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582 lines
21 KiB
C++
582 lines
21 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::cuda;
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using namespace cv::superres;
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using namespace cv::superres::detail;
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#if !defined(HAVE_CUDA) || !defined(HAVE_OPENCV_CUDAARITHM) || !defined(HAVE_OPENCV_CUDAWARPING) || !defined(HAVE_OPENCV_CUDAFILTERS)
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Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_CUDA()
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{
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CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");
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return Ptr<SuperResolution>();
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}
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#else // HAVE_CUDA
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namespace btv_l1_cudev
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{
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void buildMotionMaps(PtrStepSzf forwardMotionX, PtrStepSzf forwardMotionY,
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PtrStepSzf backwardMotionX, PtrStepSzf bacwardMotionY,
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PtrStepSzf forwardMapX, PtrStepSzf forwardMapY,
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PtrStepSzf backwardMapX, PtrStepSzf backwardMapY);
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template <int cn>
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void upscale(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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void diffSign(PtrStepSzf src1, PtrStepSzf src2, PtrStepSzf dst, cudaStream_t stream);
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void loadBtvWeights(const float* weights, size_t count);
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template <int cn> void calcBtvRegularization(PtrStepSzb src, PtrStepSzb dst, int ksize);
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}
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namespace
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{
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void calcRelativeMotions(const std::vector<std::pair<GpuMat, GpuMat> >& forwardMotions, const std::vector<std::pair<GpuMat, GpuMat> >& backwardMotions,
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std::vector<std::pair<GpuMat, GpuMat> >& relForwardMotions, std::vector<std::pair<GpuMat, GpuMat> >& 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].first.create(size, CV_32FC1);
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relForwardMotions[baseIdx].first.setTo(Scalar::all(0));
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relForwardMotions[baseIdx].second.create(size, CV_32FC1);
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relForwardMotions[baseIdx].second.setTo(Scalar::all(0));
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relBackwardMotions.resize(count);
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relBackwardMotions[baseIdx].first.create(size, CV_32FC1);
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relBackwardMotions[baseIdx].first.setTo(Scalar::all(0));
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relBackwardMotions[baseIdx].second.create(size, CV_32FC1);
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relBackwardMotions[baseIdx].second.setTo(Scalar::all(0));
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for (int i = baseIdx - 1; i >= 0; --i)
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{
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cuda::add(relForwardMotions[i + 1].first, forwardMotions[i].first, relForwardMotions[i].first);
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cuda::add(relForwardMotions[i + 1].second, forwardMotions[i].second, relForwardMotions[i].second);
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cuda::add(relBackwardMotions[i + 1].first, backwardMotions[i + 1].first, relBackwardMotions[i].first);
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cuda::add(relBackwardMotions[i + 1].second, backwardMotions[i + 1].second, relBackwardMotions[i].second);
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}
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for (int i = baseIdx + 1; i < count; ++i)
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{
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cuda::add(relForwardMotions[i - 1].first, backwardMotions[i].first, relForwardMotions[i].first);
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cuda::add(relForwardMotions[i - 1].second, backwardMotions[i].second, relForwardMotions[i].second);
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cuda::add(relBackwardMotions[i - 1].first, forwardMotions[i - 1].first, relBackwardMotions[i].first);
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cuda::add(relBackwardMotions[i - 1].second, forwardMotions[i - 1].second, relBackwardMotions[i].second);
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}
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}
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void upscaleMotions(const std::vector<std::pair<GpuMat, GpuMat> >& lowResMotions, std::vector<std::pair<GpuMat, GpuMat> >& 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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cuda::resize(lowResMotions[i].first, highResMotions[i].first, Size(), scale, scale, INTER_CUBIC);
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cuda::resize(lowResMotions[i].second, highResMotions[i].second, Size(), scale, scale, INTER_CUBIC);
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cuda::multiply(highResMotions[i].first, Scalar::all(scale), highResMotions[i].first);
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cuda::multiply(highResMotions[i].second, Scalar::all(scale), highResMotions[i].second);
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}
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}
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void buildMotionMaps(const std::pair<GpuMat, GpuMat>& forwardMotion, const std::pair<GpuMat, GpuMat>& backwardMotion,
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std::pair<GpuMat, GpuMat>& forwardMap, std::pair<GpuMat, GpuMat>& backwardMap)
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{
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forwardMap.first.create(forwardMotion.first.size(), CV_32FC1);
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forwardMap.second.create(forwardMotion.first.size(), CV_32FC1);
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backwardMap.first.create(forwardMotion.first.size(), CV_32FC1);
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backwardMap.second.create(forwardMotion.first.size(), CV_32FC1);
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btv_l1_cudev::buildMotionMaps(forwardMotion.first, forwardMotion.second,
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backwardMotion.first, backwardMotion.second,
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forwardMap.first, forwardMap.second,
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backwardMap.first, backwardMap.second);
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}
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void upscale(const GpuMat& src, GpuMat& dst, int scale, Stream& stream)
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{
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typedef void (*func_t)(const PtrStepSzb src, PtrStepSzb dst, int scale, cudaStream_t stream);
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static const func_t funcs[] =
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{
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0, btv_l1_cudev::upscale<1>, 0, btv_l1_cudev::upscale<3>, btv_l1_cudev::upscale<4>
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};
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CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
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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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const func_t func = funcs[src.channels()];
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func(src, dst, scale, StreamAccessor::getStream(stream));
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}
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void diffSign(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
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{
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dst.create(src1.size(), src1.type());
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btv_l1_cudev::diffSign(src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
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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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btv_l1_cudev::loadBtvWeights(&btvWeights[0], size);
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}
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void calcBtvRegularization(const GpuMat& src, GpuMat& dst, int btvKernelSize)
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{
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typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int ksize);
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static const func_t funcs[] =
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{
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0,
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btv_l1_cudev::calcBtvRegularization<1>,
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0,
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btv_l1_cudev::calcBtvRegularization<3>,
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btv_l1_cudev::calcBtvRegularization<4>
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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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funcs[src.channels()](src, dst, ksize);
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}
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class BTVL1_CUDA_Base : public cv::superres::SuperResolution
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{
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public:
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BTVL1_CUDA_Base();
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void process(const std::vector<GpuMat>& src, GpuMat& dst,
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const std::vector<std::pair<GpuMat, GpuMat> >& forwardMotions, const std::vector<std::pair<GpuMat, GpuMat> >& backwardMotions,
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int baseIdx);
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void collectGarbage();
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CV_IMPL_PROPERTY(int, Scale, scale_)
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CV_IMPL_PROPERTY(int, Iterations, iterations_)
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CV_IMPL_PROPERTY(double, Tau, tau_)
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CV_IMPL_PROPERTY(double, Labmda, lambda_)
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CV_IMPL_PROPERTY(double, Alpha, alpha_)
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CV_IMPL_PROPERTY(int, KernelSize, btvKernelSize_)
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CV_IMPL_PROPERTY(int, BlurKernelSize, blurKernelSize_)
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CV_IMPL_PROPERTY(double, BlurSigma, blurSigma_)
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CV_IMPL_PROPERTY(int, TemporalAreaRadius, temporalAreaRadius_)
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CV_IMPL_PROPERTY_S(Ptr<cv::superres::DenseOpticalFlowExt>, OpticalFlow, opticalFlow_)
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protected:
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int scale_;
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int iterations_;
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double lambda_;
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double tau_;
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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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int temporalAreaRadius_;
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Ptr<cv::superres::DenseOpticalFlowExt> opticalFlow_;
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private:
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std::vector<Ptr<cuda::Filter> > filters_;
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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<std::pair<GpuMat, GpuMat> > lowResForwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > lowResBackwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > highResForwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > highResBackwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > forwardMaps_;
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std::vector<std::pair<GpuMat, GpuMat> > backwardMaps_;
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GpuMat highRes_;
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std::vector<Stream> streams_;
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std::vector<GpuMat> diffTerms_;
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std::vector<GpuMat> a_, b_, c_;
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GpuMat regTerm_;
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};
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BTVL1_CUDA_Base::BTVL1_CUDA_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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#ifdef HAVE_OPENCV_CUDAOPTFLOW
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opticalFlow_ = createOptFlow_Farneback_CUDA();
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#else
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opticalFlow_ = createOptFlow_Farneback();
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#endif
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temporalAreaRadius_ = 0;
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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_CUDA_Base::process(const std::vector<GpuMat>& src, GpuMat& dst,
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const std::vector<std::pair<GpuMat, GpuMat> >& forwardMotions, const std::vector<std::pair<GpuMat, GpuMat> >& backwardMotions,
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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 && btvKernelSize_ <= 16 );
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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 (filters_.size() != src.size() || blurKernelSize_ != curBlurKernelSize_ || blurSigma_ != curBlurSigma_ || src[0].type() != curSrcType_)
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{
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filters_.resize(src.size());
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for (size_t i = 0; i < src.size(); ++i)
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filters_[i] = cuda::createGaussianFilter(src[0].type(), -1, 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 motions between input frames
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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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cuda::resize(src[baseIdx], highRes_, highResSize, 0, 0, INTER_CUBIC);
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// iterations
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streams_.resize(src.size());
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diffTerms_.resize(src.size());
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a_.resize(src.size());
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b_.resize(src.size());
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c_.resize(src.size());
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for (int i = 0; i < iterations_; ++i)
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{
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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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cuda::remap(highRes_, a_[k], backwardMaps_[k].first, backwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
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// b = HM * Ih
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filters_[k]->apply(a_[k], b_[k], streams_[k]);
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// c = DHF * Ih
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cuda::resize(b_[k], c_[k], lowResSize, 0, 0, INTER_NEAREST, streams_[k]);
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diffSign(src[k], c_[k], c_[k], streams_[k]);
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// a = Dt * diff
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upscale(c_[k], a_[k], scale_, streams_[k]);
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// b = HtDt * diff
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filters_[k]->apply(a_[k], b_[k], streams_[k]);
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// diffTerm = MtHtDt * diff
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cuda::remap(b_[k], diffTerms_[k], forwardMaps_[k].first, forwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
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}
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if (lambda_ > 0)
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{
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calcBtvRegularization(highRes_, regTerm_, btvKernelSize_);
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cuda::addWeighted(highRes_, 1.0, regTerm_, -tau_ * lambda_, 0.0, highRes_);
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}
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for (size_t k = 0; k < src.size(); ++k)
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{
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streams_[k].waitForCompletion();
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cuda::addWeighted(highRes_, 1.0, diffTerms_[k], tau_, 0.0, highRes_);
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}
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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_CUDA_Base::collectGarbage()
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{
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filters_.clear();
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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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diffTerms_.clear();
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a_.clear();
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b_.clear();
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c_.clear();
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regTerm_.release();
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}
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////////////////////////////////////////////////////////////
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class BTVL1_CUDA : public BTVL1_CUDA_Base
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{
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public:
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BTVL1_CUDA();
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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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void readNextFrame(Ptr<FrameSource>& frameSource);
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void processFrame(int idx);
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GpuMat curFrame_;
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GpuMat prevFrame_;
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std::vector<GpuMat> frames_;
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std::vector<std::pair<GpuMat, GpuMat> > forwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > backwardMotions_;
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std::vector<GpuMat> 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<GpuMat> srcFrames_;
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std::vector<std::pair<GpuMat, GpuMat> > srcForwardMotions_;
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std::vector<std::pair<GpuMat, GpuMat> > srcBackwardMotions_;
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GpuMat finalOutput_;
|
|
};
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|
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BTVL1_CUDA::BTVL1_CUDA()
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|
{
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|
temporalAreaRadius_ = 4;
|
|
}
|
|
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|
void BTVL1_CUDA::collectGarbage()
|
|
{
|
|
curFrame_.release();
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|
prevFrame_.release();
|
|
|
|
frames_.clear();
|
|
forwardMotions_.clear();
|
|
backwardMotions_.clear();
|
|
outputs_.clear();
|
|
|
|
srcFrames_.clear();
|
|
srcForwardMotions_.clear();
|
|
srcBackwardMotions_.clear();
|
|
finalOutput_.release();
|
|
|
|
SuperResolution::collectGarbage();
|
|
BTVL1_CUDA_Base::collectGarbage();
|
|
}
|
|
|
|
void BTVL1_CUDA::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_CUDA::processImpl(Ptr<FrameSource>& frameSource, OutputArray _output)
|
|
{
|
|
if (outPos_ >= storePos_)
|
|
{
|
|
_output.release();
|
|
return;
|
|
}
|
|
|
|
readNextFrame(frameSource);
|
|
|
|
if (procPos_ < storePos_)
|
|
{
|
|
++procPos_;
|
|
processFrame(procPos_);
|
|
}
|
|
|
|
++outPos_;
|
|
const GpuMat& curOutput = at(outPos_, outputs_);
|
|
|
|
if (_output.kind() == _InputArray::CUDA_GPU_MAT)
|
|
curOutput.convertTo(_output.getGpuMatRef(), CV_8U);
|
|
else
|
|
{
|
|
curOutput.convertTo(finalOutput_, CV_8U);
|
|
arrCopy(finalOutput_, _output);
|
|
}
|
|
}
|
|
|
|
void BTVL1_CUDA::readNextFrame(Ptr<FrameSource>& frameSource)
|
|
{
|
|
frameSource->nextFrame(curFrame_);
|
|
|
|
if (curFrame_.empty())
|
|
return;
|
|
|
|
++storePos_;
|
|
curFrame_.convertTo(at(storePos_, frames_), CV_32F);
|
|
|
|
if (storePos_ > 0)
|
|
{
|
|
std::pair<GpuMat, GpuMat>& forwardMotion = at(storePos_ - 1, forwardMotions_);
|
|
std::pair<GpuMat, GpuMat>& backwardMotion = at(storePos_, backwardMotions_);
|
|
|
|
opticalFlow_->calc(prevFrame_, curFrame_, forwardMotion.first, forwardMotion.second);
|
|
opticalFlow_->calc(curFrame_, prevFrame_, backwardMotion.first, backwardMotion.second);
|
|
}
|
|
|
|
curFrame_.copyTo(prevFrame_);
|
|
}
|
|
|
|
void BTVL1_CUDA::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_CUDA()
|
|
{
|
|
return makePtr<BTVL1_CUDA>();
|
|
}
|
|
|
|
#endif // HAVE_CUDA
|