bioinspired_Retina
import { bioinspired_Retina } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits Algorithm.
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.
Constructors and members
static create
Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
create(inputSize: Size): bioinspired_Retina | null;inputSizethe input frame size
The bioinspired_Retina | null result.
static create1
Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
create1(inputSize: Size, colorMode: boolean, colorSamplingMethod: number, useRetinaLogSampling: boolean, reductionFactor: number, samplingStrength: number): bioinspired_Retina | null;5 available overloads
create1(inputSize: Size, colorMode: boolean): bioinspired_Retina | null;create1(inputSize: Size, colorMode: boolean, colorSamplingMethod: number): bioinspired_Retina | null;create1(inputSize: Size, colorMode: boolean, colorSamplingMethod: number, useRetinaLogSampling: boolean): bioinspired_Retina | null;create1(inputSize: Size, colorMode: boolean, colorSamplingMethod: number, useRetinaLogSampling: boolean, reductionFactor: number): bioinspired_Retina | null;create1(inputSize: Size, colorMode: boolean, colorSamplingMethod: number, useRetinaLogSampling: boolean, reductionFactor: number, samplingStrength: number): bioinspired_Retina | null;inputSizethe input frame size
colorModethe chosen processing mode : with or without color processing
colorSamplingMethodspecifies 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
useRetinaLogSamplingactivate retina log sampling, if true, the 2 following parameters can be used
reductionFactoronly 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
samplingStrengthonly usefull if param useRetinaLogSampling=true, specifies the strength of the log scale that is applied
The bioinspired_Retina | null result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
getInputSize
Retreive retina input buffer size
getInputSize(): Size;the retina input buffer size
getOutputSize
Retreive retina output buffer size that can be different from the input if a spatial log transformation is applied
getOutputSize(): Size;the retina output buffer size
setup
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
setup(retinaParameterFile: EmbindString, applyDefaultSetupOnFailure: boolean): void;3 available overloads
setup(): void;setup(retinaParameterFile: EmbindString): void;setup(retinaParameterFile: EmbindString, applyDefaultSetupOnFailure: boolean): void;retinaParameterFilethe parameters filename
applyDefaultSetupOnFailureset 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.
printSetup
Outputs a string showing the used parameters setup
printSetup(): string;a string which contains formated parameters information
write
Write xml/yml formated parameters information
write(fs: EmbindString): void;fsthe filename of the xml file that will be open and writen with formatted parameters information
setupOPLandIPLParvoChannel
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
setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number, horizontalCellsGain: number, HcellsTemporalConstant: number, HcellsSpatialConstant: number, ganglionCellsSensitivity: number): void;10 available overloads
setupOPLandIPLParvoChannel(): void;setupOPLandIPLParvoChannel(colorMode: boolean): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number, horizontalCellsGain: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number, horizontalCellsGain: number, HcellsTemporalConstant: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number, horizontalCellsGain: number, HcellsTemporalConstant: number, HcellsSpatialConstant: number): void;setupOPLandIPLParvoChannel(colorMode: boolean, normaliseOutput: boolean, photoreceptorsLocalAdaptationSensitivity: number, photoreceptorsTemporalConstant: number, photoreceptorsSpatialConstant: number, horizontalCellsGain: number, HcellsTemporalConstant: number, HcellsSpatialConstant: number, ganglionCellsSensitivity: number): void;colorModespecifies if (true) color is processed of not (false) to then processing gray level image
normaliseOutputspecifies if (true) output is rescaled between 0 and 255 of not (false)
photoreceptorsLocalAdaptationSensitivitythe photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
photoreceptorsTemporalConstantthe 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
photoreceptorsSpatialConstantthe 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
horizontalCellsGaingain 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
HcellsTemporalConstantthe 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
HcellsSpatialConstantthe 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)
ganglionCellsSensitivitythe 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
setupIPLMagnoChannel
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.
setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number, amacrinCellsTemporalCutFrequency: number, V0CompressionParameter: number, localAdaptintegration_tau: number, localAdaptintegration_k: number): void;9 available overloads
setupIPLMagnoChannel(): void;setupIPLMagnoChannel(normaliseOutput: boolean): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number, amacrinCellsTemporalCutFrequency: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number, amacrinCellsTemporalCutFrequency: number, V0CompressionParameter: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number, amacrinCellsTemporalCutFrequency: number, V0CompressionParameter: number, localAdaptintegration_tau: number): void;setupIPLMagnoChannel(normaliseOutput: boolean, parasolCells_beta: number, parasolCells_tau: number, parasolCells_k: number, amacrinCellsTemporalCutFrequency: number, V0CompressionParameter: number, localAdaptintegration_tau: number, localAdaptintegration_k: number): void;normaliseOutputspecifies if (true) output is rescaled between 0 and 255 of not (false)
parasolCells_betathe 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_tauthe 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_kthe 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
amacrinCellsTemporalCutFrequencythe time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, typical value is 1.2
V0CompressionParameterthe 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_tauspecifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
localAdaptintegration_kspecifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
run
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
run(inputImage: Mat): void;inputImagethe input Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)
applyFastToneMapping
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.
applyFastToneMapping(inputImage: Mat, outputToneMappedImage: Mat): void;inputImagethe 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).
outputToneMappedImageOutput destination, filled by the native operation. the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format).
getParvo
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: getParvoRAW
getParvo(retinaOutput_parvo: Mat): void;retinaOutput_parvoOutput destination, filled by the native operation. 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
Accessor of the details channel of the retina (models foveal vision). See: getParvo
getParvoRAW(retinaOutput_parvo: Mat): void;retinaOutput_parvoOutput destination, filled by the native operation. retina output parvo argument (Mat).
getParvoRAW1
Accessor of the details channel of the retina (models foveal vision). See: getParvo
getParvoRAW1(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
getMagno
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: getMagnoRAW
getMagno(retinaOutput_magno: Mat): void;retinaOutput_magnoOutput destination, filled by the native operation. 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
Accessor of the motion channel of the retina (models peripheral vision). See: getMagno
getMagnoRAW(retinaOutput_magno: Mat): void;retinaOutput_magnoOutput destination, filled by the native operation. retina output magno argument (Mat).
getMagnoRAW1
Accessor of the motion channel of the retina (models peripheral vision). See: getMagno
getMagnoRAW1(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
setColorSaturation
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.
setColorSaturation(saturateColors: boolean, colorSaturationValue: number): void;3 available overloads
setColorSaturation(): void;setColorSaturation(saturateColors: boolean): void;setColorSaturation(saturateColors: boolean, colorSaturationValue: number): void;saturateColorsboolean that activates color saturation (if true) or desactivate (if false)
colorSaturationValuethe saturation factor : a simple factor applied on the chrominance buffers
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.
clearBuffers(): void;activateMovingContoursProcessing
Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated
activateMovingContoursProcessing(activate: boolean): void;activatetrue if Magnocellular output should be activated, false if not... if activated, the Magnocellular output can be retrieved using the getMagno methods
activateContoursProcessing
Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated
activateContoursProcessing(activate: boolean): void;activatetrue 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
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