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Commit 28030952 authored by Andrey Morozov's avatar Andrey Morozov
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added get_first_k_initial_global_init_global_cost in gpu::SCBP

parent 9a669b1c
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Showing with 185 additions and 147 deletions
modules/gpu/include/opencv2/gpu/gpu.hpp
View file @ 28030952
......@@ -235,7 +235,7 @@ namespace cv
class CV_EXPORTS CudaMem
{
public:
public:
enum { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };
CudaMem();
......@@ -417,7 +417,7 @@ namespace cv
//! Acync version
void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream);
//! version for user specified data term
void operator()(const GpuMat& data, GpuMat& disparity);
void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream);
......@@ -486,6 +486,8 @@ namespace cv
int min_disp_th;
int msg_type;
bool use_local_init_data_cost;
private:
GpuMat u[2], d[2], l[2], r[2];
GpuMat disp_selected_pyr[2];
......
modules/gpu/src/constantspacebp_gpu.cpp
View file @ 28030952
......@@ -62,10 +62,10 @@ namespace cv { namespace gpu { namespace csbp
const DevMem2D& left, const DevMem2D& right, const DevMem2D& temp);
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, cudaStream_t stream);
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);
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, cudaStream_t stream);
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);
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);
......@@ -111,7 +111,7 @@ cv::gpu::StereoConstantSpaceBP::StereoConstantSpaceBP(int ndisp_, int iters_, in
: ndisp(ndisp_), iters(iters_), levels(levels_), nr_plane(nr_plane_),
max_data_term(DEFAULT_MAX_DATA_TERM), data_weight(DEFAULT_DATA_WEIGHT),
max_disc_term(DEFAULT_MAX_DISC_TERM), disc_single_jump(DEFAULT_DISC_SINGLE_JUMP), min_disp_th(0),
msg_type(msg_type_)
msg_type(msg_type_), use_local_init_data_cost(true)
{
CV_Assert(msg_type_ == CV_32F || msg_type_ == CV_16S);
}
......@@ -122,7 +122,7 @@ cv::gpu::StereoConstantSpaceBP::StereoConstantSpaceBP(int ndisp_, int iters_, in
: ndisp(ndisp_), iters(iters_), levels(levels_), nr_plane(nr_plane_),
max_data_term(max_data_term_), data_weight(data_weight_),
max_disc_term(max_disc_term_), disc_single_jump(disc_single_jump_), min_disp_th(min_disp_th_),
msg_type(msg_type_)
msg_type(msg_type_), use_local_init_data_cost(true)
{
CV_Assert(msg_type_ == CV_32F || msg_type_ == CV_16S);
}
......@@ -131,7 +131,7 @@ template<class T>
static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2], GpuMat l[2], GpuMat r[2],
GpuMat disp_selected_pyr[2], GpuMat& data_cost, GpuMat& data_cost_selected,
GpuMat& temp, GpuMat& out, const GpuMat& left, const GpuMat& right, GpuMat& disp,
cudaStream_t stream)
bool use_local_init_data_cost, cudaStream_t stream)
{
CV_DbgAssert(0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levels && 0 < rthis.nr_plane
&& left.rows == right.rows && left.cols == right.cols && left.type() == right.type());
......@@ -202,7 +202,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
////////////////////////////////////////////////////////////////////////////
// Compute
csbp::load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight,
csbp::load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight,
rthis.max_disc_term, rthis.disc_single_jump, rthis.min_disp_th, left, right, temp);
l[0] = zero;
......@@ -225,7 +225,7 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
if (i == levels - 1)
{
csbp::init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<T>(), data_cost_selected.ptr<T>(),
step_pyr[i], rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), stream);
step_pyr[i], rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), use_local_init_data_cost, stream);
}
else
{
......@@ -265,20 +265,20 @@ static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2]
typedef void (*csbp_operator_t)(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2], GpuMat l[2], GpuMat r[2],
GpuMat disp_selected_pyr[2], GpuMat& data_cost, GpuMat& data_cost_selected,
GpuMat& temp, GpuMat& out, const GpuMat& left, const GpuMat& right, GpuMat& disp,
cudaStream_t stream);
bool use_local_init_data_cost, cudaStream_t stream);
const static csbp_operator_t operators[] = {0, 0, 0, csbp_operator<short>, 0, csbp_operator<float>, 0, 0};
void cv::gpu::StereoConstantSpaceBP::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp)
{
CV_Assert(msg_type == CV_32F || msg_type == CV_16S);
operators[msg_type](*this, u, d, l, r, disp_selected_pyr, data_cost, data_cost_selected, temp, out, left, right, disp, 0);
operators[msg_type](*this, u, d, l, r, disp_selected_pyr, data_cost, data_cost_selected, temp, out, left, right, disp, use_local_init_data_cost, 0);
}
void cv::gpu::StereoConstantSpaceBP::operator()(const GpuMat& left, const GpuMat& right, GpuMat& disp, Stream& stream)
{
CV_Assert(msg_type == CV_32F || msg_type == CV_16S);
operators[msg_type](*this, u, d, l, r, disp_selected_pyr, data_cost, data_cost_selected, temp, out, left, right, disp, StreamAccessor::getStream(stream));
operators[msg_type](*this, u, d, l, r, disp_selected_pyr, data_cost, data_cost_selected, temp, out, left, right, disp, use_local_init_data_cost, StreamAccessor::getStream(stream));
}
#endif /* !defined (HAVE_CUDA) */
modules/gpu/src/cuda/constantspacebp.cu
View file @ 28030952
......@@ -55,16 +55,16 @@ using namespace cv::gpu::impl;
#define SHRT_MAX 32767
#endif
template <typename T>
template <typename T>
struct TypeLimits {};
template <>
template <>
struct TypeLimits<short>
{
static __device__ short max() {return SHRT_MAX;}
};
template <>
template <>
struct TypeLimits<float>
{
static __device__ float max() {return FLT_MAX;}
......@@ -82,7 +82,7 @@ namespace csbp_krnls
__constant__ float cdata_weight;
__constant__ float cmax_disc_term;
__constant__ float cdisc_single_jump;
__constant__ int cth;
__constant__ size_t cimg_step;
......@@ -96,7 +96,7 @@ namespace csbp_krnls
__constant__ uchar* ctemp;
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
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 DevMem2D& left, const DevMem2D& right, const DevMem2D& temp)
......@@ -107,9 +107,9 @@ namespace cv { namespace gpu { namespace csbp
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cdata_weight, &data_weight, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmax_disc_term, &max_disc_term, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cdisc_single_jump, &disc_single_jump, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cth, &min_disp_th, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cimg_step, &left.step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cleft, &left.ptr, sizeof(left.ptr)) );
......@@ -123,8 +123,8 @@ namespace cv { namespace gpu { namespace csbp
///////////////////////////////////////////////////////////////
namespace csbp_krnls
{
template <int channels>
{
template <int channels>
struct DataCostPerPixel
{
static __device__ float compute(const uchar* left, const uchar* right)
......@@ -137,7 +137,7 @@ namespace csbp_krnls
}
};
template <>
template <>
struct DataCostPerPixel<1>
{
static __device__ float compute(const uchar* left, const uchar* right)
......@@ -146,12 +146,46 @@ namespace csbp_krnls
}
};
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 fmin_ = data_cost[i * cdisp_step1];
int id = i;
for(int j = 0; j < nr_plane; j++)
{
T cur = data_cost[j * cdisp_step1];
if(cur < fmin_)
{
fmin_ = cur;
id = j;
}
}
data_cost_selected[i * cdisp_step1] = fmin_;
selected_disparity[i * cdisp_step1] = id;
data_cost [id * cdisp_step1] = TypeLimits<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;
......@@ -170,7 +204,7 @@ namespace csbp_krnls
{
data_cost_selected[nr_local_minimum * cdisp_step1] = cur;
selected_disparity[nr_local_minimum * cdisp_step1] = d;
data_cost[d * cdisp_step1] = TypeLimits<T>::max();
nr_local_minimum++;
......@@ -203,11 +237,11 @@ namespace csbp_krnls
}
template <typename T, int channels>
__global__ void init_data_cost(int h, int w, int level)
__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;
......@@ -224,28 +258,28 @@ namespace csbp_krnls
for(int yi = y0; yi < yt; yi++)
{
for(int xi = x0; xi < xt; xi++)
{
{
int xr = xi - d;
if(d < cth || xr < 0)
if(d < cth || xr < 0)
val += cdata_weight * cmax_data_term;
else
{
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>
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 x_out = blockIdx.x;
int y_out = blockIdx.y % h;
int d = (blockIdx.y / h) * blockDim.z + threadIdx.z;
......@@ -269,7 +303,7 @@ namespace csbp_krnls
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;
......@@ -292,28 +326,28 @@ namespace csbp_krnls
if (winsz >= 32) if (tid < 16) dline[tid] += dline[tid + 16];
if (winsz >= 16) if (tid < 8) dline[tid] += dline[tid + 8];
if (winsz >= 8) if (tid < 4) dline[tid] += dline[tid + 4];
if (winsz >= 4) if (tid < 2) dline[tid] += dline[tid + 2];
if (winsz >= 4) if (tid < 2) dline[tid] += dline[tid + 2];
if (winsz >= 2) if (tid < 1) dline[tid] += dline[tid + 1];
T* data_cost = (T*)ctemp + y_out * cmsg_step1 + x_out;
if (tid == 0)
if (tid == 0)
data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
}
}
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
template <typename T>
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);
grid.y = divUp(h, threads.y);
switch (channels)
{
case 1: csbp_krnls::init_data_cost<T, 1><<<grid, threads, 0, stream>>>(h, w, level); break;
......@@ -322,16 +356,16 @@ namespace cv { namespace gpu { namespace csbp
}
}
template <typename T, int winsz>
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);
dim3 grid(w, h, 1);
grid.y *= divUp(ndisp, threads.z);
switch (channels)
{
case 1: csbp_krnls::init_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(level, rows, cols, h); break;
......@@ -341,19 +375,19 @@ namespace cv { namespace gpu { namespace csbp
}
template<class T>
void init_data_cost_tmpl(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, cudaStream_t stream)
{
void init_data_cost_tmpl(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[] =
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_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(csbp_krnls::cdisp_step1, &disp_step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step1, &msg_step, sizeof(size_t)) );
......@@ -368,21 +402,24 @@ namespace cv { namespace gpu { namespace csbp
grid.x = divUp(w, threads.x);
grid.y = divUp(h, threads.y);
csbp_krnls::get_first_k_initial_local<<<grid, threads, 0, stream>>>(data_cost_selected, disp_selected_pyr, h, w, nr_plane);
if (use_local_init_data_cost == true)
csbp_krnls::get_first_k_initial_local<<<grid, threads, 0, stream>>> (data_cost_selected, disp_selected_pyr, h, w, nr_plane);
else
csbp_krnls::get_first_k_initial_global<<<grid, threads, 0, stream>>>(data_cost_selected, disp_selected_pyr, h, w, nr_plane);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
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, cudaStream_t stream)
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)
{
init_data_cost_tmpl(rows, cols, disp_selected_pyr, data_cost_selected, msg_step, h, w, level, nr_plane, ndisp, channels, stream);
init_data_cost_tmpl(rows, cols, disp_selected_pyr, data_cost_selected, msg_step, h, w, level, nr_plane, ndisp, channels, use_local_init_data_cost, stream);
}
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, cudaStream_t stream)
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)
{
init_data_cost_tmpl(rows, cols, disp_selected_pyr, data_cost_selected, msg_step, h, w, level, nr_plane, ndisp, channels, stream);
init_data_cost_tmpl(rows, cols, disp_selected_pyr, data_cost_selected, msg_step, h, w, level, nr_plane, ndisp, channels, use_local_init_data_cost, stream);
}
}}}
......@@ -397,13 +434,13 @@ namespace csbp_krnls
__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;
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;
......@@ -420,9 +457,9 @@ namespace csbp_krnls
int sel_disp = selected_disparity[d * cdisp_step2];
int xr = xi - sel_disp;
if (xr < 0 || sel_disp < cth)
if (xr < 0 || sel_disp < cth)
val += cdata_weight * cmax_data_term;
else
else
{
const uchar* left_x = cleft + yi * cimg_step + xi * channels;
const uchar* right_x = cright + yi * cimg_step + xr * channels;
......@@ -436,17 +473,17 @@ namespace csbp_krnls
}
}
template <typename T, int winsz, int channels>
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 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;
T* data_cost = data_cost_ + y_out * cmsg_step1 + x_out;
if (d < nr_plane)
{
......@@ -468,7 +505,7 @@ namespace csbp_krnls
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;
......@@ -491,18 +528,18 @@ namespace csbp_krnls
if (winsz >= 32) if (tid < 16) dline[tid] += dline[tid + 16];
if (winsz >= 16) if (tid < 8) dline[tid] += dline[tid + 8];
if (winsz >= 8) if (tid < 4) dline[tid] += dline[tid + 4];
if (winsz >= 4) if (tid < 2) dline[tid] += dline[tid + 2];
if (winsz >= 4) if (tid < 2) dline[tid] += dline[tid + 2];
if (winsz >= 2) if (tid < 1) dline[tid] += dline[tid + 1];
if (tid == 0)
if (tid == 0)
data_cost[cdisp_step1 * d] = saturate_cast<T>(dline[0]);
}
}
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
template <typename T>
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)
{
......@@ -517,20 +554,20 @@ namespace cv { namespace gpu { namespace csbp
case 1: csbp_krnls::compute_data_cost<T, 1><<<grid, threads, 0, stream>>>(disp_selected_pyr, data_cost, h, w, level, nr_plane); break;
case 3: csbp_krnls::compute_data_cost<T, 3><<<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>
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);
dim3 grid(w, h, 1);
grid.y *= divUp(nr_plane, threads.z);
switch (channels)
{
case 1: csbp_krnls::compute_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane); break;
......@@ -538,19 +575,19 @@ namespace cv { namespace gpu { namespace csbp
default: cv::gpu::error("Unsupported channels count", __FILE__, __LINE__);
}
}
template<class T>
void compute_data_cost_tmpl(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,
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[] =
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_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>
};
......@@ -559,12 +596,12 @@ namespace cv { namespace gpu { namespace csbp
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cdisp_step1, &disp_step1, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cdisp_step2, &disp_step2, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step1, &msg_step1, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step2, &msg_step2, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step2, &msg_step2, sizeof(size_t)) );
callers[level](disp_selected_pyr, data_cost, rows, cols, h, w, level, nr_plane, channels, stream);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
cudaSafeCall( cudaThreadSynchronize() );
}
void compute_data_cost(const short* disp_selected_pyr, short* data_cost, size_t msg_step1, size_t msg_step2,
......@@ -587,10 +624,10 @@ namespace cv { namespace gpu { namespace csbp
namespace csbp_krnls
{
template <typename T>
__device__ void get_first_k_element_increase(T* u_new, T* d_new, T* l_new, T* r_new,
__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,
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++)
......@@ -620,17 +657,17 @@ namespace csbp_krnls
}
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_,
__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);
......@@ -644,7 +681,7 @@ namespace csbp_krnls
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;
}
......@@ -669,58 +706,58 @@ namespace csbp_krnls
}
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
template<class T>
void init_message_tmpl(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,
void init_message_tmpl(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(csbp_krnls::cdisp_step1, &disp_step1, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cdisp_step2, &disp_step2, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step1, &msg_step1, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::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);
grid.y = divUp(h, threads.y);
csbp_krnls::init_message<<<grid, threads, 0, stream>>>(u_new, d_new, l_new, r_new,
csbp_krnls::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,
selected_disp_pyr_new, selected_disp_pyr_cur,
data_cost_selected, data_cost,
h, w, nr_plane, h2, w2, nr_plane2);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
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,
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)
{
init_message_tmpl(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, msg_step1, msg_step2,
init_message_tmpl(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, msg_step1, msg_step2,
h, w, nr_plane, h2, w2, nr_plane2, stream);
}
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,
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)
{
init_message_tmpl(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, msg_step1, msg_step2,
init_message_tmpl(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, msg_step1, msg_step2,
h, w, nr_plane, h2, w2, nr_plane2, stream);
}
}}}
......@@ -732,7 +769,7 @@ namespace cv { namespace gpu { namespace csbp
namespace csbp_krnls
{
template <typename T>
__device__ void message_per_pixel(const T* data, T* msg_dst, const T* msg1, const T* msg2, const T* msg3,
__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 = TypeLimits<T>::max();
......@@ -742,7 +779,7 @@ namespace csbp_krnls
int idx = d * cdisp_step1;
T val = data[idx] + msg1[idx] + msg2[idx] + msg3[idx];
if(val < minimum)
if(val < minimum)
minimum = val;
msg_dst[idx] = val;
......@@ -756,7 +793,7 @@ namespace csbp_krnls
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;
}
......@@ -780,9 +817,9 @@ namespace csbp_krnls
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);
......@@ -793,12 +830,12 @@ namespace csbp_krnls
}
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
template<class T>
void calc_all_iterations_tmpl(T* u, T* d, T* l, T* r, const T* data_cost_selected,
void calc_all_iterations_tmpl(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(csbp_krnls::cdisp_step1, &disp_step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step1, &msg_step, sizeof(size_t)) );
......@@ -811,20 +848,20 @@ namespace cv { namespace gpu { namespace csbp
for(int t = 0; t < iters; ++t)
{
csbp_krnls::compute_message<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, selected_disp_pyr_cur, h, w, nr_plane, t & 1);
csbp_krnls::compute_message<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, selected_disp_pyr_cur, h, w, nr_plane, t & 1);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
};
void calc_all_iterations(short* u, short* d, short* l, short* r, 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)
void calc_all_iterations(short* u, short* d, short* l, short* r, 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)
{
calc_all_iterations_tmpl(u, d, l, r, data_cost_selected, selected_disp_pyr_cur, msg_step, h, w, nr_plane, iters, stream);
}
void calc_all_iterations(float*u, float* d, float* l, float* r, float* data_cost_selected,
void calc_all_iterations(float*u, float* d, float* l, float* r, 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)
{
calc_all_iterations_tmpl(u, d, l, r, data_cost_selected, selected_disp_pyr_cur, msg_step, h, w, nr_plane, iters, stream);
......@@ -839,10 +876,10 @@ namespace cv { namespace gpu { namespace csbp
namespace csbp_krnls
{
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)
{
__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;
......@@ -855,15 +892,15 @@ namespace csbp_krnls
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 = TypeLimits<T>::max();
for (int i = 0; i < nr_plane; ++i)
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)
if (val < best_val)
{
best_val = val;
best = saturate_cast<short>(disp_selected[idx]);
......@@ -874,12 +911,12 @@ namespace csbp_krnls
}
}
namespace cv { namespace gpu { namespace csbp
namespace cv { namespace gpu { namespace csbp
{
template<class T>
void compute_disp_tmpl(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,
template<class T>
void compute_disp_tmpl(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(csbp_krnls::cdisp_step1, &disp_step, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(csbp_krnls::cmsg_step1, &msg_step, sizeof(size_t)) );
......@@ -889,23 +926,23 @@ namespace cv { namespace gpu { namespace csbp
grid.x = divUp(disp.cols, threads.x);
grid.y = divUp(disp.rows, threads.y);
csbp_krnls::compute_disp<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, disp_selected,
csbp_krnls::compute_disp<<<grid, threads, 0, stream>>>(u, d, l, r, data_cost_selected, disp_selected,
disp.ptr, disp.step / disp.elemSize(), disp.cols, disp.rows, nr_plane);
if (stream == 0)
cudaSafeCall( cudaThreadSynchronize() );
}
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,
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,
DevMem2D_<short> disp, int nr_plane, cudaStream_t stream)
{
compute_disp_tmpl(u, d, l, r, data_cost_selected, disp_selected, msg_step, disp, nr_plane, stream);
}
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,
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,
DevMem2D_<short> disp, int nr_plane, cudaStream_t stream)
{
compute_disp_tmpl(u, d, l, r, data_cost_selected, disp_selected, msg_step, disp, nr_plane, stream);
}
}}}
\ No newline at end of file
}}}
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