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added retina module documentation

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Alexandre Benoit 12 years ago
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  1. 1
      modules/contrib/doc/contrib.rst
  2. 246
      modules/contrib/doc/retina/index.rst

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modules/contrib/doc/contrib.rst
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@ -9,3 +9,4 @@ The module contains some recently added functionality that has not been stabiliz
stereo
FaceRecognizer Documentation <facerec/index>
retina Documentation <retina/index>

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Bio mimetic human retina model
==============================
class which allows the Gipsa/Listic Labs model to be used. This retina model allows spatio-temporal image processing (applied on still images, images sequences and video sequences). Briefly, here are the main human retina model properties:
* spectral whithening (mid-frequency details enhancement)
* high frequency spatio-temporal noise reduction (temporal noise and high frequency spatial noise are minimized)
* low frequency luminance reduction (luminance range compression) : high luminance regions do not hide details in darker regions anymore
* local logarithmic luminance compression allows details to be enhanced even in low light conditions
USE : this model can be used basically for spatio-temporal video effects but also for :
* by using the getParvo methods : perform texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges
* by using the getMagno methods : perform motion analysis also taking benefit of the previously cited properties
for more information, refer to the following papers : Benoit A., Caplier A., DurettF 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>
Please have a look at the reference work of Jeanny Herault that you can read in 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 retina filter code 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 phD color mosaicing/demosaicing and his 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.
.. highlight:: cpp
Retina
------
.. ocv:class:: Retina
Class that provides the main controls to the human retina model, it includes protected buffer conversion methods (cv::mat <-> std::valarray)
The retina can be settled up with various parameters, by default, the retina cancels mean luminance and enforces all details of the visual scene. In order to use your own parameters, you can use at least on time the write(std::string fs) method which will write a proper XML file with all default parameters. Then, tweak it on your own and reload them at any time using method setup(std::string fs). These methods update a cv::Retina::RetinaParameters struct that is described hereafter.
class Retina
{
class RetinaParameters; // this class is detailled later
Retina (Size inputSize);
Retina (Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
Size inputSize ();
Size outputSize ();
void setup (std::string retinaParameterFile="", const bool applyDefaultSetupOnFailure=true);
void setup (cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true);
void setup (RetinaParameters newParameters);
struct Retina::RetinaParameters getParameters ();
const std::string printSetup ();
virtual void write (std::string fs) const;
virtual void write (FileStorage &fs) const;
void setupOPLandIPLParvoChannel (const bool colorMode=true, const bool normaliseOutput=true, const float photoreceptorsLocalAdaptationSensitivity=0.7, const float photoreceptorsTemporalConstant=0.5, const float photoreceptorsSpatialConstant=0.53, const float horizontalCellsGain=0, const float HcellsTemporalConstant=1, const float HcellsSpatialConstant=7, const float ganglionCellsSensitivity=0.7);
void setupIPLMagnoChannel (const bool normaliseOutput=true, const float parasolCells_beta=0, const float parasolCells_tau=0, const float parasolCells_k=7, const float amacrinCellsTemporalCutFrequency=1.2, const float V0CompressionParameter=0.95, const float localAdaptintegration_tau=0, const float localAdaptintegration_k=7);
void run (const Mat &inputImage);
void getParvo (Mat &retinaOutput_parvo);
void getParvo (std::valarray< float > &retinaOutput_parvo);
void getMagno (Mat &retinaOutput_magno);
void getMagno (std::valarray< float > &retinaOutput_magno);
const std::valarray< float > & getMagno () const;
const std::valarray< float > & getParvo () const;
void setColorSaturation (const bool saturateColors=true, const float colorSaturationValue=4.0);
void clearBuffers ();
void activateMovingContoursProcessing (const bool activate);
void activateContoursProcessing (const bool activate);
};
Retina::Retina
--------------
.. ocv:function:: Retina::Retina(Size inputSize)
.. ocv:function:: Retina::Retina(Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod = RETINA_COLOR_BAYER, const bool useRetinaLogSampling = false, const double reductionFactor = 1.0, const double samplingStrenght = 10.0 )
Constructors
: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
* 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
:param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used
:param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
:param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
Retina::activateContoursProcessing
----------------------------------
.. ocv:function:: void cv::Retina::activateContoursProcessing(const bool activate)
Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated
:param activate: true if Parvocellular (contours information extraction) output should be activated, false if not... if activated, the Parvocellular output can be retrieved using the getParvo methods
Retina::activateMovingContoursProcessing
----------------------------------------
.. ocv:function:: void cv::Retina::activateMovingContoursProcessing(const bool activate)
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
Retina::clearBuffers
--------------------
.. ocv:function:: void cv::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, some classical visual illusions can be explained
Retina::getParvo
----------------
.. ocv:function:: void cv::Retina::getParvo(Mat & retinaOutput_parvo)
.. ocv:function:: void cv::Retina::getParvo(std::valarray< float > & retinaOutput_parvo )
Accessor of the details channel of the retina (models foveal vision)
:param retinaOutput_parvo: the output buffer (reallocated if necessary), format can be :
* a cv::Mat, this output is rescaled for standard 8bits image processing use in OpenCV
* a 1D std::valarray Buffer (encoding is R1, R2, ... Rn), this output is the original retina filter model output, without any quantification or rescaling
Retina::getMagno
----------------
.. ocv:function:: void cv::Retina::getMagno(Mat & retinaOutput_magno)
.. ocv:function:: void cv::Retina::getMagno(std::valarray< float > & retinaOutput_magno)
Accessor of the motion channel of the retina (models peripheral vision)
:param retinaOutput_magno: the output buffer (reallocated if necessary), format can be :
* a cv::Mat, this output is rescaled for standard 8bits image processing use in OpenCV
* a 1D std::valarray Buffer (encoding is R1, R2, ... Rn), this output is the original retina filter model output, without any quantification or rescaling
Retina::getParameters
---------------------
.. ocv:function:: struct Retina::RetinaParameters cv::Retina::getParameters()
Returns: the current parameters setup
Retina::inputSize
-----------------
.. ocv:function:: Size cv::Retina::inputSize()
Retreive retina input buffer size
Retina::outputSize
------------------
.. ocv:function:: Size cv::Retina::outputSize()
Retreive retina output buffer size that can be different from the input if a spatial log transformation is applied
Retina::printSetup
------------------
.. ocv:function:: const std::string cv::Retina::printSetup()
Outputs a string showing the used parameters setup
:return a string which contains formatted parameters information
Retina::run
-----------
.. ocv:function:: void cv::Retina::run(const Mat & 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
:param inputImage: the input cv::Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)
Retina::setColorSaturation
--------------------------
.. ocv:function:: void cv::Retina::setColorSaturation(const bool saturateColors = true, const float colorSaturationValue = 4.0 )
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.
: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
Retina::setup
-------------
.. ocv:function:: void cv::Retina::setup(std::string retinaParameterFile = "", const bool applyDefaultSetupOnFailure = true )
.. ocv:function:: void cv::Retina::setup(cv::FileStorage & fs, const bool applyDefaultSetupOnFailure = true )
.. ocv:function:: void cv::Retina::setup(RetinaParameters newParameters)
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
:param fs: the open Filestorage which contains retina parameters
:param newParameters: a parameters structures updated with the new target configuration
Retina::write
-------------
.. ocv:function:: virtual void cv::Retina::write(std::string fs) const [virtual]
.. ocv:function:: virtual void cv::Retina::write(FileStorage & fs) const [virtual]
Write xml/yml formated parameters information
:param fs : the filename of the xml file that will be open and writen with formatted parameters information
Retina::setupIPLMagnoChannel
----------------------------
.. ocv:function:: void cv::Retina::setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta = 0, const float parasolCells_tau = 0, const float parasolCells_k = 7, const float amacrinCellsTemporalCutFrequency = 1.2, const float V0CompressionParameter = 0.95, const float localAdaptintegration_tau = 0, const float localAdaptintegration_k = 7 )
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, tipicall value is 5
:param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 200
:param localAdaptintegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
:param localAdaptintegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
Retina::setupOPLandIPLParvoChannel
----------------------------------
.. ocv:function:: void cv::Retina::setupOPLandIPLParvoChannel(const bool colorMode = true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity = 0.7, const float photoreceptorsTemporalConstant = 0.5, const float photoreceptorsSpatialConstant = 0.53, const float horizontalCellsGain = 0, const float HcellsTemporalConstant = 1, const float HcellsSpatialConstant = 7, const float ganglionCellsSensitivity = 0.7 )
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.
:param colorMode: specifies if (true) color is processed of not (false) to then processing gray level image
:param normaliseOutput: specifies if (true) output is rescaled between 0 and 255 of not (false)
:param photoreceptorsLocalAdaptationSensitivity: the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
:param photoreceptorsTemporalConstant: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
:param photoreceptorsSpatialConstant: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
:param horizontalCellsGain:gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
:param HcellsTemporalConstant:the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
:param HcellsSpatialConstant:the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
:param ganglionCellsSensitivity:the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 230
Retina::RetinaParameters
------------------------
This structure merges all the parameters that can be adjusted threw the cv::Retina::setupOPLandIPLParvoChannel and cv::Retina::setupIPLMagnoChannel setup methods
Parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel
.. ocv:class:: RetinaParameters
struct RetinaParameters{
struct OPLandIplParvoParameters{ // Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
OPLandIplParvoParameters():colorMode(true),
normaliseOutput(true),
photoreceptorsLocalAdaptationSensitivity(0.7f),
photoreceptorsTemporalConstant(0.5f),
photoreceptorsSpatialConstant(0.53f),
horizontalCellsGain(0.0f),
hcellsTemporalConstant(1.f),
hcellsSpatialConstant(7.f),
ganglionCellsSensitivity(0.7f){};// default setup
bool colorMode, normaliseOutput;
float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity;
};
struct IplMagnoParameters{ // Inner Plexiform Layer Magnocellular channel (IplMagno)
IplMagnoParameters():
normaliseOutput(true),
parasolCells_beta(0.f),
parasolCells_tau(0.f),
parasolCells_k(7.f),
amacrinCellsTemporalCutFrequency(1.2f),
V0CompressionParameter(0.95f),
localAdaptintegration_tau(0.f),
localAdaptintegration_k(7.f){};// default setup
bool normaliseOutput;
float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k;
};
struct OPLandIplParvoParameters OPLandIplParvo;
struct IplMagnoParameters IplMagno;
};
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