org.opencv.bioinspired.Retina Maven / Gradle / Ivy
//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.bioinspired;
import org.opencv.bioinspired.Retina;
import org.opencv.core.Algorithm;
import org.opencv.core.Mat;
import org.opencv.core.Size;
// C++: class Retina
/**
* 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.
*/
public class Retina extends Algorithm {
protected Retina(long addr) { super(addr); }
// internal usage only
public static Retina __fromPtr__(long addr) { return new Retina(addr); }
//
// C++: Size cv::bioinspired::Retina::getInputSize()
//
/**
* Retreive retina input buffer size
* @return the retina input buffer size
*/
public Size getInputSize() {
return new Size(getInputSize_0(nativeObj));
}
//
// C++: Size cv::bioinspired::Retina::getOutputSize()
//
/**
* 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
*/
public Size getOutputSize() {
return new Size(getOutputSize_0(nativeObj));
}
//
// C++: void cv::bioinspired::Retina::setup(String retinaParameterFile = "", bool applyDefaultSetupOnFailure = true)
//
/**
* 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.
*/
public void setup(String retinaParameterFile, boolean applyDefaultSetupOnFailure) {
setup_0(nativeObj, 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
* @param retinaParameterFile the parameters filename
*
*
*
* You can retrieve the current parameters structure using the method Retina::getParameters and update
* it before running method Retina::setup.
*/
public void setup(String retinaParameterFile) {
setup_1(nativeObj, retinaParameterFile);
}
/**
* 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
*
*
*
* You can retrieve the current parameters structure using the method Retina::getParameters and update
* it before running method Retina::setup.
*/
public void setup() {
setup_2(nativeObj);
}
//
// C++: String cv::bioinspired::Retina::printSetup()
//
/**
* Outputs a string showing the used parameters setup
* @return a string which contains formated parameters information
*/
public String printSetup() {
return printSetup_0(nativeObj);
}
//
// C++: void cv::bioinspired::Retina::write(String fs)
//
/**
* Write xml/yml formated parameters information
* @param fs the filename of the xml file that will be open and writen with formatted parameters
* information
*/
public void write(String fs) {
write_0(nativeObj, fs);
}
//
// C++: void cv::bioinspired::Retina::setupOPLandIPLParvoChannel(bool colorMode = true, bool normaliseOutput = true, float photoreceptorsLocalAdaptationSensitivity = 0.7f, float photoreceptorsTemporalConstant = 0.5f, float photoreceptorsSpatialConstant = 0.53f, float horizontalCellsGain = 0.f, float HcellsTemporalConstant = 1.f, float HcellsSpatialConstant = 7.f, float ganglionCellsSensitivity = 0.7f)
//
/**
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant, float HcellsSpatialConstant, float ganglionCellsSensitivity) {
setupOPLandIPLParvoChannel_0(nativeObj, 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
* @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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant, float HcellsSpatialConstant) {
setupOPLandIPLParvoChannel_1(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, HcellsTemporalConstant, HcellsSpatialConstant);
}
/**
* 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
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant) {
setupOPLandIPLParvoChannel_2(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, HcellsTemporalConstant);
}
/**
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain) {
setupOPLandIPLParvoChannel_3(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain);
}
/**
* 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
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant) {
setupOPLandIPLParvoChannel_4(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant);
}
/**
* 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
* the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
* pixels, typical value is 1 pixel
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant) {
setupOPLandIPLParvoChannel_5(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant);
}
/**
* 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)
* the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is
* frames, typical value is 1 frame
* the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
* pixels, typical value is 1 pixel
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity) {
setupOPLandIPLParvoChannel_6(nativeObj, colorMode, normaliseOutput, photoreceptorsLocalAdaptationSensitivity);
}
/**
* 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)
* (more log compression effect when value increases)
* the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is
* frames, typical value is 1 frame
* the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
* pixels, typical value is 1 pixel
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode, boolean normaliseOutput) {
setupOPLandIPLParvoChannel_7(nativeObj, colorMode, normaliseOutput);
}
/**
* 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
* (more log compression effect when value increases)
* the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is
* frames, typical value is 1 frame
* the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
* pixels, typical value is 1 pixel
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel(boolean colorMode) {
setupOPLandIPLParvoChannel_8(nativeObj, colorMode);
}
/**
* 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
* level image
* (more log compression effect when value increases)
* the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is
* frames, typical value is 1 frame
* the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
* pixels, typical value is 1 pixel
* 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
* horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
* frames, typical value is 1 frame, as the photoreceptors
* 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)
* 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
*/
public void setupOPLandIPLParvoChannel() {
setupOPLandIPLParvoChannel_9(nativeObj);
}
//
// C++: void cv::bioinspired::Retina::setupIPLMagnoChannel(bool normaliseOutput = true, float parasolCells_beta = 0.f, float parasolCells_tau = 0.f, float parasolCells_k = 7.f, float amacrinCellsTemporalCutFrequency = 1.2f, float V0CompressionParameter = 0.95f, float localAdaptintegration_tau = 0.f, float localAdaptintegration_k = 7.f)
//
/**
* 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
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter, float localAdaptintegration_tau, float localAdaptintegration_k) {
setupIPLMagnoChannel_0(nativeObj, 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.
*
* @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
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter, float localAdaptintegration_tau) {
setupIPLMagnoChannel_1(nativeObj, normaliseOutput, parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau);
}
/**
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter) {
setupIPLMagnoChannel_2(nativeObj, normaliseOutput, parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter);
}
/**
* 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
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency) {
setupIPLMagnoChannel_3(nativeObj, normaliseOutput, parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency);
}
/**
* 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
* the magnocellular way (motion information channel), unit is frames, typical value is 1.2
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k) {
setupIPLMagnoChannel_4(nativeObj, normaliseOutput, parasolCells_beta, parasolCells_tau, parasolCells_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.
*
* @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)
* at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical
* value is 5
* the magnocellular way (motion information channel), unit is frames, typical value is 1.2
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau) {
setupIPLMagnoChannel_5(nativeObj, normaliseOutput, parasolCells_beta, parasolCells_tau);
}
/**
* 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
* at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical
* value is 0 (immediate response)
* at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical
* value is 5
* the magnocellular way (motion information channel), unit is frames, typical value is 1.2
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput, float parasolCells_beta) {
setupIPLMagnoChannel_6(nativeObj, normaliseOutput, parasolCells_beta);
}
/**
* 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)
* IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical
* value is 0 (immediate response)
* at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical
* value is 5
* the magnocellular way (motion information channel), unit is frames, typical value is 1.2
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel(boolean normaliseOutput) {
setupIPLMagnoChannel_7(nativeObj, normaliseOutput);
}
/**
* 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.
*
* IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical
* value is 0 (immediate response)
* at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical
* value is 5
* the magnocellular way (motion information channel), unit is frames, typical value is 1.2
* 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
* involved in the computation of the local "motion mean" for the local adaptation computation
* in the computation of the local "motion mean" for the local adaptation computation
*/
public void setupIPLMagnoChannel() {
setupIPLMagnoChannel_8(nativeObj);
}
//
// C++: void cv::bioinspired::Retina::run(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 Mat image to be processed, can be gray level or BGR coded in any
* format (from 8bit to 16bits)
*/
public void run(Mat inputImage) {
run_0(nativeObj, inputImage.nativeObj);
}
//
// C++: void cv::bioinspired::Retina::applyFastToneMapping(Mat inputImage, Mat& 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.
*
* @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).
*/
public void applyFastToneMapping(Mat inputImage, Mat outputToneMappedImage) {
applyFastToneMapping_0(nativeObj, inputImage.nativeObj, outputToneMappedImage.nativeObj);
}
//
// C++: void cv::bioinspired::Retina::getParvo(Mat& 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.
*
* @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
*
*
*/
public void getParvo(Mat retinaOutput_parvo) {
getParvo_0(nativeObj, retinaOutput_parvo.nativeObj);
}
//
// C++: void cv::bioinspired::Retina::getParvoRAW(Mat& retinaOutput_parvo)
//
/**
* Accessor of the details channel of the retina (models foveal vision).
* SEE: getParvo
* @param retinaOutput_parvo automatically generated
*/
public void getParvoRAW(Mat retinaOutput_parvo) {
getParvoRAW_0(nativeObj, retinaOutput_parvo.nativeObj);
}
//
// C++: void cv::bioinspired::Retina::getMagno(Mat& 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.
* @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
*
*
*/
public void getMagno(Mat retinaOutput_magno) {
getMagno_0(nativeObj, retinaOutput_magno.nativeObj);
}
//
// C++: void cv::bioinspired::Retina::getMagnoRAW(Mat& retinaOutput_magno)
//
/**
* Accessor of the motion channel of the retina (models peripheral vision).
* SEE: getMagno
* @param retinaOutput_magno automatically generated
*/
public void getMagnoRAW(Mat retinaOutput_magno) {
getMagnoRAW_0(nativeObj, retinaOutput_magno.nativeObj);
}
//
// C++: Mat cv::bioinspired::Retina::getMagnoRAW()
//
public Mat getMagnoRAW() {
return new Mat(getMagnoRAW_1(nativeObj));
}
//
// C++: Mat cv::bioinspired::Retina::getParvoRAW()
//
public Mat getParvoRAW() {
return new Mat(getParvoRAW_1(nativeObj));
}
//
// C++: void cv::bioinspired::Retina::setColorSaturation(bool saturateColors = true, float colorSaturationValue = 4.0f)
//
/**
* 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
*/
public void setColorSaturation(boolean saturateColors, float colorSaturationValue) {
setColorSaturation_0(nativeObj, 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.
* @param saturateColors boolean that activates color saturation (if true) or desactivate (if false)
* buffers
*/
public void setColorSaturation(boolean saturateColors) {
setColorSaturation_1(nativeObj, saturateColors);
}
/**
* 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.
* buffers
*/
public void setColorSaturation() {
setColorSaturation_2(nativeObj);
}
//
// C++: void 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.
*/
public void clearBuffers() {
clearBuffers_0(nativeObj);
}
//
// C++: void cv::bioinspired::Retina::activateMovingContoursProcessing(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
*/
public void activateMovingContoursProcessing(boolean activate) {
activateMovingContoursProcessing_0(nativeObj, activate);
}
//
// C++: void cv::bioinspired::Retina::activateContoursProcessing(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
* Retina::getParvo methods
*/
public void activateContoursProcessing(boolean activate) {
activateContoursProcessing_0(nativeObj, activate);
}
//
// C++: static Ptr_Retina cv::bioinspired::Retina::create(Size inputSize)
//
public static Retina create(Size inputSize) {
return Retina.__fromPtr__(create_0(inputSize.width, inputSize.height));
}
//
// C++: static Ptr_Retina cv::bioinspired::Retina::create(Size inputSize, bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, bool useRetinaLogSampling = false, float reductionFactor = 1.0f, float samplingStrength = 10.0f)
//
/**
* 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
*
*
* @return automatically generated
*/
public static Retina create(Size inputSize, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling, float reductionFactor, float samplingStrength) {
return Retina.__fromPtr__(create_1(inputSize.width, inputSize.height, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor, samplingStrength));
}
/**
* 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
* the log scale that is applied
*
*
* @return automatically generated
*/
public static Retina create(Size inputSize, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling, float reductionFactor) {
return Retina.__fromPtr__(create_2(inputSize.width, inputSize.height, colorMode, colorSamplingMethod, useRetinaLogSampling, reductionFactor));
}
/**
* 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
* 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
* the log scale that is applied
*
*
* @return automatically generated
*/
public static Retina create(Size inputSize, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling) {
return Retina.__fromPtr__(create_3(inputSize.width, inputSize.height, colorMode, colorSamplingMethod, useRetinaLogSampling));
}
/**
* 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
* be used
* 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
* the log scale that is applied
*
*
* @return automatically generated
*/
public static Retina create(Size inputSize, boolean colorMode, int colorSamplingMethod) {
return Retina.__fromPtr__(create_4(inputSize.width, inputSize.height, colorMode, colorSamplingMethod));
}
/**
* 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
*
* -
* 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
* be used
* 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
* the log scale that is applied
*
*
* @return automatically generated
*/
public static Retina create(Size inputSize, boolean colorMode) {
return Retina.__fromPtr__(create_5(inputSize.width, inputSize.height, colorMode));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: Size cv::bioinspired::Retina::getInputSize()
private static native double[] getInputSize_0(long nativeObj);
// C++: Size cv::bioinspired::Retina::getOutputSize()
private static native double[] getOutputSize_0(long nativeObj);
// C++: void cv::bioinspired::Retina::setup(String retinaParameterFile = "", bool applyDefaultSetupOnFailure = true)
private static native void setup_0(long nativeObj, String retinaParameterFile, boolean applyDefaultSetupOnFailure);
private static native void setup_1(long nativeObj, String retinaParameterFile);
private static native void setup_2(long nativeObj);
// C++: String cv::bioinspired::Retina::printSetup()
private static native String printSetup_0(long nativeObj);
// C++: void cv::bioinspired::Retina::write(String fs)
private static native void write_0(long nativeObj, String fs);
// C++: void cv::bioinspired::Retina::setupOPLandIPLParvoChannel(bool colorMode = true, bool normaliseOutput = true, float photoreceptorsLocalAdaptationSensitivity = 0.7f, float photoreceptorsTemporalConstant = 0.5f, float photoreceptorsSpatialConstant = 0.53f, float horizontalCellsGain = 0.f, float HcellsTemporalConstant = 1.f, float HcellsSpatialConstant = 7.f, float ganglionCellsSensitivity = 0.7f)
private static native void setupOPLandIPLParvoChannel_0(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant, float HcellsSpatialConstant, float ganglionCellsSensitivity);
private static native void setupOPLandIPLParvoChannel_1(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant, float HcellsSpatialConstant);
private static native void setupOPLandIPLParvoChannel_2(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain, float HcellsTemporalConstant);
private static native void setupOPLandIPLParvoChannel_3(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant, float horizontalCellsGain);
private static native void setupOPLandIPLParvoChannel_4(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant, float photoreceptorsSpatialConstant);
private static native void setupOPLandIPLParvoChannel_5(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity, float photoreceptorsTemporalConstant);
private static native void setupOPLandIPLParvoChannel_6(long nativeObj, boolean colorMode, boolean normaliseOutput, float photoreceptorsLocalAdaptationSensitivity);
private static native void setupOPLandIPLParvoChannel_7(long nativeObj, boolean colorMode, boolean normaliseOutput);
private static native void setupOPLandIPLParvoChannel_8(long nativeObj, boolean colorMode);
private static native void setupOPLandIPLParvoChannel_9(long nativeObj);
// C++: void cv::bioinspired::Retina::setupIPLMagnoChannel(bool normaliseOutput = true, float parasolCells_beta = 0.f, float parasolCells_tau = 0.f, float parasolCells_k = 7.f, float amacrinCellsTemporalCutFrequency = 1.2f, float V0CompressionParameter = 0.95f, float localAdaptintegration_tau = 0.f, float localAdaptintegration_k = 7.f)
private static native void setupIPLMagnoChannel_0(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter, float localAdaptintegration_tau, float localAdaptintegration_k);
private static native void setupIPLMagnoChannel_1(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter, float localAdaptintegration_tau);
private static native void setupIPLMagnoChannel_2(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency, float V0CompressionParameter);
private static native void setupIPLMagnoChannel_3(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k, float amacrinCellsTemporalCutFrequency);
private static native void setupIPLMagnoChannel_4(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau, float parasolCells_k);
private static native void setupIPLMagnoChannel_5(long nativeObj, boolean normaliseOutput, float parasolCells_beta, float parasolCells_tau);
private static native void setupIPLMagnoChannel_6(long nativeObj, boolean normaliseOutput, float parasolCells_beta);
private static native void setupIPLMagnoChannel_7(long nativeObj, boolean normaliseOutput);
private static native void setupIPLMagnoChannel_8(long nativeObj);
// C++: void cv::bioinspired::Retina::run(Mat inputImage)
private static native void run_0(long nativeObj, long inputImage_nativeObj);
// C++: void cv::bioinspired::Retina::applyFastToneMapping(Mat inputImage, Mat& outputToneMappedImage)
private static native void applyFastToneMapping_0(long nativeObj, long inputImage_nativeObj, long outputToneMappedImage_nativeObj);
// C++: void cv::bioinspired::Retina::getParvo(Mat& retinaOutput_parvo)
private static native void getParvo_0(long nativeObj, long retinaOutput_parvo_nativeObj);
// C++: void cv::bioinspired::Retina::getParvoRAW(Mat& retinaOutput_parvo)
private static native void getParvoRAW_0(long nativeObj, long retinaOutput_parvo_nativeObj);
// C++: void cv::bioinspired::Retina::getMagno(Mat& retinaOutput_magno)
private static native void getMagno_0(long nativeObj, long retinaOutput_magno_nativeObj);
// C++: void cv::bioinspired::Retina::getMagnoRAW(Mat& retinaOutput_magno)
private static native void getMagnoRAW_0(long nativeObj, long retinaOutput_magno_nativeObj);
// C++: Mat cv::bioinspired::Retina::getMagnoRAW()
private static native long getMagnoRAW_1(long nativeObj);
// C++: Mat cv::bioinspired::Retina::getParvoRAW()
private static native long getParvoRAW_1(long nativeObj);
// C++: void cv::bioinspired::Retina::setColorSaturation(bool saturateColors = true, float colorSaturationValue = 4.0f)
private static native void setColorSaturation_0(long nativeObj, boolean saturateColors, float colorSaturationValue);
private static native void setColorSaturation_1(long nativeObj, boolean saturateColors);
private static native void setColorSaturation_2(long nativeObj);
// C++: void cv::bioinspired::Retina::clearBuffers()
private static native void clearBuffers_0(long nativeObj);
// C++: void cv::bioinspired::Retina::activateMovingContoursProcessing(bool activate)
private static native void activateMovingContoursProcessing_0(long nativeObj, boolean activate);
// C++: void cv::bioinspired::Retina::activateContoursProcessing(bool activate)
private static native void activateContoursProcessing_0(long nativeObj, boolean activate);
// C++: static Ptr_Retina cv::bioinspired::Retina::create(Size inputSize)
private static native long create_0(double inputSize_width, double inputSize_height);
// C++: static Ptr_Retina cv::bioinspired::Retina::create(Size inputSize, bool colorMode, int colorSamplingMethod = RETINA_COLOR_BAYER, bool useRetinaLogSampling = false, float reductionFactor = 1.0f, float samplingStrength = 10.0f)
private static native long create_1(double inputSize_width, double inputSize_height, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling, float reductionFactor, float samplingStrength);
private static native long create_2(double inputSize_width, double inputSize_height, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling, float reductionFactor);
private static native long create_3(double inputSize_width, double inputSize_height, boolean colorMode, int colorSamplingMethod, boolean useRetinaLogSampling);
private static native long create_4(double inputSize_width, double inputSize_height, boolean colorMode, int colorSamplingMethod);
private static native long create_5(double inputSize_width, double inputSize_height, boolean colorMode);
// native support for java finalize()
private static native void delete(long nativeObj);
}
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