/*#******************************************************************************
** 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.
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*******************************************************************************/
#include "precomp.hpp"
#include "retinafilter.hpp"
// @author Alexandre BENOIT, benoit.alexandre.vision@gmail.com, LISTIC : www.listic.univ-savoie.fr, Gipsa-Lab, France: www.gipsa-lab.inpg.fr/
#include <iostream>
#include <cmath>
namespace cv
{
// standard constructor without any log sampling of the input frame
RetinaFilter::RetinaFilter(const unsigned int sizeRows, const unsigned int sizeColumns, const bool colorMode, const RETINA_COLORSAMPLINGMETHOD samplingMethod, const bool useRetinaLogSampling, const double reductionFactor, const double samplingStrenght)
:
_retinaParvoMagnoMappedFrame(0),
_retinaParvoMagnoMapCoefTable(0),
_photoreceptorsPrefilter((1-(int)useRetinaLogSampling)*sizeRows+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeRows, reductionFactor), (1-(int)useRetinaLogSampling)*sizeColumns+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeColumns, reductionFactor), 4),
_ParvoRetinaFilter((1-(int)useRetinaLogSampling)*sizeRows+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeRows, reductionFactor), (1-(int)useRetinaLogSampling)*sizeColumns+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeColumns, reductionFactor)),
_MagnoRetinaFilter((1-(int)useRetinaLogSampling)*sizeRows+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeRows, reductionFactor), (1-(int)useRetinaLogSampling)*sizeColumns+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeColumns, reductionFactor)),
_colorEngine((1-(int)useRetinaLogSampling)*sizeRows+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeRows, reductionFactor), (1-(int)useRetinaLogSampling)*sizeColumns+useRetinaLogSampling*ImageLogPolProjection::predictOutputSize(sizeColumns, reductionFactor), samplingMethod),
// configure retina photoreceptors log sampling... if necessary
_photoreceptorsLogSampling(NULL)
{
#ifdef RETINADEBUG
std::cout<<"RetinaFilter::size( "<<_photoreceptorsPrefilter.getNBrows()<<", "<<_photoreceptorsPrefilter.getNBcolumns()<<")"<<" =? "<<_photoreceptorsPrefilter.getNBpixels()<<std::endl;
#endif
if (useRetinaLogSampling)
{
_photoreceptorsLogSampling = new ImageLogPolProjection(sizeRows, sizeColumns, ImageLogPolProjection::RETINALOGPROJECTION, true);
if (!_photoreceptorsLogSampling->initProjection(reductionFactor, samplingStrenght))
{
std::cerr<<"RetinaFilter::Problem initializing photoreceptors log sampling, could not setup retina filter"<<std::endl;
delete _photoreceptorsLogSampling;
_photoreceptorsLogSampling=NULL;
}
else
{
#ifdef RETINADEBUG
std::cout<<"_photoreceptorsLogSampling::size( "<<_photoreceptorsLogSampling->getNBrows()<<", "<<_photoreceptorsLogSampling->getNBcolumns()<<")"<<" =? "<<_photoreceptorsLogSampling->getNBpixels()<<std::endl;
#endif
}
}
// set default processing activities
_useParvoOutput=true;
_useMagnoOutput=true;
_useColorMode=colorMode;
// create hybrid output and related coefficient table
_createHybridTable();
// set default parameters
setGlobalParameters();
// stability controls values init
_setInitPeriodCount();
_globalTemporalConstant=25;
// reset all buffers
clearAllBuffers();
// std::cout<<"RetinaFilter::size( "<<this->getNBrows()<<", "<<this->getNBcolumns()<<")"<<_filterOutput.size()<<" =? "<<_filterOutput.getNBpixels()<<std::endl;
}
// destructor
RetinaFilter::~RetinaFilter()
{
if (_photoreceptorsLogSampling!=NULL)
delete _photoreceptorsLogSampling;
}
// function that clears all buffers of the object
void RetinaFilter::clearAllBuffers()
{
_photoreceptorsPrefilter.clearAllBuffers();
_ParvoRetinaFilter.clearAllBuffers();
_MagnoRetinaFilter.clearAllBuffers();
_colorEngine.clearAllBuffers();
if (_photoreceptorsLogSampling!=NULL)
_photoreceptorsLogSampling->clearAllBuffers();
// stability controls value init
_setInitPeriodCount();
}
/**
* resize retina filter object (resize all allocated buffers
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void RetinaFilter::resize(const unsigned int NBrows, const unsigned int NBcolumns)
{
unsigned int rows=NBrows, cols=NBcolumns;
// resize optionnal member and adjust other modules size if required
if (_photoreceptorsLogSampling)
{
_photoreceptorsLogSampling->resize(NBrows, NBcolumns);
rows=_photoreceptorsLogSampling->getOutputNBrows();
cols=_photoreceptorsLogSampling->getOutputNBcolumns();
}
_photoreceptorsPrefilter.resize(rows, cols);
_ParvoRetinaFilter.resize(rows, cols);
_MagnoRetinaFilter.resize(rows, cols);
_colorEngine.resize(rows, cols);
// reset parvo magno mapping
_createHybridTable();
// clean buffers
clearAllBuffers();
}
// stability controls value init
void RetinaFilter::_setInitPeriodCount()
{
// find out the maximum temporal constant value and apply a security factor
// false value (obviously too long) but appropriate for simple use
_globalTemporalConstant=(unsigned int)(_ParvoRetinaFilter.getPhotoreceptorsTemporalConstant()+_ParvoRetinaFilter.getHcellsTemporalConstant()+_MagnoRetinaFilter.getTemporalConstant());
// reset frame counter
_ellapsedFramesSinceLastReset=0;
}
void RetinaFilter::_createHybridTable()
{
// create hybrid output and related coefficient table
_retinaParvoMagnoMappedFrame.resize(_photoreceptorsPrefilter.getNBpixels());
_retinaParvoMagnoMapCoefTable.resize(_photoreceptorsPrefilter.getNBpixels()*2);
// fill _hybridParvoMagnoCoefTable
int i, j, halfRows=_photoreceptorsPrefilter.getNBrows()/2, halfColumns=_photoreceptorsPrefilter.getNBcolumns()/2;
float *hybridParvoMagnoCoefTablePTR= &_retinaParvoMagnoMapCoefTable[0];
float minDistance=MIN(halfRows, halfColumns)*0.7f;
for (i=0;i<(int)_photoreceptorsPrefilter.getNBrows();++i)
{
for (j=0;j<(int)_photoreceptorsPrefilter.getNBcolumns();++j)
{
float distanceToCenter=sqrt(((float)(i-halfRows)*(i-halfRows)+(j-halfColumns)*(j-halfColumns)));
if (distanceToCenter<minDistance)
{
float a=*(hybridParvoMagnoCoefTablePTR++)=0.5f+0.5f*(float)cos(CV_PI*distanceToCenter/minDistance);
*(hybridParvoMagnoCoefTablePTR++)=1.f-a;
}else
{
*(hybridParvoMagnoCoefTablePTR++)=0.f;
*(hybridParvoMagnoCoefTablePTR++)=1.f;
}
}
}
}
// setup parameters function and global data filling
void RetinaFilter::setGlobalParameters(const float OPLspatialResponse1, const float OPLtemporalresponse1, const float OPLassymetryGain, const float OPLspatialResponse2, const float OPLtemporalresponse2, const float LPfilterSpatialResponse, const float LPfilterGain, const float LPfilterTemporalresponse, const float MovingContoursExtractorCoefficient, const bool normalizeParvoOutput_0_maxOutputValue, const bool normalizeMagnoOutput_0_maxOutputValue, const float maxOutputValue, const float maxInputValue, const float meanValue)
{
_normalizeParvoOutput_0_maxOutputValue=normalizeParvoOutput_0_maxOutputValue;
_normalizeMagnoOutput_0_maxOutputValue=normalizeMagnoOutput_0_maxOutputValue;
_maxOutputValue=maxOutputValue;
_photoreceptorsPrefilter.setV0CompressionParameter(0.9f, maxInputValue, meanValue);
_photoreceptorsPrefilter.setLPfilterParameters(10, 0, 1.5, 1); // keeps low pass filter with high cut frequency in memory (usefull for the tone mapping function)
_photoreceptorsPrefilter.setLPfilterParameters(10, 0, 3.0, 2); // keeps low pass filter with low cut frequency in memory (usefull for the tone mapping function)
_photoreceptorsPrefilter.setLPfilterParameters(0, 0, 10, 3); // keeps low pass filter with low cut frequency in memory (usefull for the tone mapping function)
//this->setV0CompressionParameter(0.6, maxInputValue, meanValue); // keeps log compression sensitivity parameter (usefull for the tone mapping function)
_ParvoRetinaFilter.setOPLandParvoFiltersParameters(0,OPLtemporalresponse1, OPLspatialResponse1, OPLassymetryGain, OPLtemporalresponse2, OPLspatialResponse2);
_ParvoRetinaFilter.setV0CompressionParameter(0.9f, maxInputValue, meanValue);
_MagnoRetinaFilter.setCoefficientsTable(LPfilterGain, LPfilterTemporalresponse, LPfilterSpatialResponse, MovingContoursExtractorCoefficient, 0, 2.0f*LPfilterSpatialResponse);
_MagnoRetinaFilter.setV0CompressionParameter(0.7f, maxInputValue, meanValue);
// stability controls value init
_setInitPeriodCount();
}
bool RetinaFilter::checkInput(const std::valarray<float> &input, const bool)
{
BasicRetinaFilter *inputTarget=&_photoreceptorsPrefilter;
if (_photoreceptorsLogSampling)
inputTarget=_photoreceptorsLogSampling;
bool test=input.size()==inputTarget->getNBpixels() || input.size()==(inputTarget->getNBpixels()*3) ;
if (!test)
{
std::cerr<<"RetinaFilter::checkInput: input buffer does not match retina buffer size, conversion aborted"<<std::endl;
std::cout<<"RetinaFilter::checkInput: input size="<<input.size()<<" / "<<"retina size="<<inputTarget->getNBpixels()<<std::endl;
return false;
}
return true;
}
// main function that runs the filter for a given input frame
bool RetinaFilter::runFilter(const std::valarray<float> &imageInput, const bool useAdaptiveFiltering, const bool processRetinaParvoMagnoMapping, const bool useColorMode, const bool inputIsColorMultiplexed)
{
// preliminary check
bool processSuccess=true;
if (!checkInput(imageInput, useColorMode))
return false;
// run the color multiplexing if needed and compute each suub filter of the retina:
// -> local adaptation
// -> contours OPL extraction
// -> moving contours extraction
// stability controls value update
++_ellapsedFramesSinceLastReset;
_useColorMode=useColorMode;
/* pointer to the appropriate input data after,
* by default, if graylevel mode, the input is processed,
* if color or something else must be considered, specific preprocessing are applied
*/
const std::valarray<float> *selectedPhotoreceptorsLocalAdaptationInput= &imageInput;
const std::valarray<float> *selectedPhotoreceptorsColorInput=&imageInput;
//********** Following is input data specific photoreceptors processing
if (_photoreceptorsLogSampling)
{
_photoreceptorsLogSampling->runProjection(imageInput, useColorMode);
selectedPhotoreceptorsColorInput=selectedPhotoreceptorsLocalAdaptationInput=&(_photoreceptorsLogSampling->getSampledFrame());
}
if (useColorMode&& (!inputIsColorMultiplexed)) // not multiplexed color input case
{
_colorEngine.runColorMultiplexing(*selectedPhotoreceptorsColorInput);
selectedPhotoreceptorsLocalAdaptationInput=&(_colorEngine.getMultiplexedFrame());
}
//********** Following is generic Retina processing
// photoreceptors local adaptation
_photoreceptorsPrefilter.runFilter_LocalAdapdation(*selectedPhotoreceptorsLocalAdaptationInput, _ParvoRetinaFilter.getHorizontalCellsOutput());
// safety pixel values checks
//_photoreceptorsPrefilter.normalizeGrayOutput_0_maxOutputValue(_maxOutputValue);
// run parvo filter
_ParvoRetinaFilter.runFilter(_photoreceptorsPrefilter.getOutput(), _useParvoOutput);
if (_useParvoOutput)
{
_ParvoRetinaFilter.normalizeGrayOutputCentredSigmoide(); // models the saturation of the cells, usefull for visualisation of the ON-OFF Parvo Output, Bipolar cells outputs do not change !!!
_ParvoRetinaFilter.centerReductImageLuminance(); // best for further spectrum analysis
if (_normalizeParvoOutput_0_maxOutputValue)
_ParvoRetinaFilter.normalizeGrayOutput_0_maxOutputValue(_maxOutputValue);
}
if (_useParvoOutput&&_useMagnoOutput)
{
_MagnoRetinaFilter.runFilter(_ParvoRetinaFilter.getBipolarCellsON(), _ParvoRetinaFilter.getBipolarCellsOFF());
if (_normalizeMagnoOutput_0_maxOutputValue)
{
_MagnoRetinaFilter.normalizeGrayOutput_0_maxOutputValue(_maxOutputValue);
}
_MagnoRetinaFilter.normalizeGrayOutputNearZeroCentreredSigmoide();
}
if (_useParvoOutput&&_useMagnoOutput&&processRetinaParvoMagnoMapping)
{
_processRetinaParvoMagnoMapping();
if (_useColorMode)
_colorEngine.runColorDemultiplexing(_retinaParvoMagnoMappedFrame, useAdaptiveFiltering, _maxOutputValue);//_ColorEngine->getMultiplexedFrame());//_ParvoRetinaFilter->getPhotoreceptorsLPfilteringOutput());
return processSuccess;
}
if (_useParvoOutput&&_useColorMode)
{
_colorEngine.runColorDemultiplexing(_ParvoRetinaFilter.getOutput(), useAdaptiveFiltering, _maxOutputValue);//_ColorEngine->getMultiplexedFrame());//_ParvoRetinaFilter->getPhotoreceptorsLPfilteringOutput());
// compute A Cr1 Cr2 to LMS color space conversion
//if (true)
// _applyImageColorSpaceConversion(_ColorEngine->getChrominance(), lmsTempBuffer.Buffer(), _LMStoACr1Cr2);
}
return processSuccess;
}
const std::valarray<float> &RetinaFilter::getContours()
{
if (_useColorMode)
return _colorEngine.getLuminance();
else
return _ParvoRetinaFilter.getOutput();
}
// run the initilized retina filter in order to perform gray image tone mapping, after this call all retina outputs are updated
void RetinaFilter::runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput, const float PhotoreceptorsCompression, const float ganglionCellsCompression)
{
// preliminary check
if (!checkInput(grayImageInput, false))
return;
this->_runGrayToneMapping(grayImageInput, grayImageOutput, PhotoreceptorsCompression, ganglionCellsCompression);
}
// run the initilized retina filter in order to perform gray image tone mapping, after this call all retina outputs are updated
void RetinaFilter::_runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput, const float PhotoreceptorsCompression, const float ganglionCellsCompression)
{
// stability controls value update
++_ellapsedFramesSinceLastReset;
std::valarray<float> temp2(grayImageInput.size());
// apply tone mapping on the multiplexed image
// -> photoreceptors local adaptation (large area adaptation)
_photoreceptorsPrefilter.runFilter_LPfilter(grayImageInput, grayImageOutput, 2); // compute low pass filtering modeling the horizontal cells filtering to acess local luminance
_photoreceptorsPrefilter.setV0CompressionParameterToneMapping(PhotoreceptorsCompression, grayImageOutput.sum()/(float)_photoreceptorsPrefilter.getNBpixels());
_photoreceptorsPrefilter.runFilter_LocalAdapdation(grayImageInput, grayImageOutput, temp2); // adapt contrast to local luminance
// high pass filter
//_spatiotemporalLPfilter(_localBuffer, _filterOutput, 2); // compute low pass filtering (high cut frequency (remove spatio-temporal noise)
//for (unsigned int i=0;i<_NBpixels;++i)
// _localBuffer[i]-= _filterOutput[i]/2.0;
// -> ganglion cells local adaptation (short area adaptation)
_photoreceptorsPrefilter.runFilter_LPfilter(temp2, grayImageOutput, 1); // compute low pass filtering (high cut frequency (remove spatio-temporal noise)
_photoreceptorsPrefilter.setV0CompressionParameterToneMapping(ganglionCellsCompression, temp2.max(), temp2.sum()/(float)_photoreceptorsPrefilter.getNBpixels());
_photoreceptorsPrefilter.runFilter_LocalAdapdation(temp2, grayImageOutput, grayImageOutput); // adapt contrast to local luminance
}
// run the initilized retina filter in order to perform color tone mapping, after this call all retina outputs are updated
void RetinaFilter::runRGBToneMapping(const std::valarray<float> &RGBimageInput, std::valarray<float> &RGBimageOutput, const bool useAdaptiveFiltering, const float PhotoreceptorsCompression, const float ganglionCellsCompression)
{
// preliminary check
if (!checkInput(RGBimageInput, true))
return;
// multiplex the image with the color sampling method specified in the constructor
_colorEngine.runColorMultiplexing(RGBimageInput);
// apply tone mapping on the multiplexed image
_runGrayToneMapping(_colorEngine.getMultiplexedFrame(), RGBimageOutput, PhotoreceptorsCompression, ganglionCellsCompression);
// demultiplex tone maped image
_colorEngine.runColorDemultiplexing(RGBimageOutput, useAdaptiveFiltering, _photoreceptorsPrefilter.getMaxInputValue());//_ColorEngine->getMultiplexedFrame());//_ParvoRetinaFilter->getPhotoreceptorsLPfilteringOutput());
// rescaling result between 0 and 255
_colorEngine.normalizeRGBOutput_0_maxOutputValue(255.0);
// return the result
RGBimageOutput=_colorEngine.getDemultiplexedColorFrame();
}
void RetinaFilter::runLMSToneMapping(const std::valarray<float> &, std::valarray<float> &, const bool, const float, const float)
{
std::cerr<<"not working, sorry"<<std::endl;
/* // preliminary check
const std::valarray<float> &bufferInput=checkInput(LMSimageInput, true);
if (!bufferInput)
return NULL;
if (!_useColorMode)
std::cerr<<"RetinaFilter::Can not call tone mapping oeration if the retina filter was created for gray scale images"<<std::endl;
// create a temporary buffer of size nrows, Mcolumns, 3 layers
std::valarray<float> lmsTempBuffer(LMSimageInput);
std::cout<<"RetinaFilter::--->min LMS value="<<lmsTempBuffer.min()<<std::endl;
// setup local adaptation parameter at the photoreceptors level
setV0CompressionParameter(PhotoreceptorsCompression, _maxInputValue);
// get the local energy of each color channel
// ->L
_spatiotemporalLPfilter(LMSimageInput, _filterOutput, 1);
setV0CompressionParameterToneMapping(PhotoreceptorsCompression, _maxInputValue, this->sum()/_NBpixels);
_localLuminanceAdaptation(LMSimageInput, _filterOutput, lmsTempBuffer.Buffer());
// ->M
_spatiotemporalLPfilter(LMSimageInput+_NBpixels, _filterOutput, 1);
setV0CompressionParameterToneMapping(PhotoreceptorsCompression, _maxInputValue, this->sum()/_NBpixels);
_localLuminanceAdaptation(LMSimageInput+_NBpixels, _filterOutput, lmsTempBuffer.Buffer()+_NBpixels);
// ->S
_spatiotemporalLPfilter(LMSimageInput+_NBpixels*2, _filterOutput, 1);
setV0CompressionParameterToneMapping(PhotoreceptorsCompression, _maxInputValue, this->sum()/_NBpixels);
_localLuminanceAdaptation(LMSimageInput+_NBpixels*2, _filterOutput, lmsTempBuffer.Buffer()+_NBpixels*2);
// eliminate negative values
for (unsigned int i=0;i<lmsTempBuffer.size();++i)
if (lmsTempBuffer.Buffer()[i]<0)
lmsTempBuffer.Buffer()[i]=0;
std::cout<<"RetinaFilter::->min LMS value="<<lmsTempBuffer.min()<<std::endl;
// compute LMS to A Cr1 Cr2 color space conversion
_applyImageColorSpaceConversion(lmsTempBuffer.Buffer(), lmsTempBuffer.Buffer(), _LMStoACr1Cr2);
TemplateBuffer <float> acr1cr2TempBuffer(_NBrows, _NBcolumns, 3);
memcpy(acr1cr2TempBuffer.Buffer(), lmsTempBuffer.Buffer(), sizeof(float)*_NBpixels*3);
// compute A Cr1 Cr2 to LMS color space conversion
_applyImageColorSpaceConversion(acr1cr2TempBuffer.Buffer(), lmsTempBuffer.Buffer(), _ACr1Cr2toLMS);
// eliminate negative values
for (unsigned int i=0;i<lmsTempBuffer.size();++i)
if (lmsTempBuffer.Buffer()[i]<0)
lmsTempBuffer.Buffer()[i]=0;
// rewrite output to the appropriate buffer
_colorEngine->setDemultiplexedColorFrame(lmsTempBuffer.Buffer());
*/
}
// return image with center Parvo and peripheral Magno channels
void RetinaFilter::_processRetinaParvoMagnoMapping()
{
register float *hybridParvoMagnoPTR= &_retinaParvoMagnoMappedFrame[0];
register const float *parvoOutputPTR= get_data(_ParvoRetinaFilter.getOutput());
register const float *magnoXOutputPTR= get_data(_MagnoRetinaFilter.getOutput());
register float *hybridParvoMagnoCoefTablePTR= &_retinaParvoMagnoMapCoefTable[0];
for (unsigned int i=0 ; i<_photoreceptorsPrefilter.getNBpixels() ; ++i, hybridParvoMagnoCoefTablePTR+=2)
{
float hybridValue=*(parvoOutputPTR++)**(hybridParvoMagnoCoefTablePTR)+*(magnoXOutputPTR++)**(hybridParvoMagnoCoefTablePTR+1);
*(hybridParvoMagnoPTR++)=hybridValue;
}
TemplateBuffer<float>::normalizeGrayOutput_0_maxOutputValue(&_retinaParvoMagnoMappedFrame[0], _photoreceptorsPrefilter.getNBpixels());
}
bool RetinaFilter::getParvoFoveaResponse(std::valarray<float> &parvoFovealResponse)
{
if (!_useParvoOutput)
return false;
if (parvoFovealResponse.size() != _ParvoRetinaFilter.getNBpixels())
return false;
register const float *parvoOutputPTR= get_data(_ParvoRetinaFilter.getOutput());
register float *fovealParvoResponsePTR= &parvoFovealResponse[0];
register float *hybridParvoMagnoCoefTablePTR= &_retinaParvoMagnoMapCoefTable[0];
for (unsigned int i=0 ; i<_photoreceptorsPrefilter.getNBpixels() ; ++i, hybridParvoMagnoCoefTablePTR+=2)
{
*(fovealParvoResponsePTR++)=*(parvoOutputPTR++)**(hybridParvoMagnoCoefTablePTR);
}
return true;
}
// method to retrieve the parafoveal magnocellular pathway response (no energy motion in fovea)
bool RetinaFilter::getMagnoParaFoveaResponse(std::valarray<float> &magnoParafovealResponse)
{
if (!_useMagnoOutput)
return false;
if (magnoParafovealResponse.size() != _MagnoRetinaFilter.getNBpixels())
return false;
register const float *magnoXOutputPTR= get_data(_MagnoRetinaFilter.getOutput());
register float *parafovealMagnoResponsePTR=&magnoParafovealResponse[0];
register float *hybridParvoMagnoCoefTablePTR=&_retinaParvoMagnoMapCoefTable[0]+1;
for (unsigned int i=0 ; i<_photoreceptorsPrefilter.getNBpixels() ; ++i, hybridParvoMagnoCoefTablePTR+=2)
{
*(parafovealMagnoResponsePTR++)=*(magnoXOutputPTR++)**(hybridParvoMagnoCoefTablePTR);
}
return true;
}
}