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Open Source Computer Vision Library
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256 lines
9.9 KiB
256 lines
9.9 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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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective icvers. |
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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 Intel Corporation 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 "fast_nlmeans_denoising_invoker.hpp" |
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#include "fast_nlmeans_multi_denoising_invoker.hpp" |
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#include "fast_nlmeans_denoising_opencl.hpp" |
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void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, float h, |
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int templateWindowSize, int searchWindowSize) |
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{ |
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Size src_size = _src.size(); |
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CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) && |
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src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes |
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ocl_fastNlMeansDenoising(_src, _dst, h, templateWindowSize, searchWindowSize)) |
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Mat src = _src.getMat(); |
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_dst.create(src_size, src.type()); |
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Mat dst = _dst.getMat(); |
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#ifdef HAVE_TEGRA_OPTIMIZATION |
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if(tegra::fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize)) |
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return; |
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#endif |
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switch (src.type()) { |
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case CV_8U: |
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parallel_for_(cv::Range(0, src.rows), |
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FastNlMeansDenoisingInvoker<uchar>( |
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src, dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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case CV_8UC2: |
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parallel_for_(cv::Range(0, src.rows), |
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FastNlMeansDenoisingInvoker<cv::Vec2b>( |
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src, dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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case CV_8UC3: |
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parallel_for_(cv::Range(0, src.rows), |
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FastNlMeansDenoisingInvoker<cv::Vec3b>( |
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src, dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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default: |
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CV_Error(Error::StsBadArg, |
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"Unsupported image format! Only CV_8UC1, CV_8UC2 and CV_8UC3 are supported"); |
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} |
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} |
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void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst, |
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float h, float hForColorComponents, |
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int templateWindowSize, int searchWindowSize) |
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{ |
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int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type); |
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Size src_size = _src.size(); |
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if (type != CV_8UC3 && type != CV_8UC4) |
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{ |
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CV_Error(Error::StsBadArg, "Type of input image should be CV_8UC3!"); |
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return; |
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} |
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CV_OCL_RUN(_src.dims() <= 2 && (_dst.isUMat() || _src.isUMat()) && |
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src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes |
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ocl_fastNlMeansDenoisingColored(_src, _dst, h, hForColorComponents, |
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templateWindowSize, searchWindowSize)) |
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Mat src = _src.getMat(); |
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_dst.create(src_size, type); |
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Mat dst = _dst.getMat(); |
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Mat src_lab; |
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cvtColor(src, src_lab, COLOR_LBGR2Lab); |
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Mat l(src_size, CV_8U); |
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Mat ab(src_size, CV_8UC2); |
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Mat l_ab[] = { l, ab }; |
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int from_to[] = { 0,0, 1,1, 2,2 }; |
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mixChannels(&src_lab, 1, l_ab, 2, from_to, 3); |
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fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize); |
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fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize); |
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Mat l_ab_denoised[] = { l, ab }; |
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Mat dst_lab(src_size, CV_MAKE_TYPE(depth, 3)); |
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mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3); |
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cvtColor(dst_lab, dst, COLOR_Lab2LBGR, cn); |
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} |
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static void fastNlMeansDenoisingMultiCheckPreconditions( |
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const std::vector<Mat>& srcImgs, |
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int imgToDenoiseIndex, int temporalWindowSize, |
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int templateWindowSize, int searchWindowSize) |
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{ |
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int src_imgs_size = static_cast<int>(srcImgs.size()); |
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if (src_imgs_size == 0) |
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{ |
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CV_Error(Error::StsBadArg, "Input images vector should not be empty!"); |
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} |
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if (temporalWindowSize % 2 == 0 || |
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searchWindowSize % 2 == 0 || |
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templateWindowSize % 2 == 0) { |
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CV_Error(Error::StsBadArg, "All windows sizes should be odd!"); |
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} |
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int temporalWindowHalfSize = temporalWindowSize / 2; |
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if (imgToDenoiseIndex - temporalWindowHalfSize < 0 || |
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imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size) |
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{ |
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CV_Error(Error::StsBadArg, |
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"imgToDenoiseIndex and temporalWindowSize " |
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"should be chosen corresponding srcImgs size!"); |
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} |
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for (int i = 1; i < src_imgs_size; i++) |
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if (srcImgs[0].size() != srcImgs[i].size() || srcImgs[0].type() != srcImgs[i].type()) |
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{ |
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CV_Error(Error::StsBadArg, "Input images should have the same size and type!"); |
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} |
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} |
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void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst, |
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int imgToDenoiseIndex, int temporalWindowSize, |
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float h, int templateWindowSize, int searchWindowSize) |
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{ |
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std::vector<Mat> srcImgs; |
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_srcImgs.getMatVector(srcImgs); |
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fastNlMeansDenoisingMultiCheckPreconditions( |
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srcImgs, imgToDenoiseIndex, |
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temporalWindowSize, templateWindowSize, searchWindowSize); |
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_dst.create(srcImgs[0].size(), srcImgs[0].type()); |
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Mat dst = _dst.getMat(); |
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switch (srcImgs[0].type()) |
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{ |
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case CV_8U: |
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parallel_for_(cv::Range(0, srcImgs[0].rows), |
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FastNlMeansMultiDenoisingInvoker<uchar>( |
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srcImgs, imgToDenoiseIndex, temporalWindowSize, |
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dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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case CV_8UC2: |
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parallel_for_(cv::Range(0, srcImgs[0].rows), |
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FastNlMeansMultiDenoisingInvoker<cv::Vec2b>( |
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srcImgs, imgToDenoiseIndex, temporalWindowSize, |
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dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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case CV_8UC3: |
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parallel_for_(cv::Range(0, srcImgs[0].rows), |
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FastNlMeansMultiDenoisingInvoker<cv::Vec3b>( |
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srcImgs, imgToDenoiseIndex, temporalWindowSize, |
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dst, templateWindowSize, searchWindowSize, h)); |
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break; |
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default: |
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CV_Error(Error::StsBadArg, |
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"Unsupported matrix format! Only uchar, Vec2b, Vec3b are supported"); |
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} |
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} |
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void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst, |
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int imgToDenoiseIndex, int temporalWindowSize, |
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float h, float hForColorComponents, |
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int templateWindowSize, int searchWindowSize) |
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{ |
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std::vector<Mat> srcImgs; |
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_srcImgs.getMatVector(srcImgs); |
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fastNlMeansDenoisingMultiCheckPreconditions( |
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srcImgs, imgToDenoiseIndex, |
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temporalWindowSize, templateWindowSize, searchWindowSize); |
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_dst.create(srcImgs[0].size(), srcImgs[0].type()); |
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Mat dst = _dst.getMat(); |
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int src_imgs_size = static_cast<int>(srcImgs.size()); |
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if (srcImgs[0].type() != CV_8UC3) |
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{ |
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CV_Error(Error::StsBadArg, "Type of input images should be CV_8UC3!"); |
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return; |
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} |
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int from_to[] = { 0,0, 1,1, 2,2 }; |
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// TODO convert only required images |
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std::vector<Mat> src_lab(src_imgs_size); |
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std::vector<Mat> l(src_imgs_size); |
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std::vector<Mat> ab(src_imgs_size); |
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for (int i = 0; i < src_imgs_size; i++) |
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{ |
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src_lab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC3); |
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l[i] = Mat::zeros(srcImgs[0].size(), CV_8UC1); |
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ab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC2); |
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cvtColor(srcImgs[i], src_lab[i], COLOR_LBGR2Lab); |
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Mat l_ab[] = { l[i], ab[i] }; |
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mixChannels(&src_lab[i], 1, l_ab, 2, from_to, 3); |
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} |
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Mat dst_l; |
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Mat dst_ab; |
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fastNlMeansDenoisingMulti( |
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l, dst_l, imgToDenoiseIndex, temporalWindowSize, |
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h, templateWindowSize, searchWindowSize); |
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fastNlMeansDenoisingMulti( |
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ab, dst_ab, imgToDenoiseIndex, temporalWindowSize, |
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hForColorComponents, templateWindowSize, searchWindowSize); |
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Mat l_ab_denoised[] = { dst_l, dst_ab }; |
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Mat dst_lab(srcImgs[0].size(), srcImgs[0].type()); |
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mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3); |
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cvtColor(dst_lab, dst, COLOR_Lab2LBGR); |
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}
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