Class cv::bioinspired::Retina#

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

#include <opencv2/bioinspired/retina.hpp>

Collaboration diagram for cv::bioinspired::Retina:

cv::bioinspired::Retina Node1 cv::bioinspired::Retina   + activateContoursProcessing() + activateMovingContoursProcessing() + applyFastToneMapping() + clearBuffers() + getInputSize() + getMagno() + getMagnoRAW() + getMagnoRAW() + getOutputSize() + getParameters() and 13 more... + create() + create() Node2 cv::Algorithm   + Algorithm() + ~Algorithm() + clear() + empty() + getDefaultName() + read() + save() + write() + write() + load() + loadFromString() + read() # writeFormat() Node2->Node1

cv::bioinspired::Retina Node1 cv::bioinspired::Retina   + activateContoursProcessing() + activateMovingContoursProcessing() + applyFastToneMapping() + clearBuffers() + getInputSize() + getMagno() + getMagnoRAW() + getMagnoRAW() + getOutputSize() + getParameters() and 13 more... + create() + create() Node2 cv::Algorithm   + Algorithm() + ~Algorithm() + clear() + empty() + getDefaultName() + read() + save() + write() + write() + load() + loadFromString() + read() # writeFormat() Node2->Node1

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return

Name

Description

Algorithm()

~Algorithm()

void

clear()

Clears the algorithm state.

bool

empty()

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

String

getDefaultName()

void

read(const FileNode & fn)

Reads algorithm parameters from a file storage.

void

save(const String & filename)

void

write(
    const Ptr< FileStorage > & fs,
    const String & name = String() )

void

write(FileStorage & fs)

Stores algorithm parameters in a file storage.

void

write(
    FileStorage & fs,
    const String & name )

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return

Name

Description

static Ptr< _Tp >

load(
    const String & filename,
    const String & objname = String() )

Loads algorithm from the file.

static Ptr< _Tp >

loadFromString(
    const String & strModel,
    const String & objname = String() )

Loads algorithm from a String.

static Ptr< _Tp >

read(const FileNode & fn)

Reads algorithm from the file node.

Additional Inherited Members#

Protected Member Functions inherited from cv::Algorithm

Return

Name

Description

void

writeFormat(FileStorage & fs)

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()#

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

Python:

cv.bioinspired.Retina.activateContoursProcessing(activate)

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()#

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

Python:

cv.bioinspired.Retina.activateMovingContoursProcessing(activate)

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()#

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

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()#

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

Python:

cv.bioinspired.Retina.clearBuffers()

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.

getInputSize()#

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

Python:

cv.bioinspired.Retina.getInputSize() -> retval

Retreive retina input buffer size.

Returns

the retina input buffer size

getMagno()#

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

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.

See also

getMagnoRAW

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.

getMagnoRAW()#

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

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.

getMagnoRAW()#

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

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

getOutputSize()#

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

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()#

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

Returns

the current parameters setup

getParvo()#

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

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.

See also

getParvoRAW

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.

getParvoRAW()#

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

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.

getParvoRAW()#

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

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

printSetup()#

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

Python:

cv.bioinspired.Retina.printSetup() -> retval

Outputs a string showing the used parameters setup.

Returns

a string which contains formated parameters information

run()#

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

Python:

cv.bioinspired.Retina.run(inputImage)

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()#

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

Python:

cv.bioinspired.Retina.setColorSaturation([, saturateColors[, colorSaturationValue]])

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()#

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

Python:

cv.bioinspired.Retina.setup([, retinaParameterFile[, applyDefaultSetupOnFailure]])

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()#

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

Python:

cv.bioinspired.Retina.setup([, retinaParameterFile[, applyDefaultSetupOnFailure]])

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.

setup()#

void cv::bioinspired::Retina::setup(
String retinaParameterFile = “”,
const bool applyDefaultSetupOnFailure = true )

Python:

cv.bioinspired.Retina.setup([, retinaParameterFile[, applyDefaultSetupOnFailure]])

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

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.

setupIPLMagnoChannel()#

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 )

Python:

cv.bioinspired.Retina.setupIPLMagnoChannel([, normaliseOutput[, parasolCells_beta[, parasolCells_tau[, parasolCells_k[, amacrinCellsTemporalCutFrequency[, V0CompressionParameter[, localAdaptintegration_tau[, localAdaptintegration_k]]]]]]]])

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()#

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 )

Python:

cv.bioinspired.Retina.setupOPLandIPLParvoChannel([, colorMode[, normaliseOutput[, photoreceptorsLocalAdaptationSensitivity[, photoreceptorsTemporalConstant[, photoreceptorsSpatialConstant[, horizontalCellsGain[, HcellsTemporalConstant[, HcellsSpatialConstant[, ganglionCellsSensitivity]]]]]]]]])

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()#

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

Python:

cv.bioinspired.Retina.write(fs)

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

write()#

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

Python:

cv.bioinspired.Retina.write(fs)

Write xml/yml formated parameters information.

Parameters

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

create()#

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

Python:

cv.bioinspired.Retina.create(inputSize) -> retval

cv.bioinspired.Retina.create(inputSize, colorMode[, colorSamplingMethod[, useRetinaLogSampling[, reductionFactor[, samplingStrength]]]]) -> retval

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()#

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 )

Python:

cv.bioinspired.Retina.create(inputSize) -> retval

cv.bioinspired.Retina.create(inputSize, colorMode[, colorSamplingMethod[, useRetinaLogSampling[, reductionFactor[, samplingStrength]]]]) -> retval

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

Source file#

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