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https://github.com/opencv/opencv.git
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2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
249 lines
10 KiB
C++
249 lines
10 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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namespace cv
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{
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/****************************************************************************************\
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* DescriptorExtractor *
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\****************************************************************************************/
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/*
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* DescriptorExtractor
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*/
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DescriptorExtractor::~DescriptorExtractor()
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{}
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void DescriptorExtractor::compute( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const
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{
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if( image.empty() || keypoints.empty() )
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{
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descriptors.release();
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return;
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}
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KeyPointsFilter::runByImageBorder( keypoints, image.size(), 0 );
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KeyPointsFilter::runByKeypointSize( keypoints, std::numeric_limits<float>::epsilon() );
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computeImpl( image, keypoints, descriptors );
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}
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void DescriptorExtractor::compute( const std::vector<Mat>& imageCollection, std::vector<std::vector<KeyPoint> >& pointCollection, std::vector<Mat>& descCollection ) const
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{
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CV_Assert( imageCollection.size() == pointCollection.size() );
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descCollection.resize( imageCollection.size() );
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for( size_t i = 0; i < imageCollection.size(); i++ )
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compute( imageCollection[i], pointCollection[i], descCollection[i] );
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}
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/*void DescriptorExtractor::read( const FileNode& )
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{}
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void DescriptorExtractor::write( FileStorage& ) const
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{}*/
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bool DescriptorExtractor::empty() const
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{
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return false;
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}
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void DescriptorExtractor::removeBorderKeypoints( std::vector<KeyPoint>& keypoints,
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Size imageSize, int borderSize )
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{
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KeyPointsFilter::runByImageBorder( keypoints, imageSize, borderSize );
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}
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Ptr<DescriptorExtractor> DescriptorExtractor::create(const std::string& descriptorExtractorType)
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{
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if( descriptorExtractorType.find("Opponent") == 0 )
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{
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size_t pos = std::string("Opponent").size();
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std::string type = descriptorExtractorType.substr(pos);
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return new OpponentColorDescriptorExtractor(DescriptorExtractor::create(type));
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}
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return Algorithm::create<DescriptorExtractor>("Feature2D." + descriptorExtractorType);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/****************************************************************************************\
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* OpponentColorDescriptorExtractor *
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\****************************************************************************************/
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OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& _descriptorExtractor ) :
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descriptorExtractor(_descriptorExtractor)
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{
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CV_Assert( !descriptorExtractor.empty() );
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}
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static void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, std::vector<Mat>& opponentChannels )
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{
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if( bgrImage.type() != CV_8UC3 )
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CV_Error( CV_StsBadArg, "input image must be an BGR image of type CV_8UC3" );
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// Prepare opponent color space storage matrices.
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opponentChannels.resize( 3 );
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opponentChannels[0] = cv::Mat(bgrImage.size(), CV_8UC1); // R-G RED-GREEN
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opponentChannels[1] = cv::Mat(bgrImage.size(), CV_8UC1); // R+G-2B YELLOW-BLUE
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opponentChannels[2] = cv::Mat(bgrImage.size(), CV_8UC1); // R+G+B
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for(int y = 0; y < bgrImage.rows; ++y)
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for(int x = 0; x < bgrImage.cols; ++x)
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{
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Vec3b v = bgrImage.at<Vec3b>(y, x);
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uchar& b = v[0];
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uchar& g = v[1];
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uchar& r = v[2];
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opponentChannels[0].at<uchar>(y, x) = saturate_cast<uchar>(0.5f * (255 + g - r)); // (R - G)/sqrt(2), but converted to the destination data type
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opponentChannels[1].at<uchar>(y, x) = saturate_cast<uchar>(0.25f * (510 + r + g - 2*b)); // (R + G - 2B)/sqrt(6), but converted to the destination data type
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opponentChannels[2].at<uchar>(y, x) = saturate_cast<uchar>(1.f/3.f * (r + g + b)); // (R + G + B)/sqrt(3), but converted to the destination data type
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}
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}
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struct KP_LessThan
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{
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KP_LessThan(const std::vector<KeyPoint>& _kp) : kp(&_kp) {}
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bool operator()(int i, int j) const
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{
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return (*kp)[i].class_id < (*kp)[j].class_id;
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}
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const std::vector<KeyPoint>* kp;
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};
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void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const
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{
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std::vector<Mat> opponentChannels;
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convertBGRImageToOpponentColorSpace( bgrImage, opponentChannels );
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const int N = 3; // channels count
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std::vector<KeyPoint> channelKeypoints[N];
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Mat channelDescriptors[N];
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std::vector<int> idxs[N];
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// Compute descriptors three times, once for each Opponent channel to concatenate into a single color descriptor
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int maxKeypointsCount = 0;
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for( int ci = 0; ci < N; ci++ )
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{
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channelKeypoints[ci].insert( channelKeypoints[ci].begin(), keypoints.begin(), keypoints.end() );
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// Use class_id member to get indices into initial keypoints vector
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for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
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channelKeypoints[ci][ki].class_id = (int)ki;
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descriptorExtractor->compute( opponentChannels[ci], channelKeypoints[ci], channelDescriptors[ci] );
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idxs[ci].resize( channelKeypoints[ci].size() );
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for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
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{
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idxs[ci][ki] = (int)ki;
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}
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std::sort( idxs[ci].begin(), idxs[ci].end(), KP_LessThan(channelKeypoints[ci]) );
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maxKeypointsCount = std::max( maxKeypointsCount, (int)channelKeypoints[ci].size());
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}
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std::vector<KeyPoint> outKeypoints;
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outKeypoints.reserve( keypoints.size() );
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int dSize = descriptorExtractor->descriptorSize();
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Mat mergedDescriptors( maxKeypointsCount, 3*dSize, descriptorExtractor->descriptorType() );
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int mergedCount = 0;
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// cp - current channel position
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size_t cp[] = {0, 0, 0};
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while( cp[0] < channelKeypoints[0].size() &&
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cp[1] < channelKeypoints[1].size() &&
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cp[2] < channelKeypoints[2].size() )
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{
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const int maxInitIdx = std::max( 0, std::max( channelKeypoints[0][idxs[0][cp[0]]].class_id,
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std::max( channelKeypoints[1][idxs[1][cp[1]]].class_id,
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channelKeypoints[2][idxs[2][cp[2]]].class_id ) ) );
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while( channelKeypoints[0][idxs[0][cp[0]]].class_id < maxInitIdx && cp[0] < channelKeypoints[0].size() ) { cp[0]++; }
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while( channelKeypoints[1][idxs[1][cp[1]]].class_id < maxInitIdx && cp[1] < channelKeypoints[1].size() ) { cp[1]++; }
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while( channelKeypoints[2][idxs[2][cp[2]]].class_id < maxInitIdx && cp[2] < channelKeypoints[2].size() ) { cp[2]++; }
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if( cp[0] >= channelKeypoints[0].size() || cp[1] >= channelKeypoints[1].size() || cp[2] >= channelKeypoints[2].size() )
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break;
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if( channelKeypoints[0][idxs[0][cp[0]]].class_id == maxInitIdx &&
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channelKeypoints[1][idxs[1][cp[1]]].class_id == maxInitIdx &&
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channelKeypoints[2][idxs[2][cp[2]]].class_id == maxInitIdx )
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{
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outKeypoints.push_back( keypoints[maxInitIdx] );
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// merge descriptors
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for( int ci = 0; ci < N; ci++ )
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{
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Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*dSize, (ci+1)*dSize));
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channelDescriptors[ci].row( idxs[ci][cp[ci]] ).copyTo( dst );
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cp[ci]++;
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}
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mergedCount++;
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}
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}
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mergedDescriptors.rowRange(0, mergedCount).copyTo( descriptors );
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std::swap( outKeypoints, keypoints );
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}
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void OpponentColorDescriptorExtractor::read( const FileNode& fn )
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{
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descriptorExtractor->read(fn);
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}
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void OpponentColorDescriptorExtractor::write( FileStorage& fs ) const
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{
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descriptorExtractor->write(fs);
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}
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int OpponentColorDescriptorExtractor::descriptorSize() const
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{
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return 3*descriptorExtractor->descriptorSize();
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}
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int OpponentColorDescriptorExtractor::descriptorType() const
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{
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return descriptorExtractor->descriptorType();
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}
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bool OpponentColorDescriptorExtractor::empty() const
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{
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return descriptorExtractor.empty() || (DescriptorExtractor*)(descriptorExtractor)->empty();
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}
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}
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