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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
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// License Agreement
// For Open Source Computer Vision Library
//
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//
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#include <stddef.h>
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#include <stdio.h>
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//#include <iostream>
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#include "cuda_shared.hpp"
#include "cuda_runtime.h"
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using namespace cv::gpu;
using namespace cv::gpu::impl;
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__constant__ __align__(16) double scalar_d[4];
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namespace mat_operators
{
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//////////////////////////////////////////////////////////
// CopyTo
//////////////////////////////////////////////////////////
template<typename T>
__global__ void kernel_copy_to_with_mask(T * mat_src, T * mat_dst, const unsigned char * mask, int cols, int rows, int step_mat, int step_mask, int channels)
{
size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
{
size_t idx = y * (step_mat / sizeof(T)) + x;
mat_dst[idx] = mat_src[idx];
}
}
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//////////////////////////////////////////////////////////
// SetTo
//////////////////////////////////////////////////////////
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template<typename T>
__global__ void kernel_set_to_without_mask(T * mat, int cols, int rows, int step, int channels)
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{
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size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
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if ((x < cols * channels ) && (y < rows))
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{
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size_t idx = y * (step / sizeof(T)) + x;
mat[idx] = scalar_d[ x % channels ];
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}
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}
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template<typename T>
__global__ void kernel_set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, int step, int channels, int step_mask)
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{
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size_t x = blockIdx.x * blockDim.x + threadIdx.x;
size_t y = blockIdx.y * blockDim.y + threadIdx.y;
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if ((x < cols * channels ) && (y < rows))
if (mask[y * step_mask + x / channels] != 0)
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{
size_t idx = y * (step / sizeof(T)) + x;
mat[idx] = scalar_d[ x % channels ];
}
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}
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//////////////////////////////////////////////////////////
// ConvertTo
//////////////////////////////////////////////////////////
template <typename T, typename DT, size_t src_elem_size, size_t dst_elem_size>
struct Converter
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < width && y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
DT* dst = (DT*)(dstmat + dst_step * y);
dst[x] = (DT)__double2int_rn(alpha * src[x] + beta);
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x), divUp(height, block.y));
}
};
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template <typename T, typename DT>
struct Converter<T, DT, 1, 1>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
DT* dst = (DT*)(dstmat + dst_step * y);
if ((x << 2) + 3 < width)
{
uchar4 src4b = ((const uchar4*)src)[x];
uchar4 dst4b;
const T* src1b = (const T*) &src4b.x;
DT* dst1b = (DT*) &dst4b.x;
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dst1b[0] = (DT)__double2int_rn(alpha * src1b[0] + beta);
dst1b[1] = (DT)__double2int_rn(alpha * src1b[1] + beta);
dst1b[2] = (DT)__double2int_rn(alpha * src1b[2] + beta);
dst1b[3] = (DT)__double2int_rn(alpha * src1b[3] + beta);
((uchar4*)dst)[x] = dst4b;
}
else
{
if ((x << 2) + 0 < width)
dst[(x << 2) + 0] = (DT)__double2int_rn(alpha * src[(x << 2) + 0] + beta);
if ((x << 2) + 1 < width)
dst[(x << 2) + 1] = (DT)__double2int_rn(alpha * src[(x << 2) + 1] + beta);
if ((x << 2) + 2 < width)
dst[(x << 2) + 2] = (DT)__double2int_rn(alpha * src[(x << 2) + 2] + beta);
}
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x << 2), divUp(height, block.y));
}
};/**/
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template <typename T, typename DT>
struct Converter<T, DT, 1, 2>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
DT* dst = (DT*)(dstmat + dst_step * y);
if ((x << 1) + 1 < width)
{
uchar2 src2b = ((const uchar2*)src)[x];
ushort2 dst2s;
const T* src1b = (const T*) &src2b;
DT* dst1s = (DT*) &dst2s;
dst1s[0] = (DT)__double2int_rn(alpha * src1b[0] + beta);
dst1s[1] = (DT)__double2int_rn(alpha * src1b[1] + beta);
((ushort2*)(dst))[x] = dst2s;
}
else
{
if ((x << 1) < width)
dst[(x << 1)] = (DT)__double2int_rn(alpha * src[(x << 1)] + beta);
}
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x << 1), divUp(height, block.y));
}
};/**/
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template <typename T, typename DT>
struct Converter<T, DT, 2, 1>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
DT* dst = (DT*)(dstmat + dst_step * y);
if ((x << 2) + 3 < width)
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{
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ushort4 src4s = ((const ushort4*)src)[x];
uchar4 dst4b;
const T* src1s = (const T*) &src4s.x;
DT* dst1b = (DT*) &dst4b.x;
dst1b[0] = (DT)__double2int_rn(alpha * src1s[0] + beta);
dst1b[1] = (DT)__double2int_rn(alpha * src1s[1] + beta);
dst1b[2] = (DT)__double2int_rn(alpha * src1s[2] + beta);
dst1b[3] = (DT)__double2int_rn(alpha * src1s[3] + beta);
((uchar4*)(dst))[x] = dst4b;
}
else
{
if ((x << 2) + 0 < width)
dst[(x << 2) + 0] = (DT)__double2int_rn(alpha * src[(x << 2) + 0] + beta);
if ((x << 2) + 1 < width)
dst[(x << 2) + 1] = (DT)__double2int_rn(alpha * src[(x << 2) + 1] + beta);
if ((x << 2) + 2 < width)
dst[(x << 2) + 2] = (DT)__double2int_rn(alpha * src[(x << 2) + 2] + beta);
}
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x << 2), divUp(height, block.y));
}
};/**/
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template <typename T, typename DT>
struct Converter<T, DT, 2, 2>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
DT* dst = (DT*)(dstmat + dst_step * y);
if ((x << 1) + 1 < width)
{
ushort2 src2s = ((const ushort2*)src)[x];
ushort2 dst2s;
const T* src1s = (const T*) &src2s.x;
DT* dst1s = (DT*) &dst2s.x;
dst1s[0] = (DT)__double2int_rn(alpha * src1s[0] + beta);
dst1s[1] = (DT)__double2int_rn(alpha * src1s[1] + beta);
((ushort2*)dst)[x] = dst2s;
}
else
{
if ((x << 1) < width)
dst[(x << 1)] = (DT)__double2int_rn(alpha * src[(x << 1)] + beta);
}
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x << 1), divUp(height, block.y));
}
};/**/
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template <typename T, size_t src_elem_size, size_t dst_elem_size>
struct Converter<T, float, src_elem_size, dst_elem_size>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < width && y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
float* dst = (float*)(dstmat + dst_step * y);
dst[x] = (float)(alpha * src[x] + beta);
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x), divUp(height, block.y));
}
};
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template <typename T, size_t src_elem_size, size_t dst_elem_size>
struct Converter<T, double, src_elem_size, dst_elem_size>
{
__device__ static void convert(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
size_t x = threadIdx.x + blockIdx.x * blockDim.x;
size_t y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < width && y < height)
{
const T* src = (const T*)(srcmat + src_step * y);
double* dst = (double*)(dstmat + dst_step * y);
dst[x] = (double)(alpha * src[x] + beta);
}
}
__host__ static inline dim3 calcGrid(size_t width, size_t height, dim3 block)
{
return dim3(divUp(width, block.x), divUp(height, block.y));
}
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};
template <typename T, typename DT>
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__global__ static void kernel_convert_to(uchar* srcmat, size_t src_step, uchar* dstmat, size_t dst_step, size_t width, size_t height, double alpha, double beta)
{
Converter<T, DT, sizeof(T), sizeof(DT)>::convert(srcmat, src_step, dstmat, dst_step, width, height, alpha, beta);
}
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} // namespace mat_operators
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namespace cv
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{
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namespace gpu
{
namespace impl
{
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//////////////////////////////////////////////////////////////
// CopyTo
//////////////////////////////////////////////////////////////
typedef void (*CopyToFunc)(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels);
template<typename T>
void copy_to_with_mask_run(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels)
{
dim3 threadsPerBlock(16,16, 1);
dim3 numBlocks ( divUp(mat_src.cols * channels , threadsPerBlock.x) , divUp(mat_src.rows , threadsPerBlock.y), 1);
::mat_operators::kernel_copy_to_with_mask<T><<<numBlocks,threadsPerBlock>>>
((T*)mat_src.ptr, (T*)mat_dst.ptr, (unsigned char*)mask.ptr, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
extern "C" void copy_to_with_mask(const DevMem2D& mat_src, const DevMem2D& mat_dst, int depth, const DevMem2D& mask, int channels)
{
static CopyToFunc tab[8] =
{
copy_to_with_mask_run<unsigned char>,
copy_to_with_mask_run<char>,
copy_to_with_mask_run<unsigned short>,
copy_to_with_mask_run<short>,
copy_to_with_mask_run<int>,
copy_to_with_mask_run<float>,
copy_to_with_mask_run<double>,
0
};
CopyToFunc func = tab[depth];
if (func == 0) error("Operation \'ConvertTo\' doesn't supported on your GPU model", __FILE__, __LINE__);
func(mat_src, mat_dst, mask, channels);
}
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//////////////////////////////////////////////////////////////
// SetTo
//////////////////////////////////////////////////////////////
typedef void (*SetToFunc_with_mask)(const DevMem2D& mat, const DevMem2D& mask, int channels);
typedef void (*SetToFunc_without_mask)(const DevMem2D& mat, int channels);
template <typename T>
void set_to_with_mask_run(const DevMem2D& mat, const DevMem2D& mask, int channels)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
::mat_operators::kernel_set_to_with_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, (unsigned char *)mask.ptr, mat.cols, mat.rows, mat.step, channels, mask.step);
cudaSafeCall ( cudaThreadSynchronize() );
}
template <typename T>
void set_to_without_mask_run(const DevMem2D& mat, int channels)
{
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
::mat_operators::kernel_set_to_without_mask<T><<<numBlocks,threadsPerBlock>>>((T*)mat.ptr, mat.cols, mat.rows, mat.step, channels);
cudaSafeCall ( cudaThreadSynchronize() );
}
extern "C" void set_to_without_mask(const DevMem2D& mat, int depth, const double * scalar, int channels)
{
double data[4];
data[0] = scalar[0];
data[1] = scalar[1];
data[2] = scalar[2];
data[3] = scalar[3];
cudaSafeCall( cudaMemcpyToSymbol(scalar_d, &data, sizeof(data)));
static SetToFunc_without_mask tab[8] =
{
set_to_without_mask_run<unsigned char>,
set_to_without_mask_run<char>,
set_to_without_mask_run<unsigned short>,
set_to_without_mask_run<short>,
set_to_without_mask_run<int>,
set_to_without_mask_run<float>,
set_to_without_mask_run<double>,
0
};
SetToFunc_without_mask func = tab[depth];
if (func == 0) error("Operation \'ConvertTo\' doesn't supported on your GPU model", __FILE__, __LINE__);
func(mat, channels);
}
extern "C" void set_to_with_mask(const DevMem2D& mat, int depth, const double * scalar, const DevMem2D& mask, int channels)
{
double data[4];
data[0] = scalar[0];
data[1] = scalar[1];
data[2] = scalar[2];
data[3] = scalar[3];
cudaSafeCall( cudaMemcpyToSymbol(scalar_d, &data, sizeof(data)));
static SetToFunc_with_mask tab[8] =
{
set_to_with_mask_run<unsigned char>,
set_to_with_mask_run<char>,
set_to_with_mask_run<unsigned short>,
set_to_with_mask_run<short>,
set_to_with_mask_run<int>,
set_to_with_mask_run<float>,
set_to_with_mask_run<double>,
0
};
SetToFunc_with_mask func = tab[depth];
if (func == 0) error("Operation \'ConvertTo\' doesn't supported on your GPU model", __FILE__, __LINE__);
func(mat, mask, channels);
}
//////////////////////////////////////////////////////////////
// ConvertTo
//////////////////////////////////////////////////////////////
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typedef void (*CvtFunc)(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta);
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//#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 130)
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template<typename T, typename DT>
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void cvt_(const DevMem2D& src, DevMem2D& dst, size_t width, size_t height, double alpha, double beta)
{
dim3 block(32, 8);
dim3 grid = ::mat_operators::Converter<T, DT, sizeof(T), sizeof(DT)>::calcGrid(width, height, block);
::mat_operators::kernel_convert_to<T, DT><<<grid, block>>>(src.ptr, src.step, dst.ptr, dst.step, width, height, alpha, beta);
cudaSafeCall( cudaThreadSynchronize() );
}
//#endif
extern "C" void convert_to(const DevMem2D& src, int sdepth, DevMem2D dst, int ddepth, size_t width, size_t height, double alpha, double beta)
{
static CvtFunc tab[8][8] =
{
{cvt_<uchar, uchar>, cvt_<uchar, schar>, cvt_<uchar, ushort>, cvt_<uchar, short>,
cvt_<uchar, int>, cvt_<uchar, float>, cvt_<uchar, double>, 0},
{cvt_<schar, uchar>, cvt_<schar, schar>, cvt_<schar, ushort>, cvt_<schar, short>,
cvt_<schar, int>, cvt_<schar, float>, cvt_<schar, double>, 0},
{cvt_<ushort, uchar>, cvt_<ushort, schar>, cvt_<ushort, ushort>, cvt_<ushort, short>,
cvt_<ushort, int>, cvt_<ushort, float>, cvt_<ushort, double>, 0},
{cvt_<short, uchar>, cvt_<short, schar>, cvt_<short, ushort>, cvt_<short, short>,
cvt_<short, int>, cvt_<short, float>, cvt_<short, double>, 0},
{cvt_<int, uchar>, cvt_<int, schar>, cvt_<int, ushort>,
cvt_<int, short>, cvt_<int, int>, cvt_<int, float>, cvt_<int, double>, 0},
{cvt_<float, uchar>, cvt_<float, schar>, cvt_<float, ushort>,
cvt_<float, short>, cvt_<float, int>, cvt_<float, float>, cvt_<float, double>, 0},
{cvt_<double, uchar>, cvt_<double, schar>, cvt_<double, ushort>,
cvt_<double, short>, cvt_<double, int>, cvt_<double, float>, cvt_<double, double>, 0},
{0,0,0,0,0,0,0,0}
};
CvtFunc func = tab[sdepth][ddepth];
if (func == 0)
error("Operation \'ConvertTo\' doesn't supported on your GPU model", __FILE__, __LINE__);
func(src, dst, width, height, alpha, beta);
}
}
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}
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}
