xref: /dports/graphics/opencv/opencv-4.5.3/contrib/modules/bioinspired/src/basicretinafilter.cpp

/*#******************************************************************************
** 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.
**
**
** bioinspired : 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.
**
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** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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**               For Human Visual System tools (bioinspired)
** 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 <iostream>
#include <cstdlib>
#include "basicretinafilter.hpp"
#include <cmath>


namespace cv
{
namespace bioinspired
{
// @author Alexandre BENOIT, benoit.alexandre.vision@gmail.com, LISTIC : www.listic.univ-savoie.fr Gipsa-Lab, France: www.gipsa-lab.inpg.fr/

//////////////////////////////////////////////////////////
//                 BASIC RETINA FILTER
//////////////////////////////////////////////////////////

// Constructor and Desctructor of the basic retina filter
BasicRetinaFilter::BasicRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns, const unsigned int parametersListSize, const bool useProgressiveFilter)
:_filterOutput(NBrows, NBcolumns),
 _localBuffer(NBrows*NBcolumns),
 _filteringCoeficientsTable(3*parametersListSize),
 _progressiveSpatialConstant(0),// pointer to a local table containing local spatial constant (allocated with the object)
 _progressiveGain(0)
{
#ifdef T_BASIC_RETINA_ELEMENT_DEBUG
    std::cout<<"BasicRetinaFilter::BasicRetinaFilter: new filter, size="<<NBrows<<", "<<NBcolumns<<std::endl;
#endif
    _halfNBrows=_filterOutput.getNBrows()/2;
    _halfNBcolumns=_filterOutput.getNBcolumns()/2;

    if (useProgressiveFilter)
    {
#ifdef T_BASIC_RETINA_ELEMENT_DEBUG
        std::cout<<"BasicRetinaFilter::BasicRetinaFilter: _progressiveSpatialConstant_Tbuffer"<<std::endl;
#endif
        _progressiveSpatialConstant.resize(_filterOutput.size());
#ifdef T_BASIC_RETINA_ELEMENT_DEBUG
        std::cout<<"BasicRetinaFilter::BasicRetinaFilter: new _progressiveGain_Tbuffer"<<NBrows<<", "<<NBcolumns<<std::endl;
#endif
        _progressiveGain.resize(_filterOutput.size());
    }
#ifdef T_BASIC_RETINA_ELEMENT_DEBUG
    std::cout<<"BasicRetinaFilter::BasicRetinaFilter: new filter, size="<<NBrows<<", "<<NBcolumns<<std::endl;
#endif

    // set default values
    _maxInputValue=256.0;

    // reset all buffers
    clearAllBuffers();

#ifdef T_BASIC_RETINA_ELEMENT_DEBUG
    std::cout<<"BasicRetinaFilter::Init BasicRetinaElement at specified frame size OK, size="<<this->size()<<std::endl;
#endif

}

BasicRetinaFilter::~BasicRetinaFilter()
{

#ifdef BASIC_RETINA_ELEMENT_DEBUG
    std::cout<<"BasicRetinaFilter::BasicRetinaElement Deleted OK"<<std::endl;
#endif

}

////////////////////////////////////
// functions of the basic filter
////////////////////////////////////


// resize all allocated buffers
void BasicRetinaFilter::resize(const unsigned int NBrows, const unsigned int NBcolumns)
{

    std::cout<<"BasicRetinaFilter::resize( "<<NBrows<<", "<<NBcolumns<<")"<<std::endl;

    // resizing buffers
    _filterOutput.resizeBuffer(NBrows, NBcolumns);

    // updating variables
    _halfNBrows=_filterOutput.getNBrows()/2;
    _halfNBcolumns=_filterOutput.getNBcolumns()/2;

    _localBuffer.resize(_filterOutput.size());
    // in case of spatial adapted filter
    if (_progressiveSpatialConstant.size()>0)
    {
        _progressiveSpatialConstant.resize(_filterOutput.size());
        _progressiveGain.resize(_filterOutput.size());
    }
    // reset buffers
    clearAllBuffers();
}

// Change coefficients table
void BasicRetinaFilter::setLPfilterParameters(const float beta, const float tau, const float desired_k, const unsigned int filterIndex)
{
    float _beta = beta+tau;
    float k=desired_k;
    // check if the spatial constant is correct (avoid 0 value to avoid division by 0)
    if (desired_k<=0)
    {
        k=0.001f;
        std::cerr<<"BasicRetinaFilter::spatial constant of the low pass filter must be superior to zero !!! correcting parameter setting to 0,001"<<std::endl;
    }

    float _alpha = k*k;
    float _mu = 0.8f;
    unsigned int tableOffset=filterIndex*3;
    if (k<=0)
    {
        std::cerr<<"BasicRetinaFilter::spatial filtering coefficient must be superior to zero, correcting value to 0.01"<<std::endl;
        _alpha=0.0001f;
    }

    float _temp =  (1.0f+_beta)/(2.0f*_mu*_alpha);
    float a = _filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)std::sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
    _filteringCoeficientsTable[1+tableOffset]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
    _filteringCoeficientsTable[2+tableOffset] =tau;

    //std::cout<<"BasicRetinaFilter::normal:"<<(1.0-a)*(1.0-a)*(1.0-a)*(1.0-a)/(1.0+_beta)<<" -> old:"<<(1-a)*(1-a)*(1-a)*(1-a)/(1+_beta)<<std::endl;

    //std::cout<<"BasicRetinaFilter::a="<<a<<", gain="<<_filteringCoeficientsTable[1+tableOffset]<<", tau="<<tau<<std::endl;
}

void BasicRetinaFilter::setProgressiveFilterConstants_CentredAccuracy(const float beta, const float tau, const float alpha0, const unsigned int filterIndex)
{
    // check if dedicated buffers are already allocated, if not create them
    if (_progressiveSpatialConstant.size()!=_filterOutput.size())
    {
        _progressiveSpatialConstant.resize(_filterOutput.size());
        _progressiveGain.resize(_filterOutput.size());
    }

    float _beta = beta+tau;
    float _mu=0.8f;
    if (alpha0<=0)
    {
        std::cerr<<"BasicRetinaFilter::spatial filtering coefficient must be superior to zero, correcting value to 0.01"<<std::endl;
        //alpha0=0.0001;
    }

    unsigned int tableOffset=filterIndex*3;

    float _alpha=0.8f;
    float _temp =  (1.0f+_beta)/(2.0f*_mu*_alpha);
    float a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)std::sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
    _filteringCoeficientsTable[tableOffset+1]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
    _filteringCoeficientsTable[tableOffset+2] =tau;

    float commonFactor=alpha0/(float)std::sqrt(_halfNBcolumns*_halfNBcolumns+_halfNBrows*_halfNBrows+1.0f);
    //memset(_progressiveSpatialConstant, 255, _filterOutput.getNBpixels());
    for (unsigned int idColumn=0;idColumn<_halfNBcolumns; ++idColumn)
        for (unsigned int idRow=0;idRow<_halfNBrows; ++idRow)
        {
            // computing local spatial constant
            float localSpatialConstantValue=commonFactor*std::sqrt((float)(idColumn*idColumn)+(float)(idRow*idRow));
            if (localSpatialConstantValue>1.0f)
                localSpatialConstantValue=1.0f;

            _progressiveSpatialConstant[_halfNBcolumns-1+idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1+idRow)]=localSpatialConstantValue;
            _progressiveSpatialConstant[_halfNBcolumns-1-idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1+idRow)]=localSpatialConstantValue;
            _progressiveSpatialConstant[_halfNBcolumns-1+idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1-idRow)]=localSpatialConstantValue;
            _progressiveSpatialConstant[_halfNBcolumns-1-idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1-idRow)]=localSpatialConstantValue;

            // computing local gain
            float localGain=(1-localSpatialConstantValue)*(1-localSpatialConstantValue)*(1-localSpatialConstantValue)*(1-localSpatialConstantValue)/(1+_beta);
            _progressiveGain[_halfNBcolumns-1+idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1+idRow)]=localGain;
            _progressiveGain[_halfNBcolumns-1-idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1+idRow)]=localGain;
            _progressiveGain[_halfNBcolumns-1+idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1-idRow)]=localGain;
            _progressiveGain[_halfNBcolumns-1-idColumn+_filterOutput.getNBcolumns()*(_halfNBrows-1-idRow)]=localGain;

            //std::cout<<commonFactor<<", "<<std::sqrt((_halfNBcolumns-1-idColumn)+(_halfNBrows-idRow-1))<<", "<<(_halfNBcolumns-1-idColumn)<<", "<<(_halfNBrows-idRow-1)<<", "<<localSpatialConstantValue<<std::endl;
        }
}

void BasicRetinaFilter::setProgressiveFilterConstants_CustomAccuracy(const float beta, const float tau, const float k, const std::valarray<float> &accuracyMap, const unsigned int filterIndex)
{

    if (accuracyMap.size()!=_filterOutput.size())
    {
        std::cerr<<"BasicRetinaFilter::setProgressiveFilterConstants_CustomAccuracy: error: input accuracy map does not match filter size, init skept"<<std::endl;
        return ;
    }

    // check if dedicated buffers are already allocated, if not create them
    if (_progressiveSpatialConstant.size()!=_filterOutput.size())
    {
        _progressiveSpatialConstant.resize(accuracyMap.size());
        _progressiveGain.resize(accuracyMap.size());
    }

    float _beta = beta+tau;
    float _alpha=k*k;
    float _mu=0.8f;
    if (k<=0)
    {
        std::cerr<<"BasicRetinaFilter::spatial filtering coefficient must be superior to zero, correcting value to 0.01"<<std::endl;
        //alpha0=0.0001;
    }
    unsigned int tableOffset=filterIndex*3;
    float _temp =  (1.0f+_beta)/(2.0f*_mu*_alpha);
    float a=_filteringCoeficientsTable[tableOffset] = 1.0f + _temp - (float)std::sqrt( (1.0f+_temp)*(1.0f+_temp) - 1.0f);
    _filteringCoeficientsTable[tableOffset+1]=(1.0f-a)*(1.0f-a)*(1.0f-a)*(1.0f-a)/(1.0f+_beta);
    _filteringCoeficientsTable[tableOffset+2] =tau;

    //memset(_progressiveSpatialConstant, 255, _filterOutput.getNBpixels());
    for (unsigned int idColumn=0;idColumn<_filterOutput.getNBcolumns(); ++idColumn)
        for (unsigned int idRow=0;idRow<_filterOutput.getNBrows(); ++idRow)
        {
            // computing local spatial constant
            unsigned int index=idColumn+idRow*_filterOutput.getNBcolumns();
            float localSpatialConstantValue=_a*accuracyMap[index];
            if (localSpatialConstantValue>1)
                localSpatialConstantValue=1;

            _progressiveSpatialConstant[index]=localSpatialConstantValue;

            // computing local gain
            float localGain=(1.0f-localSpatialConstantValue)*(1.0f-localSpatialConstantValue)*(1.0f-localSpatialConstantValue)*(1.0f-localSpatialConstantValue)/(1.0f+_beta);
            _progressiveGain[index]=localGain;

            //std::cout<<commonFactor<<", "<<std::sqrt((_halfNBcolumns-1-idColumn)+(_halfNBrows-idRow-1))<<", "<<(_halfNBcolumns-1-idColumn)<<", "<<(_halfNBrows-idRow-1)<<", "<<localSpatialConstantValue<<std::endl;
        }
}

///////////////////////////////////////////////////////////////////////
/// Local luminance adaptation functions
// run local adaptation filter and save result in _filterOutput
const std::valarray<float> &BasicRetinaFilter::runFilter_LocalAdapdation(const std::valarray<float> &inputFrame, const std::valarray<float> &localLuminance)
{
    _localLuminanceAdaptation(get_data(inputFrame), get_data(localLuminance), &_filterOutput[0]);
    return _filterOutput;
}
// run local adaptation filter at a specific output adress
void BasicRetinaFilter::runFilter_LocalAdapdation(const std::valarray<float> &inputFrame, const std::valarray<float> &localLuminance, std::valarray<float> &outputFrame)
{
    _localLuminanceAdaptation(get_data(inputFrame), get_data(localLuminance), &outputFrame[0]);
}
// run local adaptation filter and save result in _filterOutput with autonomous low pass filtering before adaptation
const std::valarray<float> &BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame)
{
    _spatiotemporalLPfilter(get_data(inputFrame), &_filterOutput[0]);
    _localLuminanceAdaptation(get_data(inputFrame), &_filterOutput[0], &_filterOutput[0]);
    return _filterOutput;
}
// run local adaptation filter at a specific output adress with autonomous low pass filtering before adaptation
void BasicRetinaFilter::runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame)
{
    _spatiotemporalLPfilter(get_data(inputFrame), &_filterOutput[0]);
    _localLuminanceAdaptation(get_data(inputFrame), &_filterOutput[0], &outputFrame[0]);
}

// local luminance adaptation of the input in regard of localLuminance buffer, the input is rewrited and becomes the output
void BasicRetinaFilter::_localLuminanceAdaptation(float *inputOutputFrame, const float *localLuminance)
{
    _localLuminanceAdaptation(inputOutputFrame, localLuminance, inputOutputFrame, false);

    /*    const float *localLuminancePTR=localLuminance;
    float *inputOutputFramePTR=inputOutputFrame;

    for (unsigned int IDpixel=0 ; IDpixel<_filterOutput.getNBpixels() ; ++IDpixel, ++inputOutputFramePTR)
    {
        float X0=*(localLuminancePTR++)*_localLuminanceFactor+_localLuminanceAddon;
        *(inputOutputFramePTR) = (_maxInputValue+X0)**inputOutputFramePTR/(*inputOutputFramePTR +X0+0.00000000001);
    }
      */
}

// local luminance adaptation of the input in regard of localLuminance buffer
void BasicRetinaFilter::_localLuminanceAdaptation(const float *inputFrame, const float *localLuminance, float *outputFrame, const bool updateLuminanceMean)
{
    if (updateLuminanceMean)
    {	float meanLuminance=0;
        const float *luminancePTR=inputFrame;
        for (unsigned int i=0;i<_filterOutput.getNBpixels();++i)
            meanLuminance+=*(luminancePTR++);
        meanLuminance/=_filterOutput.getNBpixels();
        //float tempMeanValue=meanLuminance+_meanInputValue*_tau;
        updateCompressionParameter(meanLuminance);
    }
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(0,_filterOutput.getNBpixels()), Parallel_localAdaptation(localLuminance, inputFrame, outputFrame, _localLuminanceFactor, _localLuminanceAddon, _maxInputValue));
#else
    //std::cout<<meanLuminance<<std::endl;
    const float *localLuminancePTR=localLuminance;
    const float *inputFramePTR=inputFrame;
    float *outputFramePTR=outputFrame;
    for (unsigned int IDpixel=0 ; IDpixel<_filterOutput.getNBpixels() ; ++IDpixel, ++inputFramePTR, ++outputFramePTR)
    {
        float X0=*(localLuminancePTR++)*_localLuminanceFactor+_localLuminanceAddon;
        // TODO : the following line can lead to a divide by zero ! A small offset is added, take care if the offset is too large in case of High Dynamic Range images which can use very small values...
        *(outputFramePTR) = (_maxInputValue+X0)**inputFramePTR/(*inputFramePTR +X0+0.00000000001);
        //std::cout<<"BasicRetinaFilter::inputFrame[IDpixel]=%f, X0=%f, outputFrame[IDpixel]=%f\n", inputFrame[IDpixel], X0, outputFrame[IDpixel]);
    }
#endif
}

// local adaptation applied on a range of values which can be positive and negative
void BasicRetinaFilter::_localLuminanceAdaptationPosNegValues(const float *inputFrame, const float *localLuminance, float *outputFrame)
{
    const float *localLuminancePTR=localLuminance;
    const float *inputFramePTR=inputFrame;
    float *outputFramePTR=outputFrame;
    float factor=_maxInputValue*2.0f/(float)CV_PI;
    for (unsigned int IDpixel=0 ; IDpixel<_filterOutput.getNBpixels() ; ++IDpixel, ++inputFramePTR)
    {
        float X0=*(localLuminancePTR++)*_localLuminanceFactor+_localLuminanceAddon;
        *(outputFramePTR++) = factor*atan(*inputFramePTR/X0);//(_maxInputValue+X0)**inputFramePTR/(*inputFramePTR +X0);
        //std::cout<<"BasicRetinaFilter::inputFrame[IDpixel]=%f, X0=%f, outputFrame[IDpixel]=%f\n", inputFrame[IDpixel], X0, outputFrame[IDpixel]);
    }
}

///////////////////////////////////////////////////////////////////////
/// Spatio temporal Low Pass filter functions
// run LP filter and save result in the basic retina element buffer
const std::valarray<float> &BasicRetinaFilter::runFilter_LPfilter(const std::valarray<float> &inputFrame, const unsigned int filterIndex)
{
    _spatiotemporalLPfilter(get_data(inputFrame), &_filterOutput[0], filterIndex);
    return _filterOutput;
}

// run LP filter for a new frame input and save result at a specific output adress
void BasicRetinaFilter::runFilter_LPfilter(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame, const unsigned int filterIndex)
{
    _spatiotemporalLPfilter(get_data(inputFrame), &outputFrame[0], filterIndex);
}

// run LP filter on the input data and rewrite it
void BasicRetinaFilter::runFilter_LPfilter_Autonomous(std::valarray<float> &inputOutputFrame, const unsigned int filterIndex)
{
    unsigned int coefTableOffset=filterIndex*3;

    /**********/
    _a=_filteringCoeficientsTable[coefTableOffset];
    _gain=_filteringCoeficientsTable[1+coefTableOffset];
    _tau=_filteringCoeficientsTable[2+coefTableOffset];

    // launch the serie of 1D directional filters in order to compute the 2D low pass filter
    _horizontalCausalFilter(&inputOutputFrame[0], 0, _filterOutput.getNBrows());
    _horizontalAnticausalFilter(&inputOutputFrame[0], 0, _filterOutput.getNBrows());
    _verticalCausalFilter(&inputOutputFrame[0], 0, _filterOutput.getNBcolumns());
    _verticalAnticausalFilter_multGain(&inputOutputFrame[0], 0, _filterOutput.getNBcolumns());

}
// run LP filter for a new frame input and save result at a specific output adress
void BasicRetinaFilter::_spatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex)
{
    unsigned int coefTableOffset=filterIndex*3;
    /**********/
    _a=_filteringCoeficientsTable[coefTableOffset];
    _gain=_filteringCoeficientsTable[1+coefTableOffset];
    _tau=_filteringCoeficientsTable[2+coefTableOffset];

    // launch the serie of 1D directional filters in order to compute the 2D low pass filter
    _horizontalCausalFilter_addInput(inputFrame, outputFrame, 0,_filterOutput.getNBrows());
    _horizontalAnticausalFilter(outputFrame, 0, _filterOutput.getNBrows());
    _verticalCausalFilter(outputFrame, 0, _filterOutput.getNBcolumns());
    _verticalAnticausalFilter_multGain(outputFrame, 0, _filterOutput.getNBcolumns());

}

// run SQUARING LP filter for a new frame input and save result at a specific output adress
float BasicRetinaFilter::_squaringSpatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex)
{
    unsigned int coefTableOffset=filterIndex*3;
    /**********/
    _a=_filteringCoeficientsTable[coefTableOffset];
    _gain=_filteringCoeficientsTable[1+coefTableOffset];
    _tau=_filteringCoeficientsTable[2+coefTableOffset];

    // launch the serie of 1D directional filters in order to compute the 2D low pass filter

    _squaringHorizontalCausalFilter(inputFrame, outputFrame, 0, _filterOutput.getNBrows());
    _horizontalAnticausalFilter(outputFrame, 0, _filterOutput.getNBrows());
    _verticalCausalFilter(outputFrame, 0, _filterOutput.getNBcolumns());
    return _verticalAnticausalFilter_returnMeanValue(outputFrame, 0, _filterOutput.getNBcolumns());
}

/////////////////////////////////////////////////
// standard version of the 1D low pass filters

//  horizontal causal filter which adds the input inside
void BasicRetinaFilter::_horizontalCausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{


    //#pragma omp parallel for
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float* outputPTR=outputFrame+(IDrowStart+IDrow)*_filterOutput.getNBcolumns();
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(outputPTR)+  _a* result;
            *(outputPTR++) = result;
        }
    }
}
//  horizontal causal filter which adds the input inside
void BasicRetinaFilter::_horizontalCausalFilter_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDrowStart,IDrowEnd), Parallel_horizontalCausalFilter_addInput(inputFrame, outputFrame, IDrowStart, _filterOutput.getNBcolumns(), _a, _tau));
#else
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float* outputPTR=outputFrame+(IDrowStart+IDrow)*_filterOutput.getNBcolumns();
        const float* inputPTR=inputFrame+(IDrowStart+IDrow)*_filterOutput.getNBcolumns();
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(inputPTR++) + _tau**(outputPTR)+  _a* result;
            *(outputPTR++) = result;
        }
    }
#endif
}

//  horizontal anticausal filter  (basic way, no add on)
void BasicRetinaFilter::_horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{

#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDrowStart,IDrowEnd), Parallel_horizontalAnticausalFilter(outputFrame, IDrowEnd, _filterOutput.getNBcolumns(), _a ));
#else
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float* outputPTR=outputFrame+(IDrowEnd-IDrow)*(_filterOutput.getNBcolumns())-1;
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(outputPTR)+  _a* result;
            *(outputPTR--) = result;
        }
    }
#endif
}

//  horizontal anticausal filter which multiplies the output by _gain
void BasicRetinaFilter::_horizontalAnticausalFilter_multGain(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{

    //#pragma omp parallel for
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float* outputPTR=outputFrame+(IDrowEnd-IDrow)*(_filterOutput.getNBcolumns())-1;
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(outputPTR)+  _a* result;
            *(outputPTR--) = _gain*result;
        }
    }
}

//  vertical anticausal filter
void BasicRetinaFilter::_verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDcolumnStart,IDcolumnEnd), Parallel_verticalCausalFilter(outputFrame, _filterOutput.getNBrows(), _filterOutput.getNBcolumns(), _a ));
#else
        for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=outputFrame+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + _a * result;
            *(outputPTR) = result;
            outputPTR+=_filterOutput.getNBcolumns();

        }
    }
#endif
}


//  vertical anticausal filter (basic way, no add on)
void BasicRetinaFilter::_verticalAnticausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
    float* offset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    //#pragma omp parallel for
    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=offset+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + _a * result;
            *(outputPTR) = result;
            outputPTR-=_filterOutput.getNBcolumns();

        }
    }
}

//  vertical anticausal filter which multiplies the output by _gain
void BasicRetinaFilter::_verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDcolumnStart,IDcolumnEnd), Parallel_verticalAnticausalFilter_multGain(outputFrame, _filterOutput.getNBrows(), _filterOutput.getNBcolumns(), _a, _gain ));
#else
        float* offset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    //#pragma omp parallel for
    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=offset+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + _a * result;
            *(outputPTR) = _gain*result;
            outputPTR-=_filterOutput.getNBcolumns();

        }
    }
#endif
}

/////////////////////////////////////////
// specific modifications of 1D filters

// -> squaring horizontal causal filter
void BasicRetinaFilter::_squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{
    float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const float* inputPTR=inputFrame+IDrowStart*_filterOutput.getNBcolumns();
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(inputPTR)**(inputPTR) + _tau**(outputPTR)+  _a* result;
            *(outputPTR++) = result;
            ++inputPTR;
        }
    }
}

//  vertical anticausal filter that returns the mean value of its result
float BasicRetinaFilter::_verticalAnticausalFilter_returnMeanValue(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
    float meanValue=0;
    float* offset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=offset+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + _a * result;
            *(outputPTR) = _gain*result;
            meanValue+=*(outputPTR);
            outputPTR-=_filterOutput.getNBcolumns();

        }
    }

    return meanValue/(float)_filterOutput.getNBpixels();
}

// LP filter with integration in specific areas (regarding true values of a binary parameters image)
void BasicRetinaFilter::_localSquaringSpatioTemporalLPfilter(const float *inputFrame, float *LPfilterOutput, const unsigned int *integrationAreas, const unsigned int filterIndex)
{
    unsigned int coefTableOffset=filterIndex*3;
    _a=_filteringCoeficientsTable[coefTableOffset+0];
    _gain=_filteringCoeficientsTable[coefTableOffset+1];
    _tau=_filteringCoeficientsTable[coefTableOffset+2];
    // launch the serie of 1D directional filters in order to compute the 2D low pass filter

    _local_squaringHorizontalCausalFilter(inputFrame, LPfilterOutput, 0, _filterOutput.getNBrows(), integrationAreas);
    _local_horizontalAnticausalFilter(LPfilterOutput, 0, _filterOutput.getNBrows(), integrationAreas);
    _local_verticalCausalFilter(LPfilterOutput, 0, _filterOutput.getNBcolumns(), integrationAreas);
    _local_verticalAnticausalFilter_multGain(LPfilterOutput, 0, _filterOutput.getNBcolumns(), integrationAreas);

}

// LP filter on specific parts of the picture instead of all the image
// same functions (some of them) but take a binary flag to allow integration, false flag means, no data change at the output...

// this function take an image in input and squares it befor computing
void BasicRetinaFilter::_local_squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas)
{
    float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const float* inputPTR=inputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const unsigned int *integrationAreasPTR=integrationAreas;
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            if (*(integrationAreasPTR++))
                result = *(inputPTR)**(inputPTR) + _tau**(outputPTR)+  _a* result;
            else
                result=0;
            *(outputPTR++) = result;
            ++inputPTR;

        }
    }
}

void BasicRetinaFilter::_local_horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas)
{

    float* outputPTR=outputFrame+IDrowEnd*(_filterOutput.getNBcolumns())-1;
    const unsigned int *integrationAreasPTR=integrationAreas;

    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            if (*(integrationAreasPTR++))
                result = *(outputPTR)+  _a* result;
            else
                result=0;
            *(outputPTR--) = result;
        }
    }

}

void BasicRetinaFilter::_local_verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas)
{
    const unsigned int *integrationAreasPTR=integrationAreas;

    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=outputFrame+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            if (*(integrationAreasPTR++))
                result = *(outputPTR)+  _a* result;
            else
                result=0;
            *(outputPTR) = result;
            outputPTR+=_filterOutput.getNBcolumns();

        }
    }
}
// this functions affects _gain at the output
void BasicRetinaFilter::_local_verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas)
{
    const unsigned int *integrationAreasPTR=integrationAreas;
    float* offset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();

    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=offset+IDcolumn;

        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            if (*(integrationAreasPTR++))
                result = *(outputPTR)+  _a* result;
            else
                result=0;
            *(outputPTR) = _gain*result;
            outputPTR-=_filterOutput.getNBcolumns();

        }
    }
}

////////////////////////////////////////////////////
// run LP filter for a new frame input and save result at a specific output adress
// -> USE IRREGULAR SPATIAL CONSTANT

// irregular filter computed from a buffer and rewrites it
void BasicRetinaFilter::_spatiotemporalLPfilter_Irregular(float *inputOutputFrame, const unsigned int filterIndex)
{
    if (_progressiveGain.size()==0)
    {
        std::cerr<<"BasicRetinaFilter::runProgressiveFilter: cannot perform filtering, no progressive filter settled up"<<std::endl;
        return;
    }
    unsigned int coefTableOffset=filterIndex*3;
    /**********/
    //_a=_filteringCoeficientsTable[coefTableOffset];
    _tau=_filteringCoeficientsTable[2+coefTableOffset];

    // launch the serie of 1D directional filters in order to compute the 2D low pass filter
    _horizontalCausalFilter_Irregular(inputOutputFrame, 0, (int)_filterOutput.getNBrows());
    _horizontalAnticausalFilter_Irregular(inputOutputFrame, 0, (int)_filterOutput.getNBrows(), &_progressiveSpatialConstant[0]);
    _verticalCausalFilter_Irregular(inputOutputFrame, 0, (int)_filterOutput.getNBcolumns(), &_progressiveSpatialConstant[0]);
    _verticalAnticausalFilter_Irregular_multGain(inputOutputFrame, 0, (int)_filterOutput.getNBcolumns());

}
// irregular filter computed from a buffer and puts result on another
void BasicRetinaFilter::_spatiotemporalLPfilter_Irregular(const float *inputFrame, float *outputFrame, const unsigned int filterIndex)
{
    if (_progressiveGain.size()==0)
    {
        std::cerr<<"BasicRetinaFilter::runProgressiveFilter: cannot perform filtering, no progressive filter settled up"<<std::endl;
        return;
    }
    unsigned int coefTableOffset=filterIndex*3;
    /**********/
    //_a=_filteringCoeficientsTable[coefTableOffset];
    _tau=_filteringCoeficientsTable[2+coefTableOffset];

    // launch the serie of 1D directional filters in order to compute the 2D low pass filter
    _horizontalCausalFilter_Irregular_addInput(inputFrame, outputFrame, 0, (int)_filterOutput.getNBrows());
    _horizontalAnticausalFilter_Irregular(outputFrame, 0, (int)_filterOutput.getNBrows(), &_progressiveSpatialConstant[0]);
    _verticalCausalFilter_Irregular(outputFrame, 0, (int)_filterOutput.getNBcolumns(), &_progressiveSpatialConstant[0]);
    _verticalAnticausalFilter_Irregular_multGain(outputFrame, 0, (int)_filterOutput.getNBcolumns());

}
// 1D filters with irregular spatial constant
//  horizontal causal filter wich runs on its input buffer
void BasicRetinaFilter::_horizontalCausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{
    float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const float* spatialConstantPTR=&_progressiveSpatialConstant[0]+IDrowStart*_filterOutput.getNBcolumns();
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(outputPTR)+  *(spatialConstantPTR++)* result;
            *(outputPTR++) = result;
        }
    }
}

// horizontal causal filter with add input
void BasicRetinaFilter::_horizontalCausalFilter_Irregular_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd)
{
    float* outputPTR=outputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const float* inputPTR=inputFrame+IDrowStart*_filterOutput.getNBcolumns();
    const float* spatialConstantPTR=&_progressiveSpatialConstant[0]+IDrowStart*_filterOutput.getNBcolumns();
    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(inputPTR++) + _tau**(outputPTR)+  *(spatialConstantPTR++)* result;
            *(outputPTR++) = result;
        }
    }

}

//  horizontal anticausal filter  (basic way, no add on)
void BasicRetinaFilter::_horizontalAnticausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const float *spatialConstantBuffer)
{
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDrowStart,IDrowEnd), Parallel_horizontalAnticausalFilter_Irregular(outputFrame, spatialConstantBuffer, IDrowEnd, _filterOutput.getNBcolumns()));
#else
    float* outputPTR=outputFrame+IDrowEnd*(_filterOutput.getNBcolumns())-1;
    const float* spatialConstantPTR=spatialConstantBuffer+IDrowEnd*(_filterOutput.getNBcolumns())-1;

    for (unsigned int IDrow=IDrowStart; IDrow<IDrowEnd; ++IDrow)
    {
        float result=0;
        for (unsigned int index=0; index<_filterOutput.getNBcolumns(); ++index)
        {
            result = *(outputPTR)+  *(spatialConstantPTR--)* result;
            *(outputPTR--) = result;
        }
    }
#endif

}

//  vertical anticausal filter
void BasicRetinaFilter::_verticalCausalFilter_Irregular(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const float *spatialConstantBuffer)
{
#ifdef MAKE_PARALLEL
        cv::parallel_for_(cv::Range(IDcolumnStart,IDcolumnEnd), Parallel_verticalCausalFilter_Irregular(outputFrame, spatialConstantBuffer, _filterOutput.getNBrows(), _filterOutput.getNBcolumns()));
#else
    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=outputFrame+IDcolumn;
        const float *spatialConstantPTR=spatialConstantBuffer+IDcolumn;
        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + *(spatialConstantPTR) * result;
            *(outputPTR) = result;
            outputPTR+=_filterOutput.getNBcolumns();
            spatialConstantPTR+=_filterOutput.getNBcolumns();
        }
    }
#endif
}

//  vertical anticausal filter which multiplies the output by _gain
void BasicRetinaFilter::_verticalAnticausalFilter_Irregular_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
    float* outputOffset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    const float* constantOffset=&_progressiveSpatialConstant[0]+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    const float* gainOffset=&_progressiveGain[0]+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();
    for (unsigned int IDcolumn=IDcolumnStart; IDcolumn<IDcolumnEnd; ++IDcolumn)
    {
        float result=0;
        float *outputPTR=outputOffset+IDcolumn;
        const float *spatialConstantPTR=constantOffset+IDcolumn;
        const float *progressiveGainPTR=gainOffset+IDcolumn;
        for (unsigned int index=0; index<_filterOutput.getNBrows(); ++index)
        {
            result = *(outputPTR) + *(spatialConstantPTR) * result;
            *(outputPTR) = *(progressiveGainPTR)*result;
            outputPTR-=_filterOutput.getNBcolumns();
            spatialConstantPTR-=_filterOutput.getNBcolumns();
            progressiveGainPTR-=_filterOutput.getNBcolumns();
        }
    }

}
}// end of namespace bioinspired
}// end of namespace cv