// // 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 #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 nativePtrRetinaFastToneMapping; #endif #ifdef __cplusplus - (instancetype)initWithNativePtr:(cv::Ptr)nativePtr; + (instancetype)fromNative:(cv::Ptr)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