OpenCVForUnity/package/Runtime/Plugins/iOS/opencv2.framework/Headers/RetinaFastToneMapping.h

//
// This file is auto-generated. Please don't modify it!
//
#pragma once

#ifdef __cplusplus
//#import "opencv.hpp"
#import "opencv2/bioinspired.hpp"
#import "opencv2/bioinspired/retinafasttonemapping.hpp"
#else
#define CV_EXPORTS
#endif

#import <Foundation/Foundation.h>
#import "Algorithm.h"

@class Mat;
@class Size2i;



NS_ASSUME_NONNULL_BEGIN

// C++: class RetinaFastToneMapping
/**
 *  a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.
 *
 * This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc.
 * As a summary, these are the model properties:
 * - 2 stages of local luminance adaptation with a different local neighborhood for each.
 * - first stage models the retina photorecetors local luminance adaptation
 * - second stage models th ganglion cells local information adaptation
 * - compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters.
 *   this can help noise robustness and temporal stability for video sequence use cases.
 *
 * for more information, read to the following papers :
 * Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit 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
 * regarding spatio-temporal filter and the bigger retina model :
 * 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.
 *
 * Member of `Bioinspired`
 */
CV_EXPORTS @interface RetinaFastToneMapping : Algorithm


#ifdef __cplusplus
@property(readonly)cv::Ptr<cv::bioinspired::RetinaFastToneMapping> nativePtrRetinaFastToneMapping;
#endif

#ifdef __cplusplus
- (instancetype)initWithNativePtr:(cv::Ptr<cv::bioinspired::RetinaFastToneMapping>)nativePtr;
+ (instancetype)fromNative:(cv::Ptr<cv::bioinspired::RetinaFastToneMapping>)nativePtr;
#endif


#pragma mark - Methods


//
//  void cv::bioinspired::RetinaFastToneMapping::applyFastToneMapping(Mat inputImage, Mat& outputToneMappedImage)
//
/**
 * applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)
 *
 *     using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors
 *     level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal
 *     smoothing and eventually high frequencies attenuation. This is a lighter method than the one
 *     available using the regular retina::run method. It is then faster but it does not include
 *     complete temporal filtering nor retina spectral whitening. Then, it can have a more limited
 *     effect on images with a very high dynamic range. This is an adptation of the original still
 *     image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's
 *     work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local
 *     Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of
 *     America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
 *
 * @param inputImage the input image to process RGB or gray levels
 * @param outputToneMappedImage the output tone mapped image
 */
- (void)applyFastToneMapping:(Mat*)inputImage outputToneMappedImage:(Mat*)outputToneMappedImage NS_SWIFT_NAME(applyFastToneMapping(inputImage:outputToneMappedImage:));


//
//  void cv::bioinspired::RetinaFastToneMapping::setup(float photoreceptorsNeighborhoodRadius = 3.f, float ganglioncellsNeighborhoodRadius = 1.f, float meanLuminanceModulatorK = 1.f)
//
/**
 * updates tone mapping behaviors by adjusing the local luminance computation area
 *
 * @param photoreceptorsNeighborhoodRadius the first stage local adaptation area
 * @param ganglioncellsNeighborhoodRadius the second stage local adaptation area
 * @param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information
 *     (default is 1, see reference paper)
 */
- (void)setup:(float)photoreceptorsNeighborhoodRadius ganglioncellsNeighborhoodRadius:(float)ganglioncellsNeighborhoodRadius meanLuminanceModulatorK:(float)meanLuminanceModulatorK NS_SWIFT_NAME(setup(photoreceptorsNeighborhoodRadius:ganglioncellsNeighborhoodRadius:meanLuminanceModulatorK:));

/**
 * updates tone mapping behaviors by adjusing the local luminance computation area
 *
 * @param photoreceptorsNeighborhoodRadius the first stage local adaptation area
 * @param ganglioncellsNeighborhoodRadius the second stage local adaptation area
 *     (default is 1, see reference paper)
 */
- (void)setup:(float)photoreceptorsNeighborhoodRadius ganglioncellsNeighborhoodRadius:(float)ganglioncellsNeighborhoodRadius NS_SWIFT_NAME(setup(photoreceptorsNeighborhoodRadius:ganglioncellsNeighborhoodRadius:));

/**
 * updates tone mapping behaviors by adjusing the local luminance computation area
 *
 * @param photoreceptorsNeighborhoodRadius the first stage local adaptation area
 *     (default is 1, see reference paper)
 */
- (void)setup:(float)photoreceptorsNeighborhoodRadius NS_SWIFT_NAME(setup(photoreceptorsNeighborhoodRadius:));

/**
 * updates tone mapping behaviors by adjusing the local luminance computation area
 *
 *     (default is 1, see reference paper)
 */
- (void)setup NS_SWIFT_NAME(setup());


//
// static Ptr_RetinaFastToneMapping cv::bioinspired::RetinaFastToneMapping::create(Size inputSize)
//
+ (RetinaFastToneMapping*)create:(Size2i*)inputSize NS_SWIFT_NAME(create(inputSize:));



@end

NS_ASSUME_NONNULL_END