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Tetragramm
GitHub Repository: Tetragramm/opencv
Path: blob/master/modules/photo/src/denoising.cuda.cpp
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/*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) 2009, 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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#include "precomp.hpp"
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#include "opencv2/photo/cuda.hpp"
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#include "opencv2/core/private.cuda.hpp"
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_CUDAARITHM
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# include "opencv2/cudaarithm.hpp"
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#endif
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#ifdef HAVE_OPENCV_CUDAIMGPROC
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# include "opencv2/cudaimgproc.hpp"
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#endif
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using namespace cv;
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using namespace cv::cuda;
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#if !defined (HAVE_CUDA) || !defined(HAVE_OPENCV_CUDAARITHM) || !defined(HAVE_OPENCV_CUDAIMGPROC)
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void cv::cuda::nonLocalMeans(InputArray, OutputArray, float, int, int, int, Stream&) { throw_no_cuda(); }
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void cv::cuda::fastNlMeansDenoising(InputArray, OutputArray, float, int, int, Stream&) { throw_no_cuda(); }
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void cv::cuda::fastNlMeansDenoisingColored(InputArray, OutputArray, float, float, int, int, Stream&) { throw_no_cuda(); }
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#else
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//////////////////////////////////////////////////////////////////////////////////
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//// Non Local Means Denosing (brute force)
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namespace cv { namespace cuda { namespace device
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{
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namespace imgproc
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{
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template<typename T>
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void nlm_bruteforce_gpu(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
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}
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}}}
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void cv::cuda::nonLocalMeans(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, int borderMode, Stream& stream)
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{
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using cv::cuda::device::imgproc::nlm_bruteforce_gpu;
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typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
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static const func_t funcs[4] = { nlm_bruteforce_gpu<uchar>, nlm_bruteforce_gpu<uchar2>, nlm_bruteforce_gpu<uchar3>, 0/*nlm_bruteforce_gpu<uchar4>,*/ };
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const GpuMat src = _src.getGpuMat();
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CV_Assert(src.type() == CV_8U || src.type() == CV_8UC2 || src.type() == CV_8UC3);
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const func_t func = funcs[src.channels() - 1];
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CV_Assert(func != 0);
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int b = borderMode;
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CV_Assert(b == BORDER_REFLECT101 || b == BORDER_REPLICATE || b == BORDER_CONSTANT || b == BORDER_REFLECT || b == BORDER_WRAP);
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_dst.create(src.size(), src.type());
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GpuMat dst = _dst.getGpuMat();
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func(src, dst, search_window/2, block_window/2, h, borderMode, StreamAccessor::getStream(stream));
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}
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namespace cv { namespace cuda { namespace device
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{
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namespace imgproc
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{
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void nln_fast_get_buffer_size(const PtrStepSzb& src, int search_window, int block_window, int& buffer_cols, int& buffer_rows);
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template<typename T>
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void nlm_fast_gpu(const PtrStepSzb& src, PtrStepSzb dst, PtrStepi buffer,
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int search_window, int block_window, float h, cudaStream_t stream);
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void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream);
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void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream);
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}
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}}}
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void cv::cuda::fastNlMeansDenoising(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, Stream& stream)
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{
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const GpuMat src = _src.getGpuMat();
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CV_Assert(src.depth() == CV_8U && src.channels() < 4);
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int border_size = search_window/2 + block_window/2;
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Size esize = src.size() + Size(border_size, border_size) * 2;
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BufferPool pool(stream);
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GpuMat extended_src = pool.getBuffer(esize, src.type());
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cv::cuda::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), stream);
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GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
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int bcols, brows;
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device::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
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GpuMat buffer = pool.getBuffer(brows, bcols, CV_32S);
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using namespace cv::cuda::device::imgproc;
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typedef void (*nlm_fast_t)(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
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static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0};
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_dst.create(src.size(), src.type());
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GpuMat dst = _dst.getGpuMat();
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funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(stream));
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}
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void cv::cuda::fastNlMeansDenoisingColored(InputArray _src, OutputArray _dst, float h_luminance, float h_color, int search_window, int block_window, Stream& stream)
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{
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const GpuMat src = _src.getGpuMat();
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CV_Assert(src.type() == CV_8UC3);
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BufferPool pool(stream);
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GpuMat lab = pool.getBuffer(src.size(), src.type());
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cv::cuda::cvtColor(src, lab, cv::COLOR_BGR2Lab, 0, stream);
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GpuMat l = pool.getBuffer(src.size(), CV_8U);
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GpuMat ab = pool.getBuffer(src.size(), CV_8UC2);
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device::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(stream));
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fastNlMeansDenoising(l, l, h_luminance, search_window, block_window, stream);
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fastNlMeansDenoising(ab, ab, h_color, search_window, block_window, stream);
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device::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(stream));
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cv::cuda::cvtColor(lab, _dst, cv::COLOR_Lab2BGR, 0, stream);
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}
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#endif
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