/*#******************************************************************************
** 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.
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**               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 IMAGELOGPOLPROJECTION_H_
#define IMAGELOGPOLPROJECTION_H_

/**
* @class ImageLogPolProjection
* @brief class able to perform a log sampling of an image input (models the log sampling of the photoreceptors of the retina)
* or a log polar projection which models the retina information projection on the primary visual cortex: a linear projection in the center for detail analysis and a log projection of the borders (low spatial frequency motion information in general)
*
* collaboration: Barthelemy DURETTE who experimented the retina log projection
-> "Traitement visuels Bio mimtiques pour la supplance perceptive", internal technical report, May 2005, Gipsa-lab/DIS, Grenoble, FRANCE
*
* * TYPICAL USE:
*
* // create object, here for a log sampling (keyword:RETINALOGPROJECTION): (dynamic object allocation sample)
* ImageLogPolProjection *imageSamplingTool;
* imageSamplingTool = new ImageLogPolProjection(frameSizeRows, frameSizeColumns, RETINALOGPROJECTION);
*
* // init log projection:
* imageSamplingTool->initProjection(1.0, 15.0);
*
* // during program execution, call the log transform applied to a frame called "FrameBuffer" :
* imageSamplingTool->runProjection(FrameBuffer);
* // get output frame and its size:
* const unsigned int logSampledFrame_nbRows=imageSamplingTool->getOutputNBrows();
* const unsigned int logSampledFrame_nbColumns=imageSamplingTool->getOutputNBcolumns();
* const double *logSampledFrame=imageSamplingTool->getSampledFrame();
*
* // at the end of the program, destroy object:
* delete imageSamplingTool;
*
* @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
*/

//#define __IMAGELOGPOLPROJECTION_DEBUG // used for std output debug information

#include "basicretinafilter.hpp"


namespace cv
{

class ImageLogPolProjection:public BasicRetinaFilter
{
public:

	enum PROJECTIONTYPE{RETINALOGPROJECTION, CORTEXLOGPOLARPROJECTION};

	/**
	* constructor, just specifies the image input size and the projection type, no projection initialisation is done
	* -> use initLogRetinaSampling() or initLogPolarCortexSampling() for that
	* @param nbRows: number of rows of the input image
	* @param nbColumns: number of columns of the input image
	* @param projection: the type of projection, RETINALOGPROJECTION or CORTEXLOGPOLARPROJECTION
	* @param colorMode: specifies if the projection is applied on a grayscale image (false) or color images (3 layers) (true)
	*/
	ImageLogPolProjection(const unsigned int nbRows, const unsigned int nbColumns, const PROJECTIONTYPE projection, const bool colorMode=false);

	/**
	* standard destructor
	*/
	virtual ~ImageLogPolProjection();

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

	/**
	* resize retina color 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);

	/**
	* init function depending on the projection type
	* @param reductionFactor: the size reduction factor of the ouptup image in regard of the size of the input image, must be superior to 1
	* @param samplingStrenght: specifies the strenght of the log compression effect (magnifying coefficient)
	* @return true if the init was performed without any errors
	*/
	bool initProjection(const double reductionFactor, const double samplingStrenght);

	/**
	* main funtion of the class: run projection function
	* @param inputFrame: the input frame to be processed
	* @return the output frame
	*/
	std::valarray<double> &runProjection(const std::valarray<double> &inputFrame, const double colorMode=false);

	/**
	* @return the numbers of rows (height) of the images OUTPUTS of the object
	*/
	inline const unsigned int getOutputNBrows(){return _outputNBrows;};

	/**
	* @return the numbers of columns (width) of the images OUTPUTS of the object
	*/
	inline const unsigned int getOutputNBcolumns(){return _outputNBcolumns;};

	/**
	* main funtion of the class: run projection function
	* @param size: one of the input frame initial dimensions to be processed
	* @return the output frame dimension
	*/
	inline static const unsigned int predictOutputSize(const unsigned int size, const double reductionFactor){return (unsigned int)((double)size/reductionFactor);};

	/**
	* @return the output of the filter which applies an irregular Low Pass spatial filter to the imag input (see function
	*/
	inline const std::valarray<double> &getIrregularLPfilteredInputFrame() const {return _irregularLPfilteredFrame;};

	/**
	* function which allows to retrieve the output frame which was updated after the "runProjection(...) function BasicRetinaFilter::runProgressiveFilter(...)
	* @return the projection result
	*/
	inline const std::valarray<double> &getSampledFrame() const {return _sampledFrame;};

	/**
	* function which allows gives the tranformation table, its size is (getNBrows()*getNBcolumns()*2)
	* @return the transformation matrix [outputPixIndex_i, inputPixIndex_i, outputPixIndex_i+1, inputPixIndex_i+1....]
	*/
	inline const std::valarray<unsigned int> &getSamplingMap() const {return _transformTable;};

	inline const double getOriginalRadiusLength(const double projectedRadiusLength){return _azero/(_alim-projectedRadiusLength*2.0/_minDimension);};

	//    unsigned int getInputPixelIndex(const unsigned int ){ return  _transformTable[index*2+1]};

private:
	PROJECTIONTYPE _selectedProjection;

	// size of the image output
	unsigned int _outputNBrows;
	unsigned int _outputNBcolumns;
	unsigned int _outputNBpixels;
	unsigned int _outputDoubleNBpixels;
	unsigned int _inputDoubleNBpixels;

	// is the object able to manage color flag
	bool _colorModeCapable;
	// sampling strenght factor
	double _samplingStrenght;
	// sampling reduction factor
	double _reductionFactor;

	// log sampling parameters
	double _azero;
	double _alim;
	double _minDimension;

	// template buffers
	std::valarray<double>_sampledFrame;
	std::valarray<double>&_tempBuffer;
	std::valarray<unsigned int>_transformTable;

	std::valarray<double> &_irregularLPfilteredFrame; // just a reference for easier understanding
	unsigned int _usefullpixelIndex;

	// init transformation tables
	bool _computeLogProjection();
	bool _computeLogPolarProjection();

	// specifies if init was done correctly
	bool _initOK;
	// private init projections functions called by "initProjection(...)" function
	bool _initLogRetinaSampling(const double reductionFactor, const double samplingStrenght);
	bool _initLogPolarCortexSampling(const double reductionFactor, const double samplingStrenght);

};

}
#endif /*IMAGELOGPOLPROJECTION_H_*/