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