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490 lines
17 KiB
C++
490 lines
17 KiB
C++
/*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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namespace cv
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{
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/*
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* FeatureDetector
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*/
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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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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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/*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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bool FeatureDetector::empty() const
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{
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return false;
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}
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void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints )
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{
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
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}
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Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
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{
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if( detectorType.find("Grid") == 0 )
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{
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return new GridAdaptedFeatureDetector(FeatureDetector::create(
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detectorType.substr(strlen("Grid"))));
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}
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if( detectorType.find("Pyramid") == 0 )
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{
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return new PyramidAdaptedFeatureDetector(FeatureDetector::create(
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detectorType.substr(strlen("Pyramid"))));
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}
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if( detectorType.find("Dynamic") == 0 )
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{
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return new DynamicAdaptedFeatureDetector(AdjusterAdapter::create(
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detectorType.substr(strlen("Dynamic"))));
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}
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if( detectorType.compare( "HARRIS" ) == 0 )
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{
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Ptr<FeatureDetector> fd = FeatureDetector::create("GFTT");
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fd->set("useHarrisDetector", true);
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return fd;
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}
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return Algorithm::create<FeatureDetector>("Feature2D." + detectorType);
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}
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GFTTDetector::GFTTDetector( int _nfeatures, double _qualityLevel,
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double _minDistance, int _blockSize,
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bool _useHarrisDetector, double _k )
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: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
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blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
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{
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}
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void GFTTDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
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{
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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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vector<Point2f> corners;
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goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, mask,
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blockSize, useHarrisDetector, k );
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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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*keypoint_it = KeyPoint( *corner_it, (float)blockSize );
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}
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}
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static Algorithm* createGFTT() { return new GFTTDetector; }
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static Algorithm* createHarris()
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{
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GFTTDetector* d = new GFTTDetector;
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d->set("useHarris", true);
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return d;
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}
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static AlgorithmInfo gftt_info("Feature2D.GFTT", createGFTT);
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static AlgorithmInfo harris_info("Feature2D.HARRIS", createHarris);
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AlgorithmInfo* GFTTDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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GFTTDetector obj;
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gftt_info.addParam(obj, "nfeatures", obj.nfeatures);
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gftt_info.addParam(obj, "qualityLevel", obj.qualityLevel);
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gftt_info.addParam(obj, "minDistance", obj.minDistance);
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gftt_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
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gftt_info.addParam(obj, "k", obj.k);
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harris_info.addParam(obj, "nfeatures", obj.nfeatures);
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harris_info.addParam(obj, "qualityLevel", obj.qualityLevel);
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harris_info.addParam(obj, "minDistance", obj.minDistance);
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harris_info.addParam(obj, "useHarrisDetector", obj.useHarrisDetector);
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harris_info.addParam(obj, "k", obj.k);
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initialized = true;
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}
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return &gftt_info;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/*
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* DenseFeatureDetector
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*/
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DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels,
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float _featureScaleMul, int _initXyStep,
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int _initImgBound, bool _varyXyStepWithScale,
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bool _varyImgBoundWithScale ) :
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initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
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featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
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varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
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{}
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void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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{
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float curScale = initFeatureScale;
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int curStep = initXyStep;
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int curBound = initImgBound;
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for( int curLevel = 0; curLevel < featureScaleLevels; curLevel++ )
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{
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for( int x = curBound; x < image.cols - curBound; x += curStep )
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{
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for( int y = curBound; y < image.rows - curBound; y += curStep )
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{
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keypoints.push_back( KeyPoint(static_cast<float>(x), static_cast<float>(y), curScale) );
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}
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}
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curScale = curScale * featureScaleMul;
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if( varyXyStepWithScale ) curStep = static_cast<int>( curStep * featureScaleMul + 0.5f );
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if( varyImgBoundWithScale ) curBound = static_cast<int>( curBound * featureScaleMul + 0.5f );
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}
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
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}
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static Algorithm* createDense() { return new DenseFeatureDetector; }
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static AlgorithmInfo dense_info("Feature2D.Dense", createDense);
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AlgorithmInfo* DenseFeatureDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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DenseFeatureDetector obj;
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dense_info.addParam(obj, "initFeatureScale", obj.initFeatureScale);
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dense_info.addParam(obj, "featureScaleLevels", obj.featureScaleLevels);
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dense_info.addParam(obj, "featureScaleMul", obj.featureScaleMul);
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dense_info.addParam(obj, "initXyStep", obj.initXyStep);
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dense_info.addParam(obj, "initImgBound", obj.initImgBound);
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dense_info.addParam(obj, "varyXyStepWithScale", obj.varyXyStepWithScale);
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dense_info.addParam(obj, "varyImgBoundWithScale", obj.varyImgBoundWithScale);
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initialized = true;
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}
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return &dense_info;
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}
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/*
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* GridAdaptedFeatureDetector
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*/
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GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector,
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int _maxTotalKeypoints, int _gridRows, int _gridCols )
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: detector(_detector), maxTotalKeypoints(_maxTotalKeypoints), gridRows(_gridRows), gridCols(_gridCols)
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{}
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bool GridAdaptedFeatureDetector::empty() const
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{
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return detector.empty() || (FeatureDetector*)detector->empty();
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}
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struct ResponseComparator
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{
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bool operator() (const KeyPoint& a, const KeyPoint& b)
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{
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return std::abs(a.response) > std::abs(b.response);
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}
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};
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void keepStrongest( int N, vector<KeyPoint>& keypoints )
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{
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if( (int)keypoints.size() > N )
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{
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vector<KeyPoint>::iterator nth = keypoints.begin() + N;
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std::nth_element( keypoints.begin(), nth, keypoints.end(), ResponseComparator() );
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keypoints.erase( nth, keypoints.end() );
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}
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}
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void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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{
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keypoints.reserve(maxTotalKeypoints);
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int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
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for( int i = 0; i < gridRows; ++i )
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{
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Range row_range((i*image.rows)/gridRows, ((i+1)*image.rows)/gridRows);
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for( int j = 0; j < gridCols; ++j )
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{
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Range col_range((j*image.cols)/gridCols, ((j+1)*image.cols)/gridCols);
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Mat sub_image = image(row_range, col_range);
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Mat sub_mask;
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if( !mask.empty() )
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sub_mask = mask(row_range, col_range);
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vector<KeyPoint> sub_keypoints;
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detector->detect( sub_image, sub_keypoints, sub_mask );
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keepStrongest( maxPerCell, sub_keypoints );
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std::vector<cv::KeyPoint>::iterator it = sub_keypoints.begin(),
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end = sub_keypoints.end();
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for( ; it != end; ++it )
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{
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it->pt.x += col_range.start;
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it->pt.y += row_range.start;
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}
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keypoints.insert( keypoints.end(), sub_keypoints.begin(), sub_keypoints.end() );
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}
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}
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}
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/*
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* PyramidAdaptedFeatureDetector
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*/
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PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& _detector, int _maxLevel )
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: detector(_detector), maxLevel(_maxLevel)
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{}
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bool PyramidAdaptedFeatureDetector::empty() const
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{
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return detector.empty() || (FeatureDetector*)detector->empty();
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}
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void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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{
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Mat src = image;
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Mat src_mask = mask;
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Mat dilated_mask;
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if( !mask.empty() )
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{
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dilate( mask, dilated_mask, Mat() );
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Mat mask255( mask.size(), CV_8UC1, Scalar(0) );
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mask255.setTo( Scalar(255), dilated_mask != 0 );
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dilated_mask = mask255;
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}
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for( int l = 0, multiplier = 1; l <= maxLevel; ++l, multiplier *= 2 )
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{
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// Detect on current level of the pyramid
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vector<KeyPoint> new_pts;
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detector->detect( src, new_pts, src_mask );
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vector<KeyPoint>::iterator it = new_pts.begin(),
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end = new_pts.end();
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for( ; it != end; ++it)
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{
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it->pt.x *= multiplier;
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it->pt.y *= multiplier;
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it->size *= multiplier;
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it->octave = l;
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}
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keypoints.insert( keypoints.end(), new_pts.begin(), new_pts.end() );
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// Downsample
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if( l < maxLevel )
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{
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Mat dst;
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pyrDown( src, dst );
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src = dst;
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if( !mask.empty() )
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resize( dilated_mask, src_mask, src.size(), 0, 0, CV_INTER_AREA );
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}
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}
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if( !mask.empty() )
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
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}
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/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
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/* NOTE!!!
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All the AlgorithmInfo-related stuff should be in the same file as initModule_features2d().
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Otherwise, linker may throw away some seemingly unused stuff.
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*/
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static Algorithm* createBRIEF() { return new BriefDescriptorExtractor; }
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static AlgorithmInfo brief_info("Feature2D.BRIEF", createBRIEF);
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AlgorithmInfo* BriefDescriptorExtractor::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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BriefDescriptorExtractor brief;
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brief_info.addParam(brief, "bytes", brief.bytes_);
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initialized = true;
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}
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return &brief_info;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createFAST() { return new FastFeatureDetector; }
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static AlgorithmInfo fast_info("Feature2D.FAST", createFAST);
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AlgorithmInfo* FastFeatureDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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FastFeatureDetector obj;
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fast_info.addParam(obj, "threshold", obj.threshold);
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fast_info.addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
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initialized = true;
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}
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return &fast_info;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createStarDetector() { return new StarDetector; }
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static AlgorithmInfo star_info("Feature2D.STAR", createStarDetector);
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AlgorithmInfo* StarDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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StarDetector obj;
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star_info.addParam(obj, "maxSize", obj.maxSize);
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star_info.addParam(obj, "responseThreshold", obj.responseThreshold);
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star_info.addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
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star_info.addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
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star_info.addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize);
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initialized = true;
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}
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return &star_info;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createMSER() { return new MSER; }
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static AlgorithmInfo mser_info("Feature2D.MSER", createMSER);
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AlgorithmInfo* MSER::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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MSER obj;
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mser_info.addParam(obj, "delta", obj.delta);
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mser_info.addParam(obj, "minArea", obj.minArea);
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mser_info.addParam(obj, "maxArea", obj.maxArea);
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mser_info.addParam(obj, "maxVariation", obj.maxVariation);
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mser_info.addParam(obj, "minDiversity", obj.minDiversity);
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mser_info.addParam(obj, "maxEvolution", obj.maxEvolution);
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mser_info.addParam(obj, "areaThreshold", obj.areaThreshold);
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mser_info.addParam(obj, "minMargin", obj.minMargin);
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mser_info.addParam(obj, "edgeBlurSize", obj.edgeBlurSize);
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initialized = true;
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}
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return &mser_info;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createORB() { return new ORB; }
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static AlgorithmInfo orb_info("Feature2D.ORB", createORB);
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AlgorithmInfo* ORB::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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ORB obj;
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orb_info.addParam(obj, "nFeatures", obj.nfeatures);
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orb_info.addParam(obj, "scaleFactor", obj.scaleFactor);
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orb_info.addParam(obj, "nLevels", obj.nlevels);
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orb_info.addParam(obj, "firstLevel", obj.firstLevel);
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orb_info.addParam(obj, "edgeThreshold", obj.edgeThreshold);
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orb_info.addParam(obj, "patchSize", obj.patchSize);
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orb_info.addParam(obj, "WTA_K", obj.WTA_K);
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orb_info.addParam(obj, "scoreType", obj.scoreType);
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initialized = true;
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}
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return &orb_info;
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}
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bool initModule_features2d(void)
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{
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Ptr<Algorithm> brief = createBRIEF(), orb = createORB(),
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star = createStarDetector(), fastd = createFAST(), mser = createMSER();
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return brief->info() != 0 && orb->info() != 0 && star->info() != 0 &&
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fastd->info() != 0 && mser->info() != 0;
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}
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}
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