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447 lines
19 KiB
C++
447 lines
19 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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// 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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template<typename ST, typename IT, typename UIT, typename D>
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static void fastNlMeansDenoising_( const Mat& src, Mat& dst, const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize)
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{
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int hn = (int)h.size();
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double granularity = (double)std::max(1., (double)dst.total()/(1 << 17));
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switch (CV_MAT_CN(src.type())) {
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case 1:
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<ST, IT, UIT, D, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case 2:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case 3:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case 4:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported number of channels! Only 1, 2, 3, and 4 are supported");
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}
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}
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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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CV_INSTRUMENT_REGION()
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fastNlMeansDenoising(_src, _dst, std::vector<float>(1, h),
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templateWindowSize, searchWindowSize);
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}
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void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize, int normType)
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{
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CV_INSTRUMENT_REGION()
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int hn = (int)h.size(), type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
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CV_Assert(!_src.empty());
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CV_Assert(hn == 1 || hn == cn);
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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[0], hn,
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templateWindowSize, searchWindowSize, normType))
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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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switch (normType) {
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case NORM_L2:
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#ifdef HAVE_TEGRA_OPTIMIZATION
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if(hn == 1 && tegra::useTegra() &&
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tegra::fastNlMeansDenoising(src, dst, h[0], templateWindowSize, searchWindowSize))
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return;
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#endif
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switch (depth) {
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case CV_8U:
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fastNlMeansDenoising_<uchar, int, unsigned, DistSquared>(src, dst, h,
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templateWindowSize,
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searchWindowSize);
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported depth! Only CV_8U is supported for NORM_L2");
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}
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break;
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case NORM_L1:
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switch (depth) {
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case CV_8U:
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fastNlMeansDenoising_<uchar, int, unsigned, DistAbs>(src, dst, h,
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templateWindowSize,
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searchWindowSize);
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break;
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case CV_16U:
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fastNlMeansDenoising_<ushort, int64, uint64, DistAbs>(src, dst, h,
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templateWindowSize,
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searchWindowSize);
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported depth! Only CV_8U and CV_16U are supported for NORM_L1");
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}
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported norm type! Only NORM_L2 and NORM_L1 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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CV_INSTRUMENT_REGION()
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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 or CV_8UC4!");
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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_MAKE_TYPE(depth, 1));
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Mat ab(src_size, CV_MAKE_TYPE(depth, 2));
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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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template<typename ST, typename IT, typename UIT, typename D>
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static void fastNlMeansDenoisingMulti_( const std::vector<Mat>& srcImgs, Mat& dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize)
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{
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int hn = (int)h.size();
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double granularity = (double)std::max(1., (double)dst.total()/(1 << 16));
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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, IT, UIT, D, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case CV_8UC2:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 2>, IT, UIT, D, Vec2i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case CV_8UC3:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 3>, IT, UIT, D, Vec3i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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break;
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case CV_8UC4:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ST, 4>, IT, UIT, D, Vec4i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]),
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granularity);
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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_8U, CV_8UC2, CV_8UC3 and CV_8UC4 are supported");
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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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CV_INSTRUMENT_REGION()
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fastNlMeansDenoisingMulti(_srcImgs, _dst, imgToDenoiseIndex, temporalWindowSize,
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std::vector<float>(1, h), templateWindowSize, searchWindowSize);
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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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const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize, int normType)
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{
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CV_INSTRUMENT_REGION()
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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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int hn = (int)h.size();
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int type = srcImgs[0].type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
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CV_Assert(hn == 1 || hn == cn);
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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 (normType) {
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case NORM_L2:
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switch (depth) {
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case CV_8U:
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fastNlMeansDenoisingMulti_<uchar, int, unsigned,
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DistSquared>(srcImgs, dst,
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imgToDenoiseIndex, temporalWindowSize,
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h,
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templateWindowSize, searchWindowSize);
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported depth! Only CV_8U is supported for NORM_L2");
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}
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break;
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case NORM_L1:
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switch (depth) {
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case CV_8U:
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fastNlMeansDenoisingMulti_<uchar, int, unsigned,
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DistAbs>(srcImgs, dst,
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imgToDenoiseIndex, temporalWindowSize,
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h,
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templateWindowSize, searchWindowSize);
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break;
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case CV_16U:
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fastNlMeansDenoisingMulti_<ushort, int64, uint64,
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DistAbs>(srcImgs, dst,
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imgToDenoiseIndex, temporalWindowSize,
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h,
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templateWindowSize, searchWindowSize);
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported depth! Only CV_8U and CV_16U are supported for NORM_L1");
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}
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported norm type! Only NORM_L2 and NORM_L1 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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CV_INSTRUMENT_REGION()
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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 type = srcImgs[0].type(), depth = CV_MAT_DEPTH(type);
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int src_imgs_size = static_cast<int>(srcImgs.size());
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if (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(), type);
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l[i] = Mat::zeros(srcImgs[0].size(), CV_MAKE_TYPE(depth, 1));
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ab[i] = Mat::zeros(srcImgs[0].size(), CV_MAKE_TYPE(depth, 2));
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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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