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896 lines
38 KiB
Plaintext
896 lines
38 KiB
Plaintext
/*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 "internal_shared.hpp"
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#include "opencv2/gpu/device/saturate_cast.hpp"
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#include "opencv2/gpu/device/limits.hpp"
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using namespace cv::gpu;
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using namespace cv::gpu::device;
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namespace cv { namespace gpu { namespace csbp
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{
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///////////////////////////////////////////////////////////////
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/////////////////////// load constants ////////////////////////
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///////////////////////////////////////////////////////////////
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__constant__ int cndisp;
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__constant__ float cmax_data_term;
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__constant__ float cdata_weight;
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__constant__ float cmax_disc_term;
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__constant__ float cdisc_single_jump;
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__constant__ int cth;
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__constant__ size_t cimg_step;
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__constant__ size_t cmsg_step1;
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__constant__ size_t cmsg_step2;
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__constant__ size_t cdisp_step1;
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__constant__ size_t cdisp_step2;
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__constant__ uchar* cleft;
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__constant__ uchar* cright;
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__constant__ uchar* ctemp;
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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 DevMem2D& left, const DevMem2D& right, const DevMem2D& temp)
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{
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cudaSafeCall( cudaMemcpyToSymbol(cndisp, &ndisp, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(cmax_data_term, &max_data_term, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(cdata_weight, &data_weight, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(cmax_disc_term, &max_disc_term, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(cdisc_single_jump, &disc_single_jump, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(cth, &min_disp_th, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(cimg_step, &left.step, sizeof(size_t)) );
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cudaSafeCall( cudaMemcpyToSymbol(cleft, &left.data, sizeof(left.data)) );
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cudaSafeCall( cudaMemcpyToSymbol(cright, &right.data, sizeof(right.data)) );
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cudaSafeCall( cudaMemcpyToSymbol(ctemp, &temp.data, sizeof(temp.data)) );
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}
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///////////////////////////////////////////////////////////////
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/////////////////////// init data cost ////////////////////////
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///////////////////////////////////////////////////////////////
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template <int channels> struct DataCostPerPixel;
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template <> struct DataCostPerPixel<1>
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{
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static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
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{
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return fmin(cdata_weight * abs((int)*left - *right), cdata_weight * cmax_data_term);
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}
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};
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template <> struct DataCostPerPixel<3>
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{
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static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
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{
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float tb = 0.114f * abs((int)left[0] - right[0]);
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float tg = 0.587f * abs((int)left[1] - right[1]);
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float tr = 0.299f * abs((int)left[2] - right[2]);
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return fmin(cdata_weight * (tr + tg + tb), cdata_weight * cmax_data_term);
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}
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};
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template <> struct DataCostPerPixel<4>
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{
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static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
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{
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uchar4 l = *((const uchar4*)left);
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uchar4 r = *((const uchar4*)right);
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float tb = 0.114f * abs((int)l.x - r.x);
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float tg = 0.587f * abs((int)l.y - r.y);
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float tr = 0.299f * abs((int)l.z - r.z);
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return fmin(cdata_weight * (tr + tg + tb), cdata_weight * cmax_data_term);
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}
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};
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template <typename T>
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__global__ void get_first_k_initial_global(T* data_cost_selected_, T *selected_disp_pyr, int h, int w, int nr_plane)
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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 < h && x < w)
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{
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T* selected_disparity = selected_disp_pyr + y * cmsg_step1 + x;
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T* data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
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T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
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for(int i = 0; i < nr_plane; i++)
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{
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T minimum = numeric_limits<T>::max();
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int id = 0;
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for(int d = 0; d < cndisp; d++)
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{
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T cur = data_cost[d * cdisp_step1];
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if(cur < minimum)
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{
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minimum = cur;
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id = d;
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}
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}
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data_cost_selected[i * cdisp_step1] = minimum;
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selected_disparity[i * cdisp_step1] = id;
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data_cost [id * cdisp_step1] = numeric_limits<T>::max();
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}
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}
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}
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template <typename T>
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__global__ void get_first_k_initial_local(T* data_cost_selected_, T* selected_disp_pyr, int h, int w, int nr_plane)
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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 < h && x < w)
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{
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T* selected_disparity = selected_disp_pyr + y * cmsg_step1 + x;
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T* data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
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T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
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int nr_local_minimum = 0;
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T prev = data_cost[0 * cdisp_step1];
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T cur = data_cost[1 * cdisp_step1];
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T next = data_cost[2 * cdisp_step1];
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for (int d = 1; d < cndisp - 1 && nr_local_minimum < nr_plane; d++)
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{
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if (cur < prev && cur < next)
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{
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data_cost_selected[nr_local_minimum * cdisp_step1] = cur;
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selected_disparity[nr_local_minimum * cdisp_step1] = d;
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data_cost[d * cdisp_step1] = numeric_limits<T>::max();
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nr_local_minimum++;
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}
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prev = cur;
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cur = next;
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next = data_cost[(d + 1) * cdisp_step1];
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}
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for (int i = nr_local_minimum; i < nr_plane; i++)
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{
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T minimum = numeric_limits<T>::max();
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int id = 0;
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for (int d = 0; d < cndisp; d++)
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{
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cur = data_cost[d * cdisp_step1];
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if (cur < minimum)
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{
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minimum = cur;
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id = d;
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}
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}
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data_cost_selected[i * cdisp_step1] = minimum;
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selected_disparity[i * cdisp_step1] = id;
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data_cost[id * cdisp_step1] = numeric_limits<T>::max();
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}
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}
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}
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template <typename T, int channels>
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__global__ void init_data_cost(int h, int w, int level)
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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 < h && x < w)
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{
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int y0 = y << level;
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int yt = (y + 1) << level;
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int x0 = x << level;
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int xt = (x + 1) << level;
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T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
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for(int d = 0; d < cndisp; ++d)
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{
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float val = 0.0f;
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for(int yi = y0; yi < yt; yi++)
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{
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for(int xi = x0; xi < xt; xi++)
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{
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int xr = xi - d;
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if(d < cth || xr < 0)
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val += cdata_weight * cmax_data_term;
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else
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{
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const uchar* lle = cleft + yi * cimg_step + xi * channels;
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const uchar* lri = cright + yi * cimg_step + xr * channels;
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val += DataCostPerPixel<channels>::compute(lle, lri);
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}
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}
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}
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data_cost[cdisp_step1 * d] = saturate_cast<T>(val);
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}
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}
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}
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template <typename T, int winsz, int channels>
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__global__ void init_data_cost_reduce(int level, int rows, int cols, int h)
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{
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int x_out = blockIdx.x;
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int y_out = blockIdx.y % h;
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int d = (blockIdx.y / h) * blockDim.z + threadIdx.z;
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int tid = threadIdx.x;
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if (d < cndisp)
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{
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int x0 = x_out << level;
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int y0 = y_out << level;
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int len = min(y0 + winsz, rows) - y0;
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float val = 0.0f;
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if (x0 + tid < cols)
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{
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if (x0 + tid - d < 0 || d < cth)
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val = cdata_weight * cmax_data_term * len;
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else
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{
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const uchar* lle = cleft + y0 * cimg_step + channels * (x0 + tid );
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const uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - d);
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for(int y = 0; y < len; ++y)
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{
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val += DataCostPerPixel<channels>::compute(lle, lri);
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lle += cimg_step;
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lri += cimg_step;
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}
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}
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}
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extern __shared__ float smem[];
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float* dline = smem + winsz * threadIdx.z;
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dline[tid] = val;
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__syncthreads();
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if (winsz >= 256) { if (tid < 128) { dline[tid] += dline[tid + 128]; } __syncthreads(); }
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if (winsz >= 128) { if (tid < 64) { dline[tid] += dline[tid + 64]; } __syncthreads(); }
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volatile float* vdline = smem + winsz * threadIdx.z;
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if (winsz >= 64) if (tid < 32) vdline[tid] += vdline[tid + 32];
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if (winsz >= 32) if (tid < 16) vdline[tid] += vdline[tid + 16];
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if (winsz >= 16) if (tid < 8) vdline[tid] += vdline[tid + 8];
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if (winsz >= 8) if (tid < 4) vdline[tid] += vdline[tid + 4];
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if (winsz >= 4) if (tid < 2) vdline[tid] += vdline[tid + 2];
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if (winsz >= 2) if (tid < 1) vdline[tid] += vdline[tid + 1];
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T* data_cost = (T*)ctemp + y_out * cmsg_step1 + x_out;
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if (tid == 0)
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data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
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}
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}
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template <typename T>
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void init_data_cost_caller_(int /*rows*/, int /*cols*/, int h, int w, int level, int /*ndisp*/, int channels, cudaStream_t stream)
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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(w, threads.x);
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grid.y = divUp(h, threads.y);
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switch (channels)
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{
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case 1: init_data_cost<T, 1><<<grid, threads, 0, stream>>>(h, w, level); break;
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case 3: init_data_cost<T, 3><<<grid, threads, 0, stream>>>(h, w, level); break;
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case 4: init_data_cost<T, 4><<<grid, threads, 0, stream>>>(h, w, level); break;
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default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
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}
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}
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template <typename T, int winsz>
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void init_data_cost_reduce_caller_(int rows, int cols, int h, int w, int level, int ndisp, int channels, cudaStream_t stream)
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{
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const int threadsNum = 256;
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const size_t smem_size = threadsNum * sizeof(float);
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dim3 threads(winsz, 1, threadsNum / winsz);
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dim3 grid(w, h, 1);
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grid.y *= divUp(ndisp, threads.z);
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switch (channels)
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{
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case 1: init_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
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case 3: init_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
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case 4: init_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
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default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
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}
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}
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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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{
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typedef void (*InitDataCostCaller)(int cols, int rows, int w, int h, int level, int ndisp, int channels, cudaStream_t stream);
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static const InitDataCostCaller init_data_cost_callers[] =
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{
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init_data_cost_caller_<T>, init_data_cost_caller_<T>, init_data_cost_reduce_caller_<T, 4>,
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init_data_cost_reduce_caller_<T, 8>, init_data_cost_reduce_caller_<T, 16>, init_data_cost_reduce_caller_<T, 32>,
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init_data_cost_reduce_caller_<T, 64>, init_data_cost_reduce_caller_<T, 128>, init_data_cost_reduce_caller_<T, 256>
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};
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size_t disp_step = msg_step * h;
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cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step, sizeof(size_t)) );
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cudaSafeCall( cudaMemcpyToSymbol(cmsg_step1, &msg_step, sizeof(size_t)) );
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init_data_cost_callers[level](rows, cols, h, w, level, ndisp, channels, stream);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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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(w, threads.x);
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grid.y = divUp(h, threads.y);
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if (use_local_init_data_cost == true)
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get_first_k_initial_local<<<grid, threads, 0, stream>>> (data_cost_selected, disp_selected_pyr, h, w, nr_plane);
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else
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get_first_k_initial_global<<<grid, threads, 0, stream>>>(data_cost_selected, disp_selected_pyr, h, w, nr_plane);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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template void init_data_cost(int rows, int cols, short* disp_selected_pyr, short* 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 void init_data_cost(int rows, int cols, float* disp_selected_pyr, float* 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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///////////////////////////////////////////////////////////////
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////////////////////// compute data cost //////////////////////
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///////////////////////////////////////////////////////////////
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template <typename T, int channels>
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__global__ void compute_data_cost(const T* selected_disp_pyr, T* data_cost_, int h, int w, int level, int nr_plane)
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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 < h && x < w)
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{
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int y0 = y << level;
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int yt = (y + 1) << level;
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int x0 = x << level;
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int xt = (x + 1) << level;
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const T* selected_disparity = selected_disp_pyr + y/2 * cmsg_step2 + x/2;
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T* data_cost = data_cost_ + y * cmsg_step1 + x;
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for(int d = 0; d < nr_plane; d++)
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{
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float val = 0.0f;
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for(int yi = y0; yi < yt; yi++)
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{
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for(int xi = x0; xi < xt; xi++)
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{
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int sel_disp = selected_disparity[d * cdisp_step2];
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int xr = xi - sel_disp;
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if (xr < 0 || sel_disp < cth)
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val += cdata_weight * cmax_data_term;
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else
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{
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const uchar* left_x = cleft + yi * cimg_step + xi * channels;
|
|
const uchar* right_x = cright + yi * cimg_step + xr * channels;
|
|
|
|
val += DataCostPerPixel<channels>::compute(left_x, right_x);
|
|
}
|
|
}
|
|
}
|
|
data_cost[cdisp_step1 * d] = saturate_cast<T>(val);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, int winsz, int channels>
|
|
__global__ void compute_data_cost_reduce(const T* selected_disp_pyr, T* data_cost_, int level, int rows, int cols, int h, int nr_plane)
|
|
{
|
|
int x_out = blockIdx.x;
|
|
int y_out = blockIdx.y % h;
|
|
int d = (blockIdx.y / h) * blockDim.z + threadIdx.z;
|
|
|
|
int tid = threadIdx.x;
|
|
|
|
const T* selected_disparity = selected_disp_pyr + y_out/2 * cmsg_step2 + x_out/2;
|
|
T* data_cost = data_cost_ + y_out * cmsg_step1 + x_out;
|
|
|
|
if (d < nr_plane)
|
|
{
|
|
int sel_disp = selected_disparity[d * cdisp_step2];
|
|
|
|
int x0 = x_out << level;
|
|
int y0 = y_out << level;
|
|
|
|
int len = min(y0 + winsz, rows) - y0;
|
|
|
|
float val = 0.0f;
|
|
if (x0 + tid < cols)
|
|
{
|
|
if (x0 + tid - sel_disp < 0 || sel_disp < cth)
|
|
val = cdata_weight * cmax_data_term * len;
|
|
else
|
|
{
|
|
const uchar* lle = cleft + y0 * cimg_step + channels * (x0 + tid );
|
|
const uchar* lri = cright + y0 * cimg_step + channels * (x0 + tid - sel_disp);
|
|
|
|
for(int y = 0; y < len; ++y)
|
|
{
|
|
val += DataCostPerPixel<channels>::compute(lle, lri);
|
|
|
|
lle += cimg_step;
|
|
lri += cimg_step;
|
|
}
|
|
}
|
|
}
|
|
|
|
extern __shared__ float smem[];
|
|
float* dline = smem + winsz * threadIdx.z;
|
|
|
|
dline[tid] = val;
|
|
|
|
__syncthreads();
|
|
|
|
if (winsz >= 256) { if (tid < 128) { dline[tid] += dline[tid + 128]; } __syncthreads(); }
|
|
if (winsz >= 128) { if (tid < 64) { dline[tid] += dline[tid + 64]; } __syncthreads(); }
|
|
|
|
volatile float* vdline = smem + winsz * threadIdx.z;
|
|
|
|
if (winsz >= 64) if (tid < 32) vdline[tid] += vdline[tid + 32];
|
|
if (winsz >= 32) if (tid < 16) vdline[tid] += vdline[tid + 16];
|
|
if (winsz >= 16) if (tid < 8) vdline[tid] += vdline[tid + 8];
|
|
if (winsz >= 8) if (tid < 4) vdline[tid] += vdline[tid + 4];
|
|
if (winsz >= 4) if (tid < 2) vdline[tid] += vdline[tid + 2];
|
|
if (winsz >= 2) if (tid < 1) vdline[tid] += vdline[tid + 1];
|
|
|
|
if (tid == 0)
|
|
data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
void compute_data_cost_caller_(const T* disp_selected_pyr, T* data_cost, int /*rows*/, int /*cols*/,
|
|
int h, int w, int level, int nr_plane, int channels, cudaStream_t stream)
|
|
{
|
|
dim3 threads(32, 8, 1);
|
|
dim3 grid(1, 1, 1);
|
|
|
|
grid.x = divUp(w, threads.x);
|
|
grid.y = divUp(h, threads.y);
|
|
|
|
switch(channels)
|
|
{
|
|
case 1: compute_data_cost<T, 1><<<grid, threads, 0, stream>>>(disp_selected_pyr, data_cost, h, w, level, nr_plane); break;
|
|
case 3: compute_data_cost<T, 3><<<grid, threads, 0, stream>>>(disp_selected_pyr, data_cost, h, w, level, nr_plane); break;
|
|
case 4: compute_data_cost<T, 4><<<grid, threads, 0, stream>>>(disp_selected_pyr, data_cost, h, w, level, nr_plane); break;
|
|
default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
|
|
}
|
|
}
|
|
|
|
template <typename T, int winsz>
|
|
void compute_data_cost_reduce_caller_(const T* disp_selected_pyr, T* data_cost, int rows, int cols,
|
|
int h, int w, int level, int nr_plane, int channels, cudaStream_t stream)
|
|
{
|
|
const int threadsNum = 256;
|
|
const size_t smem_size = threadsNum * sizeof(float);
|
|
|
|
dim3 threads(winsz, 1, threadsNum / winsz);
|
|
dim3 grid(w, h, 1);
|
|
grid.y *= divUp(nr_plane, threads.z);
|
|
|
|
switch (channels)
|
|
{
|
|
case 1: compute_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane); break;
|
|
case 3: compute_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane); break;
|
|
case 4: compute_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane); break;
|
|
default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
|
|
}
|
|
}
|
|
|
|
template<class T>
|
|
void compute_data_cost(const T* disp_selected_pyr, T* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, cudaStream_t stream)
|
|
{
|
|
typedef void (*ComputeDataCostCaller)(const T* disp_selected_pyr, T* data_cost, int rows, int cols,
|
|
int h, int w, int level, int nr_plane, int channels, cudaStream_t stream);
|
|
|
|
static const ComputeDataCostCaller callers[] =
|
|
{
|
|
compute_data_cost_caller_<T>, compute_data_cost_caller_<T>, compute_data_cost_reduce_caller_<T, 4>,
|
|
compute_data_cost_reduce_caller_<T, 8>, compute_data_cost_reduce_caller_<T, 16>, compute_data_cost_reduce_caller_<T, 32>,
|
|
compute_data_cost_reduce_caller_<T, 64>, compute_data_cost_reduce_caller_<T, 128>, compute_data_cost_reduce_caller_<T, 256>
|
|
};
|
|
|
|
size_t disp_step1 = msg_step1 * h;
|
|
size_t disp_step2 = msg_step2 * h2;
|
|
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step1, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step2, &disp_step2, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step1, &msg_step1, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step2, &msg_step2, sizeof(size_t)) );
|
|
|
|
callers[level](disp_selected_pyr, data_cost, rows, cols, h, w, level, nr_plane, channels, stream);
|
|
cudaSafeCall( cudaGetLastError() );
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
template void compute_data_cost(const short* disp_selected_pyr, short* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, cudaStream_t stream);
|
|
|
|
template void compute_data_cost(const float* disp_selected_pyr, float* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int rows, int cols, int h, int w, int h2, int level, int nr_plane, int channels, cudaStream_t stream);
|
|
|
|
|
|
///////////////////////////////////////////////////////////////
|
|
//////////////////////// init message /////////////////////////
|
|
///////////////////////////////////////////////////////////////
|
|
|
|
|
|
template <typename T>
|
|
__device__ void get_first_k_element_increase(T* u_new, T* d_new, T* l_new, T* r_new,
|
|
const T* u_cur, const T* d_cur, const T* l_cur, const T* r_cur,
|
|
T* data_cost_selected, T* disparity_selected_new, T* data_cost_new,
|
|
const T* data_cost_cur, const T* disparity_selected_cur,
|
|
int nr_plane, int nr_plane2)
|
|
{
|
|
for(int i = 0; i < nr_plane; i++)
|
|
{
|
|
T minimum = numeric_limits<T>::max();
|
|
int id = 0;
|
|
for(int j = 0; j < nr_plane2; j++)
|
|
{
|
|
T cur = data_cost_new[j * cdisp_step1];
|
|
if(cur < minimum)
|
|
{
|
|
minimum = cur;
|
|
id = j;
|
|
}
|
|
}
|
|
|
|
data_cost_selected[i * cdisp_step1] = data_cost_cur[id * cdisp_step1];
|
|
disparity_selected_new[i * cdisp_step1] = disparity_selected_cur[id * cdisp_step2];
|
|
|
|
u_new[i * cdisp_step1] = u_cur[id * cdisp_step2];
|
|
d_new[i * cdisp_step1] = d_cur[id * cdisp_step2];
|
|
l_new[i * cdisp_step1] = l_cur[id * cdisp_step2];
|
|
r_new[i * cdisp_step1] = r_cur[id * cdisp_step2];
|
|
|
|
data_cost_new[id * cdisp_step1] = numeric_limits<T>::max();
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
__global__ void init_message(T* u_new_, T* d_new_, T* l_new_, T* r_new_,
|
|
const T* u_cur_, const T* d_cur_, const T* l_cur_, const T* r_cur_,
|
|
T* selected_disp_pyr_new, const T* selected_disp_pyr_cur,
|
|
T* data_cost_selected_, const T* data_cost_,
|
|
int h, int w, int nr_plane, int h2, int w2, int nr_plane2)
|
|
{
|
|
int x = blockIdx.x * blockDim.x + threadIdx.x;
|
|
int y = blockIdx.y * blockDim.y + threadIdx.y;
|
|
|
|
if (y < h && x < w)
|
|
{
|
|
const T* u_cur = u_cur_ + min(h2-1, y/2 + 1) * cmsg_step2 + x/2;
|
|
const T* d_cur = d_cur_ + max(0, y/2 - 1) * cmsg_step2 + x/2;
|
|
const T* l_cur = l_cur_ + y/2 * cmsg_step2 + min(w2-1, x/2 + 1);
|
|
const T* r_cur = r_cur_ + y/2 * cmsg_step2 + max(0, x/2 - 1);
|
|
|
|
T* data_cost_new = (T*)ctemp + y * cmsg_step1 + x;
|
|
|
|
const T* disparity_selected_cur = selected_disp_pyr_cur + y/2 * cmsg_step2 + x/2;
|
|
const T* data_cost = data_cost_ + y * cmsg_step1 + x;
|
|
|
|
for(int d = 0; d < nr_plane2; d++)
|
|
{
|
|
int idx2 = d * cdisp_step2;
|
|
|
|
T val = data_cost[d * cdisp_step1] + u_cur[idx2] + d_cur[idx2] + l_cur[idx2] + r_cur[idx2];
|
|
data_cost_new[d * cdisp_step1] = val;
|
|
}
|
|
|
|
T* data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
|
|
T* disparity_selected_new = selected_disp_pyr_new + y * cmsg_step1 + x;
|
|
|
|
T* u_new = u_new_ + y * cmsg_step1 + x;
|
|
T* d_new = d_new_ + y * cmsg_step1 + x;
|
|
T* l_new = l_new_ + y * cmsg_step1 + x;
|
|
T* r_new = r_new_ + y * cmsg_step1 + x;
|
|
|
|
u_cur = u_cur_ + y/2 * cmsg_step2 + x/2;
|
|
d_cur = d_cur_ + y/2 * cmsg_step2 + x/2;
|
|
l_cur = l_cur_ + y/2 * cmsg_step2 + x/2;
|
|
r_cur = r_cur_ + y/2 * cmsg_step2 + x/2;
|
|
|
|
get_first_k_element_increase(u_new, d_new, l_new, r_new, u_cur, d_cur, l_cur, r_cur,
|
|
data_cost_selected, disparity_selected_new, data_cost_new,
|
|
data_cost, disparity_selected_cur, nr_plane, nr_plane2);
|
|
}
|
|
}
|
|
|
|
|
|
template<class T>
|
|
void init_message(T* u_new, T* d_new, T* l_new, T* r_new,
|
|
const T* u_cur, const T* d_cur, const T* l_cur, const T* r_cur,
|
|
T* selected_disp_pyr_new, const T* selected_disp_pyr_cur,
|
|
T* data_cost_selected, const T* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream)
|
|
{
|
|
|
|
size_t disp_step1 = msg_step1 * h;
|
|
size_t disp_step2 = msg_step2 * h2;
|
|
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step1, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step2, &disp_step2, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step1, &msg_step1, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step2, &msg_step2, sizeof(size_t)) );
|
|
|
|
dim3 threads(32, 8, 1);
|
|
dim3 grid(1, 1, 1);
|
|
|
|
grid.x = divUp(w, threads.x);
|
|
grid.y = divUp(h, threads.y);
|
|
|
|
init_message<<<grid, threads, 0, stream>>>(u_new, d_new, l_new, r_new,
|
|
u_cur, d_cur, l_cur, r_cur,
|
|
selected_disp_pyr_new, selected_disp_pyr_cur,
|
|
data_cost_selected, data_cost,
|
|
h, w, nr_plane, h2, w2, nr_plane2);
|
|
cudaSafeCall( cudaGetLastError() );
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
|
|
template void init_message(short* u_new, short* d_new, short* l_new, short* r_new,
|
|
const short* u_cur, const short* d_cur, const short* l_cur, const short* r_cur,
|
|
short* selected_disp_pyr_new, const short* selected_disp_pyr_cur,
|
|
short* data_cost_selected, const short* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream);
|
|
|
|
template void init_message(float* u_new, float* d_new, float* l_new, float* r_new,
|
|
const float* u_cur, const float* d_cur, const float* l_cur, const float* r_cur,
|
|
float* selected_disp_pyr_new, const float* selected_disp_pyr_cur,
|
|
float* data_cost_selected, const float* data_cost, size_t msg_step1, size_t msg_step2,
|
|
int h, int w, int nr_plane, int h2, int w2, int nr_plane2, cudaStream_t stream);
|
|
|
|
///////////////////////////////////////////////////////////////
|
|
//////////////////// calc all iterations /////////////////////
|
|
///////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
__device__ void message_per_pixel(const T* data, T* msg_dst, const T* msg1, const T* msg2, const T* msg3,
|
|
const T* dst_disp, const T* src_disp, int nr_plane, T* temp)
|
|
{
|
|
T minimum = numeric_limits<T>::max();
|
|
|
|
for(int d = 0; d < nr_plane; d++)
|
|
{
|
|
int idx = d * cdisp_step1;
|
|
T val = data[idx] + msg1[idx] + msg2[idx] + msg3[idx];
|
|
|
|
if(val < minimum)
|
|
minimum = val;
|
|
|
|
msg_dst[idx] = val;
|
|
}
|
|
|
|
float sum = 0;
|
|
for(int d = 0; d < nr_plane; d++)
|
|
{
|
|
float cost_min = minimum + cmax_disc_term;
|
|
T src_disp_reg = src_disp[d * cdisp_step1];
|
|
|
|
for(int d2 = 0; d2 < nr_plane; d2++)
|
|
cost_min = fmin(cost_min, msg_dst[d2 * cdisp_step1] + cdisc_single_jump * abs(dst_disp[d2 * cdisp_step1] - src_disp_reg));
|
|
|
|
temp[d * cdisp_step1] = saturate_cast<T>(cost_min);
|
|
sum += cost_min;
|
|
}
|
|
sum /= nr_plane;
|
|
|
|
for(int d = 0; d < nr_plane; d++)
|
|
msg_dst[d * cdisp_step1] = saturate_cast<T>(temp[d * cdisp_step1] - sum);
|
|
}
|
|
|
|
template <typename T>
|
|
__global__ void compute_message(T* u_, T* d_, T* l_, T* r_, const T* data_cost_selected, const T* selected_disp_pyr_cur, int h, int w, int nr_plane, int i)
|
|
{
|
|
int y = blockIdx.y * blockDim.y + threadIdx.y;
|
|
int x = ((blockIdx.x * blockDim.x + threadIdx.x) << 1) + ((y + i) & 1);
|
|
|
|
if (y > 0 && y < h - 1 && x > 0 && x < w - 1)
|
|
{
|
|
const T* data = data_cost_selected + y * cmsg_step1 + x;
|
|
|
|
T* u = u_ + y * cmsg_step1 + x;
|
|
T* d = d_ + y * cmsg_step1 + x;
|
|
T* l = l_ + y * cmsg_step1 + x;
|
|
T* r = r_ + y * cmsg_step1 + x;
|
|
|
|
const T* disp = selected_disp_pyr_cur + y * cmsg_step1 + x;
|
|
|
|
T* temp = (T*)ctemp + y * cmsg_step1 + x;
|
|
|
|
message_per_pixel(data, u, r - 1, u + cmsg_step1, l + 1, disp, disp - cmsg_step1, nr_plane, temp);
|
|
message_per_pixel(data, d, d - cmsg_step1, r - 1, l + 1, disp, disp + cmsg_step1, nr_plane, temp);
|
|
message_per_pixel(data, l, u + cmsg_step1, d - cmsg_step1, l + 1, disp, disp - 1, nr_plane, temp);
|
|
message_per_pixel(data, r, u + cmsg_step1, d - cmsg_step1, r - 1, disp, disp + 1, nr_plane, temp);
|
|
}
|
|
}
|
|
|
|
|
|
template<class T>
|
|
void calc_all_iterations(T* u, T* d, T* l, T* r, const T* data_cost_selected,
|
|
const T* selected_disp_pyr_cur, size_t msg_step, int h, int w, int nr_plane, int iters, cudaStream_t stream)
|
|
{
|
|
size_t disp_step = msg_step * h;
|
|
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step, sizeof(size_t)) );
|
|
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step1, &msg_step, sizeof(size_t)) );
|
|
|
|
dim3 threads(32, 8, 1);
|
|
dim3 grid(1, 1, 1);
|
|
|
|
grid.x = divUp(w, threads.x << 1);
|
|
grid.y = divUp(h, threads.y);
|
|
|
|
for(int t = 0; t < iters; ++t)
|
|
{
|
|
compute_message<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, selected_disp_pyr_cur, h, w, nr_plane, t & 1);
|
|
cudaSafeCall( cudaGetLastError() );
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
};
|
|
|
|
template void calc_all_iterations(short* u, short* d, short* l, short* r, const short* data_cost_selected, const short* selected_disp_pyr_cur, size_t msg_step,
|
|
int h, int w, int nr_plane, int iters, cudaStream_t stream);
|
|
|
|
template void calc_all_iterations(float* u, float* d, float* l, float* r, const float* data_cost_selected, const float* selected_disp_pyr_cur, size_t msg_step,
|
|
int h, int w, int nr_plane, int iters, cudaStream_t stream);
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///////////////////////////////////////////////////////////////
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/////////////////////////// output ////////////////////////////
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///////////////////////////////////////////////////////////////
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template <typename T>
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__global__ void compute_disp(const T* u_, const T* d_, const T* l_, const T* r_,
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const T* data_cost_selected, const T* disp_selected_pyr,
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short* disp, size_t res_step, int cols, int rows, int nr_plane)
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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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const T* data = data_cost_selected + y * cmsg_step1 + x;
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const T* disp_selected = disp_selected_pyr + y * cmsg_step1 + x;
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const T* u = u_ + (y+1) * cmsg_step1 + (x+0);
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const T* d = d_ + (y-1) * cmsg_step1 + (x+0);
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const T* l = l_ + (y+0) * cmsg_step1 + (x+1);
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const T* r = r_ + (y+0) * cmsg_step1 + (x-1);
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int best = 0;
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T best_val = numeric_limits<T>::max();
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for (int i = 0; i < nr_plane; ++i)
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{
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int idx = i * cdisp_step1;
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T val = data[idx]+ u[idx] + d[idx] + l[idx] + r[idx];
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if (val < best_val)
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{
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best_val = val;
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best = saturate_cast<short>(disp_selected[idx]);
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}
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}
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disp[res_step * y + x] = best;
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}
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}
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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 DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream)
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{
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size_t disp_step = disp.rows * msg_step;
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cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step, sizeof(size_t)) );
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cudaSafeCall( cudaMemcpyToSymbol(cmsg_step1, &msg_step, sizeof(size_t)) );
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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(disp.cols, threads.x);
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grid.y = divUp(disp.rows, threads.y);
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compute_disp<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, disp_selected,
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disp.data, disp.step / disp.elemSize(), disp.cols, disp.rows, nr_plane);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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template void compute_disp(const short* u, const short* d, const short* l, const short* r, const short* data_cost_selected, const short* disp_selected, size_t msg_step,
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const DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream);
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template void compute_disp(const float* u, const float* d, const float* l, const float* r, const float* data_cost_selected, const float* disp_selected, size_t msg_step,
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const DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream);
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}}}
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