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133 lines
6.2 KiB
133 lines
6.2 KiB
/*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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//
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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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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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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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// indirect, incidental, special, exemplary, or consequential damages
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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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// and on any theory of liability, whether in contract, strict liability,
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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_CUDA_HPP
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#define OPENCV_PHOTO_CUDA_HPP
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#include "opencv2/core/cuda.hpp"
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namespace cv { namespace cuda {
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//! @addtogroup photo_denoise
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//! @{
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/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast.
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@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
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@param dst Destination image.
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@param h Filter sigma regulating filter strength for color.
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@param search_window Size of search window.
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@param block_size Size of block used for computing weights.
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@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
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BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
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@param stream Stream for the asynchronous version.
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@sa
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fastNlMeansDenoising
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*/
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CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
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float h,
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int search_window = 21,
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int block_size = 7,
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int borderMode = BORDER_DEFAULT,
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Stream& stream = Stream::Null());
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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 or 3-channel image.
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@param dst Output image with the same size and type as src .
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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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@param search_window 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 search_window - greater
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denoising time. Recommended value 21 pixels
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@param block_size Size in pixels of the template patch that is used to compute weights. Should be
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odd. Recommended value 7 pixels
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@param stream Stream for the asynchronous invocations.
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This function expected to be applied to grayscale images. For colored images look at
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FastNonLocalMeansDenoising::labMethod.
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@sa
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fastNlMeansDenoising
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*/
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CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
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float h,
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int search_window = 21,
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int block_size = 7,
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Stream& stream = Stream::Null());
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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 h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
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also removes image details, smaller h value preserves details but also preserves some noise
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@param photo_render float The same as h but for color components. For most images value equals 10 will be
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enough to remove colored noise and do not distort colors
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@param search_window 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 search_window - greater
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denoising time. Recommended value 21 pixels
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@param block_size Size in pixels of the template patch that is used to compute weights. Should be
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odd. Recommended value 7 pixels
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@param stream Stream for the asynchronous invocations.
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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 FastNonLocalMeansDenoising::simpleMethod function.
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@sa
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fastNlMeansDenoisingColored
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*/
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CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
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float h_luminance, float photo_render,
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int search_window = 21,
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int block_size = 7,
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Stream& stream = Stream::Null());
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//! @} photo
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}} // namespace cv { namespace cuda {
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#endif /* OPENCV_PHOTO_CUDA_HPP */
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