Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

remove constant memory use in compute_data_cost

Browse Source
pull/2983/head
Aaron Denney 11 years ago
parent 52516085d9
commit 85601e03dd
1 changed files with 20 additions and 23 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 43
      modules/cudastereo/src/cuda/stereocsbp.cu

43
modules/cudastereo/src/cuda/stereocsbp.cu
Unescape View File

@ -362,7 +362,7 @@ namespace cv { namespace cuda { namespace device
/////////////////////////////////////////////////////////////// ///////////////////////////////////////////////////////////////
template <typename T, int channels> template <typename T, int channels>
__global__ void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int h, int w, int level, int nr_plane, float data_weight, float max_data_term, int min_disp) __global__ void compute_data_cost(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int h, int w, int level, int nr_plane, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2)
{ {
int x = blockIdx.x * blockDim.x + threadIdx.x; int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y; int y = blockIdx.y * blockDim.y + threadIdx.y;
@ -375,8 +375,8 @@ namespace cv { namespace cuda { namespace device
int x0 = x << level; int x0 = x << level;
int xt = (x + 1) << level; int xt = (x + 1) << level;
const T* selected_disparity = selected_disp_pyr + y/2 * cmsg_step + x/2; const T* selected_disparity = selected_disp_pyr + y/2 * msg_step + x/2;
T* data_cost = data_cost_ + y * cmsg_step + x; T* data_cost = data_cost_ + y * msg_step + x;
for(int d = 0; d < nr_plane; d++) for(int d = 0; d < nr_plane; d++)
{ {
@ -385,7 +385,7 @@ namespace cv { namespace cuda { namespace device
{ {
for(int xi = x0; xi < xt; xi++) for(int xi = x0; xi < xt; xi++)
{ {
int sel_disp = selected_disparity[d * cdisp_step2]; int sel_disp = selected_disparity[d * disp_step2];
int xr = xi - sel_disp; int xr = xi - sel_disp;
if (xr < 0 || sel_disp < min_disp) if (xr < 0 || sel_disp < min_disp)
@ -399,13 +399,13 @@ namespace cv { namespace cuda { namespace device
} }
} }
} }
data_cost[cdisp_step1 * d] = saturate_cast<T>(val); data_cost[disp_step1 * d] = saturate_cast<T>(val);
} }
} }
} }
template <typename T, int winsz, int channels> template <typename T, int winsz, int channels>
__global__ void compute_data_cost_reduce(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int level, int rows, int cols, int h, int nr_plane, float data_weight, float max_data_term, int min_disp) __global__ void compute_data_cost_reduce(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* selected_disp_pyr, T* data_cost_, int level, int rows, int cols, int h, int nr_plane, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2)
{ {
int x_out = blockIdx.x; int x_out = blockIdx.x;
int y_out = blockIdx.y % h; int y_out = blockIdx.y % h;
@ -413,12 +413,12 @@ namespace cv { namespace cuda { namespace device
int tid = threadIdx.x; int tid = threadIdx.x;
const T* selected_disparity = selected_disp_pyr + y_out/2 * cmsg_step + x_out/2; const T* selected_disparity = selected_disp_pyr + y_out/2 * msg_step + x_out/2;
T* data_cost = data_cost_ + y_out * cmsg_step + x_out; T* data_cost = data_cost_ + y_out * msg_step + x_out;
if (d < nr_plane) if (d < nr_plane)
{ {
int sel_disp = selected_disparity[d * cdisp_step2]; int sel_disp = selected_disparity[d * disp_step2];
int x0 = x_out << level; int x0 = x_out << level;
int y0 = y_out << level; int y0 = y_out << level;
@ -450,13 +450,13 @@ namespace cv { namespace cuda { namespace device
reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>()); reduce<winsz>(smem + winsz * threadIdx.z, val, tid, plus<float>());
if (tid == 0) if (tid == 0)
data_cost[cdisp_step1 * d] = saturate_cast<T>(val); data_cost[disp_step1 * d] = saturate_cast<T>(val);
} }
} }
template <typename T> template <typename T>
void compute_data_cost_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int /*rows*/, int /*cols*/, void compute_data_cost_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int /*rows*/, int /*cols*/,
int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, cudaStream_t stream) int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream)
{ {
dim3 threads(32, 8, 1); dim3 threads(32, 8, 1);
dim3 grid(1, 1, 1); dim3 grid(1, 1, 1);
@ -466,16 +466,16 @@ namespace cv { namespace cuda { namespace device
switch(channels) switch(channels)
{ {
case 1: compute_data_cost<T, 1><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp); break; case 1: compute_data_cost<T, 1><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
case 3: compute_data_cost<T, 3><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp); break; case 3: compute_data_cost<T, 3><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
case 4: compute_data_cost<T, 4><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp); break; case 4: compute_data_cost<T, 4><<<grid, threads, 0, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, h, w, level, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count");
} }
} }
template <typename T, int winsz> template <typename T, int winsz>
void compute_data_cost_reduce_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols, void compute_data_cost_reduce_caller_(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols,
int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, cudaStream_t stream) int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream)
{ {
const int threadsNum = 256; const int threadsNum = 256;
const size_t smem_size = threadsNum * sizeof(float); const size_t smem_size = threadsNum * sizeof(float);
@ -486,9 +486,9 @@ namespace cv { namespace cuda { namespace device
switch (channels) switch (channels)
{ {
case 1: compute_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp); break; case 1: compute_data_cost_reduce<T, winsz, 1><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
case 3: compute_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp); break; case 3: compute_data_cost_reduce<T, winsz, 3><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
case 4: compute_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp); break; case 4: compute_data_cost_reduce<T, winsz, 4><<<grid, threads, smem_size, stream>>>(cleft, cright, cimg_step, disp_selected_pyr, data_cost, level, rows, cols, h, nr_plane, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2); break;
default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count"); default: CV_Error(cv::Error::BadNumChannels, "Unsupported channels count");
} }
} }
@ -499,7 +499,7 @@ namespace cv { namespace cuda { namespace device
int min_disp, cudaStream_t stream) int min_disp, cudaStream_t stream)
{ {
typedef void (*ComputeDataCostCaller)(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols, typedef void (*ComputeDataCostCaller)(const uchar *cleft, const uchar *cright, size_t cimg_step, const T* disp_selected_pyr, T* data_cost, int rows, int cols,
int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, cudaStream_t stream); int h, int w, int level, int nr_plane, int channels, float data_weight, float max_data_term, int min_disp, size_t msg_step, size_t disp_step1, size_t disp_step2, cudaStream_t stream);
static const ComputeDataCostCaller callers[] = static const ComputeDataCostCaller callers[] =
{ {
@ -510,11 +510,8 @@ namespace cv { namespace cuda { namespace device
size_t disp_step1 = msg_step * h; size_t disp_step1 = msg_step * h;
size_t disp_step2 = msg_step * h2; size_t disp_step2 = msg_step * h2;
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step1, &disp_step1, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(cdisp_step2, &disp_step2, sizeof(size_t)) );
cudaSafeCall( cudaMemcpyToSymbol(cmsg_step, &msg_step, sizeof(size_t)) );
callers[level](cleft, cright, cimg_step, disp_selected_pyr, data_cost, rows, cols, h, w, level, nr_plane, channels, data_weight, max_data_term, min_disp, stream); callers[level](cleft, cright, cimg_step, disp_selected_pyr, data_cost, rows, cols, h, w, level, nr_plane, channels, data_weight, max_data_term, min_disp, msg_step, disp_step1, disp_step2, stream);
cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaGetLastError() );
if (stream == 0) if (stream == 0)

Write Preview
Loading…
Cancel
Save