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scan operations are moved in separate header

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This commit is contained in:
Marina Kolpakova
2012-06-20 05:41:16 +00:00
parent 8748cbc232
commit e7f6c4b7ef
28 changed files with 462 additions and 720 deletions
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8
modules/gpu/src/cuda/element_operations.cu
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@@ -1253,7 +1253,7 @@ namespace cv { namespace gpu { namespace device
{
const T val;
__host__ explicit CompareScalar(T val) : val(val) {}
__host__ explicit CompareScalar(T val_) : val(val_) {}
__device__ __forceinline__ uchar operator()(T src) const
{
@@ -1266,7 +1266,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 2) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 2) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 2) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 2) operator()(const TYPE_VEC(T, 2) & src) const
{
@@ -1281,7 +1281,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 3) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 3) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 3) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 3) operator()(const TYPE_VEC(T, 3) & src) const
{
@@ -1297,7 +1297,7 @@ namespace cv { namespace gpu { namespace device
{
const TYPE_VEC(T, 4) val;
__host__ explicit CompareScalar(TYPE_VEC(T, 4) val) : val(val) {}
__host__ explicit CompareScalar(TYPE_VEC(T, 4) val_) : val(val_) {}
__device__ __forceinline__ TYPE_VEC(uchar, 4) operator()(const TYPE_VEC(T, 4) & src) const
{

2
modules/gpu/src/cuda/matrix_reductions.cu
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@@ -72,7 +72,7 @@ namespace cv { namespace gpu { namespace device
struct Mask8U
{
explicit Mask8U(PtrStepb mask): mask(mask) {}
explicit Mask8U(PtrStepb mask_): mask(mask_) {}
__device__ __forceinline__ bool operator()(int y, int x) const
{

365
modules/gpu/src/cuda/resize.cu
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@@ -46,7 +46,8 @@
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/filters.hpp"
# include <cfloat>
#include <cfloat>
#include <opencv2/gpu/device/scan.hpp>
namespace cv { namespace gpu { namespace device
{
@@ -285,367 +286,5 @@ namespace cv { namespace gpu { namespace device
typedef float scan_line_type;
};
// template <typename T>
// __global__ void resize_area_scan(const DevMem2D_<T> src, DevMem2D_<T> dst, int fx, int fy, DevMem2D_<T> buffer)
// {
// typedef typename scan_traits<T>::scan_line_type W;
// extern __shared__ W line[];
// const int x = threadIdx.x;
// const int y = blockIdx.x;
// if (y >= src.rows) return;
// int offset = 1;
// line[2 * x + 0] = src(y, 2 * x + 0);
// line[2 * x + 1] = src(y, 2 * x + 1);
// __syncthreads();//???
// // reduction
// for (int d = blockDim.x; d > 0; d >>= 1)
// {
// __syncthreads();
// if (x < d)
// {
// int ai = 2 * x * offset -1 + 1 * offset;
// int bi = 2 * x * offset -1 + 2 * offset;
// line[bi] += line[ai];
// }
// offset *= 2;
// }
// __syncthreads();
// // convolution
// if (x == 0) { line[(blockDim.x << 1) - 1] = 0; printf("offset: %d!!!!!!!!!!!!!\n", fx);}
// for (int d = 1; d < (blockDim.x << 1); d *= 2)
// {
// offset >>= 1;
// __syncthreads();
// if (x < d)
// {
// int ai = offset * 2 * x + 1 * offset - 1;
// int bi = offset * 2 * x + 2 * offset - 1;
// W t = line[ai];
// line[ai] = line[bi];
// line[bi] += t;
// }
// }
// __syncthreads();
// // calculate sum
// int start = 0;
// int out_idx = 0;
// int end = start + fx;
// while (start < (blockDim.x << 1) && end < (blockDim.x << 1))
// {
// buffer(y, out_idx) = saturate_cast<T>((line[end] - line[start]) / fx);
// start = end;
// end = start + fx;
// out_idx++;
// }
// }
template <typename T>
__device__ void scan_y(DevMem2D_<typename scan_traits<T>::scan_line_type> buffer,int fx, int fy, DevMem2D_<T> dst,
typename scan_traits<T>::scan_line_type* line, int g_base)
{
typedef typename scan_traits<T>::scan_line_type W;
const int y = threadIdx.x;
const int x = blockIdx.x;
float scale = 1.f / (fx * fy);
if (x >= buffer.cols) return;
int offset = 1;
line[2 * y + 0] = buffer((g_base * fy) + 2 * y + 1, x);
if (y != (blockDim.x -1) )
line[2 * y + 1] = buffer((g_base * fy) + 2 * y + 2, x);
else
line[2 * y + 1] = 0;
__syncthreads();
// reduction
for (int d = blockDim.x; d > 0; d >>= 1)
{
__syncthreads();
if (y < d)
{
int ai = 2 * y * offset -1 + 1 * offset;
int bi = 2 * y * offset -1 + 2 * offset;
line[bi] += line[ai];
}
offset *= 2;
}
__syncthreads();
// convolution
if (y == 0) line[(blockDim.x << 1) - 1] = (W)buffer(0, x);
for (int d = 1; d < (blockDim.x << 1); d *= 2)
{
offset >>= 1;
__syncthreads();
if (y < d)
{
int ai = offset * 2 * y + 1 * offset - 1;
int bi = offset * 2 * y + 2 * offset - 1;
W t = line[ai];
line[ai] = line[bi];
line[bi] += t;
}
}
__syncthreads();
if (y < dst.rows)
{
W start = (y == 0)? (W)0:line[y * fy -1];
W end = line[y * fy + fy - 1];
dst(g_base + y ,x) = saturate_cast<T>((end - start) * scale);
}
}
template <typename T>
__device__ void scan_x(const DevMem2D_<T> src, int fx, int fy, DevMem2D_<typename scan_traits<T>::scan_line_type> buffer,
typename scan_traits<T>::scan_line_type* line, int g_base)
{
typedef typename scan_traits<T>::scan_line_type W;
const int x = threadIdx.x;
const int y = blockIdx.x;
float scale = 1.f / (fx * fy);
if (y >= src.rows) return;
int offset = 1;
line[2 * x + 0] = (W)src(y, (g_base * fx) + 2 * x + 1);
if (x != (blockDim.x -1) )
line[2 * x + 1] = (W)src(y, (g_base * fx) + 2 * x + 2);
else
line[2 * x + 1] = 0;
__syncthreads();
// reduction
for (int d = blockDim.x; d > 0; d >>= 1)
{
__syncthreads();
if (x < d)
{
int ai = 2 * x * offset -1 + 1 * offset;
int bi = 2 * x * offset -1 + 2 * offset;
line[bi] += line[ai];
}
offset *= 2;
}
__syncthreads();
// convolution
if (x == 0) line[(blockDim.x << 1) - 1] = (W)src(y, 0);
for (int d = 1; d < (blockDim.x << 1); d *= 2)
{
offset >>= 1;
__syncthreads();
if (x < d)
{
int ai = offset * 2 * x + 1 * offset - 1;
int bi = offset * 2 * x + 2 * offset - 1;
W t = line[ai];
line[ai] = line[bi];
line[bi] += t;
}
}
__syncthreads();
if (x < buffer.cols)
{
W start = (x == 0)? (W)0:line[x * fx -1];
W end = line[x * fx + fx - 1];
buffer(y, g_base + x) =(end - start);
}
}
enum ScanKind { exclusive, inclusive } ;
template <ScanKind Kind , class T>
__device__ __forceinline__ T scan_warp ( volatile T *ptr , const unsigned int idx = threadIdx.x )
{
const unsigned int lane = idx & 31;
if ( lane >= 1) ptr [idx ] = ptr [idx - 1] + ptr [idx];
if ( lane >= 2) ptr [idx ] = ptr [idx - 2] + ptr [idx];
if ( lane >= 4) ptr [idx ] = ptr [idx - 4] + ptr [idx];
if ( lane >= 8) ptr [idx ] = ptr [idx - 8] + ptr [idx];
if ( lane >= 16) ptr [idx ] = ptr [idx - 16] + ptr [idx];
if( Kind == inclusive )
return ptr [idx ];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
template <ScanKind Kind , class T>
__device__ __forceinline__ T scan_block( volatile T *ptr)
{
const unsigned int idx = threadIdx.x;
const unsigned int lane = idx & 31;
const unsigned int warp = idx >> 5;
T val = scan_warp <Kind>( ptr , idx );
__syncthreads ();
if( lane == 31 )
ptr [ warp ] = ptr [idx ];
__syncthreads ();
if( warp == 0 )
scan_warp<inclusive>( ptr , idx );
__syncthreads ();
if ( warp > 0)
val = ptr [warp -1] + val;
__syncthreads ();
ptr[idx] = val;
__syncthreads ();
return val ;
}
template<typename T, typename W>
__global__ void resise_scan_fast_x(const DevMem2D_<T> src, DevMem2D_<W> dst, int fx, int fy, int thred_lines, int stride)
{
extern __shared__ W sbuf[];
const unsigned int tid = threadIdx. x;
// load line-block on shared memory
int y = blockIdx.x / thred_lines;
int input_stride = (blockIdx.x % thred_lines) * stride;
int x = input_stride + tid;
// store global data in shared memory
if (x < src.cols && y < src.rows)
sbuf[tid] = src(y, x);
else
sbuf[tid] = 0;
__syncthreads();
scan_block<inclusive, W>(sbuf);
float scale = __fdividef(1.f, fx);
int out_stride = input_stride / fx;
int count = blockDim.x / fx;
if (tid < count)
{
int start_idx = (tid == 0)? 0 : tid * fx - 1;
int end_idx = tid * fx + fx - 1;
W start = (tid == 0)? (W)0:sbuf[start_idx];
W end = sbuf[end_idx];
dst(y, out_stride + tid) = (end - start);
}
}
template<typename T, typename W>
__global__ void resise_scan_fast_y(const DevMem2D_<W> src, DevMem2D_<T> dst, int fx, int fy, int thred_lines, int stride)
{
extern __shared__ W sbuf[];
const unsigned int tid = threadIdx. x;
// load line-block on shared memory
int x = blockIdx.x / thred_lines;
int global_stride = (blockIdx.x % thred_lines) * stride;
int y = global_stride + tid;
// store global data in shared memory
if (x < src.cols && y < src.rows)
sbuf[tid] = src(y, x);
else
sbuf[tid] = 0;
__syncthreads();
scan_block<inclusive, W>(sbuf);
float scale = __fdividef(1.f, fx * fy);
int out_stride = global_stride / fx;
int count = blockDim.x / fx;
if (tid < count)
{
int start_idx = (tid == 0)? 0 : tid * fx - 1;
int end_idx = tid * fx + fx - 1;
W start = (tid == 0)? (W)0:sbuf[start_idx];
W end = sbuf[end_idx];
dst(out_stride + tid, x) = saturate_cast<T>((end - start) * scale);
}
}
template <typename T>
void resize_area_gpu(const DevMem2Db src, DevMem2Db dst,float fx, float fy,
int interpolation, DevMem2Df buffer, cudaStream_t stream)
{
(void)interpolation;
int iscale_x = round(fx);
int iscale_y = round(fy);
int warps = 4;
const int threads = 32 * warps;
int input_stride = threads / iscale_x;
int thred_lines = divUp(src.cols, input_stride * iscale_x);
int blocks = src.rows * thred_lines;
typedef typename scan_traits<T>::scan_line_type smem_type;
resise_scan_fast_x<T, smem_type><<<blocks, threads, warps * 32 * sizeof(smem_type)>>>
(src, buffer, iscale_x, iscale_y, thred_lines, input_stride * iscale_x);
input_stride = threads / iscale_y;
thred_lines = divUp(src.rows, input_stride * iscale_y);
blocks = dst.cols * thred_lines;
resise_scan_fast_y<T, smem_type><<<blocks, threads, warps * 32 * sizeof(smem_type)>>>
(buffer, dst, iscale_x, iscale_y, thred_lines, input_stride * iscale_y);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template void resize_area_gpu<uchar>(DevMem2Db src, DevMem2Db dst, float fx, float fy, int interpolation, DevMem2Df buffer, cudaStream_t stream);
} // namespace imgproc
}}} // namespace cv { namespace gpu { namespace device

36
modules/gpu/src/cuda/split_merge.cu
View File

@@ -228,9 +228,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC2_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC2_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC2_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
dst.rows, dst.cols, dst.data, dst.step);
@@ -244,9 +244,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC3_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC3_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC3_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
src[2].data, src[2].step,
@@ -261,9 +261,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void mergeC4_(const DevMem2Db* src, DevMem2Db& dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(dst.cols, blockDim.x), divUp(dst.rows, blockDim.y));
mergeC4_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
mergeC4_<T><<<grid, block, 0, stream>>>(
src[0].data, src[0].step,
src[1].data, src[1].step,
src[2].data, src[2].step,
@@ -437,9 +437,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC2_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC2_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC2_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step);
@@ -453,9 +453,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC3_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC3_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC3_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step,
@@ -470,9 +470,9 @@ namespace cv { namespace gpu { namespace device
template <typename T>
static void splitC4_(const DevMem2Db& src, DevMem2Db* dst, const cudaStream_t& stream)
{
dim3 blockDim(32, 8);
dim3 gridDim(divUp(src.cols, blockDim.x), divUp(src.rows, blockDim.y));
splitC4_<T><<<gridDim, blockDim, 0, stream>>>(
dim3 block(32, 8);
dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));
splitC4_<T><<<grid, block, 0, stream>>>(
src.data, src.step, src.rows, src.cols,
dst[0].data, dst[0].step,
dst[1].data, dst[1].step,