split descriptors.cpp
This commit is contained in:
Maria Dimashova
790
modules/features2d/src/matchers.cpp
Executable file
790
modules/features2d/src/matchers.cpp
Executable file
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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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#ifdef HAVE_EIGEN2
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#include <Eigen/Array>
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#endif
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using namespace std;
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namespace cv
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{
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Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
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float maxDeltaX, float maxDeltaY )
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{
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if( keypoints1.empty() || keypoints2.empty() )
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return Mat();
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Mat mask( keypoints1.size(), keypoints2.size(), CV_8UC1 );
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for( size_t i = 0; i < keypoints1.size(); i++ )
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{
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for( size_t j = 0; j < keypoints2.size(); j++ )
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{
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Point2f diff = keypoints2[j].pt - keypoints1[i].pt;
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mask.at<uchar>(i, j) = std::abs(diff.x) < maxDeltaX && std::abs(diff.y) < maxDeltaY;
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}
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}
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return mask;
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}
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/****************************************************************************************\
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* DescriptorMatcher *
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\****************************************************************************************/
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void DescriptorMatcher::add( const Mat& descriptors )
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{
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if( m_train.empty() )
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{
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m_train = descriptors;
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}
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else
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{
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// merge train and descriptors
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Mat m( m_train.rows + descriptors.rows, m_train.cols, CV_32F );
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Mat m1 = m.rowRange( 0, m_train.rows );
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m_train.copyTo( m1 );
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Mat m2 = m.rowRange( m_train.rows + 1, m.rows );
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descriptors.copyTo( m2 );
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m_train = m;
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}
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}
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void DescriptorMatcher::match( const Mat& query, vector<int>& matches ) const
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{
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matchImpl( query, m_train, matches, Mat() );
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}
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void DescriptorMatcher::match( const Mat& query, const Mat& mask,
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vector<int>& matches ) const
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{
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matchImpl( query, m_train, matches, mask );
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}
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void DescriptorMatcher::match( const Mat& query, vector<DMatch>& matches ) const
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{
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matchImpl( query, m_train, matches, Mat() );
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}
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void DescriptorMatcher::match( const Mat& query, const Mat& mask,
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vector<DMatch>& matches ) const
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{
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matchImpl( query, m_train, matches, mask );
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}
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void DescriptorMatcher::match( const Mat& query, const Mat& train, vector<DMatch>& matches, const Mat& mask ) const
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{
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matchImpl( query, train, matches, mask );
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}
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void DescriptorMatcher::match( const Mat& query, vector<vector<DMatch> >& matches, float threshold ) const
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{
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matchImpl( query, m_train, matches, threshold, Mat() );
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}
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void DescriptorMatcher::match( const Mat& query, const Mat& mask,
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vector<vector<DMatch> >& matches, float threshold ) const
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{
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matchImpl( query, m_train, matches, threshold, mask );
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}
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void DescriptorMatcher::clear()
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{
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m_train.release();
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}
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/*
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* BruteForceMatcher L2 specialization
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*/
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template<>
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void BruteForceMatcher<L2<float> >::matchImpl( const Mat& query, const Mat& train, vector<DMatch>& matches, const Mat& mask ) const
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{
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assert( mask.empty() || (mask.rows == query.rows && mask.cols == train.rows) );
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assert( query.cols == train.cols || query.empty() || train.empty() );
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matches.clear();
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matches.reserve( query.rows );
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#if (!defined HAVE_EIGEN2)
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Mat norms;
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cv::reduce( train.mul( train ), norms, 1, 0);
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norms = norms.t();
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Mat desc_2t = train.t();
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for( int i=0;i<query.rows;i++ )
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{
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Mat distances = (-2)*query.row(i)*desc_2t;
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distances += norms;
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DMatch match;
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match.indexTrain = -1;
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double minVal;
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Point minLoc;
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if( mask.empty() )
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{
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minMaxLoc ( distances, &minVal, 0, &minLoc );
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}
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else
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{
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minMaxLoc ( distances, &minVal, 0, &minLoc, 0, mask.row( i ) );
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}
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match.indexTrain = minLoc.x;
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if( match.indexTrain != -1 )
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{
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match.indexQuery = i;
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double queryNorm = norm( query.row(i) );
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match.distance = (float)sqrt( minVal + queryNorm*queryNorm );
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matches.push_back( match );
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}
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}
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#else
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Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> desc1t;
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Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> desc2;
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cv2eigen( query.t(), desc1t);
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cv2eigen( train, desc2 );
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Eigen::Matrix<float, Eigen::Dynamic, 1> norms = desc2.rowwise().squaredNorm() / 2;
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if( mask.empty() )
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{
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for( int i=0;i<query.rows;i++ )
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{
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Eigen::Matrix<float, Eigen::Dynamic, 1> distances = desc2*desc1t.col(i);
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distances -= norms;
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DMatch match;
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match.indexQuery = i;
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match.distance = sqrt( (-2)*distances.maxCoeff( &match.indexTrain ) + desc1t.col(i).squaredNorm() );
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matches.push_back( match );
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}
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}
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else
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{
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for( int i=0;i<query.rows;i++ )
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{
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Eigen::Matrix<float, Eigen::Dynamic, 1> distances = desc2*desc1t.col(i);
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distances -= norms;
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float maxCoeff = -std::numeric_limits<float>::max();
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DMatch match;
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match.indexTrain = -1;
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for( int j=0;j<train.rows;j++ )
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{
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if( possibleMatch( mask, i, j ) && distances( j, 0 ) > maxCoeff )
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{
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maxCoeff = distances( j, 0 );
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match.indexTrain = j;
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}
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}
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if( match.indexTrain != -1 )
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{
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match.indexQuery = i;
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match.distance = sqrt( (-2)*maxCoeff + desc1t.col(i).squaredNorm() );
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matches.push_back( match );
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}
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}
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}
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#endif
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}
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/****************************************************************************************\
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* Factory function for descriptor matcher creating *
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\****************************************************************************************/
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Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType )
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{
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DescriptorMatcher* dm = 0;
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if( !descriptorMatcherType.compare( "BruteForce" ) )
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{
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dm = new BruteForceMatcher<L2<float> >();
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}
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else if( !descriptorMatcherType.compare( "BruteForce-L1" ) )
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{
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dm = new BruteForceMatcher<L1<float> >();
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}
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return dm;
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}
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/****************************************************************************************\
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* GenericDescriptorMatch *
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\****************************************************************************************/
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/*
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* KeyPointCollection
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*/
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void KeyPointCollection::add( const Mat& _image, const vector<KeyPoint>& _points )
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{
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// update m_start_indices
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if( startIndices.empty() )
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startIndices.push_back(0);
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else
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startIndices.push_back((int)(*startIndices.rbegin() + points.rbegin()->size()));
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// add image and keypoints
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images.push_back(_image);
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points.push_back(_points);
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}
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KeyPoint KeyPointCollection::getKeyPoint( int index ) const
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{
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size_t i = 0;
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for(; i < startIndices.size() && startIndices[i] <= index; i++);
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i--;
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assert(i < startIndices.size() && (size_t)index - startIndices[i] < points[i].size());
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return points[i][index - startIndices[i]];
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}
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size_t KeyPointCollection::calcKeypointCount() const
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{
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if( startIndices.empty() )
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return 0;
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return *startIndices.rbegin() + points.rbegin()->size();
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}
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void KeyPointCollection::clear()
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{
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images.clear();
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points.clear();
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startIndices.clear();
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}
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/*
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* GenericDescriptorMatch
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*/
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void GenericDescriptorMatch::match( const Mat&, vector<KeyPoint>&, vector<DMatch>& )
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{
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}
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void GenericDescriptorMatch::match( const Mat&, vector<KeyPoint>&, vector<vector<DMatch> >&, float )
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{
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}
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void GenericDescriptorMatch::add( KeyPointCollection& collection )
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{
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for( size_t i = 0; i < collection.images.size(); i++ )
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add( collection.images[i], collection.points[i] );
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}
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void GenericDescriptorMatch::classify( const Mat& image, vector<cv::KeyPoint>& points )
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{
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vector<int> keypointIndices;
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match( image, points, keypointIndices );
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// remap keypoint indices to descriptors
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for( size_t i = 0; i < keypointIndices.size(); i++ )
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points[i].class_id = collection.getKeyPoint(keypointIndices[i]).class_id;
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};
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void GenericDescriptorMatch::clear()
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{
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collection.clear();
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}
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/****************************************************************************************\
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* OneWayDescriptorMatch *
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\****************************************************************************************/
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OneWayDescriptorMatch::OneWayDescriptorMatch()
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{}
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OneWayDescriptorMatch::OneWayDescriptorMatch( const Params& _params)
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{
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initialize(_params);
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}
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OneWayDescriptorMatch::~OneWayDescriptorMatch()
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{}
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void OneWayDescriptorMatch::initialize( const Params& _params, OneWayDescriptorBase *_base)
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{
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base.release();
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if (_base != 0)
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{
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base = _base;
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}
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params = _params;
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}
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void OneWayDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
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{
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if( base.empty() )
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base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
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params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
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size_t trainFeatureCount = keypoints.size();
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base->Allocate( (int)trainFeatureCount );
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IplImage _image = image;
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for( size_t i = 0; i < keypoints.size(); i++ )
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base->InitializeDescriptor( (int)i, &_image, keypoints[i], "" );
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collection.add( Mat(), keypoints );
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#if defined(_KDTREE)
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base->ConvertDescriptorsArrayToTree();
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#endif
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}
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void OneWayDescriptorMatch::add( KeyPointCollection& keypoints )
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{
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if( base.empty() )
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base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
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params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale);
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size_t trainFeatureCount = keypoints.calcKeypointCount();
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base->Allocate( (int)trainFeatureCount );
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int count = 0;
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for( size_t i = 0; i < keypoints.points.size(); i++ )
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{
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for( size_t j = 0; j < keypoints.points[i].size(); j++ )
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{
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IplImage img = keypoints.images[i];
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base->InitializeDescriptor( count++, &img, keypoints.points[i][j], "" );
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}
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collection.add( Mat(), keypoints.points[i] );
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}
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#if defined(_KDTREE)
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base->ConvertDescriptorsArrayToTree();
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#endif
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}
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void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<int>& indices)
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{
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vector<DMatch> matchings( points.size() );
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indices.resize(points.size());
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match( image, points, matchings );
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for( size_t i = 0; i < points.size(); i++ )
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indices[i] = matchings[i].indexTrain;
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}
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void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
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{
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matches.resize( points.size() );
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IplImage _image = image;
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for( size_t i = 0; i < points.size(); i++ )
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{
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int poseIdx = -1;
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DMatch match;
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match.indexQuery = (int)i;
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match.indexTrain = -1;
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base->FindDescriptor( &_image, points[i].pt, match.indexTrain, poseIdx, match.distance );
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matches[i] = match;
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}
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}
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void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<vector<DMatch> >& matches, float /*threshold*/ )
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{
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matches.clear();
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matches.resize( points.size() );
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vector<DMatch> dmatches;
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match( image, points, dmatches );
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for( size_t i=0;i<matches.size();i++ )
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{
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matches[i].push_back( dmatches[i] );
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}
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/*
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printf("Start matching %d points\n", points.size());
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//std::cout << "Start matching " << points.size() << "points\n";
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assert(collection.images.size() == 1);
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int n = collection.points[0].size();
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printf("n = %d\n", n);
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for( size_t i = 0; i < points.size(); i++ )
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{
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//printf("Matching %d\n", i);
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//int poseIdx = -1;
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DMatch match;
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match.indexQuery = i;
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match.indexTrain = -1;
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CvPoint pt = points[i].pt;
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CvRect roi = cvRect(cvRound(pt.x - 24/4),
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cvRound(pt.y - 24/4),
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24/2, 24/2);
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cvSetImageROI(&_image, roi);
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std::vector<int> desc_idxs;
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std::vector<int> pose_idxs;
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std::vector<float> distances;
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std::vector<float> _scales;
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base->FindDescriptor(&_image, n, desc_idxs, pose_idxs, distances, _scales);
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cvResetImageROI(&_image);
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for( int j=0;j<n;j++ )
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{
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match.indexTrain = desc_idxs[j];
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match.distance = distances[j];
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matches[i].push_back( match );
|
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}
|
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//sort( matches[i].begin(), matches[i].end(), compareIndexTrain );
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//for( int j=0;j<n;j++ )
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//{
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||||
//printf( "%d %f; ",matches[i][j].indexTrain, matches[i][j].distance);
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||||
//}
|
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//printf("\n\n\n");
|
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//base->FindDescriptor( &_image, 100, points[i].pt, match.indexTrain, poseIdx, match.distance );
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||||
//matches[i].push_back( match );
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||||
}
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||||
*/
|
||||
}
|
||||
|
||||
|
||||
void OneWayDescriptorMatch::read( const FileNode &fn )
|
||||
{
|
||||
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, string (), string (), string (),
|
||||
params.minScale, params.maxScale, params.stepScale );
|
||||
base->Read (fn);
|
||||
}
|
||||
|
||||
|
||||
void OneWayDescriptorMatch::write( FileStorage& fs ) const
|
||||
{
|
||||
base->Write (fs);
|
||||
}
|
||||
|
||||
void OneWayDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& points )
|
||||
{
|
||||
IplImage _image = image;
|
||||
for( size_t i = 0; i < points.size(); i++ )
|
||||
{
|
||||
int descIdx = -1;
|
||||
int poseIdx = -1;
|
||||
float distance;
|
||||
base->FindDescriptor(&_image, points[i].pt, descIdx, poseIdx, distance);
|
||||
points[i].class_id = collection.getKeyPoint(descIdx).class_id;
|
||||
}
|
||||
}
|
||||
|
||||
void OneWayDescriptorMatch::clear ()
|
||||
{
|
||||
GenericDescriptorMatch::clear();
|
||||
base->clear ();
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* FernDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
FernDescriptorMatch::Params::Params( int _nclasses, int _patchSize, int _signatureSize,
|
||||
int _nstructs, int _structSize, int _nviews, int _compressionMethod,
|
||||
const PatchGenerator& _patchGenerator ) :
|
||||
nclasses(_nclasses), patchSize(_patchSize), signatureSize(_signatureSize),
|
||||
nstructs(_nstructs), structSize(_structSize), nviews(_nviews),
|
||||
compressionMethod(_compressionMethod), patchGenerator(_patchGenerator)
|
||||
{}
|
||||
|
||||
FernDescriptorMatch::Params::Params( const string& _filename )
|
||||
{
|
||||
filename = _filename;
|
||||
}
|
||||
|
||||
FernDescriptorMatch::FernDescriptorMatch()
|
||||
{}
|
||||
|
||||
FernDescriptorMatch::FernDescriptorMatch( const Params& _params )
|
||||
{
|
||||
params = _params;
|
||||
}
|
||||
|
||||
FernDescriptorMatch::~FernDescriptorMatch()
|
||||
{}
|
||||
|
||||
void FernDescriptorMatch::initialize( const Params& _params )
|
||||
{
|
||||
classifier.release();
|
||||
params = _params;
|
||||
if( !params.filename.empty() )
|
||||
{
|
||||
classifier = new FernClassifier;
|
||||
FileStorage fs(params.filename, FileStorage::READ);
|
||||
if( fs.isOpened() )
|
||||
classifier->read( fs.getFirstTopLevelNode() );
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
|
||||
{
|
||||
if( params.filename.empty() )
|
||||
collection.add( image, keypoints );
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::trainFernClassifier()
|
||||
{
|
||||
if( classifier.empty() )
|
||||
{
|
||||
assert( params.filename.empty() );
|
||||
|
||||
vector<vector<Point2f> > points;
|
||||
for( size_t imgIdx = 0; imgIdx < collection.images.size(); imgIdx++ )
|
||||
KeyPoint::convert( collection.points[imgIdx], points[imgIdx] );
|
||||
|
||||
classifier = new FernClassifier( points, collection.images, vector<vector<int> >(), 0, // each points is a class
|
||||
params.patchSize, params.signatureSize, params.nstructs, params.structSize,
|
||||
params.nviews, params.compressionMethod, params.patchGenerator );
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
|
||||
float& bestProb, int& bestMatchIdx, vector<float>& signature )
|
||||
{
|
||||
(*classifier)( image, pt, signature);
|
||||
|
||||
bestProb = -FLT_MAX;
|
||||
bestMatchIdx = -1;
|
||||
for( int ci = 0; ci < classifier->getClassCount(); ci++ )
|
||||
{
|
||||
if( signature[ci] > bestProb )
|
||||
{
|
||||
bestProb = signature[ci];
|
||||
bestMatchIdx = ci;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices )
|
||||
{
|
||||
trainFernClassifier();
|
||||
|
||||
indices.resize( keypoints.size() );
|
||||
vector<float> signature( (size_t)classifier->getClassCount() );
|
||||
|
||||
for( size_t pi = 0; pi < keypoints.size(); pi++ )
|
||||
{
|
||||
//calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, indices[pi], signature );
|
||||
//TODO: use octave and image pyramid
|
||||
indices[pi] = (*classifier)(image, keypoints[pi].pt, signature);
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<DMatch>& matches )
|
||||
{
|
||||
trainFernClassifier();
|
||||
|
||||
matches.resize( keypoints.size() );
|
||||
vector<float> signature( (size_t)classifier->getClassCount() );
|
||||
|
||||
for( int pi = 0; pi < (int)keypoints.size(); pi++ )
|
||||
{
|
||||
matches[pi].indexQuery = pi;
|
||||
calcBestProbAndMatchIdx( image, keypoints[pi].pt, matches[pi].distance, matches[pi].indexTrain, signature );
|
||||
//matching[pi].distance is log of probability so we need to transform it
|
||||
matches[pi].distance = -matches[pi].distance;
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<vector<DMatch> >& matches, float threshold )
|
||||
{
|
||||
trainFernClassifier();
|
||||
|
||||
matches.resize( keypoints.size() );
|
||||
vector<float> signature( (size_t)classifier->getClassCount() );
|
||||
|
||||
for( int pi = 0; pi < (int)keypoints.size(); pi++ )
|
||||
{
|
||||
(*classifier)( image, keypoints[pi].pt, signature);
|
||||
|
||||
DMatch match;
|
||||
match.indexQuery = pi;
|
||||
|
||||
for( int ci = 0; ci < classifier->getClassCount(); ci++ )
|
||||
{
|
||||
if( -signature[ci] < threshold )
|
||||
{
|
||||
match.distance = -signature[ci];
|
||||
match.indexTrain = ci;
|
||||
matches[pi].push_back( match );
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& keypoints )
|
||||
{
|
||||
trainFernClassifier();
|
||||
|
||||
vector<float> signature( (size_t)classifier->getClassCount() );
|
||||
for( size_t pi = 0; pi < keypoints.size(); pi++ )
|
||||
{
|
||||
float bestProb = 0;
|
||||
int bestMatchIdx = -1;
|
||||
calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, bestMatchIdx, signature );
|
||||
keypoints[pi].class_id = collection.getKeyPoint(bestMatchIdx).class_id;
|
||||
}
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::read( const FileNode &fn )
|
||||
{
|
||||
params.nclasses = fn["nclasses"];
|
||||
params.patchSize = fn["patchSize"];
|
||||
params.signatureSize = fn["signatureSize"];
|
||||
params.nstructs = fn["nstructs"];
|
||||
params.structSize = fn["structSize"];
|
||||
params.nviews = fn["nviews"];
|
||||
params.compressionMethod = fn["compressionMethod"];
|
||||
|
||||
//classifier->read(fn);
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::write( FileStorage& fs ) const
|
||||
{
|
||||
fs << "nclasses" << params.nclasses;
|
||||
fs << "patchSize" << params.patchSize;
|
||||
fs << "signatureSize" << params.signatureSize;
|
||||
fs << "nstructs" << params.nstructs;
|
||||
fs << "structSize" << params.structSize;
|
||||
fs << "nviews" << params.nviews;
|
||||
fs << "compressionMethod" << params.compressionMethod;
|
||||
|
||||
// classifier->write(fs);
|
||||
}
|
||||
|
||||
void FernDescriptorMatch::clear ()
|
||||
{
|
||||
GenericDescriptorMatch::clear();
|
||||
classifier.release();
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* VectorDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
void VectorDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, keypoints, descriptors );
|
||||
matcher->add( descriptors );
|
||||
|
||||
collection.add( Mat(), keypoints );
|
||||
};
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<int>& keypointIndices )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, keypointIndices );
|
||||
};
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, matches );
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points,
|
||||
vector<vector<DMatch> >& matches, float threshold )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, matches, threshold );
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::clear()
|
||||
{
|
||||
GenericDescriptorMatch::clear();
|
||||
matcher->clear();
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::read( const FileNode& fn )
|
||||
{
|
||||
GenericDescriptorMatch::read(fn);
|
||||
extractor->read (fn);
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::write (FileStorage& fs) const
|
||||
{
|
||||
GenericDescriptorMatch::write(fs);
|
||||
extractor->write (fs);
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* Factory function for GenericDescriptorMatch creating *
|
||||
\****************************************************************************************/
|
||||
Ptr<GenericDescriptorMatch> createGenericDescriptorMatch( const string& genericDescritptorMatchType,
|
||||
const string ¶msFilename )
|
||||
{
|
||||
GenericDescriptorMatch *descriptorMatch = 0;
|
||||
if( ! genericDescritptorMatchType.compare("ONEWAY") )
|
||||
{
|
||||
descriptorMatch = new OneWayDescriptorMatch();
|
||||
}
|
||||
else if( ! genericDescritptorMatchType.compare("FERN") )
|
||||
{
|
||||
descriptorMatch = new FernDescriptorMatch();
|
||||
}
|
||||
else if( ! genericDescritptorMatchType.compare ("CALONDER") )
|
||||
{
|
||||
//descriptorMatch = new CalonderDescriptorMatch ();
|
||||
}
|
||||
|
||||
if( !paramsFilename.empty() && descriptorMatch != 0 )
|
||||
{
|
||||
FileStorage fs = FileStorage( paramsFilename, FileStorage::READ );
|
||||
if( fs.isOpened() )
|
||||
{
|
||||
descriptorMatch->read( fs.root() );
|
||||
fs.release();
|
||||
}
|
||||
}
|
||||
|
||||
return descriptorMatch;
|
||||
}
|
||||
|
||||
}
|
||||
Reference in New Issue
Block a user