/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" namespace cv { BOWTrainer::BOWTrainer() : size(0) {} BOWTrainer::~BOWTrainer() {} void BOWTrainer::add( const Mat& _descriptors ) { CV_Assert( !_descriptors.empty() ); if( !descriptors.empty() ) { CV_Assert( descriptors[0].cols == _descriptors.cols ); CV_Assert( descriptors[0].type() == _descriptors.type() ); size += _descriptors.rows; } else { size = _descriptors.rows; } descriptors.push_back(_descriptors); } const std::vector& BOWTrainer::getDescriptors() const { return descriptors; } int BOWTrainer::descriptorsCount() const { return descriptors.empty() ? 0 : size; } void BOWTrainer::clear() { descriptors.clear(); } BOWKMeansTrainer::BOWKMeansTrainer( int _clusterCount, const TermCriteria& _termcrit, int _attempts, int _flags ) : clusterCount(_clusterCount), termcrit(_termcrit), attempts(_attempts), flags(_flags) {} Mat BOWKMeansTrainer::cluster() const { CV_INSTRUMENT_REGION(); CV_Assert( !descriptors.empty() ); Mat mergedDescriptors( descriptorsCount(), descriptors[0].cols, descriptors[0].type() ); for( size_t i = 0, start = 0; i < descriptors.size(); i++ ) { Mat submut = mergedDescriptors.rowRange((int)start, (int)(start + descriptors[i].rows)); descriptors[i].copyTo(submut); start += descriptors[i].rows; } return cluster( mergedDescriptors ); } BOWKMeansTrainer::~BOWKMeansTrainer() {} Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const { CV_INSTRUMENT_REGION(); Mat labels, vocabulary; kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary ); return vocabulary; } BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr& _dextractor, const Ptr& _dmatcher ) : dextractor(_dextractor), dmatcher(_dmatcher) {} BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr& _dmatcher ) : dmatcher(_dmatcher) {} BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor() {} void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary ) { dmatcher->clear(); vocabulary = _vocabulary; dmatcher->add( std::vector(1, vocabulary) ); } const Mat& BOWImgDescriptorExtractor::getVocabulary() const { return vocabulary; } void BOWImgDescriptorExtractor::compute( InputArray image, std::vector& keypoints, OutputArray imgDescriptor, std::vector >* pointIdxsOfClusters, Mat* descriptors ) { CV_INSTRUMENT_REGION(); imgDescriptor.release(); if( keypoints.empty() ) return; // Compute descriptors for the image. Mat _descriptors; dextractor->compute( image, keypoints, _descriptors ); compute( _descriptors, imgDescriptor, pointIdxsOfClusters ); // Add the descriptors of image keypoints if (descriptors) { *descriptors = _descriptors.clone(); } } int BOWImgDescriptorExtractor::descriptorSize() const { return vocabulary.empty() ? 0 : vocabulary.rows; } int BOWImgDescriptorExtractor::descriptorType() const { return CV_32FC1; } void BOWImgDescriptorExtractor::compute( InputArray keypointDescriptors, OutputArray _imgDescriptor, std::vector >* pointIdxsOfClusters ) { CV_INSTRUMENT_REGION(); CV_Assert( !vocabulary.empty() ); CV_Assert(!keypointDescriptors.empty()); int clusterCount = descriptorSize(); // = vocabulary.rows // Match keypoint descriptors to cluster center (to vocabulary) std::vector matches; dmatcher->match( keypointDescriptors, matches ); // Compute image descriptor if( pointIdxsOfClusters ) { pointIdxsOfClusters->clear(); pointIdxsOfClusters->resize(clusterCount); } _imgDescriptor.create(1, clusterCount, descriptorType()); _imgDescriptor.setTo(Scalar::all(0)); Mat imgDescriptor = _imgDescriptor.getMat(); float *dptr = imgDescriptor.ptr(); for( size_t i = 0; i < matches.size(); i++ ) { int queryIdx = matches[i].queryIdx; int trainIdx = matches[i].trainIdx; // cluster index CV_Assert( queryIdx == (int)i ); dptr[trainIdx] = dptr[trainIdx] + 1.f; if( pointIdxsOfClusters ) (*pointIdxsOfClusters)[trainIdx].push_back( queryIdx ); } // Normalize image descriptor. imgDescriptor /= keypointDescriptors.size().height; } }