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0c7663eb3b
Conflicts: modules/core/include/opencv2/core/cuda.hpp modules/cudacodec/src/thread.cpp modules/cudacodec/src/thread.hpp modules/superres/perf/perf_superres.cpp modules/superres/src/btv_l1_cuda.cpp modules/superres/src/optical_flow.cpp modules/videostab/src/global_motion.cpp modules/videostab/src/inpainting.cpp samples/cpp/stitching_detailed.cpp samples/cpp/videostab.cpp samples/gpu/stereo_multi.cpp
619 lines
20 KiB
C++
619 lines
20 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) 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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// S. Farsiu , D. Robinson, M. Elad, P. Milanfar. Fast and robust multiframe super resolution.
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// Dennis Mitzel, Thomas Pock, Thomas Schoenemann, Daniel Cremers. Video Super Resolution using Duality Based TV-L1 Optical Flow.
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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::superres;
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using namespace cv::superres::detail;
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namespace
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{
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void calcRelativeMotions(const std::vector<Mat>& forwardMotions, const std::vector<Mat>& backwardMotions,
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std::vector<Mat>& relForwardMotions, std::vector<Mat>& relBackwardMotions,
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int baseIdx, Size size)
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{
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const int count = static_cast<int>(forwardMotions.size());
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relForwardMotions.resize(count);
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relForwardMotions[baseIdx].create(size, CV_32FC2);
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relForwardMotions[baseIdx].setTo(Scalar::all(0));
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relBackwardMotions.resize(count);
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relBackwardMotions[baseIdx].create(size, CV_32FC2);
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relBackwardMotions[baseIdx].setTo(Scalar::all(0));
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for (int i = baseIdx - 1; i >= 0; --i)
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{
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add(relForwardMotions[i + 1], forwardMotions[i], relForwardMotions[i]);
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add(relBackwardMotions[i + 1], backwardMotions[i + 1], relBackwardMotions[i]);
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}
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for (int i = baseIdx + 1; i < count; ++i)
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{
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add(relForwardMotions[i - 1], backwardMotions[i], relForwardMotions[i]);
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add(relBackwardMotions[i - 1], forwardMotions[i - 1], relBackwardMotions[i]);
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}
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}
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void upscaleMotions(const std::vector<Mat>& lowResMotions, std::vector<Mat>& highResMotions, int scale)
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{
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highResMotions.resize(lowResMotions.size());
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for (size_t i = 0; i < lowResMotions.size(); ++i)
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{
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resize(lowResMotions[i], highResMotions[i], Size(), scale, scale, INTER_CUBIC);
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multiply(highResMotions[i], Scalar::all(scale), highResMotions[i]);
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}
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}
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void buildMotionMaps(const Mat& forwardMotion, const Mat& backwardMotion, Mat& forwardMap, Mat& backwardMap)
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{
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forwardMap.create(forwardMotion.size(), CV_32FC2);
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backwardMap.create(forwardMotion.size(), CV_32FC2);
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for (int y = 0; y < forwardMotion.rows; ++y)
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{
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const Point2f* forwardMotionRow = forwardMotion.ptr<Point2f>(y);
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const Point2f* backwardMotionRow = backwardMotion.ptr<Point2f>(y);
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Point2f* forwardMapRow = forwardMap.ptr<Point2f>(y);
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Point2f* backwardMapRow = backwardMap.ptr<Point2f>(y);
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for (int x = 0; x < forwardMotion.cols; ++x)
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{
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Point2f base(static_cast<float>(x), static_cast<float>(y));
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forwardMapRow[x] = base + backwardMotionRow[x];
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backwardMapRow[x] = base + forwardMotionRow[x];
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}
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}
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}
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template <typename T>
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void upscaleImpl(const Mat& src, Mat& dst, int scale)
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{
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dst.create(src.rows * scale, src.cols * scale, src.type());
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dst.setTo(Scalar::all(0));
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for (int y = 0, Y = 0; y < src.rows; ++y, Y += scale)
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{
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const T* srcRow = src.ptr<T>(y);
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T* dstRow = dst.ptr<T>(Y);
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for (int x = 0, X = 0; x < src.cols; ++x, X += scale)
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dstRow[X] = srcRow[x];
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}
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}
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void upscale(const Mat& src, Mat& dst, int scale)
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{
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typedef void (*func_t)(const Mat& src, Mat& dst, int scale);
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static const func_t funcs[] =
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{
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0, upscaleImpl<float>, 0, upscaleImpl<Point3f>
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};
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CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
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const func_t func = funcs[src.channels()];
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func(src, dst, scale);
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}
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float diffSign(float a, float b)
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{
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return a > b ? 1.0f : a < b ? -1.0f : 0.0f;
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}
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Point3f diffSign(Point3f a, Point3f b)
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{
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return Point3f(
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a.x > b.x ? 1.0f : a.x < b.x ? -1.0f : 0.0f,
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a.y > b.y ? 1.0f : a.y < b.y ? -1.0f : 0.0f,
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a.z > b.z ? 1.0f : a.z < b.z ? -1.0f : 0.0f
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);
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}
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void diffSign(const Mat& src1, const Mat& src2, Mat& dst)
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{
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const int count = src1.cols * src1.channels();
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dst.create(src1.size(), src1.type());
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for (int y = 0; y < src1.rows; ++y)
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{
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const float* src1Ptr = src1.ptr<float>(y);
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const float* src2Ptr = src2.ptr<float>(y);
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float* dstPtr = dst.ptr<float>(y);
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for (int x = 0; x < count; ++x)
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dstPtr[x] = diffSign(src1Ptr[x], src2Ptr[x]);
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}
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}
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void calcBtvWeights(int btvKernelSize, double alpha, std::vector<float>& btvWeights)
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{
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const size_t size = btvKernelSize * btvKernelSize;
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btvWeights.resize(size);
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const int ksize = (btvKernelSize - 1) / 2;
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const float alpha_f = static_cast<float>(alpha);
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for (int m = 0, ind = 0; m <= ksize; ++m)
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{
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for (int l = ksize; l + m >= 0; --l, ++ind)
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btvWeights[ind] = pow(alpha_f, std::abs(m) + std::abs(l));
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}
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}
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template <typename T>
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struct BtvRegularizationBody : ParallelLoopBody
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{
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void operator ()(const Range& range) const;
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Mat src;
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mutable Mat dst;
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int ksize;
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const float* btvWeights;
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};
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template <typename T>
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void BtvRegularizationBody<T>::operator ()(const Range& range) const
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{
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for (int i = range.start; i < range.end; ++i)
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{
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const T* srcRow = src.ptr<T>(i);
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T* dstRow = dst.ptr<T>(i);
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for(int j = ksize; j < src.cols - ksize; ++j)
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{
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const T srcVal = srcRow[j];
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for (int m = 0, ind = 0; m <= ksize; ++m)
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{
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const T* srcRow2 = src.ptr<T>(i - m);
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const T* srcRow3 = src.ptr<T>(i + m);
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for (int l = ksize; l + m >= 0; --l, ++ind)
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{
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dstRow[j] += btvWeights[ind] * (diffSign(srcVal, srcRow3[j + l]) - diffSign(srcRow2[j - l], srcVal));
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}
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}
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}
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}
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}
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template <typename T>
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void calcBtvRegularizationImpl(const Mat& src, Mat& dst, int btvKernelSize, const std::vector<float>& btvWeights)
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{
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dst.create(src.size(), src.type());
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dst.setTo(Scalar::all(0));
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const int ksize = (btvKernelSize - 1) / 2;
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BtvRegularizationBody<T> body;
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body.src = src;
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body.dst = dst;
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body.ksize = ksize;
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body.btvWeights = &btvWeights[0];
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parallel_for_(Range(ksize, src.rows - ksize), body);
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}
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void calcBtvRegularization(const Mat& src, Mat& dst, int btvKernelSize, const std::vector<float>& btvWeights)
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{
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typedef void (*func_t)(const Mat& src, Mat& dst, int btvKernelSize, const std::vector<float>& btvWeights);
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static const func_t funcs[] =
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{
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0, calcBtvRegularizationImpl<float>, 0, calcBtvRegularizationImpl<Point3f>
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};
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const func_t func = funcs[src.channels()];
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func(src, dst, btvKernelSize, btvWeights);
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}
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class BTVL1_Base
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{
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public:
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BTVL1_Base();
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void process(const std::vector<Mat>& src, Mat& dst,
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const std::vector<Mat>& forwardMotions, const std::vector<Mat>& backwardMotions,
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int baseIdx);
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void collectGarbage();
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protected:
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int scale_;
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int iterations_;
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double tau_;
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double lambda_;
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double alpha_;
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int btvKernelSize_;
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int blurKernelSize_;
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double blurSigma_;
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Ptr<DenseOpticalFlowExt> opticalFlow_;
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private:
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Ptr<FilterEngine> filter_;
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int curBlurKernelSize_;
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double curBlurSigma_;
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int curSrcType_;
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std::vector<float> btvWeights_;
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int curBtvKernelSize_;
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double curAlpha_;
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std::vector<Mat> lowResForwardMotions_;
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std::vector<Mat> lowResBackwardMotions_;
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std::vector<Mat> highResForwardMotions_;
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std::vector<Mat> highResBackwardMotions_;
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std::vector<Mat> forwardMaps_;
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std::vector<Mat> backwardMaps_;
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Mat highRes_;
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Mat diffTerm_, regTerm_;
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Mat a_, b_, c_;
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};
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BTVL1_Base::BTVL1_Base()
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{
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scale_ = 4;
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iterations_ = 180;
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lambda_ = 0.03;
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tau_ = 1.3;
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alpha_ = 0.7;
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btvKernelSize_ = 7;
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blurKernelSize_ = 5;
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blurSigma_ = 0.0;
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opticalFlow_ = createOptFlow_Farneback();
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curBlurKernelSize_ = -1;
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curBlurSigma_ = -1.0;
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curSrcType_ = -1;
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curBtvKernelSize_ = -1;
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curAlpha_ = -1.0;
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}
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void BTVL1_Base::process(const std::vector<Mat>& src, Mat& dst, const std::vector<Mat>& forwardMotions, const std::vector<Mat>& backwardMotions, int baseIdx)
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{
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CV_Assert( scale_ > 1 );
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CV_Assert( iterations_ > 0 );
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CV_Assert( tau_ > 0.0 );
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CV_Assert( alpha_ > 0.0 );
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CV_Assert( btvKernelSize_ > 0 );
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CV_Assert( blurKernelSize_ > 0 );
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CV_Assert( blurSigma_ >= 0.0 );
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// update blur filter and btv weights
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if (!filter_ || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
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{
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filter_ = createGaussianFilter(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
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curBlurKernelSize_ = blurKernelSize_;
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curBlurSigma_ = blurSigma_;
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curSrcType_ = src[0].type();
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}
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if (btvWeights_.empty() || btvKernelSize_ != curBtvKernelSize_ || alpha_ != curAlpha_)
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{
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calcBtvWeights(btvKernelSize_, alpha_, btvWeights_);
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curBtvKernelSize_ = btvKernelSize_;
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curAlpha_ = alpha_;
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}
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// calc high res motions
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calcRelativeMotions(forwardMotions, backwardMotions, lowResForwardMotions_, lowResBackwardMotions_, baseIdx, src[0].size());
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upscaleMotions(lowResForwardMotions_, highResForwardMotions_, scale_);
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upscaleMotions(lowResBackwardMotions_, highResBackwardMotions_, scale_);
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forwardMaps_.resize(highResForwardMotions_.size());
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backwardMaps_.resize(highResForwardMotions_.size());
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for (size_t i = 0; i < highResForwardMotions_.size(); ++i)
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buildMotionMaps(highResForwardMotions_[i], highResBackwardMotions_[i], forwardMaps_[i], backwardMaps_[i]);
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// initial estimation
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const Size lowResSize = src[0].size();
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const Size highResSize(lowResSize.width * scale_, lowResSize.height * scale_);
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resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC);
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// iterations
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diffTerm_.create(highResSize, highRes_.type());
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a_.create(highResSize, highRes_.type());
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b_.create(highResSize, highRes_.type());
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c_.create(lowResSize, highRes_.type());
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for (int i = 0; i < iterations_; ++i)
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{
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diffTerm_.setTo(Scalar::all(0));
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for (size_t k = 0; k < src.size(); ++k)
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{
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// a = M * Ih
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remap(highRes_, a_, backwardMaps_[k], noArray(), INTER_NEAREST);
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// b = HM * Ih
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filter_->apply(a_, b_);
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// c = DHM * Ih
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resize(b_, c_, lowResSize, 0, 0, INTER_NEAREST);
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diffSign(src[k], c_, c_);
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// a = Dt * diff
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upscale(c_, a_, scale_);
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// b = HtDt * diff
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filter_->apply(a_, b_);
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// a = MtHtDt * diff
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remap(b_, a_, forwardMaps_[k], noArray(), INTER_NEAREST);
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add(diffTerm_, a_, diffTerm_);
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}
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if (lambda_ > 0)
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{
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calcBtvRegularization(highRes_, regTerm_, btvKernelSize_, btvWeights_);
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addWeighted(diffTerm_, 1.0, regTerm_, -lambda_, 0.0, diffTerm_);
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}
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addWeighted(highRes_, 1.0, diffTerm_, tau_, 0.0, highRes_);
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}
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Rect inner(btvKernelSize_, btvKernelSize_, highRes_.cols - 2 * btvKernelSize_, highRes_.rows - 2 * btvKernelSize_);
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highRes_(inner).copyTo(dst);
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}
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void BTVL1_Base::collectGarbage()
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{
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filter_.release();
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lowResForwardMotions_.clear();
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lowResBackwardMotions_.clear();
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highResForwardMotions_.clear();
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highResBackwardMotions_.clear();
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forwardMaps_.clear();
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backwardMaps_.clear();
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highRes_.release();
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diffTerm_.release();
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regTerm_.release();
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a_.release();
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b_.release();
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c_.release();
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}
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////////////////////////////////////////////////////////////////////
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class BTVL1 : public SuperResolution, private BTVL1_Base
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{
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public:
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AlgorithmInfo* info() const;
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BTVL1();
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void collectGarbage();
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protected:
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void initImpl(Ptr<FrameSource>& frameSource);
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void processImpl(Ptr<FrameSource>& frameSource, OutputArray output);
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private:
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int temporalAreaRadius_;
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void readNextFrame(Ptr<FrameSource>& frameSource);
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void processFrame(int idx);
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Mat curFrame_;
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Mat prevFrame_;
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std::vector<Mat> frames_;
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std::vector<Mat> forwardMotions_;
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std::vector<Mat> backwardMotions_;
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std::vector<Mat> outputs_;
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int storePos_;
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int procPos_;
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int outPos_;
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std::vector<Mat> srcFrames_;
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std::vector<Mat> srcForwardMotions_;
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std::vector<Mat> srcBackwardMotions_;
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Mat finalOutput_;
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};
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CV_INIT_ALGORITHM(BTVL1, "SuperResolution.BTVL1",
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obj.info()->addParam(obj, "scale", obj.scale_, false, 0, 0, "Scale factor.");
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obj.info()->addParam(obj, "iterations", obj.iterations_, false, 0, 0, "Iteration count.");
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obj.info()->addParam(obj, "tau", obj.tau_, false, 0, 0, "Asymptotic value of steepest descent method.");
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obj.info()->addParam(obj, "lambda", obj.lambda_, false, 0, 0, "Weight parameter to balance data term and smoothness term.");
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obj.info()->addParam(obj, "alpha", obj.alpha_, false, 0, 0, "Parameter of spacial distribution in Bilateral-TV.");
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obj.info()->addParam(obj, "btvKernelSize", obj.btvKernelSize_, false, 0, 0, "Kernel size of Bilateral-TV filter.");
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obj.info()->addParam(obj, "blurKernelSize", obj.blurKernelSize_, false, 0, 0, "Gaussian blur kernel size.");
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obj.info()->addParam(obj, "blurSigma", obj.blurSigma_, false, 0, 0, "Gaussian blur sigma.");
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obj.info()->addParam(obj, "temporalAreaRadius", obj.temporalAreaRadius_, false, 0, 0, "Radius of the temporal search area.");
|
|
obj.info()->addParam<DenseOpticalFlowExt>(obj, "opticalFlow", obj.opticalFlow_, false, 0, 0, "Dense optical flow algorithm."));
|
|
|
|
BTVL1::BTVL1()
|
|
{
|
|
temporalAreaRadius_ = 4;
|
|
}
|
|
|
|
void BTVL1::collectGarbage()
|
|
{
|
|
curFrame_.release();
|
|
prevFrame_.release();
|
|
|
|
frames_.clear();
|
|
forwardMotions_.clear();
|
|
backwardMotions_.clear();
|
|
outputs_.clear();
|
|
|
|
srcFrames_.clear();
|
|
srcForwardMotions_.clear();
|
|
srcBackwardMotions_.clear();
|
|
finalOutput_.release();
|
|
|
|
SuperResolution::collectGarbage();
|
|
BTVL1_Base::collectGarbage();
|
|
}
|
|
|
|
void BTVL1::initImpl(Ptr<FrameSource>& frameSource)
|
|
{
|
|
const int cacheSize = 2 * temporalAreaRadius_ + 1;
|
|
|
|
frames_.resize(cacheSize);
|
|
forwardMotions_.resize(cacheSize);
|
|
backwardMotions_.resize(cacheSize);
|
|
outputs_.resize(cacheSize);
|
|
|
|
storePos_ = -1;
|
|
|
|
for (int t = -temporalAreaRadius_; t <= temporalAreaRadius_; ++t)
|
|
readNextFrame(frameSource);
|
|
|
|
for (int i = 0; i <= temporalAreaRadius_; ++i)
|
|
processFrame(i);
|
|
|
|
procPos_ = temporalAreaRadius_;
|
|
outPos_ = -1;
|
|
}
|
|
|
|
void BTVL1::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
|
|
{
|
|
if (outPos_ >= storePos_)
|
|
{
|
|
_output.release();
|
|
return;
|
|
}
|
|
|
|
readNextFrame(frameSource);
|
|
|
|
if (procPos_ < storePos_)
|
|
{
|
|
++procPos_;
|
|
processFrame(procPos_);
|
|
}
|
|
|
|
++outPos_;
|
|
const Mat& curOutput = at(outPos_, outputs_);
|
|
|
|
if (_output.kind() < _InputArray::OPENGL_BUFFER)
|
|
curOutput.convertTo(_output, CV_8U);
|
|
else
|
|
{
|
|
curOutput.convertTo(finalOutput_, CV_8U);
|
|
arrCopy(finalOutput_, _output);
|
|
}
|
|
}
|
|
|
|
void BTVL1::readNextFrame(Ptr<FrameSource>& frameSource)
|
|
{
|
|
frameSource->nextFrame(curFrame_);
|
|
|
|
if (curFrame_.empty())
|
|
return;
|
|
|
|
++storePos_;
|
|
curFrame_.convertTo(at(storePos_, frames_), CV_32F);
|
|
|
|
if (storePos_ > 0)
|
|
{
|
|
opticalFlow_->calc(prevFrame_, curFrame_, at(storePos_ - 1, forwardMotions_));
|
|
opticalFlow_->calc(curFrame_, prevFrame_, at(storePos_, backwardMotions_));
|
|
}
|
|
|
|
curFrame_.copyTo(prevFrame_);
|
|
}
|
|
|
|
void BTVL1::processFrame(int idx)
|
|
{
|
|
const int startIdx = std::max(idx - temporalAreaRadius_, 0);
|
|
const int procIdx = idx;
|
|
const int endIdx = std::min(startIdx + 2 * temporalAreaRadius_, storePos_);
|
|
|
|
const int count = endIdx - startIdx + 1;
|
|
|
|
srcFrames_.resize(count);
|
|
srcForwardMotions_.resize(count);
|
|
srcBackwardMotions_.resize(count);
|
|
|
|
int baseIdx = -1;
|
|
|
|
for (int i = startIdx, k = 0; i <= endIdx; ++i, ++k)
|
|
{
|
|
if (i == procIdx)
|
|
baseIdx = k;
|
|
|
|
srcFrames_[k] = at(i, frames_);
|
|
|
|
if (i < endIdx)
|
|
srcForwardMotions_[k] = at(i, forwardMotions_);
|
|
if (i > startIdx)
|
|
srcBackwardMotions_[k] = at(i, backwardMotions_);
|
|
}
|
|
|
|
process(srcFrames_, at(idx, outputs_), srcForwardMotions_, srcBackwardMotions_, baseIdx);
|
|
}
|
|
}
|
|
|
|
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1()
|
|
{
|
|
return makePtr<BTVL1>();
|
|
}
|