Skip to content

O
opencv
Commit 5af529c1 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
Browse files
Options

fixed and updated gpu implementation of separable liner filters 

now it supports kernel's size up to 32
parent b96a556f
Hide whitespace changes
Inline Side-by-side
Showing with 454 additions and 318 deletions
modules/gpu/perf/perf_filters.cpp
View file @ 5af529c1
......@@ -139,6 +139,6 @@ INSTANTIATE_TEST_CASE_P(Filter, SeparableLinearFilter, testing::Combine(
ALL_DEVICES,
GPU_TYPICAL_MAT_SIZES,
testing::Values(CV_8UC1, CV_8UC4, CV_32FC1),
testing::Values(3, 5)));
testing::Values(3, 5, 7, 9, 11, 13, 15)));
#endif
modules/gpu/src/cuda/column_filter.cu
View file @ 5af529c1
......@@ -46,17 +46,14 @@
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
#include "opencv2/gpu/device/static_check.hpp"
namespace cv { namespace gpu { namespace device
{
#define MAX_KERNEL_SIZE 16
#define BLOCK_DIM_X 16
#define BLOCK_DIM_Y 4
#define RESULT_STEPS 8
#define HALO_STEPS 1
namespace column_filter
{
#define MAX_KERNEL_SIZE 32
__constant__ float c_kernel[MAX_KERNEL_SIZE];
void loadKernel(const float kernel[], int ksize)
......@@ -64,64 +61,75 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float)) );
}
template <int KERNEL_SIZE, typename T, typename D, typename B>
__global__ void linearColumnFilter(const DevMem2D_<T> src, PtrStep<D> dst, int anchor, const B b)
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int PATCH_PER_BLOCK, int HALO_SIZE, int KSIZE, typename T, typename D, typename B>
__global__ void linearColumnFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
Static<KSIZE <= MAX_KERNEL_SIZE>::check();
Static<HALO_SIZE * BLOCK_DIM_Y >= KSIZE>::check();
Static<VecTraits<T>::cn == VecTraits<D>::cn>::check();
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_t;
__shared__ T smem[BLOCK_DIM_X][(RESULT_STEPS + 2 * HALO_STEPS) * BLOCK_DIM_Y + 1];
__shared__ sum_t smem[(PATCH_PER_BLOCK + 2 * HALO_SIZE) * BLOCK_DIM_Y][BLOCK_DIM_X];
//Offset to the upper halo edge
const int x = blockIdx.x * BLOCK_DIM_X + threadIdx.x;
const int y = (blockIdx.y * RESULT_STEPS - HALO_STEPS) * BLOCK_DIM_Y + threadIdx.y;
if (x < src.cols)
{
const T* src_col = src.ptr() + x;
if (x >= src.cols)
return;
//Main data
#pragma unroll
for(int i = HALO_STEPS; i < HALO_STEPS + RESULT_STEPS; ++i)
smem[threadIdx.x][threadIdx.y + i * BLOCK_DIM_Y] = b.at_high(y + i * BLOCK_DIM_Y, src_col, src.step);
const T* src_col = src.ptr() + x;
//Upper halo
#pragma unroll
for(int i = 0; i < HALO_STEPS; ++i)
smem[threadIdx.x][threadIdx.y + i * BLOCK_DIM_Y] = b.at_low(y + i * BLOCK_DIM_Y, src_col, src.step);
const int yStart = blockIdx.y * (BLOCK_DIM_Y * PATCH_PER_BLOCK) + threadIdx.y;
//Lower halo
#pragma unroll
for(int i = HALO_STEPS + RESULT_STEPS; i < HALO_STEPS + RESULT_STEPS + HALO_STEPS; ++i)
smem[threadIdx.x][threadIdx.y + i * BLOCK_DIM_Y]= b.at_high(y + i * BLOCK_DIM_Y, src_col, src.step);
//Upper halo
#pragma unroll
for (int j = 0; j < HALO_SIZE; ++j)
smem[threadIdx.y + j * BLOCK_DIM_Y][threadIdx.x] = saturate_cast<sum_t>(brd.at_low(yStart - (HALO_SIZE - j) * BLOCK_DIM_Y, src_col, src.step));
__syncthreads();
//Main data
#pragma unroll
for (int j = 0; j < PATCH_PER_BLOCK; ++j)
smem[threadIdx.y + HALO_SIZE * BLOCK_DIM_Y + j * BLOCK_DIM_Y][threadIdx.x] = saturate_cast<sum_t>(brd.at_high(yStart + j * BLOCK_DIM_Y, src_col, src.step));
#pragma unroll
for(int i = HALO_STEPS; i < HALO_STEPS + RESULT_STEPS; ++i)
{
sum_t sum = VecTraits<sum_t>::all(0);
//Lower halo
#pragma unroll
for (int j = 0; j < HALO_SIZE; ++j)
smem[threadIdx.y + (PATCH_PER_BLOCK + HALO_SIZE) * BLOCK_DIM_Y + j * BLOCK_DIM_Y][threadIdx.x] = saturate_cast<sum_t>(brd.at_high(yStart + (PATCH_PER_BLOCK + j) * BLOCK_DIM_Y, src_col, src.step));
#pragma unroll
for(int j = 0; j < KERNEL_SIZE; ++j)
sum = sum + smem[threadIdx.x][threadIdx.y + i * BLOCK_DIM_Y + j - anchor] * c_kernel[j];
__syncthreads();
int dstY = y + i * BLOCK_DIM_Y;
#pragma unroll
for (int j = 0; j < PATCH_PER_BLOCK; ++j)
{
const int y = yStart + j * BLOCK_DIM_Y;
if (y >= src.rows)
return;
sum_t sum = VecTraits<sum_t>::all(0);
#pragma unroll
for (int k = 0; k < KSIZE; ++k)
sum = sum + smem[threadIdx.y + HALO_SIZE * BLOCK_DIM_Y + j * BLOCK_DIM_Y - anchor + k][threadIdx.x] * c_kernel[k];
if (dstY < src.rows)
dst.ptr(dstY)[x] = saturate_cast<D>(sum);
}
dst(y, x) = saturate_cast<D>(sum);
}
}
template <int ksize, typename T, typename D, template<typename> class B>
void linearColumnFilter_caller(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor, cudaStream_t stream)
{
template <int KSIZE, typename T, typename D, template<typename> class B>
void linearColumnFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream)
{
const int BLOCK_DIM_X = 16;
const int BLOCK_DIM_Y = 16;
const int PATCH_PER_BLOCK = 4;
const dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y);
const dim3 grid(divUp(src.cols, BLOCK_DIM_X), divUp(src.rows, RESULT_STEPS * BLOCK_DIM_Y));
const dim3 grid(divUp(src.cols, BLOCK_DIM_X), divUp(src.rows, BLOCK_DIM_Y * PATCH_PER_BLOCK));
B<T> brd(src.rows);
B<T> b(src.rows);
linearColumnFilter<BLOCK_DIM_X, BLOCK_DIM_Y, PATCH_PER_BLOCK, KSIZE <= 16 ? 1 : 2, KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
linearColumnFilter<ksize, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, b);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
......@@ -129,106 +137,187 @@ namespace cv { namespace gpu { namespace device
}
template <typename T, typename D>
void linearColumnFilter_gpu(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor, cudaStream_t stream);
static const caller_t callers[5][17] =
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream);
static const caller_t callers[5][33] =
{
{
0,
linearColumnFilter_caller<1 , T, D, BrdColReflect101>,
linearColumnFilter_caller<2 , T, D, BrdColReflect101>,
linearColumnFilter_caller<3 , T, D, BrdColReflect101>,
linearColumnFilter_caller<4 , T, D, BrdColReflect101>,
linearColumnFilter_caller<5 , T, D, BrdColReflect101>,
linearColumnFilter_caller<6 , T, D, BrdColReflect101>,
linearColumnFilter_caller<7 , T, D, BrdColReflect101>,
linearColumnFilter_caller<8 , T, D, BrdColReflect101>,
linearColumnFilter_caller<9 , T, D, BrdColReflect101>,
linearColumnFilter_caller<10, T, D, BrdColReflect101>,
linearColumnFilter_caller<11, T, D, BrdColReflect101>,
linearColumnFilter_caller<12, T, D, BrdColReflect101>,
linearColumnFilter_caller<13, T, D, BrdColReflect101>,
linearColumnFilter_caller<14, T, D, BrdColReflect101>,
linearColumnFilter_caller<15, T, D, BrdColReflect101>,
linearColumnFilter_caller<16, T, D, BrdColReflect101>
0,
linearColumnFilter_caller< 1, T, D, BrdColReflect101>,
linearColumnFilter_caller< 2, T, D, BrdColReflect101>,
linearColumnFilter_caller< 3, T, D, BrdColReflect101>,
linearColumnFilter_caller< 4, T, D, BrdColReflect101>,
linearColumnFilter_caller< 5, T, D, BrdColReflect101>,
linearColumnFilter_caller< 6, T, D, BrdColReflect101>,
linearColumnFilter_caller< 7, T, D, BrdColReflect101>,
linearColumnFilter_caller< 8, T, D, BrdColReflect101>,
linearColumnFilter_caller< 9, T, D, BrdColReflect101>,
linearColumnFilter_caller<10, T, D, BrdColReflect101>,
linearColumnFilter_caller<11, T, D, BrdColReflect101>,
linearColumnFilter_caller<12, T, D, BrdColReflect101>,
linearColumnFilter_caller<13, T, D, BrdColReflect101>,
linearColumnFilter_caller<14, T, D, BrdColReflect101>,
linearColumnFilter_caller<15, T, D, BrdColReflect101>,
linearColumnFilter_caller<16, T, D, BrdColReflect101>,
linearColumnFilter_caller<17, T, D, BrdColReflect101>,
linearColumnFilter_caller<18, T, D, BrdColReflect101>,
linearColumnFilter_caller<19, T, D, BrdColReflect101>,
linearColumnFilter_caller<20, T, D, BrdColReflect101>,
linearColumnFilter_caller<21, T, D, BrdColReflect101>,
linearColumnFilter_caller<22, T, D, BrdColReflect101>,
linearColumnFilter_caller<23, T, D, BrdColReflect101>,
linearColumnFilter_caller<24, T, D, BrdColReflect101>,
linearColumnFilter_caller<25, T, D, BrdColReflect101>,
linearColumnFilter_caller<26, T, D, BrdColReflect101>,
linearColumnFilter_caller<27, T, D, BrdColReflect101>,
linearColumnFilter_caller<28, T, D, BrdColReflect101>,
linearColumnFilter_caller<29, T, D, BrdColReflect101>,
linearColumnFilter_caller<30, T, D, BrdColReflect101>,
linearColumnFilter_caller<31, T, D, BrdColReflect101>,
linearColumnFilter_caller<32, T, D, BrdColReflect101>
},
{
0,
linearColumnFilter_caller<1 , T, D, BrdColReplicate>,
linearColumnFilter_caller<2 , T, D, BrdColReplicate>,
linearColumnFilter_caller<3 , T, D, BrdColReplicate>,
linearColumnFilter_caller<4 , T, D, BrdColReplicate>,
linearColumnFilter_caller<5 , T, D, BrdColReplicate>,
linearColumnFilter_caller<6 , T, D, BrdColReplicate>,
linearColumnFilter_caller<7 , T, D, BrdColReplicate>,
linearColumnFilter_caller<8 , T, D, BrdColReplicate>,
linearColumnFilter_caller<9 , T, D, BrdColReplicate>,
linearColumnFilter_caller<10, T, D, BrdColReplicate>,
linearColumnFilter_caller<11, T, D, BrdColReplicate>,
linearColumnFilter_caller<12, T, D, BrdColReplicate>,
linearColumnFilter_caller<13, T, D, BrdColReplicate>,
linearColumnFilter_caller<14, T, D, BrdColReplicate>,
linearColumnFilter_caller<15, T, D, BrdColReplicate>,
linearColumnFilter_caller<16, T, D, BrdColReplicate>
0,
linearColumnFilter_caller< 1, T, D, BrdColReplicate>,
linearColumnFilter_caller< 2, T, D, BrdColReplicate>,
linearColumnFilter_caller< 3, T, D, BrdColReplicate>,
linearColumnFilter_caller< 4, T, D, BrdColReplicate>,
linearColumnFilter_caller< 5, T, D, BrdColReplicate>,
linearColumnFilter_caller< 6, T, D, BrdColReplicate>,
linearColumnFilter_caller< 7, T, D, BrdColReplicate>,
linearColumnFilter_caller< 8, T, D, BrdColReplicate>,
linearColumnFilter_caller< 9, T, D, BrdColReplicate>,
linearColumnFilter_caller<10, T, D, BrdColReplicate>,
linearColumnFilter_caller<11, T, D, BrdColReplicate>,
linearColumnFilter_caller<12, T, D, BrdColReplicate>,
linearColumnFilter_caller<13, T, D, BrdColReplicate>,
linearColumnFilter_caller<14, T, D, BrdColReplicate>,
linearColumnFilter_caller<15, T, D, BrdColReplicate>,
linearColumnFilter_caller<16, T, D, BrdColReplicate>,
linearColumnFilter_caller<17, T, D, BrdColReplicate>,
linearColumnFilter_caller<18, T, D, BrdColReplicate>,
linearColumnFilter_caller<19, T, D, BrdColReplicate>,
linearColumnFilter_caller<20, T, D, BrdColReplicate>,
linearColumnFilter_caller<21, T, D, BrdColReplicate>,
linearColumnFilter_caller<22, T, D, BrdColReplicate>,
linearColumnFilter_caller<23, T, D, BrdColReplicate>,
linearColumnFilter_caller<24, T, D, BrdColReplicate>,
linearColumnFilter_caller<25, T, D, BrdColReplicate>,
linearColumnFilter_caller<26, T, D, BrdColReplicate>,
linearColumnFilter_caller<27, T, D, BrdColReplicate>,
linearColumnFilter_caller<28, T, D, BrdColReplicate>,
linearColumnFilter_caller<29, T, D, BrdColReplicate>,
linearColumnFilter_caller<30, T, D, BrdColReplicate>,
linearColumnFilter_caller<31, T, D, BrdColReplicate>,
linearColumnFilter_caller<32, T, D, BrdColReplicate>
},
{
0,
linearColumnFilter_caller<1 , T, D, BrdColConstant>,
linearColumnFilter_caller<2 , T, D, BrdColConstant>,
linearColumnFilter_caller<3 , T, D, BrdColConstant>,
linearColumnFilter_caller<4 , T, D, BrdColConstant>,
linearColumnFilter_caller<5 , T, D, BrdColConstant>,
linearColumnFilter_caller<6 , T, D, BrdColConstant>,
linearColumnFilter_caller<7 , T, D, BrdColConstant>,
linearColumnFilter_caller<8 , T, D, BrdColConstant>,
linearColumnFilter_caller<9 , T, D, BrdColConstant>,
linearColumnFilter_caller<10, T, D, BrdColConstant>,
linearColumnFilter_caller<11, T, D, BrdColConstant>,
linearColumnFilter_caller<12, T, D, BrdColConstant>,
linearColumnFilter_caller<13, T, D, BrdColConstant>,
linearColumnFilter_caller<14, T, D, BrdColConstant>,
linearColumnFilter_caller<15, T, D, BrdColConstant>,
linearColumnFilter_caller<16, T, D, BrdColConstant>
0,
linearColumnFilter_caller< 1, T, D, BrdColConstant>,
linearColumnFilter_caller< 2, T, D, BrdColConstant>,
linearColumnFilter_caller< 3, T, D, BrdColConstant>,
linearColumnFilter_caller< 4, T, D, BrdColConstant>,
linearColumnFilter_caller< 5, T, D, BrdColConstant>,
linearColumnFilter_caller< 6, T, D, BrdColConstant>,
linearColumnFilter_caller< 7, T, D, BrdColConstant>,
linearColumnFilter_caller< 8, T, D, BrdColConstant>,
linearColumnFilter_caller< 9, T, D, BrdColConstant>,
linearColumnFilter_caller<10, T, D, BrdColConstant>,
linearColumnFilter_caller<11, T, D, BrdColConstant>,
linearColumnFilter_caller<12, T, D, BrdColConstant>,
linearColumnFilter_caller<13, T, D, BrdColConstant>,
linearColumnFilter_caller<14, T, D, BrdColConstant>,
linearColumnFilter_caller<15, T, D, BrdColConstant>,
linearColumnFilter_caller<16, T, D, BrdColConstant>,
linearColumnFilter_caller<17, T, D, BrdColConstant>,
linearColumnFilter_caller<18, T, D, BrdColConstant>,
linearColumnFilter_caller<19, T, D, BrdColConstant>,
linearColumnFilter_caller<20, T, D, BrdColConstant>,
linearColumnFilter_caller<21, T, D, BrdColConstant>,
linearColumnFilter_caller<22, T, D, BrdColConstant>,
linearColumnFilter_caller<23, T, D, BrdColConstant>,
linearColumnFilter_caller<24, T, D, BrdColConstant>,
linearColumnFilter_caller<25, T, D, BrdColConstant>,
linearColumnFilter_caller<26, T, D, BrdColConstant>,
linearColumnFilter_caller<27, T, D, BrdColConstant>,
linearColumnFilter_caller<28, T, D, BrdColConstant>,
linearColumnFilter_caller<29, T, D, BrdColConstant>,
linearColumnFilter_caller<30, T, D, BrdColConstant>,
linearColumnFilter_caller<31, T, D, BrdColConstant>,
linearColumnFilter_caller<32, T, D, BrdColConstant>
},
{
0,
linearColumnFilter_caller<1 , T, D, BrdColReflect>,
linearColumnFilter_caller<2 , T, D, BrdColReflect>,
linearColumnFilter_caller<3 , T, D, BrdColReflect>,
linearColumnFilter_caller<4 , T, D, BrdColReflect>,
linearColumnFilter_caller<5 , T, D, BrdColReflect>,
linearColumnFilter_caller<6 , T, D, BrdColReflect>,
linearColumnFilter_caller<7 , T, D, BrdColReflect>,
linearColumnFilter_caller<8 , T, D, BrdColReflect>,
linearColumnFilter_caller<9 , T, D, BrdColReflect>,
linearColumnFilter_caller<10, T, D, BrdColReflect>,
linearColumnFilter_caller<11, T, D, BrdColReflect>,
linearColumnFilter_caller<12, T, D, BrdColReflect>,
linearColumnFilter_caller<13, T, D, BrdColReflect>,
linearColumnFilter_caller<14, T, D, BrdColReflect>,
linearColumnFilter_caller<15, T, D, BrdColReflect>,
linearColumnFilter_caller<16, T, D, BrdColReflect>
0,
linearColumnFilter_caller< 1, T, D, BrdColReflect>,
linearColumnFilter_caller< 2, T, D, BrdColReflect>,
linearColumnFilter_caller< 3, T, D, BrdColReflect>,
linearColumnFilter_caller< 4, T, D, BrdColReflect>,
linearColumnFilter_caller< 5, T, D, BrdColReflect>,
linearColumnFilter_caller< 6, T, D, BrdColReflect>,
linearColumnFilter_caller< 7, T, D, BrdColReflect>,
linearColumnFilter_caller< 8, T, D, BrdColReflect>,
linearColumnFilter_caller< 9, T, D, BrdColReflect>,
linearColumnFilter_caller<10, T, D, BrdColReflect>,
linearColumnFilter_caller<11, T, D, BrdColReflect>,
linearColumnFilter_caller<12, T, D, BrdColReflect>,
linearColumnFilter_caller<13, T, D, BrdColReflect>,
linearColumnFilter_caller<14, T, D, BrdColReflect>,
linearColumnFilter_caller<15, T, D, BrdColReflect>,
linearColumnFilter_caller<16, T, D, BrdColReflect>,
linearColumnFilter_caller<17, T, D, BrdColReflect>,
linearColumnFilter_caller<18, T, D, BrdColReflect>,
linearColumnFilter_caller<19, T, D, BrdColReflect>,
linearColumnFilter_caller<20, T, D, BrdColReflect>,
linearColumnFilter_caller<21, T, D, BrdColReflect>,
linearColumnFilter_caller<22, T, D, BrdColReflect>,
linearColumnFilter_caller<23, T, D, BrdColReflect>,
linearColumnFilter_caller<24, T, D, BrdColReflect>,
linearColumnFilter_caller<25, T, D, BrdColReflect>,
linearColumnFilter_caller<26, T, D, BrdColReflect>,
linearColumnFilter_caller<27, T, D, BrdColReflect>,
linearColumnFilter_caller<28, T, D, BrdColReflect>,
linearColumnFilter_caller<29, T, D, BrdColReflect>,
linearColumnFilter_caller<30, T, D, BrdColReflect>,
linearColumnFilter_caller<31, T, D, BrdColReflect>,
linearColumnFilter_caller<32, T, D, BrdColReflect>
},
{
0,
linearColumnFilter_caller<1 , T, D, BrdColWrap>,
linearColumnFilter_caller<2 , T, D, BrdColWrap>,
linearColumnFilter_caller<3 , T, D, BrdColWrap>,
linearColumnFilter_caller<4 , T, D, BrdColWrap>,
linearColumnFilter_caller<5 , T, D, BrdColWrap>,
linearColumnFilter_caller<6 , T, D, BrdColWrap>,
linearColumnFilter_caller<7 , T, D, BrdColWrap>,
linearColumnFilter_caller<8 , T, D, BrdColWrap>,
linearColumnFilter_caller<9 , T, D, BrdColWrap>,
linearColumnFilter_caller<10, T, D, BrdColWrap>,
linearColumnFilter_caller<11, T, D, BrdColWrap>,
linearColumnFilter_caller<12, T, D, BrdColWrap>,
linearColumnFilter_caller<13, T, D, BrdColWrap>,
linearColumnFilter_caller<14, T, D, BrdColWrap>,
linearColumnFilter_caller<15, T, D, BrdColWrap>,
0,
linearColumnFilter_caller< 1, T, D, BrdColWrap>,
linearColumnFilter_caller< 2, T, D, BrdColWrap>,
linearColumnFilter_caller< 3, T, D, BrdColWrap>,
linearColumnFilter_caller< 4, T, D, BrdColWrap>,
linearColumnFilter_caller< 5, T, D, BrdColWrap>,
linearColumnFilter_caller< 6, T, D, BrdColWrap>,
linearColumnFilter_caller< 7, T, D, BrdColWrap>,
linearColumnFilter_caller< 8, T, D, BrdColWrap>,
linearColumnFilter_caller< 9, T, D, BrdColWrap>,
linearColumnFilter_caller<10, T, D, BrdColWrap>,
linearColumnFilter_caller<11, T, D, BrdColWrap>,
linearColumnFilter_caller<12, T, D, BrdColWrap>,
linearColumnFilter_caller<13, T, D, BrdColWrap>,
linearColumnFilter_caller<14, T, D, BrdColWrap>,
linearColumnFilter_caller<15, T, D, BrdColWrap>,
linearColumnFilter_caller<16, T, D, BrdColWrap>,
}
linearColumnFilter_caller<17, T, D, BrdColWrap>,
linearColumnFilter_caller<18, T, D, BrdColWrap>,
linearColumnFilter_caller<19, T, D, BrdColWrap>,
linearColumnFilter_caller<20, T, D, BrdColWrap>,
linearColumnFilter_caller<21, T, D, BrdColWrap>,
linearColumnFilter_caller<22, T, D, BrdColWrap>,
linearColumnFilter_caller<23, T, D, BrdColWrap>,
linearColumnFilter_caller<24, T, D, BrdColWrap>,
linearColumnFilter_caller<25, T, D, BrdColWrap>,
linearColumnFilter_caller<26, T, D, BrdColWrap>,
linearColumnFilter_caller<27, T, D, BrdColWrap>,
linearColumnFilter_caller<28, T, D, BrdColWrap>,
linearColumnFilter_caller<29, T, D, BrdColWrap>,
linearColumnFilter_caller<30, T, D, BrdColWrap>,
linearColumnFilter_caller<31, T, D, BrdColWrap>,
linearColumnFilter_caller<32, T, D, BrdColWrap>
}
};
loadKernel(kernel, ksize);
......@@ -236,12 +325,10 @@ namespace cv { namespace gpu { namespace device
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, stream);
}
template void linearColumnFilter_gpu<float , uchar >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float4, uchar4>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
//template void linearColumnFilter_gpu<float , short >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
//template void linearColumnFilter_gpu<float2, short2>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float3, short3>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , int >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , float >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , uchar >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float4, uchar4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float3, short3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , int >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearColumnFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
} // namespace column_filter
}}} // namespace cv { namespace gpu { namespace device
modules/gpu/src/cuda/row_filter.cu
View file @ 5af529c1
......@@ -46,17 +46,14 @@
#include "opencv2/gpu/device/vec_math.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
#include "opencv2/gpu/device/static_check.hpp"
namespace cv { namespace gpu { namespace device
{
#define MAX_KERNEL_SIZE 16
#define BLOCK_DIM_X 16
#define BLOCK_DIM_Y 4
#define RESULT_STEPS 8
#define HALO_STEPS 1
namespace row_filter
{
#define MAX_KERNEL_SIZE 32
__constant__ float c_kernel[MAX_KERNEL_SIZE];
void loadKernel(const float kernel[], int ksize)
......@@ -64,87 +61,74 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaMemcpyToSymbol(c_kernel, kernel, ksize * sizeof(float)) );
}
namespace detail
{
template <typename T, size_t size> struct SmemType
{
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type smem_t;
};
template <typename T> struct SmemType<T, 4>
{
typedef T smem_t;
};
}
template <typename T> struct SmemType
template <int BLOCK_DIM_X, int BLOCK_DIM_Y, int PATCH_PER_BLOCK, int HALO_SIZE, int KSIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const DevMem2D_<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
typedef typename detail::SmemType<T, sizeof(T)>::smem_t smem_t;
};
Static<KSIZE <= MAX_KERNEL_SIZE>::check();
Static<HALO_SIZE * BLOCK_DIM_X >= KSIZE>::check();
Static<VecTraits<T>::cn == VecTraits<D>::cn>::check();
template <int KERNEL_SIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const DevMem2D_<T> src, PtrStep<D> dst, int anchor, const B b)
{
typedef typename SmemType<T>::smem_t smem_t;
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_t;
__shared__ smem_t smem[BLOCK_DIM_Y][(RESULT_STEPS + 2 * HALO_STEPS) * BLOCK_DIM_X];
//Offset to the left halo edge
const int x = (blockIdx.x * RESULT_STEPS - HALO_STEPS) * BLOCK_DIM_X + threadIdx.x;
__shared__ typename sum_t smem[BLOCK_DIM_Y][(PATCH_PER_BLOCK + 2 * HALO_SIZE) * BLOCK_DIM_X];
const int y = blockIdx.y * BLOCK_DIM_Y + threadIdx.y;
if (y < src.rows)
{
const T* src_row = src.ptr(y);
if (y >= src.rows)
return;
//Load main data
#pragma unroll
for(int i = HALO_STEPS; i < HALO_STEPS + RESULT_STEPS; ++i)
smem[threadIdx.y][threadIdx.x + i * BLOCK_DIM_X] = b.at_high(i * BLOCK_DIM_X + x, src_row);
const T* src_row = src.ptr(y);
//Load left halo
#pragma unroll
for(int i = 0; i < HALO_STEPS; ++i)
smem[threadIdx.y][threadIdx.x + i * BLOCK_DIM_X] = b.at_low(i * BLOCK_DIM_X + x, src_row);
const int xStart = blockIdx.x * (PATCH_PER_BLOCK * BLOCK_DIM_X) + threadIdx.x;
//Load right halo
#pragma unroll
for(int i = HALO_STEPS + RESULT_STEPS; i < HALO_STEPS + RESULT_STEPS + HALO_STEPS; ++i)
smem[threadIdx.y][threadIdx.x + i * BLOCK_DIM_X] = b.at_high(i * BLOCK_DIM_X + x, src_row);
//Load left halo
#pragma unroll
for (int j = 0; j < HALO_SIZE; ++j)
smem[threadIdx.y][threadIdx.x + j * BLOCK_DIM_X] = saturate_cast<sum_t>(brd.at_low(xStart - (HALO_SIZE - j) * BLOCK_DIM_X, src_row));
__syncthreads();
//Load main data
#pragma unroll
for (int j = 0; j < PATCH_PER_BLOCK; ++j)
smem[threadIdx.y][threadIdx.x + HALO_SIZE * BLOCK_DIM_X + j * BLOCK_DIM_X] = saturate_cast<sum_t>(brd.at_high(xStart + j * BLOCK_DIM_X, src_row));
D* dst_row = dst.ptr(y);
//Load right halo
#pragma unroll
for (int j = 0; j < HALO_SIZE; ++j)
smem[threadIdx.y][threadIdx.x + (PATCH_PER_BLOCK + HALO_SIZE) * BLOCK_DIM_X + j * BLOCK_DIM_X] = saturate_cast<sum_t>(brd.at_high(xStart + (PATCH_PER_BLOCK + j) * BLOCK_DIM_X, src_row));
#pragma unroll
for(int i = HALO_STEPS; i < HALO_STEPS + RESULT_STEPS; ++i)
{
sum_t sum = VecTraits<sum_t>::all(0);
__syncthreads();
#pragma unroll
for (int j = 0; j < PATCH_PER_BLOCK; ++j)
{
const int x = xStart + j * BLOCK_DIM_X;
#pragma unroll
for (int j = 0; j < KERNEL_SIZE; ++j)
sum = sum + smem[threadIdx.y][threadIdx.x + i * BLOCK_DIM_X + j - anchor] * c_kernel[j];
if (x >= src.cols)
return;
int dstX = x + i * BLOCK_DIM_X;
sum_t sum = VecTraits<sum_t>::all(0);
if (dstX < src.cols)
dst_row[dstX] = saturate_cast<D>(sum);
}
#pragma unroll
for (int k = 0; k < KSIZE; ++k)
sum = sum + smem[threadIdx.y][threadIdx.x + HALO_SIZE * BLOCK_DIM_X + j * BLOCK_DIM_X - anchor + k] * c_kernel[k];
dst(y, x) = saturate_cast<D>(sum);
}
}
template <int ksize, typename T, typename D, template<typename> class B>
void linearRowFilter_caller(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor, cudaStream_t stream)
template <int KSIZE, typename T, typename D, template<typename> class B>
void linearRowFilter_caller(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream)
{
typedef typename SmemType<T>::smem_t smem_t;
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 4;
const dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y);
const dim3 grid(divUp(src.cols, RESULT_STEPS * BLOCK_DIM_X), divUp(src.rows, BLOCK_DIM_Y));
const dim3 grid(divUp(src.cols, BLOCK_DIM_X * PATCH_PER_BLOCK), divUp(src.rows, BLOCK_DIM_Y));
B<smem_t> b(src.cols);
B<T> brd(src.cols);
linearRowFilter<ksize, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, b);
linearRowFilter<BLOCK_DIM_X, BLOCK_DIM_Y, PATCH_PER_BLOCK, 1, KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
......@@ -152,106 +136,187 @@ namespace cv { namespace gpu { namespace device
}
template <typename T, typename D>
void linearRowFilter_gpu(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream)
{
typedef void (*caller_t)(const DevMem2D_<T>& src, const DevMem2D_<D>& dst, int anchor, cudaStream_t stream);
static const caller_t callers[5][17] =
typedef void (*caller_t)(DevMem2D_<T> src, DevMem2D_<D> dst, int anchor, cudaStream_t stream);
static const caller_t callers[5][33] =
{
{
0,
linearRowFilter_caller<1 , T, D, BrdRowReflect101>,
linearRowFilter_caller<2 , T, D, BrdRowReflect101>,
linearRowFilter_caller<3 , T, D, BrdRowReflect101>,
linearRowFilter_caller<4 , T, D, BrdRowReflect101>,
linearRowFilter_caller<5 , T, D, BrdRowReflect101>,
linearRowFilter_caller<6 , T, D, BrdRowReflect101>,
linearRowFilter_caller<7 , T, D, BrdRowReflect101>,
linearRowFilter_caller<8 , T, D, BrdRowReflect101>,
linearRowFilter_caller<9 , T, D, BrdRowReflect101>,
linearRowFilter_caller<10, T, D, BrdRowReflect101>,
linearRowFilter_caller<11, T, D, BrdRowReflect101>,
linearRowFilter_caller<12, T, D, BrdRowReflect101>,
linearRowFilter_caller<13, T, D, BrdRowReflect101>,
0,
linearRowFilter_caller< 1, T, D, BrdRowReflect101>,
linearRowFilter_caller< 2, T, D, BrdRowReflect101>,
linearRowFilter_caller< 3, T, D, BrdRowReflect101>,
linearRowFilter_caller< 4, T, D, BrdRowReflect101>,
linearRowFilter_caller< 5, T, D, BrdRowReflect101>,
linearRowFilter_caller< 6, T, D, BrdRowReflect101>,
linearRowFilter_caller< 7, T, D, BrdRowReflect101>,
linearRowFilter_caller< 8, T, D, BrdRowReflect101>,
linearRowFilter_caller< 9, T, D, BrdRowReflect101>,
linearRowFilter_caller<10, T, D, BrdRowReflect101>,
linearRowFilter_caller<11, T, D, BrdRowReflect101>,
linearRowFilter_caller<12, T, D, BrdRowReflect101>,
linearRowFilter_caller<13, T, D, BrdRowReflect101>,
linearRowFilter_caller<14, T, D, BrdRowReflect101>,
linearRowFilter_caller<15, T, D, BrdRowReflect101>,
linearRowFilter_caller<16, T, D, BrdRowReflect101>
linearRowFilter_caller<15, T, D, BrdRowReflect101>,
linearRowFilter_caller<16, T, D, BrdRowReflect101>,
linearRowFilter_caller<17, T, D, BrdRowReflect101>,
linearRowFilter_caller<18, T, D, BrdRowReflect101>,
linearRowFilter_caller<19, T, D, BrdRowReflect101>,
linearRowFilter_caller<20, T, D, BrdRowReflect101>,
linearRowFilter_caller<21, T, D, BrdRowReflect101>,
linearRowFilter_caller<22, T, D, BrdRowReflect101>,
linearRowFilter_caller<23, T, D, BrdRowReflect101>,
linearRowFilter_caller<24, T, D, BrdRowReflect101>,
linearRowFilter_caller<25, T, D, BrdRowReflect101>,
linearRowFilter_caller<26, T, D, BrdRowReflect101>,
linearRowFilter_caller<27, T, D, BrdRowReflect101>,
linearRowFilter_caller<28, T, D, BrdRowReflect101>,
linearRowFilter_caller<29, T, D, BrdRowReflect101>,
linearRowFilter_caller<30, T, D, BrdRowReflect101>,
linearRowFilter_caller<31, T, D, BrdRowReflect101>,
linearRowFilter_caller<32, T, D, BrdRowReflect101>
},
{
0,
linearRowFilter_caller<1 , T, D, BrdRowReplicate>,
linearRowFilter_caller<2 , T, D, BrdRowReplicate>,
linearRowFilter_caller<3 , T, D, BrdRowReplicate>,
linearRowFilter_caller<4 , T, D, BrdRowReplicate>,
linearRowFilter_caller<5 , T, D, BrdRowReplicate>,
linearRowFilter_caller<6 , T, D, BrdRowReplicate>,
linearRowFilter_caller<7 , T, D, BrdRowReplicate>,
linearRowFilter_caller<8 , T, D, BrdRowReplicate>,
linearRowFilter_caller<9 , T, D, BrdRowReplicate>,
linearRowFilter_caller<10, T, D, BrdRowReplicate>,
linearRowFilter_caller<11, T, D, BrdRowReplicate>,
linearRowFilter_caller<12, T, D, BrdRowReplicate>,
linearRowFilter_caller<13, T, D, BrdRowReplicate>,
0,
linearRowFilter_caller< 1, T, D, BrdRowReplicate>,
linearRowFilter_caller< 2, T, D, BrdRowReplicate>,
linearRowFilter_caller< 3, T, D, BrdRowReplicate>,
linearRowFilter_caller< 4, T, D, BrdRowReplicate>,
linearRowFilter_caller< 5, T, D, BrdRowReplicate>,
linearRowFilter_caller< 6, T, D, BrdRowReplicate>,
linearRowFilter_caller< 7, T, D, BrdRowReplicate>,
linearRowFilter_caller< 8, T, D, BrdRowReplicate>,
linearRowFilter_caller< 9, T, D, BrdRowReplicate>,
linearRowFilter_caller<10, T, D, BrdRowReplicate>,
linearRowFilter_caller<11, T, D, BrdRowReplicate>,
linearRowFilter_caller<12, T, D, BrdRowReplicate>,
linearRowFilter_caller<13, T, D, BrdRowReplicate>,
linearRowFilter_caller<14, T, D, BrdRowReplicate>,
linearRowFilter_caller<15, T, D, BrdRowReplicate>,
linearRowFilter_caller<16, T, D, BrdRowReplicate>
linearRowFilter_caller<15, T, D, BrdRowReplicate>,
linearRowFilter_caller<16, T, D, BrdRowReplicate>,
linearRowFilter_caller<17, T, D, BrdRowReplicate>,
linearRowFilter_caller<18, T, D, BrdRowReplicate>,
linearRowFilter_caller<19, T, D, BrdRowReplicate>,
linearRowFilter_caller<20, T, D, BrdRowReplicate>,
linearRowFilter_caller<21, T, D, BrdRowReplicate>,
linearRowFilter_caller<22, T, D, BrdRowReplicate>,
linearRowFilter_caller<23, T, D, BrdRowReplicate>,
linearRowFilter_caller<24, T, D, BrdRowReplicate>,
linearRowFilter_caller<25, T, D, BrdRowReplicate>,
linearRowFilter_caller<26, T, D, BrdRowReplicate>,
linearRowFilter_caller<27, T, D, BrdRowReplicate>,
linearRowFilter_caller<28, T, D, BrdRowReplicate>,
linearRowFilter_caller<29, T, D, BrdRowReplicate>,
linearRowFilter_caller<30, T, D, BrdRowReplicate>,
linearRowFilter_caller<31, T, D, BrdRowReplicate>,
linearRowFilter_caller<32, T, D, BrdRowReplicate>
},
{
0,
linearRowFilter_caller<1 , T, D, BrdRowConstant>,
linearRowFilter_caller<2 , T, D, BrdRowConstant>,
linearRowFilter_caller<3 , T, D, BrdRowConstant>,
linearRowFilter_caller<4 , T, D, BrdRowConstant>,
linearRowFilter_caller<5 , T, D, BrdRowConstant>,
linearRowFilter_caller<6 , T, D, BrdRowConstant>,
linearRowFilter_caller<7 , T, D, BrdRowConstant>,
linearRowFilter_caller<8 , T, D, BrdRowConstant>,
linearRowFilter_caller<9 , T, D, BrdRowConstant>,
linearRowFilter_caller<10, T, D, BrdRowConstant>,
linearRowFilter_caller<11, T, D, BrdRowConstant>,
linearRowFilter_caller<12, T, D, BrdRowConstant>,
0,
linearRowFilter_caller< 1, T, D, BrdRowConstant>,
linearRowFilter_caller< 2, T, D, BrdRowConstant>,
linearRowFilter_caller< 3, T, D, BrdRowConstant>,
linearRowFilter_caller< 4, T, D, BrdRowConstant>,
linearRowFilter_caller< 5, T, D, BrdRowConstant>,
linearRowFilter_caller< 6, T, D, BrdRowConstant>,
linearRowFilter_caller< 7, T, D, BrdRowConstant>,
linearRowFilter_caller< 8, T, D, BrdRowConstant>,
linearRowFilter_caller< 9, T, D, BrdRowConstant>,
linearRowFilter_caller<10, T, D, BrdRowConstant>,
linearRowFilter_caller<11, T, D, BrdRowConstant>,
linearRowFilter_caller<12, T, D, BrdRowConstant>,
linearRowFilter_caller<13, T, D, BrdRowConstant>,
linearRowFilter_caller<14, T, D, BrdRowConstant>,
linearRowFilter_caller<15, T, D, BrdRowConstant>,
linearRowFilter_caller<16, T, D, BrdRowConstant>
linearRowFilter_caller<15, T, D, BrdRowConstant>,
linearRowFilter_caller<16, T, D, BrdRowConstant>,
linearRowFilter_caller<17, T, D, BrdRowConstant>,
linearRowFilter_caller<18, T, D, BrdRowConstant>,
linearRowFilter_caller<19, T, D, BrdRowConstant>,
linearRowFilter_caller<20, T, D, BrdRowConstant>,
linearRowFilter_caller<21, T, D, BrdRowConstant>,
linearRowFilter_caller<22, T, D, BrdRowConstant>,
linearRowFilter_caller<23, T, D, BrdRowConstant>,
linearRowFilter_caller<24, T, D, BrdRowConstant>,
linearRowFilter_caller<25, T, D, BrdRowConstant>,
linearRowFilter_caller<26, T, D, BrdRowConstant>,
linearRowFilter_caller<27, T, D, BrdRowConstant>,
linearRowFilter_caller<28, T, D, BrdRowConstant>,
linearRowFilter_caller<29, T, D, BrdRowConstant>,
linearRowFilter_caller<30, T, D, BrdRowConstant>,
linearRowFilter_caller<31, T, D, BrdRowConstant>,
linearRowFilter_caller<32, T, D, BrdRowConstant>
},
{
0,
linearRowFilter_caller<1 , T, D, BrdRowReflect>,
linearRowFilter_caller<2 , T, D, BrdRowReflect>,
linearRowFilter_caller<3 , T, D, BrdRowReflect>,
linearRowFilter_caller<4 , T, D, BrdRowReflect>,
linearRowFilter_caller<5 , T, D, BrdRowReflect>,
linearRowFilter_caller<6 , T, D, BrdRowReflect>,
linearRowFilter_caller<7 , T, D, BrdRowReflect>,
linearRowFilter_caller<8 , T, D, BrdRowReflect>,
linearRowFilter_caller<9 , T, D, BrdRowReflect>,
linearRowFilter_caller<10, T, D, BrdRowReflect>,
linearRowFilter_caller<11, T, D, BrdRowReflect>,
linearRowFilter_caller<12, T, D, BrdRowReflect>,
0,
linearRowFilter_caller< 1, T, D, BrdRowReflect>,
linearRowFilter_caller< 2, T, D, BrdRowReflect>,
linearRowFilter_caller< 3, T, D, BrdRowReflect>,
linearRowFilter_caller< 4, T, D, BrdRowReflect>,
linearRowFilter_caller< 5, T, D, BrdRowReflect>,
linearRowFilter_caller< 6, T, D, BrdRowReflect>,
linearRowFilter_caller< 7, T, D, BrdRowReflect>,
linearRowFilter_caller< 8, T, D, BrdRowReflect>,
linearRowFilter_caller< 9, T, D, BrdRowReflect>,
linearRowFilter_caller<10, T, D, BrdRowReflect>,
linearRowFilter_caller<11, T, D, BrdRowReflect>,
linearRowFilter_caller<12, T, D, BrdRowReflect>,
linearRowFilter_caller<13, T, D, BrdRowReflect>,
linearRowFilter_caller<14, T, D, BrdRowReflect>,
linearRowFilter_caller<15, T, D, BrdRowReflect>,
linearRowFilter_caller<16, T, D, BrdRowReflect>
linearRowFilter_caller<15, T, D, BrdRowReflect>,
linearRowFilter_caller<16, T, D, BrdRowReflect>,
linearRowFilter_caller<17, T, D, BrdRowReflect>,
linearRowFilter_caller<18, T, D, BrdRowReflect>,
linearRowFilter_caller<19, T, D, BrdRowReflect>,
linearRowFilter_caller<20, T, D, BrdRowReflect>,
linearRowFilter_caller<21, T, D, BrdRowReflect>,
linearRowFilter_caller<22, T, D, BrdRowReflect>,
linearRowFilter_caller<23, T, D, BrdRowReflect>,
linearRowFilter_caller<24, T, D, BrdRowReflect>,
linearRowFilter_caller<25, T, D, BrdRowReflect>,
linearRowFilter_caller<26, T, D, BrdRowReflect>,
linearRowFilter_caller<27, T, D, BrdRowReflect>,
linearRowFilter_caller<28, T, D, BrdRowReflect>,
linearRowFilter_caller<29, T, D, BrdRowReflect>,
linearRowFilter_caller<30, T, D, BrdRowReflect>,
linearRowFilter_caller<31, T, D, BrdRowReflect>,
linearRowFilter_caller<32, T, D, BrdRowReflect>
},
{
0,
linearRowFilter_caller<1 , T, D, BrdRowWrap>,
linearRowFilter_caller<2 , T, D, BrdRowWrap>,
linearRowFilter_caller<3 , T, D, BrdRowWrap>,
linearRowFilter_caller<4 , T, D, BrdRowWrap>,
linearRowFilter_caller<5 , T, D, BrdRowWrap>,
linearRowFilter_caller<6 , T, D, BrdRowWrap>,
linearRowFilter_caller<7 , T, D, BrdRowWrap>,
linearRowFilter_caller<8 , T, D, BrdRowWrap>,
linearRowFilter_caller<9 , T, D, BrdRowWrap>,
linearRowFilter_caller<10, T, D, BrdRowWrap>,
linearRowFilter_caller<11, T, D, BrdRowWrap>,
linearRowFilter_caller<12, T, D, BrdRowWrap>,
0,
linearRowFilter_caller< 1, T, D, BrdRowWrap>,
linearRowFilter_caller< 2, T, D, BrdRowWrap>,
linearRowFilter_caller< 3, T, D, BrdRowWrap>,
linearRowFilter_caller< 4, T, D, BrdRowWrap>,
linearRowFilter_caller< 5, T, D, BrdRowWrap>,
linearRowFilter_caller< 6, T, D, BrdRowWrap>,
linearRowFilter_caller< 7, T, D, BrdRowWrap>,
linearRowFilter_caller< 8, T, D, BrdRowWrap>,
linearRowFilter_caller< 9, T, D, BrdRowWrap>,
linearRowFilter_caller<10, T, D, BrdRowWrap>,
linearRowFilter_caller<11, T, D, BrdRowWrap>,
linearRowFilter_caller<12, T, D, BrdRowWrap>,
linearRowFilter_caller<13, T, D, BrdRowWrap>,
linearRowFilter_caller<14, T, D, BrdRowWrap>,
linearRowFilter_caller<15, T, D, BrdRowWrap>,
linearRowFilter_caller<16, T, D, BrdRowWrap>
}
linearRowFilter_caller<15, T, D, BrdRowWrap>,
linearRowFilter_caller<16, T, D, BrdRowWrap>,
linearRowFilter_caller<17, T, D, BrdRowWrap>,
linearRowFilter_caller<18, T, D, BrdRowWrap>,
linearRowFilter_caller<19, T, D, BrdRowWrap>,
linearRowFilter_caller<20, T, D, BrdRowWrap>,
linearRowFilter_caller<21, T, D, BrdRowWrap>,
linearRowFilter_caller<22, T, D, BrdRowWrap>,
linearRowFilter_caller<23, T, D, BrdRowWrap>,
linearRowFilter_caller<24, T, D, BrdRowWrap>,
linearRowFilter_caller<25, T, D, BrdRowWrap>,
linearRowFilter_caller<26, T, D, BrdRowWrap>,
linearRowFilter_caller<27, T, D, BrdRowWrap>,
linearRowFilter_caller<28, T, D, BrdRowWrap>,
linearRowFilter_caller<29, T, D, BrdRowWrap>,
linearRowFilter_caller<30, T, D, BrdRowWrap>,
linearRowFilter_caller<31, T, D, BrdRowWrap>,
linearRowFilter_caller<32, T, D, BrdRowWrap>
}
};
loadKernel(kernel, ksize);
......@@ -259,12 +324,10 @@ namespace cv { namespace gpu { namespace device
callers[brd_type][ksize]((DevMem2D_<T>)src, (DevMem2D_<D>)dst, anchor, stream);
}
template void linearRowFilter_gpu<uchar , float >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<uchar4, float4>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
//template void linearRowFilter_gpu<short , float >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
//template void linearRowFilter_gpu<short2, float2>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<short3, float3>(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<int , float >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<float , float >(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<uchar , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<uchar4, float4>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<short3, float3>(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<int , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
template void linearRowFilter_gpu<float , float >(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
} // namespace row_filter
}}} // namespace cv { namespace gpu { namespace device
modules/gpu/src/filtering.cpp
View file @ 5af529c1
......@@ -740,13 +740,13 @@ namespace cv { namespace gpu { namespace device
namespace row_filter
{
template <typename T, typename D>
void linearRowFilter_gpu(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
void linearRowFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
}
namespace column_filter
{
template <typename T, typename D>
void linearColumnFilter_gpu(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
void linearColumnFilter_gpu(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
}
}}}
......@@ -755,7 +755,7 @@ namespace
typedef NppStatus (*nppFilter1D_t)(const Npp8u * pSrc, Npp32s nSrcStep, Npp8u * pDst, Npp32s nDstStep, NppiSize oROI,
const Npp32s * pKernel, Npp32s nMaskSize, Npp32s nAnchor, Npp32s nDivisor);
typedef void (*gpuFilter1D_t)(const DevMem2Db& src, const DevMem2Db& dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
typedef void (*gpuFilter1D_t)(DevMem2Db src, DevMem2Db dst, const float kernel[], int ksize, int anchor, int brd_type, cudaStream_t stream);
struct NppLinearRowFilter : public BaseRowFilter_GPU
{
......@@ -825,8 +825,7 @@ Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int srcType, int bufType,
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderType, gpuBorderType));
CV_Assert(srcType == CV_8UC1 || srcType == CV_8UC4 /*|| srcType == CV_16SC1*/ /*|| srcType == CV_16SC2*/
|| srcType == CV_16SC3 || srcType == CV_32SC1 || srcType == CV_32FC1);
CV_Assert(srcType == CV_8UC1 || srcType == CV_8UC4 || srcType == CV_16SC3 || srcType == CV_32SC1 || srcType == CV_32FC1);
CV_Assert(CV_MAT_DEPTH(bufType) == CV_32F && CV_MAT_CN(srcType) == CV_MAT_CN(bufType));
......@@ -836,7 +835,7 @@ Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int srcType, int bufType,
int ksize = cont_krnl.cols;
CV_Assert(ksize > 0 && ksize <= 16);
CV_Assert(ksize > 0 && ksize <= 32);
normalizeAnchor(anchor, ksize);
......@@ -850,12 +849,6 @@ Ptr<BaseRowFilter_GPU> cv::gpu::getLinearRowFilter_GPU(int srcType, int bufType,
case CV_8UC4:
func = linearRowFilter_gpu<uchar4, float4>;
break;
/*case CV_16SC1:
func = linearRowFilter_gpu<short, float>;
break;*/
/*case CV_16SC2:
func = linearRowFilter_gpu<short2, float2>;
break;*/
case CV_16SC3:
func = linearRowFilter_gpu<short3, float3>;
break;
......@@ -940,8 +933,7 @@ Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int bufType, int ds
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderType, gpuBorderType));
CV_Assert(dstType == CV_8UC1 || dstType == CV_8UC4 /*|| dstType == CV_16SC1*/ /*|| dstType == CV_16SC2*/
|| dstType == CV_16SC3 || dstType == CV_32SC1 || dstType == CV_32FC1);
CV_Assert(dstType == CV_8UC1 || dstType == CV_8UC4 || dstType == CV_16SC3 || dstType == CV_32SC1 || dstType == CV_32FC1);
CV_Assert(CV_MAT_DEPTH(bufType) == CV_32F && CV_MAT_CN(dstType) == CV_MAT_CN(bufType));
......@@ -951,7 +943,7 @@ Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int bufType, int ds
int ksize = cont_krnl.cols;
CV_Assert(ksize > 0 && ksize <= 16);
CV_Assert(ksize > 0 && ksize <= 32);
normalizeAnchor(anchor, ksize);
......@@ -965,12 +957,6 @@ Ptr<BaseColumnFilter_GPU> cv::gpu::getLinearColumnFilter_GPU(int bufType, int ds
case CV_8UC4:
func = linearColumnFilter_gpu<float4, uchar4>;
break;
/*case CV_16SC1:
func = linearColumnFilter_gpu<float, short>;
break;*/
/*case CV_16SC2:
func = linearColumnFilter_gpu<float2, short2>;
break;*/
case CV_16SC3:
func = linearColumnFilter_gpu<float3, short3>;
break;
......
modules/gpu/test/test_filters.cpp
View file @ 5af529c1
......@@ -188,7 +188,7 @@ TEST_P(Sobel, Rgba)
dev_dst_rgba.download(dst_rgba);
EXPECT_MAT_NEAR_KSIZE(dst_gold_rgba, dst_rgba, ksize, 0.0);
EXPECT_MAT_NEAR(dst_gold_rgba, dst_rgba, 0.0);
}
TEST_P(Sobel, Gray)
......@@ -204,7 +204,7 @@ TEST_P(Sobel, Gray)
dev_dst_gray.download(dst_gray);
EXPECT_MAT_NEAR_KSIZE(dst_gold_gray, dst_gray, ksize, 0.0);
EXPECT_MAT_NEAR(dst_gold_gray, dst_gray, 0.0);
}
INSTANTIATE_TEST_CASE_P(Filter, Sobel, Combine(
......@@ -342,7 +342,7 @@ TEST_P(GaussianBlur, Rgba)
dev_dst_rgba.download(dst_rgba);
EXPECT_MAT_NEAR_KSIZE(dst_gold_rgba, dst_rgba, ksize, 3.0);
EXPECT_MAT_NEAR(dst_gold_rgba, dst_rgba, 4.0);
}
TEST_P(GaussianBlur, Gray)
......@@ -355,12 +355,12 @@ TEST_P(GaussianBlur, Gray)
dev_dst_gray.download(dst_gray);
EXPECT_MAT_NEAR_KSIZE(dst_gold_gray, dst_gray, ksize, 3.0);
EXPECT_MAT_NEAR(dst_gold_gray, dst_gray, 4.0);
}
INSTANTIATE_TEST_CASE_P(Filter, GaussianBlur, Combine(
ALL_DEVICES,
Values(cv::Size(3, 3), cv::Size(5, 5), cv::Size(7, 7)),
Values(cv::Size(3, 3), cv::Size(5, 5), cv::Size(7, 7), cv::Size(9, 9), cv::Size(11, 11), cv::Size(13, 13), cv::Size(15, 15), cv::Size(17, 17), cv::Size(19, 19), cv::Size(21, 21), cv::Size(23, 23), cv::Size(25, 25), cv::Size(27, 27), cv::Size(29, 29), cv::Size(31, 31)),
USE_ROI));
/////////////////////////////////////////////////////////////////////////////////////////////////
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment