class which allows the Gipsa/Listic Labs model to be used with OpenCV. More...

#include <opencv2/bioinspired/retina.hpp>

Inheritance diagram for cv::bioinspired::Retina:

Public Member Functions
virtual void	activateContoursProcessing (const bool activate)=0
	Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated. More...

virtual void	activateMovingContoursProcessing (const bool activate)=0
	Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated. More...

virtual void	applyFastToneMapping (InputArray inputImage, OutputArray outputToneMappedImage)=0
	Method which processes an image in the aim to correct its luminance correct backlight problems, enhance details in shadows. More...

virtual void	clearBuffers ()=0
	Clears all retina buffers. More...

virtual Size	getInputSize ()=0
	Retreive retina input buffer size. More...

virtual void	getMagno (OutputArray retinaOutput_magno)=0
	Accessor of the motion channel of the retina (models peripheral vision). More...

virtual void	getMagnoRAW (OutputArray retinaOutput_magno)=0
	Accessor of the motion channel of the retina (models peripheral vision). More...

virtual const Mat	getMagnoRAW () const =0

virtual Size	getOutputSize ()=0
	Retreive retina output buffer size that can be different from the input if a spatial log transformation is applied. More...

virtual RetinaParameters	getParameters ()=0

virtual void	getParvo (OutputArray retinaOutput_parvo)=0
	Accessor of the details channel of the retina (models foveal vision). More...

virtual void	getParvoRAW (OutputArray retinaOutput_parvo)=0
	Accessor of the details channel of the retina (models foveal vision). More...

virtual const Mat	getParvoRAW () const =0

virtual const String	printSetup ()=0
	Outputs a string showing the used parameters setup. More...

virtual void	run (InputArray inputImage)=0
	Method which allows retina to be applied on an input image,. More...

virtual void	setColorSaturation (const bool saturateColors=true, const float colorSaturationValue=4.0f)=0
	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. More...

virtual void	setup (String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)=0
	Try to open an XML retina parameters file to adjust current retina instance setup. More...

virtual void	setup (cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0

virtual void	setup (RetinaParameters newParameters)=0

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
	Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel. More...

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
	Setup the OPL and IPL parvo channels (see biologocal model) More...

virtual void	write (String fs) const =0
	Write xml/yml formated parameters information. More...

virtual void	write (FileStorage &fs) const CV_OVERRIDE=0

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state. More...

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

Static Public Member Functions
static Ptr< Retina >	create (Size inputSize)

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)
	Constructors from standardized interfaces : retreive a smart pointer to a Retina instance. More...

Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

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.

Member Function Documentation

◆ activateContoursProcessing()

virtual void cv::bioinspired::Retina::activateContoursProcessing ( const bool activate )

pure virtual

Python:
	cv.bioinspired_Retina.activateContoursProcessing(	activate	) ->	None

Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated.

Parameters

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

◆ activateMovingContoursProcessing()

virtual void cv::bioinspired::Retina::activateMovingContoursProcessing ( const bool activate )

pure virtual

Python:
	cv.bioinspired_Retina.activateMovingContoursProcessing(	activate	) ->	None

Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated.

Parameters

activate true if Magnocellular output should be activated, false if not... if activated, the Magnocellular output can be retrieved using the getMagno methods

◆ applyFastToneMapping()

virtual void cv::bioinspired::Retina::applyFastToneMapping	(	InputArray	inputImage,
		OutputArray	outputToneMappedImage
	)

pure virtual

Python:
	cv.bioinspired_Retina.applyFastToneMapping(	inputImage[, outputToneMappedImage]	) ->	outputToneMappedImage

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.

Parameters

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).
outputToneMappedImage	the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format).

◆ clearBuffers()

virtual void cv::bioinspired::Retina::clearBuffers ( )

pure virtual

Python:
	cv.bioinspired_Retina.clearBuffers(		) ->	None

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.

◆ create() [1/2]

static Ptr<Retina> cv::bioinspired::Retina::create ( Size inputSize )

static

Python:
	cv.bioinspired.Retina_create(	inputSize	) ->	retval
	cv.bioinspired.Retina_create(	inputSize, colorMode[, colorSamplingMethod[, useRetinaLogSampling[, reductionFactor[, samplingStrength]]]]	) ->	retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ create() [2/2]

static Ptr<Retina> cv::bioinspired::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`
	)

static

Python:
	cv.bioinspired.Retina_create(	inputSize	) ->	retval
	cv.bioinspired.Retina_create(	inputSize, colorMode[, colorSamplingMethod[, useRetinaLogSampling[, reductionFactor[, samplingStrength]]]]	) ->	retval

Constructors from standardized interfaces : retreive a smart pointer to a Retina instance.

Parameters

inputSize	the input frame size
colorMode	the chosen processing mode : with or without color processing
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
useRetinaLogSampling	activate retina log sampling, if true, the 2 following parameters can be used
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
samplingStrength	only usefull if param useRetinaLogSampling=true, specifies the strength of the log scale that is applied

◆ getInputSize()

virtual Size cv::bioinspired::Retina::getInputSize ( )

pure virtual

Python:
	cv.bioinspired_Retina.getInputSize(		) ->	retval

Retreive retina input buffer size.

Returns: the retina input buffer size

◆ getMagno()

virtual void cv::bioinspired::Retina::getMagno ( OutputArray retinaOutput_magno )

pure virtual

Python:
	cv.bioinspired_Retina.getMagno(	[, retinaOutput_magno]	) ->	retinaOutput_magno

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.

Parameters

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 also: getMagnoRAW

◆ getMagnoRAW() [1/2]

virtual void cv::bioinspired::Retina::getMagnoRAW ( OutputArray retinaOutput_magno )

pure virtual

Python:
	cv.bioinspired_Retina.getMagnoRAW(	[, retinaOutput_magno]	) ->	retinaOutput_magno
	cv.bioinspired_Retina.getMagnoRAW(		) ->	retval

Accessor of the motion channel of the retina (models peripheral vision).

See also: getMagno

◆ getMagnoRAW() [2/2]

virtual const Mat cv::bioinspired::Retina::getMagnoRAW ( ) const

pure virtual

Python:
	cv.bioinspired_Retina.getMagnoRAW(	[, retinaOutput_magno]	) ->	retinaOutput_magno
	cv.bioinspired_Retina.getMagnoRAW(		) ->	retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ getOutputSize()

virtual Size cv::bioinspired::Retina::getOutputSize ( )

pure virtual

Python:
	cv.bioinspired_Retina.getOutputSize(		) ->	retval

Retreive retina output buffer size that can be different from the input if a spatial log transformation is applied.

Returns: the retina output buffer size

◆ getParameters()

virtual RetinaParameters cv::bioinspired::Retina::getParameters ( )

pure virtual

Returns: the current parameters setup

◆ getParvo()

virtual void cv::bioinspired::Retina::getParvo ( OutputArray retinaOutput_parvo )

pure virtual

Python:
	cv.bioinspired_Retina.getParvo(	[, retinaOutput_parvo]	) ->	retinaOutput_parvo

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.

Parameters

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 also: getParvoRAW

◆ getParvoRAW() [1/2]

virtual void cv::bioinspired::Retina::getParvoRAW ( OutputArray retinaOutput_parvo )

pure virtual

Python:
	cv.bioinspired_Retina.getParvoRAW(	[, retinaOutput_parvo]	) ->	retinaOutput_parvo
	cv.bioinspired_Retina.getParvoRAW(		) ->	retval

Accessor of the details channel of the retina (models foveal vision).

See also: getParvo

◆ getParvoRAW() [2/2]

virtual const Mat cv::bioinspired::Retina::getParvoRAW ( ) const

pure virtual

Python:
	cv.bioinspired_Retina.getParvoRAW(	[, retinaOutput_parvo]	) ->	retinaOutput_parvo
	cv.bioinspired_Retina.getParvoRAW(		) ->	retval

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ printSetup()

virtual const String cv::bioinspired::Retina::printSetup ( )

pure virtual

Python:
	cv.bioinspired_Retina.printSetup(		) ->	retval

Outputs a string showing the used parameters setup.

Returns: a string which contains formated parameters information

◆ run()

virtual void cv::bioinspired::Retina::run ( InputArray inputImage )

pure virtual

Python:
	cv.bioinspired_Retina.run(	inputImage	) ->	None

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

Parameters

inputImage the input Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)

◆ setColorSaturation()

virtual void cv::bioinspired::Retina::setColorSaturation	(	const bool	saturateColors = `true`,
		const float	colorSaturationValue = `4.0f`
	)

pure virtual

Python:
	cv.bioinspired_Retina.setColorSaturation(	[, saturateColors[, colorSaturationValue]]	) ->	None

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.

Parameters

saturateColors	boolean that activates color saturation (if true) or desactivate (if false)
colorSaturationValue	the saturation factor : a simple factor applied on the chrominance buffers

◆ setup() [1/3]

virtual void cv::bioinspired::Retina::setup	(	String	retinaParameterFile = `""`,
		const bool	applyDefaultSetupOnFailure = `true`
	)

pure virtual

Python:
	cv.bioinspired_Retina.setup(	[, retinaParameterFile[, applyDefaultSetupOnFailure]]	) ->	None

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
Parameters

retinaParameterFile the parameters filename

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.

◆ setup() [2/3]

virtual void cv::bioinspired::Retina::setup	(	cv::FileStorage &	fs,
		const bool	applyDefaultSetupOnFailure = `true`
	)

pure virtual

Python:
	cv.bioinspired_Retina.setup(	[, retinaParameterFile[, applyDefaultSetupOnFailure]]	) ->	None

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

fs	the open Filestorage which contains retina parameters
applyDefaultSetupOnFailure	set to true if an error must be thrown on error

◆ setup() [3/3]

virtual void cv::bioinspired::Retina::setup ( RetinaParameters newParameters )

pure virtual

Python:
	cv.bioinspired_Retina.setup(	[, retinaParameterFile[, applyDefaultSetupOnFailure]]	) ->	None

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

newParameters a parameters structures updated with the new target configuration.

◆ setupIPLMagnoChannel()

virtual void cv::bioinspired::Retina::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`
	)

pure virtual

Python:
	cv.bioinspired_Retina.setupIPLMagnoChannel(	[, normaliseOutput[, parasolCells_beta[, parasolCells_tau[, parasolCells_k[, amacrinCellsTemporalCutFrequency[, V0CompressionParameter[, localAdaptintegration_tau[, localAdaptintegration_k]]]]]]]]	) ->	None

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.

Parameters

normaliseOutput	specifies if (true) output is rescaled between 0 and 255 of not (false)
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
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)
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
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
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
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
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

◆ setupOPLandIPLParvoChannel()

virtual void cv::bioinspired::Retina::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`
	)

pure virtual

Python:
	cv.bioinspired_Retina.setupOPLandIPLParvoChannel(	[, colorMode[, normaliseOutput[, photoreceptorsLocalAdaptationSensitivity[, photoreceptorsTemporalConstant[, photoreceptorsSpatialConstant[, horizontalCellsGain[, HcellsTemporalConstant[, HcellsSpatialConstant[, ganglionCellsSensitivity]]]]]]]]]	) ->	None

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

Parameters

colorMode	specifies if (true) color is processed of not (false) to then processing gray level image
normaliseOutput	specifies if (true) output is rescaled between 0 and 255 of not (false)
photoreceptorsLocalAdaptationSensitivity	the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
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
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
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
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
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)
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

◆ write() [1/2]

virtual void cv::bioinspired::Retina::write ( String fs ) const

pure virtual

Python:
	cv.bioinspired_Retina.write(	fs	) ->	None

Write xml/yml formated parameters information.

Parameters

fs	the filename of the xml file that will be open and writen with formatted parameters information

◆ write() [2/2]

virtual void cv::bioinspired::Retina::write ( FileStorage & fs ) const

pure virtual

Python:
	cv.bioinspired_Retina.write(	fs	) ->	None

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Reimplemented from cv::Algorithm.

The documentation for this class was generated from the following file:

opencv2/bioinspired/retina.hpp

retinaParameterFile	the parameters filename
applyDefaultSetupOnFailure	set to true if an error must be thrown on error

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ activateContoursProcessing()

◆ activateMovingContoursProcessing()

◆ applyFastToneMapping()

◆ clearBuffers()

◆ create() [1/2]

◆ create() [2/2]

◆ getInputSize()

◆ getMagno()

◆ getMagnoRAW() [1/2]

◆ getMagnoRAW() [2/2]

◆ getOutputSize()

◆ getParameters()

◆ getParvo()

◆ getParvoRAW() [1/2]

◆ getParvoRAW() [2/2]

◆ printSetup()

◆ run()

◆ setColorSaturation()

◆ setup() [1/3]

◆ setup() [2/3]

◆ setup() [3/3]

◆ setupIPLMagnoChannel()

◆ setupOPLandIPLParvoChannel()

◆ write() [1/2]

◆ write() [2/2]