// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/bioinspired.hpp" #import "opencv2/bioinspired/retina.hpp" #else #define CV_EXPORTS #endif #import #import "Algorithm.h" @class Mat; @class Size2i; NS_ASSUME_NONNULL_BEGIN // 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. * * Member of `Bioinspired` */ CV_EXPORTS @interface Retina : Algorithm #ifdef __cplusplus @property(readonly)cv::Ptr nativePtrRetina; #endif #ifdef __cplusplus - (instancetype)initWithNativePtr:(cv::Ptr)nativePtr; + (instancetype)fromNative:(cv::Ptr)nativePtr; #endif #pragma mark - Methods // // Size cv::bioinspired::Retina::getInputSize() // /** * Retreive retina input buffer size * @return the retina input buffer size */ - (Size2i*)getInputSize NS_SWIFT_NAME(getInputSize()); // // 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 */ - (Size2i*)getOutputSize NS_SWIFT_NAME(getOutputSize()); // // 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. */ - (void)setup:(NSString*)retinaParameterFile applyDefaultSetupOnFailure:(BOOL)applyDefaultSetupOnFailure NS_SWIFT_NAME(setup(retinaParameterFile:applyDefaultSetupOnFailure:)); /** * Try to open an XML retina parameters file to adjust current retina instance setup * * - if the xml file does not exist, then default setup is applied * - warning, Exceptions are thrown if read XML file is not valid * @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. */ - (void)setup:(NSString*)retinaParameterFile NS_SWIFT_NAME(setup(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. */ - (void)setup NS_SWIFT_NAME(setup()); // // String cv::bioinspired::Retina::printSetup() // /** * Outputs a string showing the used parameters setup * @return a string which contains formated parameters information */ - (NSString*)printSetup NS_SWIFT_NAME(printSetup()); // // 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 */ - (void)write:(NSString*)fs NS_SWIFT_NAME(write(fs:)); // // 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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant photoreceptorsSpatialConstant:(float)photoreceptorsSpatialConstant horizontalCellsGain:(float)horizontalCellsGain HcellsTemporalConstant:(float)HcellsTemporalConstant HcellsSpatialConstant:(float)HcellsSpatialConstant ganglionCellsSensitivity:(float)ganglionCellsSensitivity NS_SWIFT_NAME(setupOPLandIPLParvoChannel(colorMode:normaliseOutput:photoreceptorsLocalAdaptationSensitivity:photoreceptorsTemporalConstant:photoreceptorsSpatialConstant:horizontalCellsGain:HcellsTemporalConstant:HcellsSpatialConstant:ganglionCellsSensitivity:)); /** * Setup the OPL and IPL parvo channels (see biologocal model) * * OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering * which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance * (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the * Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See * reference papers for more informations. * for more informations, please have a look at the paper Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011 * @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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant photoreceptorsSpatialConstant:(float)photoreceptorsSpatialConstant horizontalCellsGain:(float)horizontalCellsGain HcellsTemporalConstant:(float)HcellsTemporalConstant HcellsSpatialConstant:(float)HcellsSpatialConstant NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant photoreceptorsSpatialConstant:(float)photoreceptorsSpatialConstant horizontalCellsGain:(float)horizontalCellsGain HcellsTemporalConstant:(float)HcellsTemporalConstant NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant photoreceptorsSpatialConstant:(float)photoreceptorsSpatialConstant horizontalCellsGain:(float)horizontalCellsGain NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant photoreceptorsSpatialConstant:(float)photoreceptorsSpatialConstant NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity photoreceptorsTemporalConstant:(float)photoreceptorsTemporalConstant NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput photoreceptorsLocalAdaptationSensitivity:(float)photoreceptorsLocalAdaptationSensitivity NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode normaliseOutput:(BOOL)normaliseOutput NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel:(BOOL)colorMode NS_SWIFT_NAME(setupOPLandIPLParvoChannel(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 */ - (void)setupOPLandIPLParvoChannel NS_SWIFT_NAME(setupOPLandIPLParvoChannel()); // // 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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau parasolCells_k:(float)parasolCells_k amacrinCellsTemporalCutFrequency:(float)amacrinCellsTemporalCutFrequency V0CompressionParameter:(float)V0CompressionParameter localAdaptintegration_tau:(float)localAdaptintegration_tau localAdaptintegration_k:(float)localAdaptintegration_k NS_SWIFT_NAME(setupIPLMagnoChannel(normaliseOutput:parasolCells_beta:parasolCells_tau:parasolCells_k:amacrinCellsTemporalCutFrequency:V0CompressionParameter:localAdaptintegration_tau:localAdaptintegration_k:)); /** * Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel * * this channel processes signals output from OPL processing stage in peripheral vision, it allows * motion information enhancement. It is decorrelated from the details channel. See reference * papers for more details. * * @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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau parasolCells_k:(float)parasolCells_k amacrinCellsTemporalCutFrequency:(float)amacrinCellsTemporalCutFrequency V0CompressionParameter:(float)V0CompressionParameter localAdaptintegration_tau:(float)localAdaptintegration_tau NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau parasolCells_k:(float)parasolCells_k amacrinCellsTemporalCutFrequency:(float)amacrinCellsTemporalCutFrequency V0CompressionParameter:(float)V0CompressionParameter NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau parasolCells_k:(float)parasolCells_k amacrinCellsTemporalCutFrequency:(float)amacrinCellsTemporalCutFrequency NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau parasolCells_k:(float)parasolCells_k NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta parasolCells_tau:(float)parasolCells_tau NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput parasolCells_beta:(float)parasolCells_beta NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel:(BOOL)normaliseOutput NS_SWIFT_NAME(setupIPLMagnoChannel(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 */ - (void)setupIPLMagnoChannel NS_SWIFT_NAME(setupIPLMagnoChannel()); // // 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) */ - (void)run:(Mat*)inputImage NS_SWIFT_NAME(run(inputImage:)); // // 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). */ - (void)applyFastToneMapping:(Mat*)inputImage outputToneMappedImage:(Mat*)outputToneMappedImage NS_SWIFT_NAME(applyFastToneMapping(inputImage:outputToneMappedImage:)); // // 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:` */ - (void)getParvo:(Mat*)retinaOutput_parvo NS_SWIFT_NAME(getParvo(retinaOutput_parvo:)); // // void cv::bioinspired::Retina::getParvoRAW(Mat& retinaOutput_parvo) // /** * Accessor of the details channel of the retina (models foveal vision). * @see `-getParvo:` */ - (void)getParvoRAW:(Mat*)retinaOutput_parvo NS_SWIFT_NAME(getParvoRAW(retinaOutput_parvo:)); // // 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:` */ - (void)getMagno:(Mat*)retinaOutput_magno NS_SWIFT_NAME(getMagno(retinaOutput_magno:)); // // void cv::bioinspired::Retina::getMagnoRAW(Mat& retinaOutput_magno) // /** * Accessor of the motion channel of the retina (models peripheral vision). * @see `-getMagno:` */ - (void)getMagnoRAW:(Mat*)retinaOutput_magno NS_SWIFT_NAME(getMagnoRAW(retinaOutput_magno:)); // // Mat cv::bioinspired::Retina::getMagnoRAW() // - (Mat*)getMagnoRAW NS_SWIFT_NAME(getMagnoRAW()); // // Mat cv::bioinspired::Retina::getParvoRAW() // - (Mat*)getParvoRAW NS_SWIFT_NAME(getParvoRAW()); // // 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 */ - (void)setColorSaturation:(BOOL)saturateColors colorSaturationValue:(float)colorSaturationValue NS_SWIFT_NAME(setColorSaturation(saturateColors:colorSaturationValue:)); /** * Activate color saturation as the final step of the color demultiplexing process -\> this * saturation is a sigmoide function applied to each channel of the demultiplexed image. * @param saturateColors boolean that activates color saturation (if true) or desactivate (if false) * buffers */ - (void)setColorSaturation:(BOOL)saturateColors NS_SWIFT_NAME(setColorSaturation(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 */ - (void)setColorSaturation NS_SWIFT_NAME(setColorSaturation()); // // 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. */ - (void)clearBuffers NS_SWIFT_NAME(clearBuffers()); // // 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 */ - (void)activateMovingContoursProcessing:(BOOL)activate NS_SWIFT_NAME(activateMovingContoursProcessing(activate:)); // // 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 */ - (void)activateContoursProcessing:(BOOL)activate NS_SWIFT_NAME(activateContoursProcessing(activate:)); // // static Ptr_Retina cv::bioinspired::Retina::create(Size inputSize) // + (Retina*)create:(Size2i*)inputSize NS_SWIFT_NAME(create(inputSize:)); // // 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 */ + (Retina*)create:(Size2i*)inputSize colorMode:(BOOL)colorMode colorSamplingMethod:(int)colorSamplingMethod useRetinaLogSampling:(BOOL)useRetinaLogSampling reductionFactor:(float)reductionFactor samplingStrength:(float)samplingStrength NS_SWIFT_NAME(create(inputSize: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 */ + (Retina*)create:(Size2i*)inputSize colorMode:(BOOL)colorMode colorSamplingMethod:(int)colorSamplingMethod useRetinaLogSampling:(BOOL)useRetinaLogSampling reductionFactor:(float)reductionFactor NS_SWIFT_NAME(create(inputSize: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 */ + (Retina*)create:(Size2i*)inputSize colorMode:(BOOL)colorMode colorSamplingMethod:(int)colorSamplingMethod useRetinaLogSampling:(BOOL)useRetinaLogSampling NS_SWIFT_NAME(create(inputSize: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 */ + (Retina*)create:(Size2i*)inputSize colorMode:(BOOL)colorMode colorSamplingMethod:(int)colorSamplingMethod NS_SWIFT_NAME(create(inputSize: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 */ + (Retina*)create:(Size2i*)inputSize colorMode:(BOOL)colorMode NS_SWIFT_NAME(create(inputSize:colorMode:)); @end NS_ASSUME_NONNULL_END