added gpu belief propagation stereo matching
This commit is contained in:
Vladislav Vinogradov
179
modules/gpu/src/beliefpropagation_gpu.cpp
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179
modules/gpu/src/beliefpropagation_gpu.cpp
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/*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 GpuMaterials 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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// This software is provided by the copyright holders and contributors "as is" and
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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::gpu;
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using namespace std;
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const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_DISC_COST = 1.7f;
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const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_DATA_COST = 10.0f;
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const float cv::gpu::StereoBeliefPropagation_GPU::DEFAULT_LAMBDA_COST = 0.07f;
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#if !defined (HAVE_CUDA)
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cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int, int, int, float, float, float) { throw_nogpu(); }
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void cv::gpu::StereoBeliefPropagation_GPU::operator() (const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
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#else /* !defined (HAVE_CUDA) */
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typedef DevMem2D_<float> DevMem2Df;
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namespace cv { namespace gpu { namespace impl {
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extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda);
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extern "C" void comp_data_caller(const DevMem2D& l, const DevMem2D& r, DevMem2Df mdata);
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extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2Df& src, DevMem2Df dst);
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extern "C" void level_up(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2Df* mu, DevMem2Df* md, DevMem2Df* ml, DevMem2Df* mr);
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extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2Df& u, DevMem2Df& d, DevMem2Df& l, DevMem2Df& r, const DevMem2Df& data);
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extern "C" void output_caller(const DevMem2Df& u, const DevMem2Df& d, const DevMem2Df& l, const DevMem2Df& r, const DevMem2Df& data, DevMem2D disp);
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}}}
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cv::gpu::StereoBeliefPropagation_GPU::StereoBeliefPropagation_GPU(int ndisp_, int iters_, int levels_, float disc_cost_, float data_cost_, float lambda_)
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: ndisp(ndisp_), iters(iters_), levels(levels_), disc_cost(disc_cost_), data_cost(data_cost_), lambda(lambda_), datas(levels_)
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{
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const int max_supported_ndisp = 1 << (sizeof(unsigned char) * 8);
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CV_Assert(0 < ndisp && ndisp <= max_supported_ndisp);
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CV_Assert(ndisp % 8 == 0);
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}
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void cv::gpu::StereoBeliefPropagation_GPU::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp)
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{
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CV_DbgAssert(left.cols == right.cols && left.rows == right.rows && left.type() == right.type() && left.type() == CV_8U);
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const Scalar zero = Scalar::all(0);
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int rows = left.rows;
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int cols = left.cols;
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int divisor = (int)pow(2.f, levels - 1.0f);
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int lowest_cols = cols / divisor;
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int lowest_rows = rows / divisor;
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const int min_image_dim_size = 20;
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CV_Assert(min(lowest_cols, lowest_rows) > min_image_dim_size);
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disp.create(rows, cols, CV_8U);
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u.create(rows * ndisp, cols, CV_32F);
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d.create(rows * ndisp, cols, CV_32F);
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l.create(rows * ndisp, cols, CV_32F);
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r.create(rows * ndisp, cols, CV_32F);
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if (levels & 1)
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{
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u = zero; //can clear less area
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d = zero;
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l = zero;
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r = zero;
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}
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if (levels > 1)
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{
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int less_rows = (rows + 1) / 2;
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int less_cols = (cols + 1) / 2;
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u2.create(less_rows * ndisp, less_cols, CV_32F);
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d2.create(less_rows * ndisp, less_cols, CV_32F);
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l2.create(less_rows * ndisp, less_cols, CV_32F);
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r2.create(less_rows * ndisp, less_cols, CV_32F);
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if ((levels & 1) == 0)
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{
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u2 = zero;
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d2 = zero;
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l2 = zero;
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r2 = zero;
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}
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}
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impl::load_constants(ndisp, disc_cost, data_cost, lambda);
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vector<int> cols_all(levels);
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vector<int> rows_all(levels);
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vector<int> iters_all(levels);
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cols_all[0] = cols;
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rows_all[0] = rows;
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iters_all[0] = iters;
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datas[0].create(rows * ndisp, cols, CV_32F);
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//datas[0] = Scalar(data_cost); //DOTO did in kernel, but not sure if correct
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impl::comp_data_caller(left, right, datas.front());
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for (int i = 1; i < levels; i++)
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{
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cols_all[i] = (cols_all[i-1] + 1)/2;
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rows_all[i] = (rows_all[i-1] + 1)/2;
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// this is difference from Felzenszwalb algorithm
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// we reduce iters num for each next level
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iters_all[i] = max(2 * iters_all[i-1] / 3, 1);
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datas[i].create(rows_all[i] * ndisp, cols_all[i], CV_32F);
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impl::data_down_kernel_caller(cols_all[i], rows_all[i], rows_all[i-1], datas[i-1], datas[i]);
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}
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DevMem2D_<float> mus[] = {u, u2};
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DevMem2D_<float> mds[] = {d, d2};
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DevMem2D_<float> mrs[] = {r, r2};
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DevMem2D_<float> mls[] = {l, l2};
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int mem_idx = (levels & 1) ? 0 : 1;
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for (int i = levels - 1; i >= 0; i--) // for lower level we have already computed messages by setting to zero
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{
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if (i != levels - 1)
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impl::level_up(mem_idx, cols_all[i], rows_all[i], rows_all[i+1], mus, mds, mls, mrs);
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impl::call_all_iterations(cols_all[i], rows_all[i], iters_all[i], mus[mem_idx], mds[mem_idx], mls[mem_idx], mrs[mem_idx], datas[i]);
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mem_idx = (mem_idx + 1) & 1;
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}
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impl::output_caller(u, d, l, r, datas.front(), disp);
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}
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#endif /* !defined (HAVE_CUDA) */
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