|
else if( !detectorType.compare( "GFTT" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
fd = new GoodFeaturesToTrackDetector();
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
|
|
|
else if( !detectorType.compare( "HARRIS" ) )
|
|
|
|
{
|
2010-11-23 02:27:08 +08:00
|
|
|
GoodFeaturesToTrackDetector::Params params;
|
|
|
|
params.useHarrisDetector = true;
|
|
|
|
fd = new GoodFeaturesToTrackDetector(params);
|
2010-09-25 00:55:12 +08:00
|
|
|
}
|
|
|
|
return fd;
|
|
|
|
}
|
|
|
|
|
2010-06-12 02:44:22 +08:00
|
|
|
}
|