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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/bioinspired.hpp"

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/*#******************************************************************************
** 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 2007-2015
** 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 (bioinspired)
** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
**
** Third party copyrights are property of their respective owners.
**
** Redistribution and use in source and binary forms, with or without modification,
** are permitted provided that the following conditions are met:
**
** * Redistributions of source code must retain the above copyright notice,
** this list of conditions and the following disclaimer.
**
** * Redistributions in binary form must reproduce the above copyright notice,
** this list of conditions and the following disclaimer in the documentation
** and/or other materials provided with the distribution.
**
** * The name of the copyright holders may not be used to endorse or promote products
** derived from this software without specific prior written permission.
**
** This software is provided by the copyright holders and contributors "as is" and
** any express or implied warranties, including, but not limited to, the implied
** warranties of merchantability and fitness for a particular purpose are disclaimed.
** In no event shall the Intel Corporation or contributors be liable for any direct,
** indirect, incidental, special, exemplary, or consequential damages
** (including, but not limited to, procurement of substitute goods or services;
** loss of use, data, or profits; or business interruption) however caused
** and on any theory of liability, whether in contract, strict liability,
** or tort (including negligence or otherwise) arising in any way out of
** the use of this software, even if advised of the possibility of such damage.
*******************************************************************************/
#ifndef __OPENCV_BIOINSPIRED_RETINA_HPP__
#define __OPENCV_BIOINSPIRED_RETINA_HPP__
/**
@file
@date Jul 19, 2011
@author Alexandre Benoit
*/
#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
namespace cv{
namespace bioinspired{
//! @addtogroup bioinspired
//! @{
enum {
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
};
/** @brief retina model parameters structure
For better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel
Here is the default configuration file of the retina module. It gives results such as the first
retina output shown on the top of this page.
@code{xml}
<?xml version="1.0"?>
<opencv_storage>
<OPLandIPLparvo>
<colorMode>1</colorMode>
<normaliseOutput>1</normaliseOutput>
<photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
<horizontalCellsGain>0.01</horizontalCellsGain>
<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
<hcellsSpatialConstant>7.</hcellsSpatialConstant>
<ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
<IPLmagno>
<normaliseOutput>1</normaliseOutput>
<parasolCells_beta>0.</parasolCells_beta>
<parasolCells_tau>0.</parasolCells_tau>
<parasolCells_k>7.</parasolCells_k>
<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
<V0CompressionParameter>9.5e-01</V0CompressionParameter>
<localAdaptintegration_tau>0.</localAdaptintegration_tau>
<localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
</opencv_storage>
@endcode
Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page.
@code{xml}
<?xml version="1.0"?>
<opencv_storage>
<OPLandIPLparvo>
<colorMode>1</colorMode>
<normaliseOutput>1</normaliseOutput>
<photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
<horizontalCellsGain>0.3</horizontalCellsGain>
<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
<hcellsSpatialConstant>7.</hcellsSpatialConstant>
<ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity></OPLandIPLparvo>
<IPLmagno>
<normaliseOutput>1</normaliseOutput>
<parasolCells_beta>0.</parasolCells_beta>
<parasolCells_tau>0.</parasolCells_tau>
<parasolCells_k>7.</parasolCells_k>
<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
<V0CompressionParameter>9.5e-01</V0CompressionParameter>
<localAdaptintegration_tau>0.</localAdaptintegration_tau>
<localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
</opencv_storage>
@endcode
*/
struct RetinaParameters{
//! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
struct OPLandIplParvoParameters{
OPLandIplParvoParameters():colorMode(true),
normaliseOutput(true),
photoreceptorsLocalAdaptationSensitivity(0.75f),
photoreceptorsTemporalConstant(0.9f),
photoreceptorsSpatialConstant(0.53f),
horizontalCellsGain(0.01f),
hcellsTemporalConstant(0.5f),
hcellsSpatialConstant(7.f),
ganglionCellsSensitivity(0.75f) { } // default setup
bool colorMode, normaliseOutput;
float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity;
};
//! Inner Plexiform Layer Magnocellular channel (IplMagno)
struct IplMagnoParameters{
IplMagnoParameters():
normaliseOutput(true),
parasolCells_beta(0.f),
parasolCells_tau(0.f),
parasolCells_k(7.f),
amacrinCellsTemporalCutFrequency(2.0f),
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;
};
OPLandIplParvoParameters OPLandIplParvo;
IplMagnoParameters IplMagno;
};
/** @brief class which allows the Gipsa/Listic Labs model to be used with OpenCV.
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_W Retina : public Algorithm {
public:
/** @brief Retreive retina input buffer size
@return the retina input buffer size
*/
CV_WRAP virtual Size getInputSize()=0;
/** @brief Retreive retina output buffer size that can be different from the input if a spatial log
transformation is applied
@return the retina output buffer size
*/
CV_WRAP virtual Size getOutputSize()=0;
/** @brief 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
You can retrieve the current parameters structure using the method Retina::getParameters and update
it before running method Retina::setup.
*/
CV_WRAP virtual void setup(String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)=0;
/** @overload
@param fs the open Filestorage which contains retina parameters
@param applyDefaultSetupOnFailure set to true if an error must be thrown on error
*/
virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;
/** @overload
@param newParameters a parameters structures updated with the new target configuration.
*/
virtual void setup(RetinaParameters newParameters)=0;
/**
@return the current parameters setup
*/
virtual RetinaParameters getParameters()=0;
/** @brief Outputs a string showing the used parameters setup
@return a string which contains formated parameters information
*/
CV_WRAP virtual const String printSetup()=0;
/** @brief Write xml/yml formated parameters information
@param fs the filename of the xml file that will be open and writen with formatted parameters
information
*/
CV_WRAP virtual void write( String fs ) const=0;
/** @overload */
virtual void write( FileStorage& fs ) const CV_OVERRIDE = 0;
/** @brief 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 a part of the
Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See
reference papers for more informations.
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 0.6 and 1 for best results, a high value increases more the low
value sensitivity... and the output saturates faster, recommended value: 0.7
*/
CV_WRAP virtual void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7f, const float photoreceptorsTemporalConstant=0.5f, const float photoreceptorsSpatialConstant=0.53f, const float horizontalCellsGain=0.f, const float HcellsTemporalConstant=1.f, const float HcellsSpatialConstant=7.f, const float ganglionCellsSensitivity=0.7f)=0;
/** @brief Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
this channel processes signals output from OPL processing stage in peripheral vision, it allows
motion information enhancement. It is decorrelated from the details channel. See reference
papers 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, typical value is 1.2
@param V0CompressionParameter the compression strengh of the ganglion cells local adaptation
output, set a value between 0.6 and 1 for best results, a high value increases more the low
value sensitivity... and the output saturates faster, recommended value: 0.95
@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
*/
CV_WRAP virtual void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0.f, const float parasolCells_tau=0.f, const float parasolCells_k=7.f, const float amacrinCellsTemporalCutFrequency=1.2f, const float V0CompressionParameter=0.95f, const float localAdaptintegration_tau=0.f, const float localAdaptintegration_k=7.f)=0;
/** @brief 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 Mat image to be processed, can be gray level or BGR coded in any
format (from 8bit to 16bits)
*/
CV_WRAP virtual void run(InputArray inputImage)=0;
/** @brief Method which processes an image in the aim to correct its luminance correct
backlight problems, enhance details in shadows.
This method is designed to perform High Dynamic Range image tone mapping (compress \>8bit/pixel
images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model
(simplified version of the run/getParvo methods call) since it does not include the
spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral
whitening and many other stuff. However, it works great for tone mapping and in a faster way.
Check the demos and experiments section to see examples and the way to perform tone mapping
using the original retina model and the method.
@param inputImage the input image to process (should be coded in float format : CV_32F,
CV_32FC1, CV_32F_C3, CV_32F_C4, the 4th channel won't be considered).
@param outputToneMappedImage the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format).
*/
CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0;
/** @brief Accessor of the details channel of the retina (models foveal vision).
Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while
the non RAW method allows a normalized matrix to be retrieved.
@param retinaOutput_parvo the output buffer (reallocated if necessary), format can be :
- a Mat, this output is rescaled for standard 8bits image processing use in OpenCV
- RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1,
B2, ...Bn), this output is the original retina filter model output, without any
quantification or rescaling.
@see getParvoRAW
*/
CV_WRAP virtual void getParvo(OutputArray retinaOutput_parvo)=0;
/** @brief Accessor of the details channel of the retina (models foveal vision).
@see getParvo
*/
CV_WRAP virtual void getParvoRAW(OutputArray retinaOutput_parvo)=0;
/** @brief Accessor of the motion channel of the retina (models peripheral vision).
Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while
the non RAW method allows a normalized matrix to be retrieved.
@param retinaOutput_magno the output buffer (reallocated if necessary), format can be :
- a Mat, this output is rescaled for standard 8bits image processing use in OpenCV
- RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the
original retina filter model output, without any quantification or rescaling.
@see getMagnoRAW
*/
CV_WRAP virtual void getMagno(OutputArray retinaOutput_magno)=0;
/** @brief Accessor of the motion channel of the retina (models peripheral vision).
@see getMagno
*/
CV_WRAP virtual void getMagnoRAW(OutputArray retinaOutput_magno)=0;
/** @overload */
CV_WRAP virtual const Mat getMagnoRAW() const=0;
/** @overload */
CV_WRAP virtual const Mat getParvoRAW() const=0;
/** @brief 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 : a simple factor applied on the chrominance
buffers
*/
CV_WRAP virtual void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0f)=0;
/** @brief Clears all retina buffers
(equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal
transition occuring just after this method call.
*/
CV_WRAP virtual void clearBuffers()=0;
/** @brief 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... if activated,
the Magnocellular output can be retrieved using the **getMagno** methods
*/
CV_WRAP virtual void activateMovingContoursProcessing(const bool activate)=0;
/** @brief 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... if activated, the Parvocellular output can be retrieved using the
Retina::getParvo methods
*/
CV_WRAP virtual void activateContoursProcessing(const bool activate)=0;
/** @overload */
CV_WRAP static Ptr<Retina> create(Size inputSize);
/** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
@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 :
- cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice
- cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
- cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling
@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 samplingStrength only usefull if param useRetinaLogSampling=true, specifies the strength of
the log scale that is applied
*/
CV_WRAP static Ptr<Retina> create(Size inputSize, const bool colorMode,
int colorSamplingMethod=RETINA_COLOR_BAYER,
const bool useRetinaLogSampling=false,
const float reductionFactor=1.0f, const float samplingStrength=10.0f);
};
//! @}
}
}
#endif /* __OPENCV_BIOINSPIRED_RETINA_HPP__ */

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/*#******************************************************************************
** 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.
**
** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
**
** Creation - enhancement process 2007-2013
** 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.
**
**
**
**
**
** This class is based on image processing tools of the author and already used within the Retina class (this is the same code as method retina::applyFastToneMapping, but in an independent class, it is ligth from a memory requirement point of view). It implements an adaptation of the efficient tone mapping algorithm propose by David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
** -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
**
**
** 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 (bioinspired)
** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
**
** Third party copyrights are property of their respective owners.
**
** Redistribution and use in source and binary forms, with or without modification,
** are permitted provided that the following conditions are met:
**
** * Redistributions of source code must retain the above copyright notice,
** this list of conditions and the following disclaimer.
**
** * Redistributions in binary form must reproduce the above copyright notice,
** this list of conditions and the following disclaimer in the documentation
** and/or other materials provided with the distribution.
**
** * The name of the copyright holders may not be used to endorse or promote products
** derived from this software without specific prior written permission.
**
** This software is provided by the copyright holders and contributors "as is" and
** any express or implied warranties, including, but not limited to, the implied
** warranties of merchantability and fitness for a particular purpose are disclaimed.
** In no event shall the Intel Corporation or contributors be liable for any direct,
** indirect, incidental, special, exemplary, or consequential damages
** (including, but not limited to, procurement of substitute goods or services;
** loss of use, data, or profits; or business interruption) however caused
** and on any theory of liability, whether in contract, strict liability,
** or tort (including negligence or otherwise) arising in any way out of
** the use of this software, even if advised of the possibility of such damage.
*******************************************************************************/
#ifndef __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__
#define __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__
/**
@file
@date May 26, 2013
@author Alexandre Benoit
*/
#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
namespace cv{
namespace bioinspired{
//! @addtogroup bioinspired
//! @{
/** @brief a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.
This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc.
As a summary, these are the model properties:
- 2 stages of local luminance adaptation with a different local neighborhood for each.
- first stage models the retina photorecetors local luminance adaptation
- second stage models th ganglion cells local information adaptation
- compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters.
this can help noise robustness and temporal stability for video sequence use cases.
for more information, read to the following papers :
Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit 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
regarding spatio-temporal filter and the bigger retina model :
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.
*/
class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm
{
public:
/** @brief applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)
using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors
level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal
smoothing and eventually high frequencies attenuation. This is a lighter method than the one
available using the regular retina::run method. It is then faster but it does not include
complete temporal filtering nor retina spectral whitening. Then, it can have a more limited
effect on images with a very high dynamic range. This is an adptation of the original still
image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's
work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local
Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of
America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
@param inputImage the input image to process RGB or gray levels
@param outputToneMappedImage the output tone mapped image
*/
CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0;
/** @brief updates tone mapping behaviors by adjusing the local luminance computation area
@param photoreceptorsNeighborhoodRadius the first stage local adaptation area
@param ganglioncellsNeighborhoodRadius the second stage local adaptation area
@param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information
(default is 1, see reference paper)
*/
CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0;
CV_WRAP static Ptr<RetinaFastToneMapping> create(Size inputSize);
};
//! @}
}
}
#endif /* __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ */

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/*#******************************************************************************
** 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.
** TransientAreasSegmentationModule Use: extract areas that present spatio-temporal changes.
** => It should be used at the output of the cv::bioinspired::Retina::getMagnoRAW() output that enhances spatio-temporal changes
**
** Maintainers : Listic lab (code author current affiliation & applications)
**
** Creation - enhancement process 2007-2015
** 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:
** Strat, S.T.; Benoit, A.; Lambert, P., "Retina enhanced bag of words descriptors for video classification," Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European , vol., no., pp.1307,1311, 1-5 Sept. 2014 (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952461&isnumber=6951911)
** 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.
**
**
** 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 (bioinspired)
** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
**
** Third party copyrights are property of their respective owners.
**
** Redistribution and use in source and binary forms, with or without modification,
** are permitted provided that the following conditions are met:
**
** * Redistributions of source code must retain the above copyright notice,
** this list of conditions and the following disclaimer.
**
** * Redistributions in binary form must reproduce the above copyright notice,
** this list of conditions and the following disclaimer in the documentation
** and/or other materials provided with the distribution.
**
** * The name of the copyright holders may not be used to endorse or promote products
** derived from this software without specific prior written permission.
**
** This software is provided by the copyright holders and contributors "as is" and
** any express or implied warranties, including, but not limited to, the implied
** warranties of merchantability and fitness for a particular purpose are disclaimed.
** In no event shall the Intel Corporation or contributors be liable for any direct,
** indirect, incidental, special, exemplary, or consequential damages
** (including, but not limited to, procurement of substitute goods or services;
** loss of use, data, or profits; or business interruption) however caused
** and on any theory of liability, whether in contract, strict liability,
** or tort (including negligence or otherwise) arising in any way out of
** the use of this software, even if advised of the possibility of such damage.
*******************************************************************************/
#ifndef SEGMENTATIONMODULE_HPP_
#define SEGMENTATIONMODULE_HPP_
/**
@file
@date 2007-2013
@author Alexandre BENOIT, benoit.alexandre.vision@gmail.com
*/
#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
namespace cv
{
namespace bioinspired
{
//! @addtogroup bioinspired
//! @{
/** @brief parameter structure that stores the transient events detector setup parameters
*/
struct SegmentationParameters{ // CV_EXPORTS_W_MAP to export to python native dictionnaries
// default structure instance construction with default values
SegmentationParameters():
thresholdON(100),
thresholdOFF(100),
localEnergy_temporalConstant(0.5),
localEnergy_spatialConstant(5),
neighborhoodEnergy_temporalConstant(1),
neighborhoodEnergy_spatialConstant(15),
contextEnergy_temporalConstant(1),
contextEnergy_spatialConstant(75){};
// all properties list
float thresholdON;
float thresholdOFF;
//! the time constant of the first order low pass filter, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 0.5 frame
float localEnergy_temporalConstant;
//! the spatial constant of the first order low pass filter, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 5 pixel
float localEnergy_spatialConstant;
//! local neighborhood energy filtering parameters : the aim is to get information about the energy neighborhood to perform a center surround energy analysis
float neighborhoodEnergy_temporalConstant;
float neighborhoodEnergy_spatialConstant;
//! context neighborhood energy filtering parameters : the aim is to get information about the energy on a wide neighborhood area to filtered out local effects
float contextEnergy_temporalConstant;
float contextEnergy_spatialConstant;
};
/** @brief class which provides a transient/moving areas segmentation module
perform a locally adapted segmentation by using the retina magno input data Based on Alexandre
BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur"
3 spatio temporal filters are used:
- a first one which filters the noise and local variations of the input motion energy
- a second (more powerfull low pass spatial filter) which gives the neighborhood motion energy the
segmentation consists in the comparison of these both outputs, if the local motion energy is higher
to the neighborhood otion energy, then the area is considered as moving and is segmented
- a stronger third low pass filter helps decision by providing a smooth information about the
"motion context" in a wider area
*/
class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
{
public:
/** @brief return the sze of the manage input and output images
*/
CV_WRAP virtual Size getSize()=0;
/** @brief try to open an XML segmentation parameters file to adjust current segmentation 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 segmentationParameterFile : the parameters filename
@param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
CV_WRAP virtual void setup(String segmentationParameterFile="", const bool applyDefaultSetupOnFailure=true)=0;
/** @brief try to open an XML segmentation parameters file to adjust current segmentation 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 segmentation parameters
@param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;
/** @brief try to open an XML segmentation parameters file to adjust current segmentation 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
*/
virtual void setup(SegmentationParameters newParameters)=0;
/** @brief return the current parameters setup
*/
virtual SegmentationParameters getParameters()=0;
/** @brief parameters setup display method
@return a string which contains formatted parameters information
*/
CV_WRAP virtual const String printSetup()=0;
/** @brief write xml/yml formated parameters information
@param fs : the filename of the xml file that will be open and writen with formatted parameters information
*/
CV_WRAP virtual void write( String fs ) const=0;
/** @brief write xml/yml formated parameters information
@param fs : a cv::Filestorage object ready to be filled
*/
virtual void write( cv::FileStorage& fs ) const CV_OVERRIDE = 0;
/** @brief main processing method, get result using methods getSegmentationPicture()
@param inputToSegment : the image to process, it must match the instance buffer size !
@param channelIndex : the channel to process in case of multichannel images
*/
CV_WRAP virtual void run(InputArray inputToSegment, const int channelIndex=0)=0;
/** @brief access function
return the last segmentation result: a boolean picture which is resampled between 0 and 255 for a display purpose
*/
CV_WRAP virtual void getSegmentationPicture(OutputArray transientAreas)=0;
/** @brief cleans all the buffers of the instance
*/
CV_WRAP virtual void clearAllBuffers()=0;
/** @brief allocator
@param inputSize : size of the images input to segment (output will be the same size)
*/
CV_WRAP static Ptr<TransientAreasSegmentationModule> create(Size inputSize);
};
//! @}
}} // namespaces end : cv and bioinspired
#endif