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added gpu belief propagation stereo matching

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This commit is contained in:
Vladislav Vinogradov
2010-07-28 14:46:44 +00:00
parent dc69cf3ab4
commit 5bd128fac8
6 changed files with 602 additions and 15 deletions
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modules/gpu/src/cuda/beliefpropagation.cu Normal file
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//M*/
#include "opencv2/gpu/devmem2d.hpp"
#include "safe_call.hpp"
using namespace cv::gpu;
static inline int divUp(int a, int b) { return (a % b == 0) ? a/b : a/b + 1; }
#ifndef FLT_MAX
#define FLT_MAX 3.402823466e+38F
#endif
typedef unsigned char uchar;
namespace beliefpropagation_gpu
{
__constant__ int cndisp;
__constant__ float cdisc_cost;
__constant__ float cdata_cost;
__constant__ float clambda;
};
///////////////////////////////////////////////////////////////
////////////////// comp data /////////////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__global__ void comp_data_kernel(uchar* l, uchar* r, size_t step, float* data, size_t data_step, int cols, int rows)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y > 0 && y < rows - 1 && x > 0 && x < cols - 1)
{
uchar *ls = l + y * step + x;
uchar *rs = r + y * step + x;
float *ds = data + y * data_step + x;
size_t disp_step = data_step * rows;
for (int disp = 0; disp < cndisp; disp++)
{
if (x - disp >= 0)
{
int le = ls[0];
int re = rs[-disp];
float val = abs(le - re);
ds[disp * disp_step] = clambda * fmin(val, cdata_cost);
}
else
{
ds[disp * disp_step] = cdata_cost;
}
}
}
}
}
namespace cv { namespace gpu { namespace impl {
extern "C" void load_constants(int ndisp, float disc_cost, float data_cost, float lambda)
{
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cndisp, &ndisp, sizeof(ndisp)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdisc_cost, &disc_cost, sizeof(disc_cost)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::cdata_cost, &data_cost, sizeof(data_cost)) );
cudaSafeCall( cudaMemcpyToSymbol(beliefpropagation_gpu::clambda, &lambda, sizeof(lambda)) );
}
extern "C" void comp_data_caller(const DevMem2D& l, const DevMem2D& r, DevMem2D_<float> mdata)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(l.cols, threads.x);
grid.y = divUp(l.rows, threads.y);
beliefpropagation_gpu::comp_data_kernel<<<grid, threads>>>(l.ptr, r.ptr, l.step, mdata.ptr, mdata.step/sizeof(float), l.cols, l.rows);
cudaSafeCall( cudaThreadSynchronize() );
}
}}}
///////////////////////////////////////////////////////////////
////////////////// data_step_down ////////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__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)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < dst_cols && y < dst_rows)
{
const size_t dst_disp_step = dst_step * dst_rows;
const size_t src_disp_step = src_step * src_rows;
for (int d = 0; d < cndisp; ++d)
{
float dst_reg = src[d * src_disp_step + src_step * (2*y+0) + (2*x+0)];
dst_reg += src[d * src_disp_step + src_step * (2*y+1) + (2*x+0)];
dst_reg += src[d * src_disp_step + src_step * (2*y+0) + (2*x+1)];
dst_reg += src[d * src_disp_step + src_step * (2*y+1) + (2*x+1)];
dst[d * dst_disp_step + y * dst_step + x] = dst_reg;
}
}
}
}
namespace cv { namespace gpu { namespace impl {
extern "C" void data_down_kernel_caller(int dst_cols, int dst_rows, int src_rows, const DevMem2D_<float>& src, DevMem2D_<float> dst)
{
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(dst_cols, threads.x);
grid.y = divUp(dst_rows, threads.y);
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));
cudaSafeCall( cudaThreadSynchronize() );
}
}}}
///////////////////////////////////////////////////////////////
////////////////// level up messages ////////////////////////
///////////////////////////////////////////////////////////////
namespace beliefpropagation_gpu
{
__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)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x < dst_cols && y < dst_rows)
{
const size_t dst_disp_step = dst_step * dst_rows;
const size_t src_disp_step = src_step * src_rows;
float *dstr = dst + y * dst_step + x;
float *srcr = src + y/2 * src_step + x/2;
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() );
}
}}}