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
388 lines
17 KiB
C++
388 lines
17 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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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::cuda;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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void cv::cuda::StereoConstantSpaceBP::estimateRecommendedParams(int, int, int&, int&, int&, int&) { throw_no_cuda(); }
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Ptr<cuda::StereoConstantSpaceBP> cv::cuda::createStereoConstantSpaceBP(int, int, int, int, int) { throw_no_cuda(); return Ptr<cuda::StereoConstantSpaceBP>(); }
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#else /* !defined (HAVE_CUDA) */
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namespace cv { namespace cuda { namespace device
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{
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namespace stereocsbp
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{
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void load_constants(int ndisp, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int min_disp_th,
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const PtrStepSzb& left, const PtrStepSzb& right, const PtrStepSzb& temp);
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template<class T>
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void init_data_cost(int rows, int cols, T* disp_selected_pyr, T* data_cost_selected, size_t msg_step,
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int h, int w, int level, int nr_plane, int ndisp, int channels, bool use_local_init_data_cost, cudaStream_t stream);
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template<class T>
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void compute_data_cost(const T* disp_selected_pyr, T* data_cost, size_t msg_step,
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int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, cudaStream_t stream);
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template<class T>
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void init_message(T* u_new, T* d_new, T* l_new, T* r_new,
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const T* u_cur, const T* d_cur, const T* l_cur, const T* r_cur,
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T* selected_disp_pyr_new, const T* selected_disp_pyr_cur,
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T* data_cost_selected, const T* data_cost, size_t msg_step,
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int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream);
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template<class T>
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void calc_all_iterations(T* u, T* d, T* l, T* r, const T* data_cost_selected,
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const T* selected_disp_pyr_cur, size_t msg_step, int h, int w, int nr_plane, int iters, cudaStream_t stream);
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template<class T>
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void compute_disp(const T* u, const T* d, const T* l, const T* r, const T* data_cost_selected, const T* disp_selected, size_t msg_step,
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const PtrStepSz<short>& disp, int nr_plane, cudaStream_t stream);
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}
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}}}
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namespace
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{
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class StereoCSBPImpl : public cuda::StereoConstantSpaceBP
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{
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public:
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StereoCSBPImpl(int ndisp, int iters, int levels, int nr_plane, int msg_type);
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void compute(InputArray left, InputArray right, OutputArray disparity);
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void compute(InputArray left, InputArray right, OutputArray disparity, Stream& stream);
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void compute(InputArray data, OutputArray disparity, Stream& stream);
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int getMinDisparity() const { return min_disp_th_; }
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void setMinDisparity(int minDisparity) { min_disp_th_ = minDisparity; }
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int getNumDisparities() const { return ndisp_; }
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void setNumDisparities(int numDisparities) { ndisp_ = numDisparities; }
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int getBlockSize() const { return 0; }
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void setBlockSize(int /*blockSize*/) {}
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int getSpeckleWindowSize() const { return 0; }
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void setSpeckleWindowSize(int /*speckleWindowSize*/) {}
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int getSpeckleRange() const { return 0; }
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void setSpeckleRange(int /*speckleRange*/) {}
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int getDisp12MaxDiff() const { return 0; }
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void setDisp12MaxDiff(int /*disp12MaxDiff*/) {}
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int getNumIters() const { return iters_; }
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void setNumIters(int iters) { iters_ = iters; }
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int getNumLevels() const { return levels_; }
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void setNumLevels(int levels) { levels_ = levels; }
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double getMaxDataTerm() const { return max_data_term_; }
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void setMaxDataTerm(double max_data_term) { max_data_term_ = (float) max_data_term; }
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double getDataWeight() const { return data_weight_; }
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void setDataWeight(double data_weight) { data_weight_ = (float) data_weight; }
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double getMaxDiscTerm() const { return max_disc_term_; }
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void setMaxDiscTerm(double max_disc_term) { max_disc_term_ = (float) max_disc_term; }
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double getDiscSingleJump() const { return disc_single_jump_; }
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void setDiscSingleJump(double disc_single_jump) { disc_single_jump_ = (float) disc_single_jump; }
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int getMsgType() const { return msg_type_; }
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void setMsgType(int msg_type) { msg_type_ = msg_type; }
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int getNrPlane() const { return nr_plane_; }
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void setNrPlane(int nr_plane) { nr_plane_ = nr_plane; }
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bool getUseLocalInitDataCost() const { return use_local_init_data_cost_; }
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void setUseLocalInitDataCost(bool use_local_init_data_cost) { use_local_init_data_cost_ = use_local_init_data_cost; }
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private:
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int min_disp_th_;
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int ndisp_;
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int iters_;
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int levels_;
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float max_data_term_;
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float data_weight_;
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float max_disc_term_;
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float disc_single_jump_;
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int msg_type_;
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int nr_plane_;
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bool use_local_init_data_cost_;
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GpuMat mbuf_;
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GpuMat temp_;
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GpuMat outBuf_;
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};
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const float DEFAULT_MAX_DATA_TERM = 30.0f;
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const float DEFAULT_DATA_WEIGHT = 1.0f;
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const float DEFAULT_MAX_DISC_TERM = 160.0f;
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const float DEFAULT_DISC_SINGLE_JUMP = 10.0f;
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StereoCSBPImpl::StereoCSBPImpl(int ndisp, int iters, int levels, int nr_plane, int msg_type) :
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min_disp_th_(0), ndisp_(ndisp), iters_(iters), levels_(levels),
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max_data_term_(DEFAULT_MAX_DATA_TERM), data_weight_(DEFAULT_DATA_WEIGHT),
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max_disc_term_(DEFAULT_MAX_DISC_TERM), disc_single_jump_(DEFAULT_DISC_SINGLE_JUMP),
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msg_type_(msg_type), nr_plane_(nr_plane), use_local_init_data_cost_(true)
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{
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}
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void StereoCSBPImpl::compute(InputArray left, InputArray right, OutputArray disparity)
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{
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compute(left, right, disparity, Stream::Null());
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}
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void StereoCSBPImpl::compute(InputArray _left, InputArray _right, OutputArray disp, Stream& _stream)
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{
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using namespace cv::cuda::device::stereocsbp;
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CV_Assert( msg_type_ == CV_32F || msg_type_ == CV_16S );
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CV_Assert( 0 < ndisp_ && 0 < iters_ && 0 < levels_ && 0 < nr_plane_ && levels_ <= 8 );
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GpuMat left = _left.getGpuMat();
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GpuMat right = _right.getGpuMat();
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CV_Assert( left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4 );
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CV_Assert( left.size() == right.size() && left.type() == right.type() );
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cudaStream_t stream = StreamAccessor::getStream(_stream);
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////////////////////////////////////////////////////////////////////////////////////////////
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// Init
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int rows = left.rows;
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int cols = left.cols;
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levels_ = std::min(levels_, int(log((double)ndisp_) / log(2.0)));
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// compute sizes
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AutoBuffer<int> buf(levels_ * 3);
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int* cols_pyr = buf;
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int* rows_pyr = cols_pyr + levels_;
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int* nr_plane_pyr = rows_pyr + levels_;
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cols_pyr[0] = cols;
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rows_pyr[0] = rows;
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nr_plane_pyr[0] = nr_plane_;
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for (int i = 1; i < levels_; i++)
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{
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cols_pyr[i] = cols_pyr[i-1] / 2;
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rows_pyr[i] = rows_pyr[i-1] / 2;
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nr_plane_pyr[i] = nr_plane_pyr[i-1] * 2;
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}
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GpuMat u[2], d[2], l[2], r[2], disp_selected_pyr[2], data_cost, data_cost_selected;
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//allocate buffers
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int buffers_count = 10; // (up + down + left + right + disp_selected_pyr) * 2
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buffers_count += 2; // data_cost has twice more rows than other buffers, what's why +2, not +1;
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buffers_count += 1; // data_cost_selected
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mbuf_.create(rows * nr_plane_ * buffers_count, cols, msg_type_);
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data_cost = mbuf_.rowRange(0, rows * nr_plane_ * 2);
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data_cost_selected = mbuf_.rowRange(data_cost.rows, data_cost.rows + rows * nr_plane_);
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for(int k = 0; k < 2; ++k) // in/out
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{
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GpuMat sub1 = mbuf_.rowRange(data_cost.rows + data_cost_selected.rows, mbuf_.rows);
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GpuMat sub2 = sub1.rowRange((k+0)*sub1.rows/2, (k+1)*sub1.rows/2);
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GpuMat *buf_ptrs[] = { &u[k], &d[k], &l[k], &r[k], &disp_selected_pyr[k] };
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for(int _r = 0; _r < 5; ++_r)
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{
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*buf_ptrs[_r] = sub2.rowRange(_r * sub2.rows/5, (_r+1) * sub2.rows/5);
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CV_DbgAssert( buf_ptrs[_r]->cols == cols && buf_ptrs[_r]->rows == rows * nr_plane_ );
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}
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};
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size_t elem_step = mbuf_.step / mbuf_.elemSize();
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Size temp_size = data_cost.size();
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if ((size_t)temp_size.area() < elem_step * rows_pyr[levels_ - 1] * ndisp_)
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temp_size = Size(static_cast<int>(elem_step), rows_pyr[levels_ - 1] * ndisp_);
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temp_.create(temp_size, msg_type_);
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////////////////////////////////////////////////////////////////////////////
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// Compute
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load_constants(ndisp_, max_data_term_, data_weight_, max_disc_term_, disc_single_jump_, min_disp_th_, left, right, temp_);
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l[0].setTo(0, _stream);
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d[0].setTo(0, _stream);
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r[0].setTo(0, _stream);
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u[0].setTo(0, _stream);
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l[1].setTo(0, _stream);
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d[1].setTo(0, _stream);
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r[1].setTo(0, _stream);
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u[1].setTo(0, _stream);
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data_cost.setTo(0, _stream);
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data_cost_selected.setTo(0, _stream);
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int cur_idx = 0;
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if (msg_type_ == CV_32F)
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{
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for (int i = levels_ - 1; i >= 0; i--)
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{
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if (i == levels_ - 1)
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{
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init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<float>(), data_cost_selected.ptr<float>(),
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elem_step, rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], ndisp_, left.channels(), use_local_init_data_cost_, stream);
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}
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else
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{
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compute_data_cost(disp_selected_pyr[cur_idx].ptr<float>(), data_cost.ptr<float>(), elem_step,
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left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), stream);
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int new_idx = (cur_idx + 1) & 1;
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init_message(u[new_idx].ptr<float>(), d[new_idx].ptr<float>(), l[new_idx].ptr<float>(), r[new_idx].ptr<float>(),
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u[cur_idx].ptr<float>(), d[cur_idx].ptr<float>(), l[cur_idx].ptr<float>(), r[cur_idx].ptr<float>(),
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disp_selected_pyr[new_idx].ptr<float>(), disp_selected_pyr[cur_idx].ptr<float>(),
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data_cost_selected.ptr<float>(), data_cost.ptr<float>(), elem_step, rows_pyr[i],
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cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], stream);
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cur_idx = new_idx;
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}
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calc_all_iterations(u[cur_idx].ptr<float>(), d[cur_idx].ptr<float>(), l[cur_idx].ptr<float>(), r[cur_idx].ptr<float>(),
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data_cost_selected.ptr<float>(), disp_selected_pyr[cur_idx].ptr<float>(), elem_step,
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rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], iters_, stream);
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}
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}
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else
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{
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for (int i = levels_ - 1; i >= 0; i--)
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{
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if (i == levels_ - 1)
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{
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init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<short>(), data_cost_selected.ptr<short>(),
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elem_step, rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], ndisp_, left.channels(), use_local_init_data_cost_, stream);
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}
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else
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{
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compute_data_cost(disp_selected_pyr[cur_idx].ptr<short>(), data_cost.ptr<short>(), elem_step,
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left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), stream);
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int new_idx = (cur_idx + 1) & 1;
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init_message(u[new_idx].ptr<short>(), d[new_idx].ptr<short>(), l[new_idx].ptr<short>(), r[new_idx].ptr<short>(),
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u[cur_idx].ptr<short>(), d[cur_idx].ptr<short>(), l[cur_idx].ptr<short>(), r[cur_idx].ptr<short>(),
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disp_selected_pyr[new_idx].ptr<short>(), disp_selected_pyr[cur_idx].ptr<short>(),
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data_cost_selected.ptr<short>(), data_cost.ptr<short>(), elem_step, rows_pyr[i],
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cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], stream);
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cur_idx = new_idx;
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}
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calc_all_iterations(u[cur_idx].ptr<short>(), d[cur_idx].ptr<short>(), l[cur_idx].ptr<short>(), r[cur_idx].ptr<short>(),
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data_cost_selected.ptr<short>(), disp_selected_pyr[cur_idx].ptr<short>(), elem_step,
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rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], iters_, stream);
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}
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}
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const int dtype = disp.fixedType() ? disp.type() : CV_16SC1;
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disp.create(rows, cols, dtype);
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GpuMat out = disp.getGpuMat();
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if (dtype != CV_16SC1)
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{
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outBuf_.create(rows, cols, CV_16SC1);
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out = outBuf_;
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}
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out.setTo(0, _stream);
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if (msg_type_ == CV_32F)
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{
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compute_disp(u[cur_idx].ptr<float>(), d[cur_idx].ptr<float>(), l[cur_idx].ptr<float>(), r[cur_idx].ptr<float>(),
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data_cost_selected.ptr<float>(), disp_selected_pyr[cur_idx].ptr<float>(), elem_step, out, nr_plane_pyr[0], stream);
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}
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else
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{
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compute_disp(u[cur_idx].ptr<short>(), d[cur_idx].ptr<short>(), l[cur_idx].ptr<short>(), r[cur_idx].ptr<short>(),
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data_cost_selected.ptr<short>(), disp_selected_pyr[cur_idx].ptr<short>(), elem_step, out, nr_plane_pyr[0], stream);
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}
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if (dtype != CV_16SC1)
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out.convertTo(disp, dtype, _stream);
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}
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void StereoCSBPImpl::compute(InputArray /*data*/, OutputArray /*disparity*/, Stream& /*stream*/)
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{
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CV_Error(Error::StsNotImplemented, "Not implemented");
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}
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}
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Ptr<cuda::StereoConstantSpaceBP> cv::cuda::createStereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane, int msg_type)
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{
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return makePtr<StereoCSBPImpl>(ndisp, iters, levels, nr_plane, msg_type);
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}
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void cv::cuda::StereoConstantSpaceBP::estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane)
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{
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ndisp = (int) ((float) width / 3.14f);
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if ((ndisp & 1) != 0)
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ndisp++;
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int mm = std::max(width, height);
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iters = mm / 100 + ((mm > 1200)? - 4 : 4);
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levels = (int)::log(static_cast<double>(mm)) * 2 / 3;
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if (levels == 0) levels++;
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nr_plane = (int) ((float) ndisp / std::pow(2.0, levels + 1));
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}
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#endif /* !defined (HAVE_CUDA) */
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