/*#******************************************************************************
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**
** 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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#ifndef __OPENCV_CONTRIB_RETINA_HPP__
#define __OPENCV_CONTRIB_RETINA_HPP__
/*
* Retina.hpp
*
* Created on: Jul 19, 2011
* Author: Alexandre Benoit
*/
#include "opencv2/core/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
#include <valarray>
namespace cv
{
enum RETINA_COLORSAMPLINGMETHOD
{
RETINA_COLOR_RANDOM, //!< each pixel position is either R, G or B in a random choice
RETINA_COLOR_DIAGONAL,//!< color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
RETINA_COLOR_BAYER//!< standard bayer sampling
};
class RetinaFilter;
/**
* @class Retina a wrapper class which allows the Gipsa/Listic Labs model to be used.
* This retina model allows spatio-temporal image processing (applied on still images, video sequences).
* As a summary, these are the retina model properties:
* => It applies a spectral whithening (mid-frequency details enhancement)
* => high frequency spatio-temporal noise reduction
* => low frequency luminance to be reduced (luminance range compression)
* => local logarithmic luminance compression allows details to be enhanced in low light conditions
*
* USE : this model can be used basically for spatio-temporal video effects but also for :
* _using the getParvo method output matrix : texture analysiswith enhanced signal to noise ratio and enhanced details robust against input images luminance ranges
* _using the getMagno method output matrix : motion analysis also with the previously cited properties
*
* for more information, reer to the following papers :
* 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
* 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.
*/
class CV_EXPORTS Retina {
public:
// parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel
struct RetinaParameters{
struct OPLandIplParvoParameters{ // Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
OPLandIplParvoParameters():colorMode(true),
normaliseOutput(true),
photoreceptorsLocalAdaptationSensitivity(0.7f),
photoreceptorsTemporalConstant(0.5f),
photoreceptorsSpatialConstant(0.53f),
horizontalCellsGain(0.0f),
hcellsTemporalConstant(1.f),
hcellsSpatialConstant(7.f),
ganglionCellsSensitivity(0.7f){};// default setup
bool colorMode, normaliseOutput;
float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity;
};
struct IplMagnoParameters{ // Inner Plexiform Layer Magnocellular channel (IplMagno)
IplMagnoParameters():
normaliseOutput(true),
parasolCells_beta(0.f),
parasolCells_tau(0.f),
parasolCells_k(7.f),
amacrinCellsTemporalCutFrequency(1.2f),
V0CompressionParameter(0.95f),
localAdaptintegration_tau(0.f),
localAdaptintegration_k(7.f){};// default setup
bool normaliseOutput;
float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k;
};
struct OPLandIplParvoParameters OPLandIplParvo;
struct IplMagnoParameters IplMagno;
};
/**
* Main constructor with most commun use setup : create an instance of color ready retina model
* @param inputSize : the input frame size
*/
Retina(Size inputSize);
/**
* Complete Retina filter constructor which allows all basic structural parameters definition
* @param inputSize : the input frame size
* @param colorMode : the chosen processing mode : with or without color processing
* @param colorSamplingMethod: specifies which kind of color sampling will be used
* @param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used
* @param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
* @param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
*/
Retina(Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
virtual ~Retina();
/**
* retreive retina input buffer size
*/
Size inputSize();
/**
* retreive retina output buffer size
*/
Size outputSize();
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param retinaParameterFile : the parameters filename
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(std::string retinaParameterFile="", const bool applyDefaultSetupOnFailure=true);
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param fs : the open Filestorage which contains retina parameters
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true);
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param newParameters : a parameters structures updated with the new target configuration
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(RetinaParameters newParameters);
/**
* @return the current parameters setup
*/
Retina::RetinaParameters getParameters();
/**
* parameters setup display method
* @return a string which contains formatted parameters information
*/
const std::string printSetup();
/**
* write xml/yml formated parameters information
* @rparam fs : the filename of the xml file that will be open and writen with formatted parameters information
*/
virtual void write( std::string fs ) const;
/**
* write xml/yml formated parameters information
* @param fs : a cv::Filestorage object ready to be filled
*/
virtual void write( FileStorage& fs ) const;
/**
* setup the OPL and IPL parvo channels (see biologocal model)
* OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance (low frequency energy)
* IPL parvo is the OPL next processing stage, it refers to Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision.
* for more informations, please have a look at the paper 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
* @param colorMode : specifies if (true) color is processed of not (false) to then processing gray level image
* @param normaliseOutput : specifies if (true) output is rescaled between 0 and 255 of not (false)
* @param photoreceptorsLocalAdaptationSensitivity: the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
* @param photoreceptorsTemporalConstant: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param photoreceptorsSpatialConstant: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param horizontalCellsGain: gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param HcellsTemporalConstant: the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param HcellsSpatialConstant: the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
* @param ganglionCellsSensitivity: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 230
*/
void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7, const float photoreceptorsTemporalConstant=0.5, const float photoreceptorsSpatialConstant=0.53, const float horizontalCellsGain=0, const float HcellsTemporalConstant=1, const float HcellsSpatialConstant=7, const float ganglionCellsSensitivity=0.7);
/**
* set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
* this channel processes signals outpint from OPL processing stage in peripheral vision, it allows motion information enhancement. It is decorrelated from the details channel. See reference paper for more details.
* @param normaliseOutput : specifies if (true) output is rescaled between 0 and 255 of not (false)
* @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 V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 200
* @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 setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0, const float parasolCells_tau=0, const float parasolCells_k=7, const float amacrinCellsTemporalCutFrequency=1.2, const float V0CompressionParameter=0.95, const float localAdaptintegration_tau=0, const float localAdaptintegration_k=7);
/**
* method which allows retina to be applied on an input image, after run, encapsulated retina module is ready to deliver its outputs using dedicated acccessors, see getParvo and getMagno methods
* @param inputImage : the input cv::Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)
*/
void run(const Mat &inputImage);
/**
* accessor of the details channel of the retina (models foveal vision)
* @param retinaOutput_parvo : the output buffer (reallocated if necessary), this output is rescaled for standard 8bits image processing use in OpenCV
*/
void getParvo(Mat &retinaOutput_parvo);
/**
* accessor of the details channel of the retina (models foveal vision)
* @param retinaOutput_parvo : the output buffer (reallocated if necessary), this output is the original retina filter model output, without any quantification or rescaling
*/
void getParvo(std::valarray<float> &retinaOutput_parvo);
/**
* accessor of the motion channel of the retina (models peripheral vision)
* @param retinaOutput_magno : the output buffer (reallocated if necessary), this output is rescaled for standard 8bits image processing use in OpenCV
*/
void getMagno(Mat &retinaOutput_magno);
/**
* accessor of the motion channel of the retina (models peripheral vision)
* @param retinaOutput_magno : the output buffer (reallocated if necessary), this output is the original retina filter model output, without any quantification or rescaling
*/
void getMagno(std::valarray<float> &retinaOutput_magno);
// original API level data accessors : get buffers addresses...
const std::valarray<float> & getMagno() const;
const std::valarray<float> & getParvo() const;
/**
* activate color saturation as the final step of the color demultiplexing process
* -> this saturation is a sigmoide function applied to each channel of the demultiplexed image.
* @param saturateColors: boolean that activates color saturation (if true) or desactivate (if false)
* @param colorSaturationValue: the saturation factor
*/
void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0);
/**
* clear all retina buffers (equivalent to opening the eyes after a long period of eye close ;o)
*/
void clearBuffers();
/**
* Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated
* @param activate: true if Magnocellular output should be activated, false if not
*/
void activateMovingContoursProcessing(const bool activate);
/**
* Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated
* @param activate: true if Parvocellular (contours information extraction) output should be activated, false if not
*/
void activateContoursProcessing(const bool activate);
protected:
// Parameteres setup members
RetinaParameters _retinaParameters; // structure of parameters
// Retina model related modules
std::valarray<float> _inputBuffer; //!< buffer used to convert input cv::Mat to internal retina buffers format (valarrays)
// pointer to retina model
RetinaFilter* _retinaFilter; //!< the pointer to the retina module, allocated with instance construction
/**
* exports a valarray buffer outing from HVStools objects to a cv::Mat in CV_8UC1 (gray level picture) or CV_8UC3 (color) format
* @param grayMatrixToConvert the valarray to export to OpenCV
* @param nbRows : the number of rows of the valarray flatten matrix
* @param nbColumns : the number of rows of the valarray flatten matrix
* @param colorMode : a flag which mentions if matrix is color (true) or graylevel (false)
* @param outBuffer : the output matrix which is reallocated to satisfy Retina output buffer dimensions
*/
void _convertValarrayBuffer2cvMat(const std::valarray<float> &grayMatrixToConvert, const unsigned int nbRows, const unsigned int nbColumns, const bool colorMode, Mat &outBuffer);
/**
*
* @param inputMatToConvert : the OpenCV cv::Mat that has to be converted to gray or RGB valarray buffer that will be processed by the retina model
* @param outputValarrayMatrix : the output valarray
* @return the input image color mode (color=true, gray levels=false)
*/
bool _convertCvMat2ValarrayBuffer(const cv::Mat inputMatToConvert, std::valarray<float> &outputValarrayMatrix);
//! private method called by constructors, gathers their parameters and use them in a unified way
void _init(const Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
};
}
#endif /* __OPENCV_CONTRIB_RETINA_HPP__ */
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