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https://github.com/opencv/opencv.git
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added gpu belief propagation stereo matching
This commit is contained in:
Vladislav Vinogradov
parent
dc69cf3ab4
commit
5bd128fac8
@ -43,6 +43,7 @@
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#ifndef __OPENCV_GPU_HPP__
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#define __OPENCV_GPU_HPP__
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#include <vector>
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#include "opencv2/core/core.hpp"
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#include "opencv2/gpu/devmem2d.hpp"
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@ -368,6 +369,42 @@ namespace cv
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private:
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GpuMat minSSD, leBuf, riBuf;
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};
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//////////////////////// StereoBeliefPropagation_GPU /////////////////////////
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class CV_EXPORTS StereoBeliefPropagation_GPU
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{
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public:
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enum { DEFAULT_NDISP = 64 };
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enum { DEFAULT_ITERS = 5 };
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enum { DEFAULT_LEVELS = 5 };
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static const float DEFAULT_DISC_COST;
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static const float DEFAULT_DATA_COST;
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static const float DEFAULT_LAMBDA_COST;
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explicit StereoBeliefPropagation_GPU(int ndisp = DEFAULT_NDISP,
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int iters = DEFAULT_ITERS,
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int levels = DEFAULT_LEVELS,
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float disc_cost = DEFAULT_DISC_COST,
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float data_cost = DEFAULT_DATA_COST,
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float lambda = DEFAULT_LAMBDA_COST);
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void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity);
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int ndisp;
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int iters;
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int levels;
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float disc_cost;
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float data_cost;
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float lambda;
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private:
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GpuMat u, d, l, r, u2, d2, l2, r2;
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std::vector<GpuMat> datas;
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};
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}
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}
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#include "opencv2/gpu/matrix_operations.hpp"
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179
modules/gpu/src/beliefpropagation_gpu.cpp
Normal file
179
modules/gpu/src/beliefpropagation_gpu.cpp
Normal file
@ -0,0 +1,179 @@
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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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//
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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::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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372
modules/gpu/src/cuda/beliefpropagation.cu
Normal file
372
modules/gpu/src/cuda/beliefpropagation.cu
Normal file
@ -0,0 +1,372 @@
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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,
|
||||
// this list of conditions and the following disclaimer.
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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
|
||||
// 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
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// 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 "opencv2/gpu/devmem2d.hpp"
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#include "safe_call.hpp"
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using namespace cv::gpu;
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static inline int divUp(int a, int b) { return (a % b == 0) ? a/b : a/b + 1; }
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#ifndef FLT_MAX
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#define FLT_MAX 3.402823466e+38F
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#endif
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typedef unsigned char uchar;
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namespace beliefpropagation_gpu
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{
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__constant__ int cndisp;
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__constant__ float cdisc_cost;
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__constant__ float cdata_cost;
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__constant__ float clambda;
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};
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///////////////////////////////////////////////////////////////
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////////////////// comp data /////////////////////////////////
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///////////////////////////////////////////////////////////////
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namespace beliefpropagation_gpu
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{
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__global__ void comp_data_kernel(uchar* l, uchar* r, size_t step, float* data, size_t data_step, int cols, int rows)
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{
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int x = blockIdx.x * blockDim.x + threadIdx.x;
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int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
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{
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uchar *ls = l + y * step + x;
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uchar *rs = r + y * step + x;
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float *ds = data + y * data_step + x;
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size_t disp_step = data_step * rows;
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for (int disp = 0; disp < cndisp; disp++)
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{
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if (x - disp >= 0)
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{
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int le = ls[0];
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int re = rs[-disp];
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float val = abs(le - re);
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ds[disp * disp_step] = clambda * fmin(val, cdata_cost);
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}
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else
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{
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ds[disp * disp_step] = cdata_cost;
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}
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}
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}
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}
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}
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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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{
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cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cndisp, &ndisp, sizeof(ndisp)) );
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cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdisc_cost, &disc_cost, sizeof(disc_cost)) );
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cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdata_cost, &data_cost, sizeof(data_cost)) );
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cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::clambda, &lambda, sizeof(lambda)) );
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}
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extern "C" void comp_data_caller(const DevMem2D& l, const DevMem2D& r, DevMem2D_<float> mdata)
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{
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dim3 threads(32, 8, 1);
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dim3 grid(1, 1, 1);
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grid.x = divUp(l.cols, threads.x);
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grid.y = divUp(l.rows, threads.y);
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beliefpropagation_gpu::comp_data_kernel<<<grid, threads>>>(l.ptr, r.ptr, l.step, mdata.ptr, mdata.step/sizeof(float), l.cols, l.rows);
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cudaSafeCall( cudaThreadSynchronize() );
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}
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}}}
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///////////////////////////////////////////////////////////////
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////////////////// data_step_down ////////////////////////////
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///////////////////////////////////////////////////////////////
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namespace beliefpropagation_gpu
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{
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__global__ void data_down_kernel(int dst_cols, int dst_rows, int src_rows, float *src, size_t src_step, float *dst, size_t dst_step)
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{
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int x = blockIdx.x * blockDim.x + threadIdx.x;
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int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < dst_cols && y < dst_rows)
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{
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const size_t dst_disp_step = dst_step * dst_rows;
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const size_t src_disp_step = src_step * src_rows;
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for (int d = 0; d < cndisp; ++d)
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{
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float dst_reg = src[d * src_disp_step + src_step * (2*y+0) + (2*x+0)];
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dst_reg += src[d * src_disp_step + src_step * (2*y+1) + (2*x+0)];
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dst_reg += src[d * src_disp_step + src_step * (2*y+0) + (2*x+1)];
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dst_reg += src[d * src_disp_step + src_step * (2*y+1) + (2*x+1)];
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dst[d * dst_disp_step + y * dst_step + x] = dst_reg;
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}
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}
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}
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}
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namespace cv { namespace gpu { namespace impl {
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extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2D_<float>& src, DevMem2D_<float> dst)
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{
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dim3 threads(32, 8, 1);
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dim3 grid(1, 1, 1);
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grid.x = divUp(dst_cols, threads.x);
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grid.y = divUp(dst_rows, threads.y);
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beliefpropagation_gpu::data_down_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, src.ptr, src.step/sizeof(float), dst.ptr, dst.step/sizeof(float));
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cudaSafeCall( cudaThreadSynchronize() );
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}
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}}}
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///////////////////////////////////////////////////////////////
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////////////////// level up messages ////////////////////////
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///////////////////////////////////////////////////////////////
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namespace beliefpropagation_gpu
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{
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__global__ void level_up_kernel(int dst_cols, int dst_rows, int src_rows, float *src, size_t src_step, float *dst, size_t dst_step)
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{
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int x = blockIdx.x * blockDim.x + threadIdx.x;
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int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < dst_cols && y < dst_rows)
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{
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const size_t dst_disp_step = dst_step * dst_rows;
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const size_t src_disp_step = src_step * src_rows;
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float *dstr = dst + y * dst_step + x;
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float *srcr = src + y/2 * src_step + x/2;
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|
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for (int d = 0; d < cndisp; ++d)
|
||||
dstr[d * dst_disp_step] = srcr[d * src_disp_step];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
namespace cv { namespace gpu { namespace impl {
|
||||
extern "C" void level_up(int dst_idx, int dst_cols, int dst_rows, int src_rows, DevMem2D_<float>* mu, DevMem2D_<float>* md, DevMem2D_<float>* ml, DevMem2D_<float>* mr)
|
||||
{
|
||||
dim3 threads(32, 8, 1);
|
||||
dim3 grid(1, 1, 1);
|
||||
|
||||
grid.x = divUp(dst_cols, threads.x);
|
||||
grid.y = divUp(dst_rows, threads.y);
|
||||
|
||||
int src_idx = (dst_idx + 1) & 1;
|
||||
|
||||
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, mu[src_idx].ptr, mu[src_idx].step/sizeof(float), mu[dst_idx].ptr, mu[dst_idx].step/sizeof(float));
|
||||
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, md[src_idx].ptr, md[src_idx].step/sizeof(float), md[dst_idx].ptr, md[dst_idx].step/sizeof(float));
|
||||
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, ml[src_idx].ptr, ml[src_idx].step/sizeof(float), ml[dst_idx].ptr, ml[dst_idx].step/sizeof(float));
|
||||
beliefpropagation_gpu::level_up_kernel<<<grid, threads>>>(dst_cols, dst_rows, src_rows, mr[src_idx].ptr, mr[src_idx].step/sizeof(float), mr[dst_idx].ptr, mr[dst_idx].step/sizeof(float));
|
||||
|
||||
cudaSafeCall( cudaThreadSynchronize() );
|
||||
}
|
||||
}}}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////
|
||||
///////////////// Calcs all iterations ///////////////////////
|
||||
///////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
namespace beliefpropagation_gpu
|
||||
{
|
||||
__device__ void calc_min_linear_penalty(float *dst, size_t step)
|
||||
{
|
||||
float prev = dst[0];
|
||||
float cur;
|
||||
for (int disp = 1; disp < cndisp; ++disp)
|
||||
{
|
||||
prev += 1.0f;
|
||||
cur = dst[step * disp];
|
||||
if (prev < cur)
|
||||
cur = prev;
|
||||
dst[step * disp] = prev = cur;
|
||||
}
|
||||
|
||||
prev = dst[(cndisp - 1) * step];
|
||||
for (int disp = cndisp - 2; disp >= 0; disp--)
|
||||
{
|
||||
prev += 1.0f;
|
||||
cur = dst[step * disp];
|
||||
if (prev < cur)
|
||||
cur = prev;
|
||||
dst[step * disp] = prev = cur;
|
||||
}
|
||||
}
|
||||
|
||||
__device__ void message(float *msg1, float *msg2, float *msg3, float *data, float *dst, size_t msg_disp_step, size_t data_disp_step)
|
||||
{
|
||||
float minimum = FLT_MAX;
|
||||
|
||||
for(int i = 0; i < cndisp; ++i)
|
||||
{
|
||||
float dst_reg = msg1[msg_disp_step * i] + msg2[msg_disp_step * i] + msg3[msg_disp_step * i] + data[data_disp_step * i];
|
||||
|
||||
if (dst_reg < minimum)
|
||||
minimum = dst_reg;
|
||||
|
||||
dst[msg_disp_step * i] = dst_reg;
|
||||
|
||||
}
|
||||
|
||||
calc_min_linear_penalty(dst, msg_disp_step);
|
||||
|
||||
minimum += cdisc_cost;
|
||||
|
||||
float sum = 0;
|
||||
for(int i = 0; i < cndisp; ++i)
|
||||
{
|
||||
float dst_reg = dst[msg_disp_step * i];
|
||||
if (dst_reg > minimum)
|
||||
{
|
||||
dst[msg_disp_step * i] = dst_reg = minimum;
|
||||
}
|
||||
sum += dst_reg;
|
||||
}
|
||||
sum /= cndisp;
|
||||
|
||||
for(int i = 0; i < cndisp; ++i)
|
||||
dst[msg_disp_step * i] -= sum;
|
||||
}
|
||||
|
||||
__global__ void one_iteration(int t, float* u, float *d, float *l, float *r, size_t msg_step, float *data, size_t data_step, int cols, int rows)
|
||||
{
|
||||
int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + t) & 1);
|
||||
|
||||
if ( (y > 0) && (y < rows - 1) && (x > 0) && (x < cols - 1))
|
||||
{
|
||||
float *us = u + y * msg_step + x;
|
||||
float *ds = d + y * msg_step + x;
|
||||
float *ls = l + y * msg_step + x;
|
||||
float *rs = r + y * msg_step + x;
|
||||
float *dt = data + y * data_step + x;
|
||||
size_t msg_disp_step = msg_step * rows;
|
||||
size_t data_disp_step = data_step * rows;
|
||||
|
||||
message(us + msg_step, ls + 1, rs - 1, dt, us, msg_disp_step, data_disp_step);
|
||||
message(ds - msg_step, ls + 1, rs - 1, dt, ds, msg_disp_step, data_disp_step);
|
||||
message(us + msg_step, ds - msg_step, rs - 1, dt, rs, msg_disp_step, data_disp_step);
|
||||
message(us + msg_step, ds - msg_step, ls + 1, dt, ls, msg_disp_step, data_disp_step);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
namespace cv { namespace gpu { namespace impl {
|
||||
extern "C" void call_all_iterations(int cols, int rows, int iters, DevMem2D_<float>& u, DevMem2D_<float>& d, DevMem2D_<float>& l, DevMem2D_<float>& r, const DevMem2D_<float>& data)
|
||||
{
|
||||
dim3 threads(32, 8, 1);
|
||||
dim3 grid(1, 1, 1);
|
||||
|
||||
grid.x = divUp(cols, threads.x << 1);
|
||||
grid.y = divUp(rows, threads.y);
|
||||
|
||||
for(int t = 0; t < iters; ++t)
|
||||
beliefpropagation_gpu::one_iteration<<<grid, threads>>>(t, u.ptr, d.ptr, l.ptr, r.ptr, u.step/sizeof(float), data.ptr, data.step/sizeof(float), cols, rows);
|
||||
|
||||
cudaSafeCall( cudaThreadSynchronize() );
|
||||
}
|
||||
}}}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////
|
||||
////////////////// Output caller /////////////////////////////
|
||||
///////////////////////////////////////////////////////////////
|
||||
|
||||
namespace beliefpropagation_gpu
|
||||
{
|
||||
__global__ void output(int cols, int rows, float *u, float *d, float *l, float *r, float* data, size_t step, unsigned char *disp, size_t res_step)
|
||||
{
|
||||
int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
|
||||
if (y > 0 && y < rows - 1)
|
||||
if (x > 0 && x < cols - 1)
|
||||
{
|
||||
float *us = u + (y + 1) * step + x;
|
||||
float *ds = d + (y - 1) * step + x;
|
||||
float *ls = l + y * step + (x + 1);
|
||||
float *rs = r + y * step + (x - 1);
|
||||
float *dt = data + y * step + x;
|
||||
|
||||
size_t disp_step = rows * step;
|
||||
|
||||
int best = 0;
|
||||
float best_val = FLT_MAX;
|
||||
for (int d = 0; d < cndisp; ++d)
|
||||
{
|
||||
float val = us[d * disp_step] + ds[d * disp_step] + ls[d * disp_step] + rs[d * disp_step] + dt[d * disp_step];
|
||||
|
||||
if (val < best_val)
|
||||
{
|
||||
best_val = val;
|
||||
best = d;
|
||||
}
|
||||
}
|
||||
|
||||
disp[res_step * y + x] = best & 0xFF;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
namespace cv { namespace gpu { namespace impl {
|
||||
extern "C" void output_caller(const DevMem2D_<float>& u, const DevMem2D_<float>& d, const DevMem2D_<float>& l, const DevMem2D_<float>& r, const DevMem2D_<float>& data, DevMem2D disp)
|
||||
{
|
||||
dim3 threads(32, 8, 1);
|
||||
dim3 grid(1, 1, 1);
|
||||
|
||||
grid.x = divUp(disp.cols, threads.x);
|
||||
grid.y = divUp(disp.rows, threads.y);
|
||||
|
||||
beliefpropagation_gpu::output<<<grid, threads>>>(disp.cols, disp.rows, u.ptr, d.ptr, l.ptr, r.ptr, data.ptr, u.step/sizeof(float), disp.ptr, disp.step);
|
||||
cudaSafeCall( cudaThreadSynchronize() );
|
||||
}
|
||||
}}}
|
||||
@ -52,18 +52,18 @@ void cv::gpu::CudaStream::create() { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::release() { throw_nogpu(); }
|
||||
cv::gpu::CudaStream::CudaStream() : impl(0) { throw_nogpu(); }
|
||||
cv::gpu::CudaStream::~CudaStream() { throw_nogpu(); }
|
||||
cv::gpu::CudaStream::CudaStream(const CudaStream& stream) { throw_nogpu(); }
|
||||
CudaStream& cv::gpu::CudaStream::operator=(const CudaStream& stream) { throw_nogpu(); return *this; }
|
||||
cv::gpu::CudaStream::CudaStream(const CudaStream& /*stream*/) { throw_nogpu(); }
|
||||
CudaStream& cv::gpu::CudaStream::operator=(const CudaStream& /*stream*/) { throw_nogpu(); return *this; }
|
||||
bool cv::gpu::CudaStream::queryIfComplete() { throw_nogpu(); return true; }
|
||||
void cv::gpu::CudaStream::waitForCompletion() { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueDownload(const GpuMat& src, Mat& dst) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueDownload(const GpuMat& src, MatPL& dst) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueUpload(const MatPL& src, GpuMat& dst) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueUpload(const Mat& src, GpuMat& dst) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueCopy(const GpuMat& src, GpuMat& dst) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& src, Scalar val) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& src, Scalar val, const GpuMat& mask) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a, double b) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueDownload(const GpuMat& /*src*/, Mat& /*dst*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueDownload(const GpuMat& /*src*/, MatPL& /*dst*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueUpload(const MatPL& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueUpload(const Mat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueCopy(const GpuMat& /*src*/, GpuMat& /*dst*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& /*src*/, Scalar /*val*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueMemSet(const GpuMat& /*src*/, Scalar /*val*/, const GpuMat& /*mask*/) { throw_nogpu(); }
|
||||
void cv::gpu::CudaStream::enqueueConvert(const GpuMat& /*src*/, GpuMat& /*dst*/, int /*type*/, double /*a*/, double /*b*/) { throw_nogpu(); }
|
||||
|
||||
#else /* !defined (HAVE_CUDA) */
|
||||
|
||||
|
||||
@ -54,13 +54,12 @@
|
||||
#include <limits>
|
||||
|
||||
#include "opencv2/gpu/gpu.hpp"
|
||||
#include "opencv2/gpu/stream_accessor.hpp"
|
||||
|
||||
|
||||
#if defined(HAVE_CUDA)
|
||||
|
||||
#include "cuda_shared.hpp"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "opencv2/gpu/stream_accessor.hpp"
|
||||
|
||||
#else /* defined(HAVE_CUDA) */
|
||||
|
||||
|
||||
@ -98,7 +98,7 @@ bool cv::gpu::StereoBM_GPU::checkIfGpuCallReasonable()
|
||||
return false;
|
||||
}
|
||||
|
||||
void stereo_gpu_operator ( GpuMat& minSSD, GpuMat& leBuf, GpuMat& riBuf, int preset, int ndisp, int winSize, float avergeTexThreshold, const GpuMat& left, const GpuMat& right, GpuMat& disparity, const cudaStream_t & stream)
|
||||
static void stereo_bm_gpu_operator ( GpuMat& minSSD, GpuMat& leBuf, GpuMat& riBuf, int preset, int ndisp, int winSize, float avergeTexThreshold, const GpuMat& left, const GpuMat& right, GpuMat& disparity, const cudaStream_t & stream)
|
||||
{
|
||||
CV_DbgAssert(left.rows == right.rows && left.cols == right.cols);
|
||||
CV_DbgAssert(left.type() == CV_8UC1);
|
||||
@ -131,12 +131,12 @@ void stereo_gpu_operator ( GpuMat& minSSD, GpuMat& leBuf, GpuMat& riBuf, int
|
||||
|
||||
void cv::gpu::StereoBM_GPU::operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity)
|
||||
{
|
||||
::stereo_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, 0);
|
||||
::stereo_bm_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, 0);
|
||||
}
|
||||
|
||||
void cv::gpu::StereoBM_GPU::operator() ( const GpuMat& left, const GpuMat& right, GpuMat& disparity, const CudaStream& stream)
|
||||
{
|
||||
::stereo_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, StreamAccessor::getStream(stream));
|
||||
::stereo_bm_gpu_operator(minSSD, leBuf, riBuf, preset, ndisp, winSize, avergeTexThreshold, left, right, disparity, StreamAccessor::getStream(stream));
|
||||
}
|
||||
|
||||
#endif /* !defined (HAVE_CUDA) */
|
||||
|
||||
Loading…
Reference in New Issue
Block a user