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Commit b2acf50c authored by Andrey Kamaev's avatar Andrey Kamaev
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Fix build warning introduced in commit:424bc609

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modules/contrib/src/retinacolor.cpp
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/*#******************************************************************************
** 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.
**
**
**
**
** HVStools : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping.
**
**
** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
**
**
** Creation - enhancement process 2007-2011
** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
**
**
** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
** Refer to the following research paper for more information:
** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
**
**
** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
** ====> more informations in the above cited Jeanny Heraults's book.
**
**
** License Agreement
** For Open Source Computer Vision Library
**
**
** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
**
**
** For Human Visual System tools (hvstools)
** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, 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:
**
**
** * Redistributions of source code must retain the above copyright notice,
** this list of conditions and the following disclaimer.
**
**
** * Redistributions 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 the copyright holders 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.
......@@ -91,28 +91,28 @@ RetinaColor::RetinaColor(const unsigned int NBrows, const unsigned int NBcolumns
_colorLocalDensity(NBrows*NBcolumns*3),
_imageGradient(NBrows*NBcolumns*2)
{
// link to parent buffers (let's recycle !)
_luminance=&_filterOutput;
_multiplexedFrame=&_localBuffer;
// link to parent buffers (let's recycle !)
_luminance=&_filterOutput;
_multiplexedFrame=&_localBuffer;
_objectInit=false;
_samplingMethod=samplingMethod;
_saturateColors=false;
_colorSaturationValue=4.0;
_objectInit=false;
_samplingMethod=samplingMethod;
_saturateColors=false;
_colorSaturationValue=4.0;
// set default spatio-temporal filter parameters
setLPfilterParameters(0.0, 0.0, 1.5);
setLPfilterParameters(0.0, 0.0, 10.5, 1);// for the low pass filter dedicated to contours energy extraction (demultiplexing process)
setLPfilterParameters(0.f, 0.f, 0.9f, 2);
// set default spatio-temporal filter parameters
setLPfilterParameters(0.0, 0.0, 1.5);
setLPfilterParameters(0.0, 0.0, 10.5, 1);// for the low pass filter dedicated to contours energy extraction (demultiplexing process)
setLPfilterParameters(0.f, 0.f, 0.9f, 2);
// init default value on image Gradient
_imageGradient=0.57f;
// init default value on image Gradient
_imageGradient=0.57f;
// init color sampling map
_initColorSampling();
// init color sampling map
_initColorSampling();
// flush all buffers
clearAllBuffers();
// flush all buffers
clearAllBuffers();
}
RetinaColor::~RetinaColor()
......@@ -125,13 +125,13 @@ RetinaColor::~RetinaColor()
*/
void RetinaColor::clearAllBuffers()
{
BasicRetinaFilter::clearAllBuffers();
_tempMultiplexedFrame=0.f;
_demultiplexedTempBuffer=0.f;
BasicRetinaFilter::clearAllBuffers();
_tempMultiplexedFrame=0.f;
_demultiplexedTempBuffer=0.f;
_demultiplexedColorFrame=0.f;
_chrominance=0.f;
_imageGradient=0.57f;
_demultiplexedColorFrame=0.f;
_chrominance=0.f;
_imageGradient=0.57f;
}
/**
......@@ -141,421 +141,421 @@ void RetinaColor::clearAllBuffers()
*/
void RetinaColor::resize(const unsigned int NBrows, const unsigned int NBcolumns)
{
BasicRetinaFilter::clearAllBuffers();
_colorSampling.resize(NBrows*NBcolumns);
_RGBmosaic.resize(NBrows*NBcolumns*3);
_tempMultiplexedFrame.resize(NBrows*NBcolumns);
_demultiplexedTempBuffer.resize(NBrows*NBcolumns*3);
_demultiplexedColorFrame.resize(NBrows*NBcolumns*3);
_chrominance.resize(NBrows*NBcolumns*3);
_colorLocalDensity.resize(NBrows*NBcolumns*3);
_imageGradient.resize(NBrows*NBcolumns*2);
// link to parent buffers (let's recycle !)
_luminance=&_filterOutput;
_multiplexedFrame=&_localBuffer;
// init color sampling map
_initColorSampling();
// clean buffers
clearAllBuffers();
BasicRetinaFilter::clearAllBuffers();
_colorSampling.resize(NBrows*NBcolumns);
_RGBmosaic.resize(NBrows*NBcolumns*3);
_tempMultiplexedFrame.resize(NBrows*NBcolumns);
_demultiplexedTempBuffer.resize(NBrows*NBcolumns*3);
_demultiplexedColorFrame.resize(NBrows*NBcolumns*3);
_chrominance.resize(NBrows*NBcolumns*3);
_colorLocalDensity.resize(NBrows*NBcolumns*3);
_imageGradient.resize(NBrows*NBcolumns*2);
// link to parent buffers (let's recycle !)
_luminance=&_filterOutput;
_multiplexedFrame=&_localBuffer;
// init color sampling map
_initColorSampling();
// clean buffers
clearAllBuffers();
}
void RetinaColor::_initColorSampling()
{
// filling the conversion table for multiplexed <=> demultiplexed frame
srand((unsigned)time(NULL));
// preInit cones probabilities
_pR=_pB=_pG=0;
switch (_samplingMethod)
{
case RETINA_COLOR_RANDOM:
for (unsigned int index=0 ; index<this->getNBpixels(); ++index)
{
// random RGB sampling
unsigned int colorIndex=rand()%24;
if (colorIndex<8){
colorIndex=0;
++_pR;
}else
{
if (colorIndex<21){
colorIndex=1;
++_pG;
}else{
colorIndex=2;
++_pB;
}
}
_colorSampling[index] = colorIndex*this->getNBpixels()+index;
}
_pR/=(float)this->getNBpixels();
_pG/=(float)this->getNBpixels();
_pB/=(float)this->getNBpixels();
std::cout<<"Color channels proportions: pR, pG, pB= "<<_pR<<", "<<_pG<<", "<<_pB<<", "<<std::endl;
break;
case RETINA_COLOR_DIAGONAL:
for (unsigned int index=0 ; index<this->getNBpixels(); ++index)
{
_colorSampling[index] = index+((index%3+(index%_filterOutput.getNBcolumns()))%3)*_filterOutput.getNBpixels();
}
_pR=_pB=_pG=1.f/3;
break;
case RETINA_COLOR_BAYER: // default sets bayer sampling
for (unsigned int index=0 ; index<_filterOutput.getNBpixels(); ++index)
{
//First line: R G R G
_colorSampling[index] = index+((index/_filterOutput.getNBcolumns())%2)*_filterOutput.getNBpixels()+((index%_filterOutput.getNBcolumns())%2)*_filterOutput.getNBpixels();
//First line: G R G R
//_colorSampling[index] = 3*index+((index/_filterOutput.getNBcolumns())%2)+((index%_filterOutput.getNBcolumns()+1)%2);
}
_pR=_pB=0.25;
_pG=0.5;
break;
default:
// filling the conversion table for multiplexed <=> demultiplexed frame
srand((unsigned)time(NULL));
// preInit cones probabilities
_pR=_pB=_pG=0;
switch (_samplingMethod)
{
case RETINA_COLOR_RANDOM:
for (unsigned int index=0 ; index<this->getNBpixels(); ++index)
{
// random RGB sampling
unsigned int colorIndex=rand()%24;
if (colorIndex<8){
colorIndex=0;
++_pR;
}else
{
if (colorIndex<21){
colorIndex=1;
++_pG;
}else{
colorIndex=2;
++_pB;
}
}
_colorSampling[index] = colorIndex*this->getNBpixels()+index;
}
_pR/=(float)this->getNBpixels();
_pG/=(float)this->getNBpixels();
_pB/=(float)this->getNBpixels();
std::cout<<"Color channels proportions: pR, pG, pB= "<<_pR<<", "<<_pG<<", "<<_pB<<", "<<std::endl;
break;
case RETINA_COLOR_DIAGONAL:
for (unsigned int index=0 ; index<this->getNBpixels(); ++index)
{
_colorSampling[index] = index+((index%3+(index%_filterOutput.getNBcolumns()))%3)*_filterOutput.getNBpixels();
}
_pR=_pB=_pG=1.f/3;
break;
case RETINA_COLOR_BAYER: // default sets bayer sampling
for (unsigned int index=0 ; index<_filterOutput.getNBpixels(); ++index)
{
//First line: R G R G
_colorSampling[index] = index+((index/_filterOutput.getNBcolumns())%2)*_filterOutput.getNBpixels()+((index%_filterOutput.getNBcolumns())%2)*_filterOutput.getNBpixels();
//First line: G R G R
//_colorSampling[index] = 3*index+((index/_filterOutput.getNBcolumns())%2)+((index%_filterOutput.getNBcolumns()+1)%2);
}
_pR=_pB=0.25;
_pG=0.5;
break;
default:
#ifdef RETINACOLORDEBUG
std::cerr<<"RetinaColor::No or wrong color sampling method, skeeping"<<std::endl;
std::cerr<<"RetinaColor::No or wrong color sampling method, skeeping"<<std::endl;
#endif
return;
break;//.. not useful, yes
}
// feeling the mosaic buffer:
_RGBmosaic=0;
for (unsigned int index=0 ; index<_filterOutput.getNBpixels(); ++index)
// the RGB _RGBmosaic buffer contains 1 where the pixel corresponds to a sampled color
_RGBmosaic[_colorSampling[index]]=1.0;
// computing photoreceptors local density
_spatiotemporalLPfilter(&_RGBmosaic[0], &_colorLocalDensity[0]);
_spatiotemporalLPfilter(&_RGBmosaic[0]+_filterOutput.getNBpixels(), &_colorLocalDensity[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_RGBmosaic[0]+_filterOutput.getDoubleNBpixels(), &_colorLocalDensity[0]+_filterOutput.getDoubleNBpixels());
unsigned int maxNBpixels=3*_filterOutput.getNBpixels();
register float *colorLocalDensityPTR=&_colorLocalDensity[0];
for (unsigned int i=0;i<maxNBpixels;++i, ++colorLocalDensityPTR)
*colorLocalDensityPTR=1.f/ *colorLocalDensityPTR;
return;
break;//.. not useful, yes
}
// feeling the mosaic buffer:
_RGBmosaic=0;
for (unsigned int index=0 ; index<_filterOutput.getNBpixels(); ++index)
// the RGB _RGBmosaic buffer contains 1 where the pixel corresponds to a sampled color
_RGBmosaic[_colorSampling[index]]=1.0;
// computing photoreceptors local density
_spatiotemporalLPfilter(&_RGBmosaic[0], &_colorLocalDensity[0]);
_spatiotemporalLPfilter(&_RGBmosaic[0]+_filterOutput.getNBpixels(), &_colorLocalDensity[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_RGBmosaic[0]+_filterOutput.getDoubleNBpixels(), &_colorLocalDensity[0]+_filterOutput.getDoubleNBpixels());
unsigned int maxNBpixels=3*_filterOutput.getNBpixels();
register float *colorLocalDensityPTR=&_colorLocalDensity[0];
for (unsigned int i=0;i<maxNBpixels;++i, ++colorLocalDensityPTR)
*colorLocalDensityPTR=1.f/ *colorLocalDensityPTR;
#ifdef RETINACOLORDEBUG
std::cout<<"INIT _colorLocalDensity max, min: "<<_colorLocalDensity.max()<<", "<<_colorLocalDensity.min()<<std::endl;
std::cout<<"INIT _colorLocalDensity max, min: "<<_colorLocalDensity.max()<<", "<<_colorLocalDensity.min()<<std::endl;
#endif
// end of the init step
_objectInit=true;
// end of the init step
_objectInit=true;
}
// public functions
void RetinaColor::runColorDemultiplexing(const std::valarray<float> &multiplexedColorFrame, const bool adaptiveFiltering, const float maxInputValue)
{
// demultiplex the grey frame to RGB frame
// -> first set demultiplexed frame to 0
_demultiplexedTempBuffer=0;
// -> demultiplex process
register unsigned int *colorSamplingPRT=&_colorSampling[0];
register const float *multiplexedColorFramePTR=get_data(multiplexedColorFrame);
for (unsigned int indexa=0; indexa<_filterOutput.getNBpixels() ; ++indexa)
_demultiplexedTempBuffer[*(colorSamplingPRT++)]=*(multiplexedColorFramePTR++);
// interpolate the demultiplexed frame depending on the color sampling method
if (!adaptiveFiltering)
_interpolateImageDemultiplexedImage(&_demultiplexedTempBuffer[0]);
// low pass filtering the demultiplexed frame
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0], &_chrominance[0]);
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_chrominance[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_chrominance[0]+_filterOutput.getDoubleNBpixels());
/*if (_samplingMethod=BAYER)
{
_applyRIFfilter(_chrominance, _chrominance);
_applyRIFfilter(_chrominance+_filterOutput.getNBpixels(), _chrominance+_filterOutput.getNBpixels());
_applyRIFfilter(_chrominance+_filterOutput.getDoubleNBpixels(), _chrominance+_filterOutput.getDoubleNBpixels());
}*/
// normalize by the photoreceptors local density and retrieve the local luminance
register float *chrominancePTR= &_chrominance[0];
register float *colorLocalDensityPTR= &_colorLocalDensity[0];
register float *luminance= &(*_luminance)[0];
if (!adaptiveFiltering)// compute the gradient on the luminance
{
if (_samplingMethod==RETINA_COLOR_RANDOM)
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance)
{
// normalize by photoreceptors density
float Cr=*(chrominancePTR)*_colorLocalDensity[indexc];
float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()];
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()];
*luminance=(Cr+Cg+Cb)*_pG;
*(chrominancePTR)=Cr-*luminance;
*(chrominancePTR+_filterOutput.getNBpixels())=Cg-*luminance;
*(chrominancePTR+_filterOutput.getDoubleNBpixels())=Cb-*luminance;
}
else
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance)
{
float Cr=*(chrominancePTR);
float Cg=*(chrominancePTR+_filterOutput.getNBpixels());
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels());
*luminance=_pR*Cr+_pG*Cg+_pB*Cb;
*(chrominancePTR)=Cr-*luminance;
*(chrominancePTR+_filterOutput.getNBpixels())=Cg-*luminance;
*(chrominancePTR+_filterOutput.getDoubleNBpixels())=Cb-*luminance;
}
// in order to get the color image, each colored map needs to be added the luminance
// -> to do so, compute: multiplexedColorFrame - remultiplexed chrominances
runColorMultiplexing(_chrominance, _tempMultiplexedFrame);
//lum = 1/3((f*(ImR))/(f*mR) + (f*(ImG))/(f*mG) + (f*(ImB))/(f*mB));
float *luminancePTR= &(*_luminance)[0];
chrominancePTR= &_chrominance[0];
float *demultiplexedColorFramePTR= &_demultiplexedColorFrame[0];
for (unsigned int indexp=0; indexp<_filterOutput.getNBpixels() ; ++indexp, ++luminancePTR, ++chrominancePTR, ++demultiplexedColorFramePTR)
{
*luminancePTR=(multiplexedColorFrame[indexp]-_tempMultiplexedFrame[indexp]);
*(demultiplexedColorFramePTR)=*(chrominancePTR)+*luminancePTR;
*(demultiplexedColorFramePTR+_filterOutput.getNBpixels())=*(chrominancePTR+_filterOutput.getNBpixels())+*luminancePTR;
*(demultiplexedColorFramePTR+_filterOutput.getDoubleNBpixels())=*(chrominancePTR+_filterOutput.getDoubleNBpixels())+*luminancePTR;
}
}else
{
register const float *multiplexedColorFramePTR= get_data(multiplexedColorFrame);
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR)
{
// normalize by photoreceptors density
float Cr=*(chrominancePTR)*_colorLocalDensity[indexc];
float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()];
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()];
*luminance=(Cr+Cg+Cb)*_pG;
_demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR - *luminance;
}
// compute the gradient of the luminance
_computeGradient(&(*_luminance)[0]);
// adaptively filter the submosaics to get the adaptive densities, here the buffer _chrominance is used as a temp buffer
_adaptiveSpatialLPfilter(&_RGBmosaic[0], &_chrominance[0]);
_adaptiveSpatialLPfilter(&_RGBmosaic[0]+_filterOutput.getNBpixels(), &_chrominance[0]+_filterOutput.getNBpixels());
_adaptiveSpatialLPfilter(&_RGBmosaic[0]+_filterOutput.getDoubleNBpixels(), &_chrominance[0]+_filterOutput.getDoubleNBpixels());
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0], &_demultiplexedColorFrame[0]);
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels());
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getDoubleNBpixels());
/* for (unsigned int index=0; index<_filterOutput.getNBpixels()*3 ; ++index) // cette boucle pourrait �tre supprimee en passant la densit� � la fonction de filtrage
_demultiplexedColorFrame[index] /= _chrominance[index];*/
_demultiplexedColorFrame/=_chrominance; // more optimal ;o)
// compute and substract the residual luminance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
float residu = _pR*_demultiplexedColorFrame[index] + _pG*_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] + _pB*_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()];
_demultiplexedColorFrame[index] = _demultiplexedColorFrame[index] - residu;
_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] = _demultiplexedColorFrame[index+_filterOutput.getNBpixels()] - residu;
_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] = _demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] - residu;
}
// multiplex the obtained chrominance
runColorMultiplexing(_demultiplexedColorFrame, _tempMultiplexedFrame);
_demultiplexedTempBuffer=0;
// get the luminance, et and add it to each chrominance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
(*_luminance)[index]=multiplexedColorFrame[index]-_tempMultiplexedFrame[index];
_demultiplexedTempBuffer[_colorSampling[index]] = _demultiplexedColorFrame[_colorSampling[index]];//multiplexedColorFrame[index] - (*_luminance)[index];
}
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0], &_demultiplexedTempBuffer[0]);
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels());
// get the luminance and add it to each chrominance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
_demultiplexedColorFrame[index] = _demultiplexedTempBuffer[index]*_colorLocalDensity[index]+ (*_luminance)[index];
_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] = _demultiplexedTempBuffer[index+_filterOutput.getNBpixels()]*_colorLocalDensity[index+_filterOutput.getNBpixels()]+ (*_luminance)[index];
_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] = _demultiplexedTempBuffer[index+_filterOutput.getDoubleNBpixels()]*_colorLocalDensity[index+_filterOutput.getDoubleNBpixels()]+ (*_luminance)[index];
}
}
// eliminate saturated colors by simple clipping values to the input range
clipRGBOutput_0_maxInputValue(NULL, maxInputValue);
/* transfert image gradient in order to check validity
// demultiplex the grey frame to RGB frame
// -> first set demultiplexed frame to 0
_demultiplexedTempBuffer=0;
// -> demultiplex process
register unsigned int *colorSamplingPRT=&_colorSampling[0];
register const float *multiplexedColorFramePtr=get_data(multiplexedColorFrame);
for (unsigned int indexa=0; indexa<_filterOutput.getNBpixels() ; ++indexa)
_demultiplexedTempBuffer[*(colorSamplingPRT++)]=*(multiplexedColorFramePtr++);
// interpolate the demultiplexed frame depending on the color sampling method
if (!adaptiveFiltering)
_interpolateImageDemultiplexedImage(&_demultiplexedTempBuffer[0]);
// low pass filtering the demultiplexed frame
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0], &_chrominance[0]);
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_chrominance[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_chrominance[0]+_filterOutput.getDoubleNBpixels());
/*if (_samplingMethod=BAYER)
{
_applyRIFfilter(_chrominance, _chrominance);
_applyRIFfilter(_chrominance+_filterOutput.getNBpixels(), _chrominance+_filterOutput.getNBpixels());
_applyRIFfilter(_chrominance+_filterOutput.getDoubleNBpixels(), _chrominance+_filterOutput.getDoubleNBpixels());
}*/
// normalize by the photoreceptors local density and retrieve the local luminance
register float *chrominancePTR= &_chrominance[0];
register float *colorLocalDensityPTR= &_colorLocalDensity[0];
register float *luminance= &(*_luminance)[0];
if (!adaptiveFiltering)// compute the gradient on the luminance
{
if (_samplingMethod==RETINA_COLOR_RANDOM)
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance)
{
// normalize by photoreceptors density
float Cr=*(chrominancePTR)*_colorLocalDensity[indexc];
float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()];
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()];
*luminance=(Cr+Cg+Cb)*_pG;
*(chrominancePTR)=Cr-*luminance;
*(chrominancePTR+_filterOutput.getNBpixels())=Cg-*luminance;
*(chrominancePTR+_filterOutput.getDoubleNBpixels())=Cb-*luminance;
}
else
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance)
{
float Cr=*(chrominancePTR);
float Cg=*(chrominancePTR+_filterOutput.getNBpixels());
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels());
*luminance=_pR*Cr+_pG*Cg+_pB*Cb;
*(chrominancePTR)=Cr-*luminance;
*(chrominancePTR+_filterOutput.getNBpixels())=Cg-*luminance;
*(chrominancePTR+_filterOutput.getDoubleNBpixels())=Cb-*luminance;
}
// in order to get the color image, each colored map needs to be added the luminance
// -> to do so, compute: multiplexedColorFrame - remultiplexed chrominances
runColorMultiplexing(_chrominance, _tempMultiplexedFrame);
//lum = 1/3((f*(ImR))/(f*mR) + (f*(ImG))/(f*mG) + (f*(ImB))/(f*mB));
float *luminancePTR= &(*_luminance)[0];
chrominancePTR= &_chrominance[0];
float *demultiplexedColorFramePTR= &_demultiplexedColorFrame[0];
for (unsigned int indexp=0; indexp<_filterOutput.getNBpixels() ; ++indexp, ++luminancePTR, ++chrominancePTR, ++demultiplexedColorFramePTR)
{
*luminancePTR=(multiplexedColorFrame[indexp]-_tempMultiplexedFrame[indexp]);
*(demultiplexedColorFramePTR)=*(chrominancePTR)+*luminancePTR;
*(demultiplexedColorFramePTR+_filterOutput.getNBpixels())=*(chrominancePTR+_filterOutput.getNBpixels())+*luminancePTR;
*(demultiplexedColorFramePTR+_filterOutput.getDoubleNBpixels())=*(chrominancePTR+_filterOutput.getDoubleNBpixels())+*luminancePTR;
}
}else
{
register const float *multiplexedColorFramePTR= get_data(multiplexedColorFrame);
for (unsigned int indexc=0; indexc<_filterOutput.getNBpixels() ; ++indexc, ++chrominancePTR, ++colorLocalDensityPTR, ++luminance, ++multiplexedColorFramePTR)
{
// normalize by photoreceptors density
float Cr=*(chrominancePTR)*_colorLocalDensity[indexc];
float Cg=*(chrominancePTR+_filterOutput.getNBpixels())*_colorLocalDensity[indexc+_filterOutput.getNBpixels()];
float Cb=*(chrominancePTR+_filterOutput.getDoubleNBpixels())*_colorLocalDensity[indexc+_filterOutput.getDoubleNBpixels()];
*luminance=(Cr+Cg+Cb)*_pG;
_demultiplexedTempBuffer[_colorSampling[indexc]] = *multiplexedColorFramePTR - *luminance;
}
// compute the gradient of the luminance
_computeGradient(&(*_luminance)[0]);
// adaptively filter the submosaics to get the adaptive densities, here the buffer _chrominance is used as a temp buffer
_adaptiveSpatialLPfilter(&_RGBmosaic[0], &_chrominance[0]);
_adaptiveSpatialLPfilter(&_RGBmosaic[0]+_filterOutput.getNBpixels(), &_chrominance[0]+_filterOutput.getNBpixels());
_adaptiveSpatialLPfilter(&_RGBmosaic[0]+_filterOutput.getDoubleNBpixels(), &_chrominance[0]+_filterOutput.getDoubleNBpixels());
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0], &_demultiplexedColorFrame[0]);
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels());
_adaptiveSpatialLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getDoubleNBpixels());
/* for (unsigned int index=0; index<_filterOutput.getNBpixels()*3 ; ++index) // cette boucle pourrait �tre supprimee en passant la densit� � la fonction de filtrage
_demultiplexedColorFrame[index] /= _chrominance[index];*/
_demultiplexedColorFrame/=_chrominance; // more optimal ;o)
// compute and substract the residual luminance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
float residu = _pR*_demultiplexedColorFrame[index] + _pG*_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] + _pB*_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()];
_demultiplexedColorFrame[index] = _demultiplexedColorFrame[index] - residu;
_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] = _demultiplexedColorFrame[index+_filterOutput.getNBpixels()] - residu;
_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] = _demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] - residu;
}
// multiplex the obtained chrominance
runColorMultiplexing(_demultiplexedColorFrame, _tempMultiplexedFrame);
_demultiplexedTempBuffer=0;
// get the luminance, et and add it to each chrominance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
(*_luminance)[index]=multiplexedColorFrame[index]-_tempMultiplexedFrame[index];
_demultiplexedTempBuffer[_colorSampling[index]] = _demultiplexedColorFrame[_colorSampling[index]];//multiplexedColorFrame[index] - (*_luminance)[index];
}
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0], &_demultiplexedTempBuffer[0]);
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels(), &_demultiplexedTempBuffer[0]+_filterOutput.getNBpixels());
_spatiotemporalLPfilter(&_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels(), &_demultiplexedTempBuffer[0]+_filterOutput.getDoubleNBpixels());
// get the luminance and add it to each chrominance
for (unsigned int index=0; index<_filterOutput.getNBpixels() ; ++index)
{
_demultiplexedColorFrame[index] = _demultiplexedTempBuffer[index]*_colorLocalDensity[index]+ (*_luminance)[index];
_demultiplexedColorFrame[index+_filterOutput.getNBpixels()] = _demultiplexedTempBuffer[index+_filterOutput.getNBpixels()]*_colorLocalDensity[index+_filterOutput.getNBpixels()]+ (*_luminance)[index];
_demultiplexedColorFrame[index+_filterOutput.getDoubleNBpixels()] = _demultiplexedTempBuffer[index+_filterOutput.getDoubleNBpixels()]*_colorLocalDensity[index+_filterOutput.getDoubleNBpixels()]+ (*_luminance)[index];
}
}
// eliminate saturated colors by simple clipping values to the input range
clipRGBOutput_0_maxInputValue(NULL, maxInputValue);
/* transfert image gradient in order to check validity
memcpy((*_luminance), _imageGradient, sizeof(float)*_filterOutput.getNBpixels());
memcpy(_demultiplexedColorFrame, _imageGradient+_filterOutput.getNBpixels(), sizeof(float)*_filterOutput.getNBpixels());
memcpy(_demultiplexedColorFrame+_filterOutput.getNBpixels(), _imageGradient+_filterOutput.getNBpixels(), sizeof(float)*_filterOutput.getNBpixels());
memcpy(_demultiplexedColorFrame+2*_filterOutput.getNBpixels(), _imageGradient+_filterOutput.getNBpixels(), sizeof(float)*_filterOutput.getNBpixels());
*/
if (_saturateColors)
{
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0], &_demultiplexedColorFrame[0], _filterOutput.getNBpixels());
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels(), _filterOutput.getNBpixels());
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels()*2, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels()*2, _filterOutput.getNBpixels());
}
*/
if (_saturateColors)
{
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0], &_demultiplexedColorFrame[0], _filterOutput.getNBpixels());
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels(), &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels(), _filterOutput.getNBpixels());
TemplateBuffer<float>::normalizeGrayOutputCentredSigmoide(128, _colorSaturationValue, maxInputValue, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels()*2, &_demultiplexedColorFrame[0]+_filterOutput.getNBpixels()*2, _filterOutput.getNBpixels());
}
}
// color multiplexing: input frame size=_NBrows*_filterOutput.getNBcolumns()*3, multiplexedFrame output size=_NBrows*_filterOutput.getNBcolumns()
void RetinaColor::runColorMultiplexing(const std::valarray<float> &demultiplexedInputFrame, std::valarray<float> &multiplexedFrame)
{
// multiply each color layer by its bayer mask
register unsigned int *colorSamplingPTR= &_colorSampling[0];
register float *multiplexedFramePTR= &multiplexedFrame[0];
for (unsigned int indexp=0; indexp<_filterOutput.getNBpixels(); ++indexp)
*(multiplexedFramePTR++)=demultiplexedInputFrame[*(colorSamplingPTR++)];
// multiply each color layer by its bayer mask
register unsigned int *colorSamplingPTR= &_colorSampling[0];
register float *multiplexedFramePTR= &multiplexedFrame[0];
for (unsigned int indexp=0; indexp<_filterOutput.getNBpixels(); ++indexp)
*(multiplexedFramePTR++)=demultiplexedInputFrame[*(colorSamplingPTR++)];
}
void RetinaColor::normalizeRGBOutput_0_maxOutputValue(const float maxOutputValue)
{
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance);
TemplateBuffer<float>::normalizeGrayOutput_0_maxOutputValue(&_demultiplexedColorFrame[0], 3*_filterOutput.getNBpixels(), maxOutputValue);
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance+_filterOutput.getNBpixels());
//normalizeGrayOutput_0_maxOutputValue(_demultiplexedColorFrame+_filterOutput.getNBpixels(), _filterOutput.getNBpixels(), maxOutputValue);
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance+2*_filterOutput.getNBpixels());
//normalizeGrayOutput_0_maxOutputValue(_demultiplexedColorFrame+_filterOutput.getDoubleNBpixels(), _filterOutput.getNBpixels(), maxOutputValue);
TemplateBuffer<float>::normalizeGrayOutput_0_maxOutputValue(&(*_luminance)[0], _filterOutput.getNBpixels(), maxOutputValue);
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance);
TemplateBuffer<float>::normalizeGrayOutput_0_maxOutputValue(&_demultiplexedColorFrame[0], 3*_filterOutput.getNBpixels(), maxOutputValue);
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance+_filterOutput.getNBpixels());
//normalizeGrayOutput_0_maxOutputValue(_demultiplexedColorFrame+_filterOutput.getNBpixels(), _filterOutput.getNBpixels(), maxOutputValue);
//normalizeGrayOutputCentredSigmoide(0.0, 2, _chrominance+2*_filterOutput.getNBpixels());
//normalizeGrayOutput_0_maxOutputValue(_demultiplexedColorFrame+_filterOutput.getDoubleNBpixels(), _filterOutput.getNBpixels(), maxOutputValue);
TemplateBuffer<float>::normalizeGrayOutput_0_maxOutputValue(&(*_luminance)[0], _filterOutput.getNBpixels(), maxOutputValue);
}
/// normalize output between 0 and maxOutputValue;
void RetinaColor::clipRGBOutput_0_maxInputValue(float *inputOutputBuffer, const float maxInputValue)
{
//std::cout<<"RetinaColor::normalizing RGB frame..."<<std::endl;
// if outputBuffer unsassigned, the rewrite the buffer
if (inputOutputBuffer==NULL)
inputOutputBuffer= &_demultiplexedColorFrame[0];
//std::cout<<"RetinaColor::normalizing RGB frame..."<<std::endl;
// if outputBuffer unsassigned, the rewrite the buffer
if (inputOutputBuffer==NULL)
inputOutputBuffer= &_demultiplexedColorFrame[0];
#ifdef HAVE_TBB // call the TemplateBuffer TBB clipping method
tbb::parallel_for(tbb::blocked_range<size_t>(0,_filterOutput.getNBpixels()*3), Parallel_clipBufferValues<float>(inputOutputBuffer, 0, maxInputValue), tbb::auto_partitioner());
#else
register float *inputOutputBufferPTR=inputOutputBuffer;
for (register unsigned int jf = 0; jf < _filterOutput.getNBpixels()*3; ++jf, ++inputOutputBufferPTR)
{
if (*inputOutputBufferPTR>maxInputValue)
*inputOutputBufferPTR=maxInputValue;
else if (*inputOutputBufferPTR<0)
*inputOutputBufferPTR=0;
}
register float *inputOutputBufferPTR=inputOutputBuffer;
for (register unsigned int jf = 0; jf < _filterOutput.getNBpixels()*3; ++jf, ++inputOutputBufferPTR)
{
if (*inputOutputBufferPTR>maxInputValue)
*inputOutputBufferPTR=maxInputValue;
else if (*inputOutputBufferPTR<0)
*inputOutputBufferPTR=0;
}
#endif
//std::cout<<"RetinaColor::...normalizing RGB frame OK"<<std::endl;
//std::cout<<"RetinaColor::...normalizing RGB frame OK"<<std::endl;
}
void RetinaColor::_interpolateImageDemultiplexedImage(float *inputOutputBuffer)
{
switch(_samplingMethod)
{
switch(_samplingMethod)
{
case RETINA_COLOR_RANDOM:
return; // no need to interpolate
break;
case RETINA_COLOR_RANDOM:
return; // no need to interpolate
break;
case RETINA_COLOR_DIAGONAL:
_interpolateSingleChannelImage111(inputOutputBuffer);
break;
case RETINA_COLOR_DIAGONAL:
_interpolateSingleChannelImage111(inputOutputBuffer);
break;
case RETINA_COLOR_BAYER: // default sets bayer sampling
_interpolateBayerRGBchannels(inputOutputBuffer);
break;
default:
std::cerr<<"RetinaColor::No or wrong color sampling method, skeeping"<<std::endl;
return;
break;//.. not useful, yes
case RETINA_COLOR_BAYER: // default sets bayer sampling
_interpolateBayerRGBchannels(inputOutputBuffer);
break;
default:
std::cerr<<"RetinaColor::No or wrong color sampling method, skeeping"<<std::endl;
return;
break;//.. not useful, yes
}
}
}
void RetinaColor::_interpolateSingleChannelImage111(float *inputOutputBuffer)
{
for (unsigned int indexr=0 ; indexr<_filterOutput.getNBrows(); ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
inputOutputBuffer[index]=(inputOutputBuffer[index-1]+inputOutputBuffer[index]+inputOutputBuffer[index+1])/3.f;
}
}
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); ++indexc)
{
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
inputOutputBuffer[index]=(inputOutputBuffer[index-_filterOutput.getNBcolumns()]+inputOutputBuffer[index]+inputOutputBuffer[index+_filterOutput.getNBcolumns()])/3.f;
}
}
for (unsigned int indexr=0 ; indexr<_filterOutput.getNBrows(); ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
inputOutputBuffer[index]=(inputOutputBuffer[index-1]+inputOutputBuffer[index]+inputOutputBuffer[index+1])/3.f;
}
}
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); ++indexc)
{
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
inputOutputBuffer[index]=(inputOutputBuffer[index-_filterOutput.getNBcolumns()]+inputOutputBuffer[index]+inputOutputBuffer[index+_filterOutput.getNBcolumns()])/3.f;
}
}
}
void RetinaColor::_interpolateBayerRGBchannels(float *inputOutputBuffer)
{
for (unsigned int indexr=0 ; indexr<_filterOutput.getNBrows()-1; indexr+=2)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; indexc+=2)
{
unsigned int indexR=indexc+indexr*_filterOutput.getNBcolumns();
unsigned int indexB=_filterOutput.getDoubleNBpixels()+indexc+1+(indexr+1)*_filterOutput.getNBcolumns();
inputOutputBuffer[indexR]=(inputOutputBuffer[indexR-1]+inputOutputBuffer[indexR+1])/2.f;
inputOutputBuffer[indexB]=(inputOutputBuffer[indexB-1]+inputOutputBuffer[indexB+1])/2.f;
}
}
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; indexr+=2)
{
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); ++indexc)
{
unsigned int indexR=indexc+indexr*_filterOutput.getNBcolumns();
unsigned int indexB=_filterOutput.getDoubleNBpixels()+indexc+1+(indexr+1)*_filterOutput.getNBcolumns();
inputOutputBuffer[indexR]=(inputOutputBuffer[indexR-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexR+_filterOutput.getNBcolumns()])/2.f;
inputOutputBuffer[indexB]=(inputOutputBuffer[indexB-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexB+_filterOutput.getNBcolumns()])/2.f;
}
}
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); indexc+=2)
{
unsigned int indexG=_filterOutput.getNBpixels()+indexc+(indexr)*_filterOutput.getNBcolumns()+indexr%2;
inputOutputBuffer[indexG]=(inputOutputBuffer[indexG-1]+inputOutputBuffer[indexG+1]+inputOutputBuffer[indexG-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexG+_filterOutput.getNBcolumns()])*0.25f;
}
for (unsigned int indexr=0 ; indexr<_filterOutput.getNBrows()-1; indexr+=2)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; indexc+=2)
{
unsigned int indexR=indexc+indexr*_filterOutput.getNBcolumns();
unsigned int indexB=_filterOutput.getDoubleNBpixels()+indexc+1+(indexr+1)*_filterOutput.getNBcolumns();
inputOutputBuffer[indexR]=(inputOutputBuffer[indexR-1]+inputOutputBuffer[indexR+1])/2.f;
inputOutputBuffer[indexB]=(inputOutputBuffer[indexB-1]+inputOutputBuffer[indexB+1])/2.f;
}
}
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; indexr+=2)
{
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); ++indexc)
{
unsigned int indexR=indexc+indexr*_filterOutput.getNBcolumns();
unsigned int indexB=_filterOutput.getDoubleNBpixels()+indexc+1+(indexr+1)*_filterOutput.getNBcolumns();
inputOutputBuffer[indexR]=(inputOutputBuffer[indexR-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexR+_filterOutput.getNBcolumns()])/2.f;
inputOutputBuffer[indexB]=(inputOutputBuffer[indexB-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexB+_filterOutput.getNBcolumns()])/2.f;
}
}
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
for (unsigned int indexc=0 ; indexc<_filterOutput.getNBcolumns(); indexc+=2)
{
unsigned int indexG=_filterOutput.getNBpixels()+indexc+(indexr)*_filterOutput.getNBcolumns()+indexr%2;
inputOutputBuffer[indexG]=(inputOutputBuffer[indexG-1]+inputOutputBuffer[indexG+1]+inputOutputBuffer[indexG-_filterOutput.getNBcolumns()]+inputOutputBuffer[indexG+_filterOutput.getNBcolumns()])*0.25f;
}
}
void RetinaColor::_applyRIFfilter(const float *sourceBuffer, float *destinationBuffer)
{
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
_tempMultiplexedFrame[index]=(4.f*sourceBuffer[index]+sourceBuffer[index-1-_filterOutput.getNBcolumns()]+sourceBuffer[index-1+_filterOutput.getNBcolumns()]+sourceBuffer[index+1-_filterOutput.getNBcolumns()]+sourceBuffer[index+1+_filterOutput.getNBcolumns()])*0.125f;
}
}
memcpy(destinationBuffer, &_tempMultiplexedFrame[0], sizeof(float)*_filterOutput.getNBpixels());
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
_tempMultiplexedFrame[index]=(4.f*sourceBuffer[index]+sourceBuffer[index-1-_filterOutput.getNBcolumns()]+sourceBuffer[index-1+_filterOutput.getNBcolumns()]+sourceBuffer[index+1-_filterOutput.getNBcolumns()]+sourceBuffer[index+1+_filterOutput.getNBcolumns()])*0.125f;
}
}
memcpy(destinationBuffer, &_tempMultiplexedFrame[0], sizeof(float)*_filterOutput.getNBpixels());
}
void RetinaColor::_getNormalizedContoursImage(const float *inputFrame, float *outputFrame)
{
float maxValue=0.f;
float normalisationFactor=1.f/3.f;
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
outputFrame[index]=normalisationFactor*fabs(8.f*inputFrame[index]-inputFrame[index-1]-inputFrame[index+1]-inputFrame[index-_filterOutput.getNBcolumns()]-inputFrame[index+_filterOutput.getNBcolumns()]-inputFrame[index-1-_filterOutput.getNBcolumns()]-inputFrame[index-1+_filterOutput.getNBcolumns()]-inputFrame[index+1-_filterOutput.getNBcolumns()]-inputFrame[index+1+_filterOutput.getNBcolumns()]);
if (outputFrame[index]>maxValue)
maxValue=outputFrame[index];
}
}
normalisationFactor=1.f/maxValue;
// normalisation [0, 1]
float maxValue=0.f;
float normalisationFactor=1.f/3.f;
for (unsigned int indexr=1 ; indexr<_filterOutput.getNBrows()-1; ++indexr)
{
for (unsigned int indexc=1 ; indexc<_filterOutput.getNBcolumns()-1; ++indexc)
{
unsigned int index=indexc+indexr*_filterOutput.getNBcolumns();
outputFrame[index]=normalisationFactor*fabs(8.f*inputFrame[index]-inputFrame[index-1]-inputFrame[index+1]-inputFrame[index-_filterOutput.getNBcolumns()]-inputFrame[index+_filterOutput.getNBcolumns()]-inputFrame[index-1-_filterOutput.getNBcolumns()]-inputFrame[index-1+_filterOutput.getNBcolumns()]-inputFrame[index+1-_filterOutput.getNBcolumns()]-inputFrame[index+1+_filterOutput.getNBcolumns()]);
if (outputFrame[index]>maxValue)
maxValue=outputFrame[index];
}
}
normalisationFactor=1.f/maxValue;
// normalisation [0, 1]
for (unsigned int indexp=1 ; indexp<_filterOutput.getNBrows()-1; ++indexp)
outputFrame[indexp]=outputFrame[indexp]*normalisationFactor;
outputFrame[indexp]=outputFrame[indexp]*normalisationFactor;
}
//////////////////////////////////////////////////////////
......@@ -565,16 +565,16 @@ void RetinaColor::_getNormalizedContoursImage(const float *inputFrame, float *ou
void RetinaColor::_adaptiveSpatialLPfilter(const float *inputFrame, float *outputFrame)
{
/**********/
_gain = (1-0.57f)*(1-0.57f)*(1-0.06f)*(1-0.06f);
/**********/
_gain = (1-0.57f)*(1-0.57f)*(1-0.06f)*(1-0.06f);
// launch the serie of 1D directional filters in order to compute the 2D low pass filter
// -> horizontal filters work with the first layer of imageGradient
_adaptiveHorizontalCausalFilter_addInput(inputFrame, outputFrame, 0, _filterOutput.getNBrows());
_horizontalAnticausalFilter_Irregular(outputFrame, 0, _filterOutput.getNBrows(), &_imageGradient[0]);
// -> horizontal filters work with the second layer of imageGradient
_verticalCausalFilter_Irregular(outputFrame, 0, _filterOutput.getNBcolumns(), &_imageGradient[0]+_filterOutput.getNBpixels());
_adaptiveVerticalAnticausalFilter_multGain(outputFrame, 0, _filterOutput.getNBcolumns());
// launch the serie of 1D directional filters in order to compute the 2D low pass filter
// -> horizontal filters work with the first layer of imageGradient
_adaptiveHorizontalCausalFilter_addInput(inputFrame, outputFrame, 0, _filterOutput.getNBrows());
_horizontalAnticausalFilter_Irregular(outputFrame, 0, _filterOutput.getNBrows(), &_imageGradient[0]);
// -> horizontal filters work with the second layer of imageGradient
_verticalCausalFilter_Irregular(outputFrame, 0, _filterOutput.getNBcolumns(), &_imageGradient[0]+_filterOutput.getNBpixels());
_adaptiveVerticalAnticausalFilter_multGain(outputFrame, 0, _filterOutput.getNBcolumns());
}
// horizontal causal filter which adds the input inside... replaces the parent _horizontalCausalFilter_Irregular_addInput by avoiding a product for each pixel
......@@ -583,21 +583,21 @@ void RetinaColor::_adaptiveHorizontalCausalFilter_addInput(const float *inputFra
#ifdef HAVE_TBB
tbb::parallel_for(tbb::blocked_range<size_t>(IDrowStart,IDrowEnd), Parallel_adaptiveHorizontalCausalFilter_addInput(inputFrame, outputFrame, &_imageGradient[0], _filterOutput.getNBcolumns()), tbb::auto_partitioner());
#else
register float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
register const float* inputPTR=inputFrame+IDrowStart*_filterOutput.getNBcolumns();
register const float *imageGradientPTR= &_imageGradient[0]+IDrowStart*_filterOutput.getNBcolumns();
for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
{
register float result=0;
for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
{
//std::cout<<(*imageGradientPTR)<<" ";
result = *(inputPTR++) + (*imageGradientPTR)* result;
*(outputPTR++) = result;
++imageGradientPTR;
}
// std::cout<<" "<<std::endl;
}
register float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
register const float* inputPTR=inputFrame+IDrowStart*_filterOutput.getNBcolumns();
register const float *imageGradientPTR= &_imageGradient[0]+IDrowStart*_filterOutput.getNBcolumns();
for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
{
register float result=0;
for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
{
//std::cout<<(*imageGradientPTR)<<" ";
result = *(inputPTR++) + (*imageGradientPTR)* result;
*(outputPTR++) = result;
++imageGradientPTR;
}
// std::cout<<" "<<std::endl;
}
#endif
}
......@@ -607,114 +607,114 @@ void RetinaColor::_adaptiveVerticalAnticausalFilter_multGain(float *outputFrame,
#ifdef HAVE_TBB
tbb::parallel_for(tbb::blocked_range<size_t>(IDcolumnStart,IDcolumnEnd), Parallel_adaptiveVerticalAnticausalFilter_multGain(outputFrame, &_imageGradient[0]+_filterOutput.getNBpixels(), _filterOutput.getNBrows(), _filterOutput.getNBcolumns(), _gain), tbb::auto_partitioner());
#else
float* outputOffset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
float* gradOffset= &_imageGradient[0]+_filterOutput.getNBpixels()*2-_filterOutput.getNBcolumns();
for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
{
register float result=0;
register float *outputPTR=outputOffset+IDcolumn;
register float *imageGradientPTR=gradOffset+IDcolumn;
for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
{
result = *(outputPTR) + (*(imageGradientPTR)) * result;
*(outputPTR) = _gain*result;
outputPTR-=_filterOutput.getNBcolumns();
imageGradientPTR-=_filterOutput.getNBcolumns();
}
}
float* outputOffset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
float* gradOffset= &_imageGradient[0]+_filterOutput.getNBpixels()*2-_filterOutput.getNBcolumns();
for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
{
register float result=0;
register float *outputPTR=outputOffset+IDcolumn;
register float *imageGradientPTR=gradOffset+IDcolumn;
for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
{
result = *(outputPTR) + (*(imageGradientPTR)) * result;
*(outputPTR) = _gain*result;
outputPTR-=_filterOutput.getNBcolumns();
imageGradientPTR-=_filterOutput.getNBcolumns();
}
}
#endif
}
///////////////////////////
void RetinaColor::_computeGradient(const float *luminance)
{
for (unsigned int idLine=2;idLine<_filterOutput.getNBrows()-2;++idLine)
{
for (unsigned int idColumn=2;idColumn<_filterOutput.getNBcolumns()-2;++idColumn)
{
const unsigned int pixelIndex=idColumn+_filterOutput.getNBcolumns()*idLine;
// horizontal and vertical local gradients
const float verticalGrad=fabs(luminance[pixelIndex+_filterOutput.getNBcolumns()]-luminance[pixelIndex-_filterOutput.getNBcolumns()]);
const float horizontalGrad=fabs(luminance[pixelIndex+1]-luminance[pixelIndex-1]);
// neighborhood horizontal and vertical gradients
const float verticalGrad_p=fabs(luminance[pixelIndex]-luminance[pixelIndex-2*_filterOutput.getNBcolumns()]);
const float horizontalGrad_p=fabs(luminance[pixelIndex]-luminance[pixelIndex-2]);
const float verticalGrad_n=fabs(luminance[pixelIndex+2*_filterOutput.getNBcolumns()]-luminance[pixelIndex]);
const float horizontalGrad_n=fabs(luminance[pixelIndex+2]-luminance[pixelIndex]);
const float horizontalGradient=0.5f*horizontalGrad+0.25f*(horizontalGrad_p+horizontalGrad_n);
const float verticalGradient=0.5f*verticalGrad+0.25f*(verticalGrad_p+verticalGrad_n);
// compare local gradient means and fill the appropriate filtering coefficient value that will be used in adaptative filters
if (horizontalGradient<verticalGradient)
{
_imageGradient[pixelIndex+_filterOutput.getNBpixels()]=0.06f;
_imageGradient[pixelIndex]=0.57f;
}
else
{
_imageGradient[pixelIndex+_filterOutput.getNBpixels()]=0.57f;
_imageGradient[pixelIndex]=0.06f;
}
}
}
for (unsigned int idLine=2;idLine<_filterOutput.getNBrows()-2;++idLine)
{
for (unsigned int idColumn=2;idColumn<_filterOutput.getNBcolumns()-2;++idColumn)
{
const unsigned int pixelIndex=idColumn+_filterOutput.getNBcolumns()*idLine;
// horizontal and vertical local gradients
const float verticalGrad=fabs(luminance[pixelIndex+_filterOutput.getNBcolumns()]-luminance[pixelIndex-_filterOutput.getNBcolumns()]);
const float horizontalGrad=fabs(luminance[pixelIndex+1]-luminance[pixelIndex-1]);
// neighborhood horizontal and vertical gradients
const float verticalGrad_p=fabs(luminance[pixelIndex]-luminance[pixelIndex-2*_filterOutput.getNBcolumns()]);
const float horizontalGrad_p=fabs(luminance[pixelIndex]-luminance[pixelIndex-2]);
const float verticalGrad_n=fabs(luminance[pixelIndex+2*_filterOutput.getNBcolumns()]-luminance[pixelIndex]);
const float horizontalGrad_n=fabs(luminance[pixelIndex+2]-luminance[pixelIndex]);
const float horizontalGradient=0.5f*horizontalGrad+0.25f*(horizontalGrad_p+horizontalGrad_n);
const float verticalGradient=0.5f*verticalGrad+0.25f*(verticalGrad_p+verticalGrad_n);
// compare local gradient means and fill the appropriate filtering coefficient value that will be used in adaptative filters
if (horizontalGradient<verticalGradient)
{
_imageGradient[pixelIndex+_filterOutput.getNBpixels()]=0.06f;
_imageGradient[pixelIndex]=0.57f;
}
else
{
_imageGradient[pixelIndex+_filterOutput.getNBpixels()]=0.57f;
_imageGradient[pixelIndex]=0.06f;
}
}
}
}
bool RetinaColor::applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result)
{
bool processSuccess=true;
// basic preliminary error check
if (result.size()!=_demultiplexedColorFrame.size())
{
std::cerr<<"RetinaColor::applyKrauskopfLMS2Acr1cr2Transform: input buffer does not match retina buffer size, conversion aborted"<<std::endl;
return false;
}
// apply transformation
_applyImageColorSpaceConversion(_demultiplexedColorFrame, result, _LMStoACr1Cr2);
return processSuccess;
bool processSuccess=true;
// basic preliminary error check
if (result.size()!=_demultiplexedColorFrame.size())
{
std::cerr<<"RetinaColor::applyKrauskopfLMS2Acr1cr2Transform: input buffer does not match retina buffer size, conversion aborted"<<std::endl;
return false;
}
// apply transformation
_applyImageColorSpaceConversion(_demultiplexedColorFrame, result, _LMStoACr1Cr2);
return processSuccess;
}
bool RetinaColor::applyLMS2LabTransform(std::valarray<float> &result)
{
bool processSuccess=true;
// basic preliminary error check
if (result.size()!=_demultiplexedColorFrame.size())
{
std::cerr<<"RetinaColor::applyKrauskopfLMS2Acr1cr2Transform: input buffer does not match retina buffer size, conversion aborted"<<std::endl;
return false;
}
// apply transformation
_applyImageColorSpaceConversion(_demultiplexedColorFrame, result, _LMStoLab);
return processSuccess;
bool processSuccess=true;
// basic preliminary error check
if (result.size()!=_demultiplexedColorFrame.size())
{
std::cerr<<"RetinaColor::applyKrauskopfLMS2Acr1cr2Transform: input buffer does not match retina buffer size, conversion aborted"<<std::endl;
return false;
}
// apply transformation
_applyImageColorSpaceConversion(_demultiplexedColorFrame, result, _LMStoLab);
return processSuccess;
}
// template function able to perform a custom color space transformation
void RetinaColor::_applyImageColorSpaceConversion(const std::valarray<float> &inputFrameBuffer, std::valarray<float> &outputFrameBuffer, const float *transformTable)
{
// two step methods in order to allow inputFrame and outputFrame to be the same
unsigned int nbPixels=(unsigned int)(inputFrameBuffer.size()/3), dbpixels=(unsigned int)(2*inputFrameBuffer.size()/3);
const float *inputFrame=get_data(inputFrameBuffer);
float *outputFrame= &outputFrameBuffer[0];
for (unsigned int dataIndex=0; dataIndex<nbPixels;++dataIndex, ++outputFrame, ++inputFrame)
{
// first step, compute each new values
float layer1 = *(inputFrame)**(transformTable+0) +*(inputFrame+nbPixels)**(transformTable+1) +*(inputFrame+dbpixels)**(transformTable+2);
float layer2 = *(inputFrame)**(transformTable+3) +*(inputFrame+nbPixels)**(transformTable+4) +*(inputFrame+dbpixels)**(transformTable+5);
float layer3 = *(inputFrame)**(transformTable+6) +*(inputFrame+nbPixels)**(transformTable+7) +*(inputFrame+dbpixels)**(transformTable+8);
// second, affect the output
*(outputFrame) = layer1;
*(outputFrame+nbPixels) = layer2;
*(outputFrame+dbpixels) = layer3;
}
// two step methods in order to allow inputFrame and outputFrame to be the same
unsigned int nbPixels=(unsigned int)(inputFrameBuffer.size()/3), dbpixels=(unsigned int)(2*inputFrameBuffer.size()/3);
const float *inputFrame=get_data(inputFrameBuffer);
float *outputFrame= &outputFrameBuffer[0];
for (unsigned int dataIndex=0; dataIndex<nbPixels;++dataIndex, ++outputFrame, ++inputFrame)
{
// first step, compute each new values
float layer1 = *(inputFrame)**(transformTable+0) +*(inputFrame+nbPixels)**(transformTable+1) +*(inputFrame+dbpixels)**(transformTable+2);
float layer2 = *(inputFrame)**(transformTable+3) +*(inputFrame+nbPixels)**(transformTable+4) +*(inputFrame+dbpixels)**(transformTable+5);
float layer3 = *(inputFrame)**(transformTable+6) +*(inputFrame+nbPixels)**(transformTable+7) +*(inputFrame+dbpixels)**(transformTable+8);
// second, affect the output
*(outputFrame) = layer1;
*(outputFrame+nbPixels) = layer2;
*(outputFrame+dbpixels) = layer3;
}
}
}
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