/*#******************************************************************************
** 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.
**
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** 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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#ifndef MagnoRetinaFilter_H_
#define MagnoRetinaFilter_H_

/**
* @class MagnoRetinaFilter
* @brief class which describes the magnocellular channel of the retina:
* -> performs a moving contours extraction with powerfull local data enhancement
*
* TYPICAL USE:
*
* // create object at a specified picture size
* MagnoRetinaFilter *movingContoursExtractor;
* movingContoursExtractor =new MagnoRetinaFilter(frameSizeRows, frameSizeColumns);
*
* // init gain, spatial and temporal parameters:
* movingContoursExtractor->setCoefficientsTable(0, 0.7, 5, 3);
*
* // during program execution, call the filter for contours extraction for an input picture called "FrameBuffer":
* movingContoursExtractor->runfilter(FrameBuffer);
*
* // get the output frame, check in the class description below for more outputs:
* const float *movingContours=movingContoursExtractor->getMagnoYsaturated();
*
* // at the end of the program, destroy object:
* delete movingContoursExtractor;

* @author Alexandre BENOIT, benoit.alexandre.vision@gmail.com, LISTIC : www.listic.univ-savoie.fr, Gipsa-Lab, France: www.gipsa-lab.inpg.fr/
* Creation date 2007
* Based on Alexandre BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur"
*/

#include "basicretinafilter.hpp"

//#define _IPL_RETINA_ELEMENT_DEBUG

namespace cv
{

    class MagnoRetinaFilter: public BasicRetinaFilter
    {
    public:
        /**
        * constructor parameters are only linked to image input size
        * @param NBrows: number of rows of the input image
        * @param NBcolumns: number of columns of the input image
        */
        MagnoRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns);


        /**
        * destructor
        */
        virtual ~MagnoRetinaFilter();

        /**
        * function that clears all buffers of the object
        */
        void clearAllBuffers();

        /**
        * resize retina magno filter object (resize all allocated buffers)
        * @param NBrows: the new height size
        * @param NBcolumns: the new width size
        */
        void resize(const unsigned int NBrows, const unsigned int NBcolumns);

        /**
        * set parameters values
        * @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
        * @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
        * @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
        * @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
        * @param localAdaptIntegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
        * @param localAdaptIntegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
        */
        void setCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float localAdaptIntegration_tau, const float localAdaptIntegration_k);

        /**
        * launch filter that runs all the IPL magno filter (model of the magnocellular channel of the Inner Plexiform Layer of the retina)
        * @param OPL_ON: the output of the bipolar ON cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsON() function)
        * @param OPL_OFF: the output of the bipolar OFF cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsOFF() function)
        * @return the processed result without post-processing
        */
        const std::valarray<float> &runFilter(const std::valarray<float> &OPL_ON, const std::valarray<float> &OPL_OFF);

        /**
        * @return the Magnocellular ON channel filtering output
        */
        inline const std::valarray<float> &getMagnoON() const {return _magnoXOutputON;};

        /**
        * @return the Magnocellular OFF channel filtering output
        */
        inline const std::valarray<float> &getMagnoOFF() const {return _magnoXOutputOFF;};

        /**
        * @return the Magnocellular Y (sum of the ON and OFF magno channels) filtering output
        */
        inline const std::valarray<float> &getMagnoYsaturated() const {return *_magnoYsaturated;};

        /**
        * applies an image normalization which saturates the high output values by the use of an assymetric sigmoide
        */
        inline void normalizeGrayOutputNearZeroCentreredSigmoide(){_filterOutput.normalizeGrayOutputNearZeroCentreredSigmoide(&(*_magnoYOutput)[0], &(*_magnoYsaturated)[0]);};

        /**
        * @return the horizontal cells' temporal constant
        */
        inline float getTemporalConstant(){return this->_filteringCoeficientsTable[2];};

    private:

        // related pointers to these buffers
        std::valarray<float> _previousInput_ON;
        std::valarray<float> _previousInput_OFF;
        std::valarray<float> _amacrinCellsTempOutput_ON;
        std::valarray<float> _amacrinCellsTempOutput_OFF;
        std::valarray<float> _magnoXOutputON;
        std::valarray<float> _magnoXOutputOFF;
        std::valarray<float> _localProcessBufferON;
        std::valarray<float> _localProcessBufferOFF;
        // reference to parent buffers and allow better readability
        TemplateBuffer<float> *_magnoYOutput;
        std::valarray<float> *_magnoYsaturated;

        // varialbles
        float _temporalCoefficient;

        // amacrine cells filter : high pass temporal filter
        void _amacrineCellsComputing(const float *ONinput, const float *OFFinput);
#ifdef MAKE_PARALLEL
        /******************************************************
        ** IF some parallelizing thread methods are available, then, main loops are parallelized using these functors
        ** ==> main idea parallelize main filters loops, then, only the most used methods are parallelized... TODO : increase the number of parallelized methods as necessary
        ** ==> functors names = Parallel_$$$ where $$$= the name of the serial method that is parallelized
        ** ==> functors constructors can differ from the parameters used with their related serial functions
        */
        class Parallel_amacrineCellsComputing: public cv::ParallelLoopBody
        {
        private:
            const float *OPL_ON, *OPL_OFF;
            float *previousInput_ON, *previousInput_OFF, *amacrinCellsTempOutput_ON, *amacrinCellsTempOutput_OFF;
            float temporalCoefficient;
        public:
            Parallel_amacrineCellsComputing(const float *OPL_ON_PTR, const float *OPL_OFF_PTR, float *previousInput_ON_PTR, float *previousInput_OFF_PTR, float *amacrinCellsTempOutput_ON_PTR, float *amacrinCellsTempOutput_OFF_PTR, float temporalCoefficientVal)
                :OPL_ON(OPL_ON_PTR), OPL_OFF(OPL_OFF_PTR), previousInput_ON(previousInput_ON_PTR), previousInput_OFF(previousInput_OFF_PTR), amacrinCellsTempOutput_ON(amacrinCellsTempOutput_ON_PTR), amacrinCellsTempOutput_OFF(amacrinCellsTempOutput_OFF_PTR), temporalCoefficient(temporalCoefficientVal) {}

            virtual void operator()( const Range& r ) const {
                const float *OPL_ON_PTR=OPL_ON+r.start;
                const float *OPL_OFF_PTR=OPL_OFF+r.start;
                float *previousInput_ON_PTR= previousInput_ON+r.start;
                float *previousInput_OFF_PTR= previousInput_OFF+r.start;
                float *amacrinCellsTempOutput_ON_PTR= amacrinCellsTempOutput_ON+r.start;
                float *amacrinCellsTempOutput_OFF_PTR= amacrinCellsTempOutput_OFF+r.start;

                for (int IDpixel=r.start ; IDpixel!=r.end; ++IDpixel)
                {

                    /* Compute ON and OFF amacrin cells high pass temporal filter */
                    float magnoXonPixelResult = temporalCoefficient*(*amacrinCellsTempOutput_ON_PTR+ *OPL_ON_PTR-*previousInput_ON_PTR);
                    *(amacrinCellsTempOutput_ON_PTR++)=((float)(magnoXonPixelResult>0))*magnoXonPixelResult;

                    float magnoXoffPixelResult = temporalCoefficient*(*amacrinCellsTempOutput_OFF_PTR+ *OPL_OFF_PTR-*previousInput_OFF_PTR);
                    *(amacrinCellsTempOutput_OFF_PTR++)=((float)(magnoXoffPixelResult>0))*magnoXoffPixelResult;

                    /* prepare next loop */
                    *(previousInput_ON_PTR++)=*(OPL_ON_PTR++);
                    *(previousInput_OFF_PTR++)=*(OPL_OFF_PTR++);

                }
            }

        };
#endif
    };

}

#endif /*MagnoRetinaFilter_H_*/
