opencv/modules/gpu/src/cuda/stereocsbp.cu
2011-11-21 11:58:52 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "internal_shared.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/limits.hpp"
namespace cv { namespace gpu { namespace device
{
namespace stereocsbp
{
///////////////////////////////////////////////////////////////
/////////////////////// load constants ////////////////////////
///////////////////////////////////////////////////////////////
__constant__ int cndisp;
__constant__ float cmax_data_term;
__constant__ float cdata_weight;
__constant__ float cmax_disc_term;
__constant__ float cdisc_single_jump;
__constant__ int cth;
__constant__ size_t cimg_step;
__constant__ size_t cmsg_step1;
__constant__ size_t cmsg_step2;
__constant__ size_t cdisp_step1;
__constant__ size_t cdisp_step2;
__constant__ uchar* cleft;
__constant__ uchar* cright;
__constant__ uchar* ctemp;
void load_constants(int ndisp, float max_data_term, float data_weight, float max_disc_term, float disc_single_jump, int min_disp_th,
const DevMem2Db& left, const DevMem2Db& right, const DevMem2Db& temp)
{
cudaSafeCall( cudaMemcpyToSymbol(cndisp, &ndisp, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(cmax_data_term, &max_data_term, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(cdata_weight, &data_weight, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(cmax_disc_term, &max_disc_term, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(cdisc_single_jump, &disc_single_jump, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(cth, &min_disp_th, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(cimg_step, &left.step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(cleft, &left.data, sizeof(left.data)) );
cudaSafeCall( cudaMemcpyToSymbol(cright, &right.data, sizeof(right.data)) );
cudaSafeCall( cudaMemcpyToSymbol(ctemp, &temp.data, sizeof(temp.data)) );
}
///////////////////////////////////////////////////////////////
/////////////////////// init data cost ////////////////////////
///////////////////////////////////////////////////////////////
template <int channels> struct DataCostPerPixel;
template <> struct DataCostPerPixel<1>
{
static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
{
return fmin(cdata_weight * ::abs((int)*left - *right), cdata_weight * cmax_data_term);
}
};
template <> struct DataCostPerPixel<3>
{
static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
{
float tb = 0.114f * ::abs((int)left[0] - right[0]);
float tg = 0.587f * ::abs((int)left[1] - right[1]);
float tr = 0.299f * ::abs((int)left[2] - right[2]);
return fmin(cdata_weight * (tr + tg + tb), cdata_weight * cmax_data_term);
}
};
template <> struct DataCostPerPixel<4>
{
static __device__ __forceinline__ float compute(const uchar* left, const uchar* right)
{
uchar4 l = *((const uchar4*)left);
uchar4 r = *((const uchar4*)right);
float tb = 0.114f * ::abs((int)l.x - r.x);
float tg = 0.587f * ::abs((int)l.y - r.y);
float tr = 0.299f * ::abs((int)l.z - r.z);
return fmin(cdata_weight * (tr + tg + tb), cdata_weight * cmax_data_term);
}
};
template <typename T>
__global__ void get_first_k_initial_global(T* data_cost_selected_, T *selected_disp_pyr, int h, int w, int nr_plane)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < h && x < w)
{
T* selected_disparity = selected_disp_pyr + y * cmsg_step1 + x;
T* data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
for(int i = 0; i < nr_plane; i++)
{
T minimum = device::numeric_limits<T>::max();
int id = 0;
for(int d = 0; d < cndisp; d++)
{
T cur = data_cost[d * cdisp_step1];
if(cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i * cdisp_step1] = minimum;
selected_disparity[i * cdisp_step1] = id;
data_cost [id * cdisp_step1] = numeric_limits<T>::max();
}
}
}
template <typename T>
__global__ void get_first_k_initial_local(T* data_cost_selected_, T* selected_disp_pyr, int h, int w, int nr_plane)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < h && x < w)
{
T* selected_disparity = selected_disp_pyr + y * cmsg_step1 + x;
T* data_cost_selected = data_cost_selected_ + y * cmsg_step1 + x;
T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
int nr_local_minimum = 0;
T prev = data_cost[0 * cdisp_step1];
T cur = data_cost[1 * cdisp_step1];
T next = data_cost[2 * cdisp_step1];
for (int d = 1; d < cndisp - 1 && nr_local_minimum < nr_plane; d++)
{
if (cur < prev && cur < next)
{
data_cost_selected[nr_local_minimum * cdisp_step1] = cur;
selected_disparity[nr_local_minimum * cdisp_step1] = d;
data_cost[d * cdisp_step1] = numeric_limits<T>::max();
nr_local_minimum++;
}
prev = cur;
cur = next;
next = data_cost[(d + 1) * cdisp_step1];
}
for (int i = nr_local_minimum; i < nr_plane; i++)
{
T minimum = numeric_limits<T>::max();
int id = 0;
for (int d = 0; d < cndisp; d++)
{
cur = data_cost[d * cdisp_step1];
if (cur < minimum)
{
minimum = cur;
id = d;
}
}
data_cost_selected[i * cdisp_step1] = minimum;
selected_disparity[i * cdisp_step1] = id;
data_cost[id * cdisp_step1] = numeric_limits<T>::max();
}
}
}
template <typename T, int channels>
__global__ void init_data_cost(int h, int w, int level)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
T* data_cost = (T*)ctemp + y * cmsg_step1 + x;
for(int d = 0; d < cndisp; ++d)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int xr = xi - d;
if(d < cth || xr < 0)
val += cdata_weight * cmax_data_term;
else
{
const uchar* lle = cleft + yi * cimg_step + xi * channels;
const uchar* lri = cright + yi * cimg_step + xr * channels;
val += DataCostPerPixel<channels>::compute(lle, lri);
}
}
}
data_cost[cdisp_step1 * d] = saturate_cast<T>(val);
}
}
}
template <typename T, int winsz, int channels>
__global__ void init_data_cost_reduce(int level, int rows, int cols, int h)
{
int x_out = blockIdx.x;
int y_out = blockIdx.y % h;
int d = (blockIdx.y / h) * blockDim.z + threadIdx.z;
int tid = threadIdx.x;
if (d < cndisp)
{
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 - d < 0 || d < 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 - d);
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];
T* data_cost = (T*)ctemp + y_out * cmsg_step1 + x_out;
if (tid == 0)
data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
}
}
template <typename T>
void init_data_cost_caller_(int /*rows*/, int /*cols*/, int h, int w, int level, int /*ndisp*/, 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: init_data_cost<T, 1><<<grid, threads, 0, stream>>>(h, w, level); break;
case 3: init_data_cost<T, 3><<<grid, threads, 0, stream>>>(h, w, level); break;
case 4: init_data_cost<T, 4><<<grid, threads, 0, stream>>>(h, w, level); break;
default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__, "init_data_cost_caller_");
}
}
template <typename T, int winsz>
void init_data_cost_reduce_caller_(int rows, int cols, int h, int w, int level, int ndisp, 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(ndisp, threads.z);
switch (channels)
{
case 1: init_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
case 3: init_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
case 4: init_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__, "init_data_cost_reduce_caller_");
}
}
template<class T>
void init_data_cost(int rows, int cols, T* disp_selected_pyr, T* data_cost_selected, size_t msg_step,
int h, int w, int level, int nr_plane, int ndisp, int channels, bool use_local_init_data_cost, cudaStream_t stream)
{
typedef void (*InitDataCostCaller)(int cols, int rows, int w, int h, int level, int ndisp, int channels, cudaStream_t stream);
static const InitDataCostCaller init_data_cost_callers[] =
{
init_data_cost_caller_<T>, init_data_cost_caller_<T>, init_data_cost_reduce_caller_<T, 4>,
init_data_cost_reduce_caller_<T, 8>, init_data_cost_reduce_caller_<T, 16>, init_data_cost_reduce_caller_<T, 32>,
init_data_cost_reduce_caller_<T, 64>, init_data_cost_reduce_caller_<T, 128>, init_data_cost_reduce_caller_<T, 256>
};
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)) );
init_data_cost_callers[level](rows, cols, h, w, level, ndisp, channels, stream);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1);
grid.x = divUp(w, threads.x);
grid.y = divUp(h, threads.y);
if (use_local_init_data_cost == true)
get_first_k_initial_local<<<grid, threads, 0, stream>>> (data_cost_selected, disp_selected_pyr, h, w, nr_plane);
else
get_first_k_initial_global<<<grid, threads, 0, stream>>>(data_cost_selected, disp_selected_pyr, h, w, nr_plane);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void init_data_cost(int rows, int cols, short* disp_selected_pyr, short* data_cost_selected, size_t msg_step,
int h, int w, int level, int nr_plane, int ndisp, int channels, bool use_local_init_data_cost, cudaStream_t stream);
template void init_data_cost(int rows, int cols, float* disp_selected_pyr, float* data_cost_selected, size_t msg_step,
int h, int w, int level, int nr_plane, int ndisp, int channels, bool use_local_init_data_cost, cudaStream_t stream);
///////////////////////////////////////////////////////////////
////////////////////// compute data cost //////////////////////
///////////////////////////////////////////////////////////////
template <typename T, int channels>
__global__ void compute_data_cost(const T* selected_disp_pyr, T* data_cost_, int h, int w, int level, int nr_plane)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < h && x < w)
{
int y0 = y << level;
int yt = (y + 1) << level;
int x0 = x << level;
int xt = (x + 1) << level;
const T* selected_disparity = selected_disp_pyr + y/2 * cmsg_step2 + x/2;
T* data_cost = data_cost_ + y * cmsg_step1 + x;
for(int d = 0; d < nr_plane; d++)
{
float val = 0.0f;
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
int sel_disp = selected_disparity[d * cdisp_step2];
int xr = xi - sel_disp;
if (xr < 0 || sel_disp < cth)
val += cdata_weight * cmax_data_term;
else
{
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__, "compute_data_cost_caller_");
}
}
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__, "compute_data_cost_reduce_caller_");
}
}
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);
///////////////////////////////////////////////////////////////
/////////////////////////// output ////////////////////////////
///////////////////////////////////////////////////////////////
template <typename T>
__global__ void compute_disp(const T* u_, const T* d_, const T* l_, const T* r_,
const T* data_cost_selected, const T* disp_selected_pyr,
short* disp, size_t res_step, int cols, int rows, int nr_plane)
{
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)
{
const T* data = data_cost_selected + y * cmsg_step1 + x;
const T* disp_selected = disp_selected_pyr + y * cmsg_step1 + x;
const T* u = u_ + (y+1) * cmsg_step1 + (x+0);
const T* d = d_ + (y-1) * cmsg_step1 + (x+0);
const T* l = l_ + (y+0) * cmsg_step1 + (x+1);
const T* r = r_ + (y+0) * cmsg_step1 + (x-1);
int best = 0;
T best_val = numeric_limits<T>::max();
for (int i = 0; i < nr_plane; ++i)
{
int idx = i * cdisp_step1;
T val = data[idx]+ u[idx] + d[idx] + l[idx] + r[idx];
if (val < best_val)
{
best_val = val;
best = saturate_cast<short>(disp_selected[idx]);
}
}
disp[res_step * y + x] = best;
}
}
template<class T>
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,
const DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream)
{
size_t disp_step = disp.rows * msg_step;
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(disp.cols, threads.x);
grid.y = divUp(disp.rows, threads.y);
compute_disp<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, disp_selected,
disp.data, disp.step / disp.elemSize(), disp.cols, disp.rows, nr_plane);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
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,
const DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream);
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,
const DevMem2D_<short>& disp, int nr_plane, cudaStream_t stream);
} // namespace stereocsbp
}}} // namespace cv { namespace gpu { namespace device {