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858 lines
37 KiB
C++
858 lines
37 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_PHOTO_HPP
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#define OPENCV_PHOTO_HPP
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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/**
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@defgroup photo Computational Photography
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This module includes photo processing algorithms
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@{
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@defgroup photo_inpaint Inpainting
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@defgroup photo_denoise Denoising
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@defgroup photo_hdr HDR imaging
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This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
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camera calibration with multiple exposures and exposure fusion.
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@defgroup photo_decolor Contrast Preserving Decolorization
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Useful links:
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http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
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@defgroup photo_clone Seamless Cloning
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Useful links:
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https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp
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@defgroup photo_render Non-Photorealistic Rendering
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Useful links:
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http://www.inf.ufrgs.br/~eslgastal/DomainTransform
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https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/
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@}
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*/
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namespace cv
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{
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//! @addtogroup photo
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//! @{
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//! @addtogroup photo_inpaint
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//! @{
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//! the inpainting algorithm
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enum
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{
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INPAINT_NS = 0, //!< Use Navier-Stokes based method
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INPAINT_TELEA = 1 //!< Use the algorithm proposed by Alexandru Telea @cite Telea04
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};
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/** @brief Restores the selected region in an image using the region neighborhood.
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@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
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@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
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needs to be inpainted.
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@param dst Output image with the same size and type as src .
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@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
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by the algorithm.
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@param flags Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA
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The function reconstructs the selected image area from the pixel near the area boundary. The
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function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
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objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
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@note
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- An example using the inpainting technique can be found at
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opencv_source_code/samples/cpp/inpaint.cpp
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- (Python) An example using the inpainting technique can be found at
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opencv_source_code/samples/python/inpaint.py
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*/
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CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
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OutputArray dst, double inpaintRadius, int flags );
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//! @} photo_inpaint
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//! @addtogroup photo_denoise
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//! @{
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/** @brief Perform image denoising using Non-local Means Denoising algorithm
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<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
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optimizations. Noise expected to be a gaussian white noise
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@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
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@param dst Output image with the same size and type as src .
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
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removes image details, smaller h value preserves details but also preserves some noise
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This function expected to be applied to grayscale images. For colored images look at
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fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
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image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
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image to CIELAB colorspace and then separately denoise L and AB components with different h
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parameter.
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*/
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CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Perform image denoising using Non-local Means Denoising algorithm
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<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
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optimizations. Noise expected to be a gaussian white noise
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@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
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2-channel, 3-channel or 4-channel image.
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@param dst Output image with the same size and type as src .
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Array of parameters regulating filter strength, either one
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parameter applied to all channels or one per channel in dst. Big h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
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This function expected to be applied to grayscale images. For colored images look at
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fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
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image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
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image to CIELAB colorspace and then separately denoise L and AB components with different h
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parameter.
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*/
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CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
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const std::vector<float>& h,
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int templateWindowSize = 7, int searchWindowSize = 21,
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int normType = NORM_L2);
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/** @brief Modification of fastNlMeansDenoising function for colored images
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@param src Input 8-bit 3-channel image.
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@param dst Output image with the same size and type as src .
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
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removes noise but also removes image details, smaller h value preserves details but also preserves
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some noise
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@param hColor The same as h but for color components. For most images value equals 10
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will be enough to remove colored noise and do not distort colors
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The function converts image to CIELAB colorspace and then separately denoise L and AB components
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with given h parameters using fastNlMeansDenoising function.
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
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float h = 3, float hColor = 3,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
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captured in small period of time. For example video. This version of the function is for grayscale
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images or for manual manipulation with colorspaces. See @cite Buades2005DenoisingIS for more details
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(open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
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@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
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4-channel images sequence. All images should have the same type and
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size.
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@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
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@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
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be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
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imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
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srcImgs[imgToDenoiseIndex] image.
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@param dst Output image with the same size and type as srcImgs images.
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength. Bigger h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
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captured in small period of time. For example video. This version of the function is for grayscale
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images or for manual manipulation with colorspaces. See @cite Buades2005DenoisingIS for more details
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(open access [here](https://static.aminer.org/pdf/PDF/000/317/196/spatio_temporal_wiener_filtering_of_image_sequences_using_a_parametric.pdf)).
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@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
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2-channel, 3-channel or 4-channel images sequence. All images should
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have the same type and size.
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@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
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@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
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be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
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imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
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srcImgs[imgToDenoiseIndex] image.
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@param dst Output image with the same size and type as srcImgs images.
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Array of parameters regulating filter strength, either one
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parameter applied to all channels or one per channel in dst. Big h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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const std::vector<float>& h,
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int templateWindowSize = 7, int searchWindowSize = 21,
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int normType = NORM_L2);
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/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
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@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
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size.
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@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
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@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
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be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
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imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
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srcImgs[imgToDenoiseIndex] image.
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@param dst Output image with the same size and type as srcImgs images.
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
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removes noise but also removes image details, smaller h value preserves details but also preserves
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some noise.
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@param hColor The same as h but for color components.
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The function converts images to CIELAB colorspace and then separately denoise L and AB components
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with given h parameters using fastNlMeansDenoisingMulti function.
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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float h = 3, float hColor = 3,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
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finding a function to minimize some functional). As the image denoising, in particular, may be seen
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as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
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exactly what is implemented.
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It should be noted, that this implementation was taken from the July 2013 blog entry
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@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
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Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
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of July 2013 and finally it was slightly adapted by later authors.
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Although the thorough discussion and justification of the algorithm involved may be found in
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@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
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with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
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pixels (it may be seen as set
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\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
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\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
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this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
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\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
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\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
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image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
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we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
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exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
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@param observations This array should contain one or more noised versions of the image that is to
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be restored.
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@param result Here the denoised image will be stored. There is no need to do pre-allocation of
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storage space, as it will be automatically allocated, if necessary.
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@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
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(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
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speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
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removed.
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@param niters Number of iterations that the algorithm will run. Of course, as more iterations as
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better, but it is hard to quantitatively refine this statement, so just use the default and
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increase it if the results are poor.
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*/
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CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
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//! @} photo_denoise
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//! @addtogroup photo_hdr
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//! @{
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enum { LDR_SIZE = 256 };
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/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
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*/
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class CV_EXPORTS_W Tonemap : public Algorithm
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{
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public:
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/** @brief Tonemaps image
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@param src source image - CV_32FC3 Mat (float 32 bits 3 channels)
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@param dst destination image - CV_32FC3 Mat with values in [0, 1] range
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*/
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CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
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CV_WRAP virtual float getGamma() const = 0;
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CV_WRAP virtual void setGamma(float gamma) = 0;
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};
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/** @brief Creates simple linear mapper with gamma correction
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@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
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equal to 2.2f is suitable for most displays.
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Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
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*/
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CV_EXPORTS_W Ptr<Tonemap> createTonemap(float gamma = 1.0f);
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/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
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logarithmic domain.
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Since it's a global operator the same function is applied to all the pixels, it is controlled by the
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bias parameter.
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Optional saturation enhancement is possible as described in @cite FL02 .
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For more information see @cite DM03 .
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*/
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class CV_EXPORTS_W TonemapDrago : public Tonemap
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{
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public:
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CV_WRAP virtual float getSaturation() const = 0;
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CV_WRAP virtual void setSaturation(float saturation) = 0;
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CV_WRAP virtual float getBias() const = 0;
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CV_WRAP virtual void setBias(float bias) = 0;
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};
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/** @brief Creates TonemapDrago object
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@param gamma gamma value for gamma correction. See createTonemap
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@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
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than 1 increase saturation and values less than 1 decrease it.
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@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
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results, default value is 0.85.
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*/
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CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f);
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/** @brief This is a global tonemapping operator that models human visual system.
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Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
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color adaptation.
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For more information see @cite RD05 .
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*/
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class CV_EXPORTS_W TonemapReinhard : public Tonemap
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{
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public:
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CV_WRAP virtual float getIntensity() const = 0;
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CV_WRAP virtual void setIntensity(float intensity) = 0;
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CV_WRAP virtual float getLightAdaptation() const = 0;
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CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
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CV_WRAP virtual float getColorAdaptation() const = 0;
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CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
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};
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/** @brief Creates TonemapReinhard object
|
|
|
|
@param gamma gamma value for gamma correction. See createTonemap
|
|
@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
|
|
@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
|
|
value, if 0 it's global, otherwise it's a weighted mean of this two cases.
|
|
@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
|
|
if 0 adaptation level is the same for each channel.
|
|
*/
|
|
CV_EXPORTS_W Ptr<TonemapReinhard>
|
|
createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
|
|
|
|
/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
|
|
transforms contrast values to HVS response and scales the response. After this the image is
|
|
reconstructed from new contrast values.
|
|
|
|
For more information see @cite MM06 .
|
|
*/
|
|
class CV_EXPORTS_W TonemapMantiuk : public Tonemap
|
|
{
|
|
public:
|
|
CV_WRAP virtual float getScale() const = 0;
|
|
CV_WRAP virtual void setScale(float scale) = 0;
|
|
|
|
CV_WRAP virtual float getSaturation() const = 0;
|
|
CV_WRAP virtual void setSaturation(float saturation) = 0;
|
|
};
|
|
|
|
/** @brief Creates TonemapMantiuk object
|
|
|
|
@param gamma gamma value for gamma correction. See createTonemap
|
|
@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
|
|
dynamic range. Values from 0.6 to 0.9 produce best results.
|
|
@param saturation saturation enhancement value. See createTonemapDrago
|
|
*/
|
|
CV_EXPORTS_W Ptr<TonemapMantiuk>
|
|
createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f);
|
|
|
|
/** @brief The base class for algorithms that align images of the same scene with different exposures
|
|
*/
|
|
class CV_EXPORTS_W AlignExposures : public Algorithm
|
|
{
|
|
public:
|
|
/** @brief Aligns images
|
|
|
|
@param src vector of input images
|
|
@param dst vector of aligned images
|
|
@param times vector of exposure time values for each image
|
|
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
|
|
have the same number of channels as images.
|
|
*/
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
|
|
InputArray times, InputArray response) = 0;
|
|
};
|
|
|
|
/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
|
|
luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
|
|
|
|
It is invariant to exposure, so exposure values and camera response are not necessary.
|
|
|
|
In this implementation new image regions are filled with zeros.
|
|
|
|
For more information see @cite GW03 .
|
|
*/
|
|
class CV_EXPORTS_W AlignMTB : public AlignExposures
|
|
{
|
|
public:
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
|
|
InputArray times, InputArray response) CV_OVERRIDE = 0;
|
|
|
|
/** @brief Short version of process, that doesn't take extra arguments.
|
|
|
|
@param src vector of input images
|
|
@param dst vector of aligned images
|
|
*/
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst) = 0;
|
|
|
|
/** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
|
|
first.
|
|
|
|
@param img0 first image
|
|
@param img1 second image
|
|
*/
|
|
CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0;
|
|
/** @brief Helper function, that shift Mat filling new regions with zeros.
|
|
|
|
@param src input image
|
|
@param dst result image
|
|
@param shift shift value
|
|
*/
|
|
CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0;
|
|
/** @brief Computes median threshold and exclude bitmaps of given image.
|
|
|
|
@param img input image
|
|
@param tb median threshold bitmap
|
|
@param eb exclude bitmap
|
|
*/
|
|
CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0;
|
|
|
|
CV_WRAP virtual int getMaxBits() const = 0;
|
|
CV_WRAP virtual void setMaxBits(int max_bits) = 0;
|
|
|
|
CV_WRAP virtual int getExcludeRange() const = 0;
|
|
CV_WRAP virtual void setExcludeRange(int exclude_range) = 0;
|
|
|
|
CV_WRAP virtual bool getCut() const = 0;
|
|
CV_WRAP virtual void setCut(bool value) = 0;
|
|
};
|
|
|
|
/** @brief Creates AlignMTB object
|
|
|
|
@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
|
|
usually good enough (31 and 63 pixels shift respectively).
|
|
@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
|
|
median value.
|
|
@param cut if true cuts images, otherwise fills the new regions with zeros.
|
|
*/
|
|
CV_EXPORTS_W Ptr<AlignMTB> createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true);
|
|
|
|
/** @brief The base class for camera response calibration algorithms.
|
|
*/
|
|
class CV_EXPORTS_W CalibrateCRF : public Algorithm
|
|
{
|
|
public:
|
|
/** @brief Recovers inverse camera response.
|
|
|
|
@param src vector of input images
|
|
@param dst 256x1 matrix with inverse camera response function
|
|
@param times vector of exposure time values for each image
|
|
*/
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
|
|
};
|
|
|
|
/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
|
|
function as linear system. Objective function is constructed using pixel values on the same position
|
|
in all images, extra term is added to make the result smoother.
|
|
|
|
For more information see @cite DM97 .
|
|
*/
|
|
class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF
|
|
{
|
|
public:
|
|
CV_WRAP virtual float getLambda() const = 0;
|
|
CV_WRAP virtual void setLambda(float lambda) = 0;
|
|
|
|
CV_WRAP virtual int getSamples() const = 0;
|
|
CV_WRAP virtual void setSamples(int samples) = 0;
|
|
|
|
CV_WRAP virtual bool getRandom() const = 0;
|
|
CV_WRAP virtual void setRandom(bool random) = 0;
|
|
};
|
|
|
|
/** @brief Creates CalibrateDebevec object
|
|
|
|
@param samples number of pixel locations to use
|
|
@param lambda smoothness term weight. Greater values produce smoother results, but can alter the
|
|
response.
|
|
@param random if true sample pixel locations are chosen at random, otherwise they form a
|
|
rectangular grid.
|
|
*/
|
|
CV_EXPORTS_W Ptr<CalibrateDebevec> createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false);
|
|
|
|
/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
|
|
function as linear system. This algorithm uses all image pixels.
|
|
|
|
For more information see @cite RB99 .
|
|
*/
|
|
class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF
|
|
{
|
|
public:
|
|
CV_WRAP virtual int getMaxIter() const = 0;
|
|
CV_WRAP virtual void setMaxIter(int max_iter) = 0;
|
|
|
|
CV_WRAP virtual float getThreshold() const = 0;
|
|
CV_WRAP virtual void setThreshold(float threshold) = 0;
|
|
|
|
CV_WRAP virtual Mat getRadiance() const = 0;
|
|
};
|
|
|
|
/** @brief Creates CalibrateRobertson object
|
|
|
|
@param max_iter maximal number of Gauss-Seidel solver iterations.
|
|
@param threshold target difference between results of two successive steps of the minimization.
|
|
*/
|
|
CV_EXPORTS_W Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f);
|
|
|
|
/** @brief The base class algorithms that can merge exposure sequence to a single image.
|
|
*/
|
|
class CV_EXPORTS_W MergeExposures : public Algorithm
|
|
{
|
|
public:
|
|
/** @brief Merges images.
|
|
|
|
@param src vector of input images
|
|
@param dst result image
|
|
@param times vector of exposure time values for each image
|
|
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
|
|
have the same number of channels as images.
|
|
*/
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
|
|
InputArray times, InputArray response) = 0;
|
|
};
|
|
|
|
/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
|
|
values and camera response.
|
|
|
|
For more information see @cite DM97 .
|
|
*/
|
|
class CV_EXPORTS_W MergeDebevec : public MergeExposures
|
|
{
|
|
public:
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
|
|
InputArray times, InputArray response) CV_OVERRIDE = 0;
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
|
|
};
|
|
|
|
/** @brief Creates MergeDebevec object
|
|
*/
|
|
CV_EXPORTS_W Ptr<MergeDebevec> createMergeDebevec();
|
|
|
|
/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
|
|
combined using laplacian pyramids.
|
|
|
|
The resulting image weight is constructed as weighted average of contrast, saturation and
|
|
well-exposedness measures.
|
|
|
|
The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
|
|
by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
|
|
|
|
For more information see @cite MK07 .
|
|
*/
|
|
class CV_EXPORTS_W MergeMertens : public MergeExposures
|
|
{
|
|
public:
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
|
|
InputArray times, InputArray response) CV_OVERRIDE = 0;
|
|
/** @brief Short version of process, that doesn't take extra arguments.
|
|
|
|
@param src vector of input images
|
|
@param dst result image
|
|
*/
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0;
|
|
|
|
CV_WRAP virtual float getContrastWeight() const = 0;
|
|
CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0;
|
|
|
|
CV_WRAP virtual float getSaturationWeight() const = 0;
|
|
CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0;
|
|
|
|
CV_WRAP virtual float getExposureWeight() const = 0;
|
|
CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0;
|
|
};
|
|
|
|
/** @brief Creates MergeMertens object
|
|
|
|
@param contrast_weight contrast measure weight. See MergeMertens.
|
|
@param saturation_weight saturation measure weight
|
|
@param exposure_weight well-exposedness measure weight
|
|
*/
|
|
CV_EXPORTS_W Ptr<MergeMertens>
|
|
createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f);
|
|
|
|
/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
|
|
values and camera response.
|
|
|
|
For more information see @cite RB99 .
|
|
*/
|
|
class CV_EXPORTS_W MergeRobertson : public MergeExposures
|
|
{
|
|
public:
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
|
|
InputArray times, InputArray response) CV_OVERRIDE = 0;
|
|
CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
|
|
};
|
|
|
|
/** @brief Creates MergeRobertson object
|
|
*/
|
|
CV_EXPORTS_W Ptr<MergeRobertson> createMergeRobertson();
|
|
|
|
//! @} photo_hdr
|
|
|
|
//! @addtogroup photo_decolor
|
|
//! @{
|
|
|
|
/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
|
|
black-and-white photograph rendering, and in many single channel image processing applications
|
|
@cite CL12 .
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param grayscale Output 8-bit 1-channel image.
|
|
@param color_boost Output 8-bit 3-channel image.
|
|
|
|
This function is to be applied on color images.
|
|
*/
|
|
CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost);
|
|
|
|
//! @} photo_decolor
|
|
|
|
//! @addtogroup photo_clone
|
|
//! @{
|
|
|
|
|
|
//! seamlessClone algorithm flags
|
|
enum
|
|
{
|
|
/** The power of the method is fully expressed when inserting objects with complex outlines into a new background*/
|
|
NORMAL_CLONE = 1,
|
|
/** The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable
|
|
halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective.*/
|
|
MIXED_CLONE = 2,
|
|
/** Monochrome transfer allows the user to easily replace certain features of one object by alternative features.*/
|
|
MONOCHROME_TRANSFER = 3};
|
|
|
|
|
|
/** @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp
|
|
An example using seamlessClone function
|
|
*/
|
|
/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
|
|
deformations) or local changes concerned to a selection. Here we are interested in achieving local
|
|
changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
|
|
manner. The extent of the changes ranges from slight distortions to complete replacement by novel
|
|
content @cite PM03 .
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param dst Input 8-bit 3-channel image.
|
|
@param mask Input 8-bit 1 or 3-channel image.
|
|
@param p Point in dst image where object is placed.
|
|
@param blend Output image with the same size and type as dst.
|
|
@param flags Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER
|
|
*/
|
|
CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p,
|
|
OutputArray blend, int flags);
|
|
|
|
/** @brief Given an original color image, two differently colored versions of this image can be mixed
|
|
seamlessly.
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param mask Input 8-bit 1 or 3-channel image.
|
|
@param dst Output image with the same size and type as src .
|
|
@param red_mul R-channel multiply factor.
|
|
@param green_mul G-channel multiply factor.
|
|
@param blue_mul B-channel multiply factor.
|
|
|
|
Multiplication factor is between .5 to 2.5.
|
|
*/
|
|
CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f,
|
|
float green_mul = 1.0f, float blue_mul = 1.0f);
|
|
|
|
/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
|
|
then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param mask Input 8-bit 1 or 3-channel image.
|
|
@param dst Output image with the same size and type as src.
|
|
@param alpha Value ranges between 0-2.
|
|
@param beta Value ranges between 0-2.
|
|
|
|
This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
|
|
*/
|
|
CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst,
|
|
float alpha = 0.2f, float beta = 0.4f);
|
|
|
|
/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
|
|
washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param mask Input 8-bit 1 or 3-channel image.
|
|
@param dst Output image with the same size and type as src.
|
|
@param low_threshold %Range from 0 to 100.
|
|
@param high_threshold Value \> 100.
|
|
@param kernel_size The size of the Sobel kernel to be used.
|
|
|
|
@note
|
|
The algorithm assumes that the color of the source image is close to that of the destination. This
|
|
assumption means that when the colors don't match, the source image color gets tinted toward the
|
|
color of the destination image.
|
|
*/
|
|
CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
|
|
float low_threshold = 30, float high_threshold = 45,
|
|
int kernel_size = 3);
|
|
|
|
//! @} photo_clone
|
|
|
|
//! @addtogroup photo_render
|
|
//! @{
|
|
|
|
//! Edge preserving filters
|
|
enum
|
|
{
|
|
RECURS_FILTER = 1, //!< Recursive Filtering
|
|
NORMCONV_FILTER = 2 //!< Normalized Convolution Filtering
|
|
};
|
|
|
|
/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
|
|
filters are used in many different applications @cite EM11 .
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param dst Output 8-bit 3-channel image.
|
|
@param flags Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
|
|
@param sigma_s %Range between 0 to 200.
|
|
@param sigma_r %Range between 0 to 1.
|
|
*/
|
|
CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
|
|
float sigma_s = 60, float sigma_r = 0.4f);
|
|
|
|
/** @brief This filter enhances the details of a particular image.
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param dst Output image with the same size and type as src.
|
|
@param sigma_s %Range between 0 to 200.
|
|
@param sigma_r %Range between 0 to 1.
|
|
*/
|
|
CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
|
|
float sigma_r = 0.15f);
|
|
|
|
/** @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp
|
|
An example using non-photorealistic line drawing functions
|
|
*/
|
|
/** @brief Pencil-like non-photorealistic line drawing
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param dst1 Output 8-bit 1-channel image.
|
|
@param dst2 Output image with the same size and type as src.
|
|
@param sigma_s %Range between 0 to 200.
|
|
@param sigma_r %Range between 0 to 1.
|
|
@param shade_factor %Range between 0 to 0.1.
|
|
*/
|
|
CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2,
|
|
float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f);
|
|
|
|
/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
|
|
photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
|
|
contrast while preserving, or enhancing, high-contrast features.
|
|
|
|
@param src Input 8-bit 3-channel image.
|
|
@param dst Output image with the same size and type as src.
|
|
@param sigma_s %Range between 0 to 200.
|
|
@param sigma_r %Range between 0 to 1.
|
|
*/
|
|
CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60,
|
|
float sigma_r = 0.45f);
|
|
|
|
//! @} photo_render
|
|
|
|
//! @} photo
|
|
|
|
} // cv
|
|
|
|
#endif
|