This source file includes following definitions.
- linearRowFilter
- caller
- linearRow
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/border_interpolate.hpp"
using namespace cv::cuda;
using namespace cv::cuda::device;
namespace row_filter
{
#define MAX_KERNEL_SIZE 32
__constant__ float c_kernel[MAX_KERNEL_SIZE];
template <int KSIZE, typename T, typename D, typename B>
__global__ void linearRowFilter(const PtrStepSz<T> src, PtrStep<D> dst, const int anchor, const B brd)
{
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 8;
const int PATCH_PER_BLOCK = 4;
const int HALO_SIZE = 1;
#else
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 4;
const int PATCH_PER_BLOCK = 4;
const int HALO_SIZE = 1;
#endif
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_t;
__shared__ 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)
return;
const T* src_row = src.ptr(y);
const int xStart = blockIdx.x * (PATCH_PER_BLOCK * BLOCK_DIM_X) + threadIdx.x;
if (blockIdx.x > 0)
{
#pragma unroll
for (int j = 0; j < HALO_SIZE; ++j)
smem[threadIdx.y][threadIdx.x + j * BLOCK_DIM_X] = saturate_cast<sum_t>(src_row[xStart - (HALO_SIZE - j) * BLOCK_DIM_X]);
}
else
{
#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));
}
if (blockIdx.x + 2 < gridDim.x)
{
#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>(src_row[xStart + j * BLOCK_DIM_X]);
#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>(src_row[xStart + (PATCH_PER_BLOCK + j) * BLOCK_DIM_X]);
}
else
{
#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));
#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));
}
__syncthreads();
#pragma unroll
for (int j = 0; j < PATCH_PER_BLOCK; ++j)
{
const int x = xStart + j * BLOCK_DIM_X;
if (x < src.cols)
{
sum_t sum = VecTraits<sum_t>::all(0);
#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 caller(PtrStepSz<T> src, PtrStepSz<D> dst, int anchor, int cc, cudaStream_t stream)
{
int BLOCK_DIM_X;
int BLOCK_DIM_Y;
int PATCH_PER_BLOCK;
if (cc >= 20)
{
BLOCK_DIM_X = 32;
BLOCK_DIM_Y = 8;
PATCH_PER_BLOCK = 4;
}
else
{
BLOCK_DIM_X = 32;
BLOCK_DIM_Y = 4;
PATCH_PER_BLOCK = 4;
}
const dim3 block(BLOCK_DIM_X, BLOCK_DIM_Y);
const dim3 grid(divUp(src.cols, BLOCK_DIM_X * PATCH_PER_BLOCK), divUp(src.rows, BLOCK_DIM_Y));
B<T> brd(src.cols);
linearRowFilter<KSIZE, T, D><<<grid, block, 0, stream>>>(src, dst, anchor, brd);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
namespace filter
{
template <typename T, typename D>
void linearRow(PtrStepSzb src, PtrStepSzb dst, const float* kernel, int ksize, int anchor, int brd_type, int cc, cudaStream_t stream)
{
typedef void (*caller_t)(PtrStepSz<T> src, PtrStepSz<D> dst, int anchor, int cc, cudaStream_t stream);
static const caller_t callers[5][33] =
{
{
0,
row_filter::caller< 1, T, D, BrdRowConstant>,
row_filter::caller< 2, T, D, BrdRowConstant>,
row_filter::caller< 3, T, D, BrdRowConstant>,
row_filter::caller< 4, T, D, BrdRowConstant>,
row_filter::caller< 5, T, D, BrdRowConstant>,
row_filter::caller< 6, T, D, BrdRowConstant>,
row_filter::caller< 7, T, D, BrdRowConstant>,
row_filter::caller< 8, T, D, BrdRowConstant>,
row_filter::caller< 9, T, D, BrdRowConstant>,
row_filter::caller<10, T, D, BrdRowConstant>,
row_filter::caller<11, T, D, BrdRowConstant>,
row_filter::caller<12, T, D, BrdRowConstant>,
row_filter::caller<13, T, D, BrdRowConstant>,
row_filter::caller<14, T, D, BrdRowConstant>,
row_filter::caller<15, T, D, BrdRowConstant>,
row_filter::caller<16, T, D, BrdRowConstant>,
row_filter::caller<17, T, D, BrdRowConstant>,
row_filter::caller<18, T, D, BrdRowConstant>,
row_filter::caller<19, T, D, BrdRowConstant>,
row_filter::caller<20, T, D, BrdRowConstant>,
row_filter::caller<21, T, D, BrdRowConstant>,
row_filter::caller<22, T, D, BrdRowConstant>,
row_filter::caller<23, T, D, BrdRowConstant>,
row_filter::caller<24, T, D, BrdRowConstant>,
row_filter::caller<25, T, D, BrdRowConstant>,
row_filter::caller<26, T, D, BrdRowConstant>,
row_filter::caller<27, T, D, BrdRowConstant>,
row_filter::caller<28, T, D, BrdRowConstant>,
row_filter::caller<29, T, D, BrdRowConstant>,
row_filter::caller<30, T, D, BrdRowConstant>,
row_filter::caller<31, T, D, BrdRowConstant>,
row_filter::caller<32, T, D, BrdRowConstant>
},
{
0,
row_filter::caller< 1, T, D, BrdRowReplicate>,
row_filter::caller< 2, T, D, BrdRowReplicate>,
row_filter::caller< 3, T, D, BrdRowReplicate>,
row_filter::caller< 4, T, D, BrdRowReplicate>,
row_filter::caller< 5, T, D, BrdRowReplicate>,
row_filter::caller< 6, T, D, BrdRowReplicate>,
row_filter::caller< 7, T, D, BrdRowReplicate>,
row_filter::caller< 8, T, D, BrdRowReplicate>,
row_filter::caller< 9, T, D, BrdRowReplicate>,
row_filter::caller<10, T, D, BrdRowReplicate>,
row_filter::caller<11, T, D, BrdRowReplicate>,
row_filter::caller<12, T, D, BrdRowReplicate>,
row_filter::caller<13, T, D, BrdRowReplicate>,
row_filter::caller<14, T, D, BrdRowReplicate>,
row_filter::caller<15, T, D, BrdRowReplicate>,
row_filter::caller<16, T, D, BrdRowReplicate>,
row_filter::caller<17, T, D, BrdRowReplicate>,
row_filter::caller<18, T, D, BrdRowReplicate>,
row_filter::caller<19, T, D, BrdRowReplicate>,
row_filter::caller<20, T, D, BrdRowReplicate>,
row_filter::caller<21, T, D, BrdRowReplicate>,
row_filter::caller<22, T, D, BrdRowReplicate>,
row_filter::caller<23, T, D, BrdRowReplicate>,
row_filter::caller<24, T, D, BrdRowReplicate>,
row_filter::caller<25, T, D, BrdRowReplicate>,
row_filter::caller<26, T, D, BrdRowReplicate>,
row_filter::caller<27, T, D, BrdRowReplicate>,
row_filter::caller<28, T, D, BrdRowReplicate>,
row_filter::caller<29, T, D, BrdRowReplicate>,
row_filter::caller<30, T, D, BrdRowReplicate>,
row_filter::caller<31, T, D, BrdRowReplicate>,
row_filter::caller<32, T, D, BrdRowReplicate>
},
{
0,
row_filter::caller< 1, T, D, BrdRowReflect>,
row_filter::caller< 2, T, D, BrdRowReflect>,
row_filter::caller< 3, T, D, BrdRowReflect>,
row_filter::caller< 4, T, D, BrdRowReflect>,
row_filter::caller< 5, T, D, BrdRowReflect>,
row_filter::caller< 6, T, D, BrdRowReflect>,
row_filter::caller< 7, T, D, BrdRowReflect>,
row_filter::caller< 8, T, D, BrdRowReflect>,
row_filter::caller< 9, T, D, BrdRowReflect>,
row_filter::caller<10, T, D, BrdRowReflect>,
row_filter::caller<11, T, D, BrdRowReflect>,
row_filter::caller<12, T, D, BrdRowReflect>,
row_filter::caller<13, T, D, BrdRowReflect>,
row_filter::caller<14, T, D, BrdRowReflect>,
row_filter::caller<15, T, D, BrdRowReflect>,
row_filter::caller<16, T, D, BrdRowReflect>,
row_filter::caller<17, T, D, BrdRowReflect>,
row_filter::caller<18, T, D, BrdRowReflect>,
row_filter::caller<19, T, D, BrdRowReflect>,
row_filter::caller<20, T, D, BrdRowReflect>,
row_filter::caller<21, T, D, BrdRowReflect>,
row_filter::caller<22, T, D, BrdRowReflect>,
row_filter::caller<23, T, D, BrdRowReflect>,
row_filter::caller<24, T, D, BrdRowReflect>,
row_filter::caller<25, T, D, BrdRowReflect>,
row_filter::caller<26, T, D, BrdRowReflect>,
row_filter::caller<27, T, D, BrdRowReflect>,
row_filter::caller<28, T, D, BrdRowReflect>,
row_filter::caller<29, T, D, BrdRowReflect>,
row_filter::caller<30, T, D, BrdRowReflect>,
row_filter::caller<31, T, D, BrdRowReflect>,
row_filter::caller<32, T, D, BrdRowReflect>
},
{
0,
row_filter::caller< 1, T, D, BrdRowWrap>,
row_filter::caller< 2, T, D, BrdRowWrap>,
row_filter::caller< 3, T, D, BrdRowWrap>,
row_filter::caller< 4, T, D, BrdRowWrap>,
row_filter::caller< 5, T, D, BrdRowWrap>,
row_filter::caller< 6, T, D, BrdRowWrap>,
row_filter::caller< 7, T, D, BrdRowWrap>,
row_filter::caller< 8, T, D, BrdRowWrap>,
row_filter::caller< 9, T, D, BrdRowWrap>,
row_filter::caller<10, T, D, BrdRowWrap>,
row_filter::caller<11, T, D, BrdRowWrap>,
row_filter::caller<12, T, D, BrdRowWrap>,
row_filter::caller<13, T, D, BrdRowWrap>,
row_filter::caller<14, T, D, BrdRowWrap>,
row_filter::caller<15, T, D, BrdRowWrap>,
row_filter::caller<16, T, D, BrdRowWrap>,
row_filter::caller<17, T, D, BrdRowWrap>,
row_filter::caller<18, T, D, BrdRowWrap>,
row_filter::caller<19, T, D, BrdRowWrap>,
row_filter::caller<20, T, D, BrdRowWrap>,
row_filter::caller<21, T, D, BrdRowWrap>,
row_filter::caller<22, T, D, BrdRowWrap>,
row_filter::caller<23, T, D, BrdRowWrap>,
row_filter::caller<24, T, D, BrdRowWrap>,
row_filter::caller<25, T, D, BrdRowWrap>,
row_filter::caller<26, T, D, BrdRowWrap>,
row_filter::caller<27, T, D, BrdRowWrap>,
row_filter::caller<28, T, D, BrdRowWrap>,
row_filter::caller<29, T, D, BrdRowWrap>,
row_filter::caller<30, T, D, BrdRowWrap>,
row_filter::caller<31, T, D, BrdRowWrap>,
row_filter::caller<32, T, D, BrdRowWrap>
},
{
0,
row_filter::caller< 1, T, D, BrdRowReflect101>,
row_filter::caller< 2, T, D, BrdRowReflect101>,
row_filter::caller< 3, T, D, BrdRowReflect101>,
row_filter::caller< 4, T, D, BrdRowReflect101>,
row_filter::caller< 5, T, D, BrdRowReflect101>,
row_filter::caller< 6, T, D, BrdRowReflect101>,
row_filter::caller< 7, T, D, BrdRowReflect101>,
row_filter::caller< 8, T, D, BrdRowReflect101>,
row_filter::caller< 9, T, D, BrdRowReflect101>,
row_filter::caller<10, T, D, BrdRowReflect101>,
row_filter::caller<11, T, D, BrdRowReflect101>,
row_filter::caller<12, T, D, BrdRowReflect101>,
row_filter::caller<13, T, D, BrdRowReflect101>,
row_filter::caller<14, T, D, BrdRowReflect101>,
row_filter::caller<15, T, D, BrdRowReflect101>,
row_filter::caller<16, T, D, BrdRowReflect101>,
row_filter::caller<17, T, D, BrdRowReflect101>,
row_filter::caller<18, T, D, BrdRowReflect101>,
row_filter::caller<19, T, D, BrdRowReflect101>,
row_filter::caller<20, T, D, BrdRowReflect101>,
row_filter::caller<21, T, D, BrdRowReflect101>,
row_filter::caller<22, T, D, BrdRowReflect101>,
row_filter::caller<23, T, D, BrdRowReflect101>,
row_filter::caller<24, T, D, BrdRowReflect101>,
row_filter::caller<25, T, D, BrdRowReflect101>,
row_filter::caller<26, T, D, BrdRowReflect101>,
row_filter::caller<27, T, D, BrdRowReflect101>,
row_filter::caller<28, T, D, BrdRowReflect101>,
row_filter::caller<29, T, D, BrdRowReflect101>,
row_filter::caller<30, T, D, BrdRowReflect101>,
row_filter::caller<31, T, D, BrdRowReflect101>,
row_filter::caller<32, T, D, BrdRowReflect101>
}
};
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(row_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(row_filter::c_kernel, kernel, ksize * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
callers[brd_type][ksize]((PtrStepSz<T>)src, (PtrStepSz<D>)dst, anchor, cc, stream);
}
}