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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "internal_shared.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/transform.hpp"
using namespace cv::gpu::device;
namespace cv { namespace gpu { namespace matrix_operations {
template <typename T> struct shift_and_sizeof;
template <> struct shift_and_sizeof<signed char> { enum { shift = 0 }; };
template <> struct shift_and_sizeof<unsigned char> { enum { shift = 0 }; };
template <> struct shift_and_sizeof<short> { enum { shift = 1 }; };
template <> struct shift_and_sizeof<unsigned short> { enum { shift = 1 }; };
template <> struct shift_and_sizeof<int> { enum { shift = 2 }; };
template <> struct shift_and_sizeof<float> { enum { shift = 2 }; };
template <> struct shift_and_sizeof<double> { enum { shift = 3 }; };
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// CopyTo /////////////////////////////////
///////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ void 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 >> shift_and_sizeof<T>::shift ) + x;
mat_dst[idx] = mat_src[idx];
}
}
typedef void (*CopyToFunc)(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream);
template<typename T>
void copy_to_with_mask_run(const DevMem2D& mat_src, const DevMem2D& mat_dst, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
dim3 threadsPerBlock(16,16, 1);
dim3 numBlocks ( divUp(mat_src.cols * channels , threadsPerBlock.x) , divUp(mat_src.rows , threadsPerBlock.y), 1);
copy_to_with_mask<T><<<numBlocks,threadsPerBlock, 0, stream>>>
((T*)mat_src.data, (T*)mat_dst.data, (unsigned char*)mask.data, mat_src.cols, mat_src.rows, mat_src.step, mask.step, channels);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall ( cudaThreadSynchronize() );
}
void copy_to_with_mask(const DevMem2D& mat_src, DevMem2D mat_dst, int depth, const DevMem2D& mask, int channels, const cudaStream_t & stream)
{
static CopyToFunc tab[8] =
{
copy_to_with_mask_run<unsigned char>,
copy_to_with_mask_run<signed 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) cv::gpu::error("Unsupported copyTo operation", __FILE__, __LINE__);
func(mat_src, mat_dst, mask, channels, stream);
}
///////////////////////////////////////////////////////////////////////////
////////////////////////////////// SetTo //////////////////////////////////
///////////////////////////////////////////////////////////////////////////
__constant__ uchar scalar_8u[4];
__constant__ schar scalar_8s[4];
__constant__ ushort scalar_16u[4];
__constant__ short scalar_16s[4];
__constant__ int scalar_32s[4];
__constant__ float scalar_32f[4];
__constant__ double scalar_64f[4];
template <typename T> __device__ T readScalar(int i);
template <> __device__ uchar readScalar<uchar>(int i) {return scalar_8u[i];}
template <> __device__ schar readScalar<schar>(int i) {return scalar_8s[i];}
template <> __device__ ushort readScalar<ushort>(int i) {return scalar_16u[i];}
template <> __device__ short readScalar<short>(int i) {return scalar_16s[i];}
template <> __device__ int readScalar<int>(int i) {return scalar_32s[i];}
template <> __device__ float readScalar<float>(int i) {return scalar_32f[i];}
template <> __device__ double readScalar<double>(int i) {return scalar_64f[i];}
void writeScalar(const uchar* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_8u, vals, sizeof(uchar) * 4) );
}
void writeScalar(const schar* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_8s, vals, sizeof(schar) * 4) );
}
void writeScalar(const ushort* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_16u, vals, sizeof(ushort) * 4) );
}
void writeScalar(const short* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_16s, vals, sizeof(short) * 4) );
}
void writeScalar(const int* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_32s, vals, sizeof(int) * 4) );
}
void writeScalar(const float* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_32f, vals, sizeof(float) * 4) );
}
void writeScalar(const double* vals)
{
cudaSafeCall( cudaMemcpyToSymbol(scalar_64f, vals, sizeof(double) * 4) );
}
template<typename T>
__global__ void set_to_without_mask(T * mat, int cols, int rows, int step, 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))
{
size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = readScalar<T>(x % channels);
}
}
template<typename T>
__global__ void set_to_with_mask(T * mat, const unsigned char * mask, int cols, int rows, int step, int channels, int step_mask)
{
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 >> shift_and_sizeof<T>::shift ) + x;
mat[idx] = readScalar<T>(x % channels);
}
}
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, const DevMem2D& mask, int channels, cudaStream_t stream)
{
writeScalar(scalar);
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
set_to_with_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, (uchar*)mask.data, mat.cols, mat.rows, mat.step, channels, mask.step);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall ( cudaThreadSynchronize() );
}
template void set_to_gpu<uchar >(const DevMem2D& mat, const uchar* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<schar >(const DevMem2D& mat, const schar* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<ushort>(const DevMem2D& mat, const ushort* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<short >(const DevMem2D& mat, const short* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<int >(const DevMem2D& mat, const int* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<float >(const DevMem2D& mat, const float* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template void set_to_gpu<double>(const DevMem2D& mat, const double* scalar, const DevMem2D& mask, int channels, cudaStream_t stream);
template <typename T>
void set_to_gpu(const DevMem2D& mat, const T* scalar, int channels, cudaStream_t stream)
{
writeScalar(scalar);
dim3 threadsPerBlock(32, 8, 1);
dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
set_to_without_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, mat.cols, mat.rows, mat.step, channels);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall ( cudaThreadSynchronize() );
}
template void set_to_gpu<uchar >(const DevMem2D& mat, const uchar* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<schar >(const DevMem2D& mat, const schar* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<ushort>(const DevMem2D& mat, const ushort* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<short >(const DevMem2D& mat, const short* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<int >(const DevMem2D& mat, const int* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<float >(const DevMem2D& mat, const float* scalar, int channels, cudaStream_t stream);
template void set_to_gpu<double>(const DevMem2D& mat, const double* scalar, int channels, cudaStream_t stream);
///////////////////////////////////////////////////////////////////////////
//////////////////////////////// ConvertTo ////////////////////////////////
///////////////////////////////////////////////////////////////////////////
template <typename T, typename D>
class Convertor
{
public:
Convertor(double alpha_, double beta_) : alpha(alpha_), beta(beta_) {}
__device__ D operator()(const T& src)
{
return saturate_cast<D>(alpha * src + beta);
}
private:
double alpha, beta;
};
template<typename T, typename D>
void cvt_(const DevMem2D& src, const DevMem2D& dst, double alpha, double beta, cudaStream_t stream)
{
cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
cudaSafeCall( cudaSetDoubleForDevice(&beta) );
Convertor<T, D> op(alpha, beta);
transform((DevMem2D_<T>)src, (DevMem2D_<D>)dst, op, stream);
}
void convert_gpu(const DevMem2D& src, int sdepth, const DevMem2D& dst, int ddepth, double alpha, double beta,
cudaStream_t stream = 0)
{
typedef void (*caller_t)(const DevMem2D& src, const DevMem2D& dst, double alpha, double beta,
cudaStream_t stream);
static const caller_t 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}
};
caller_t func = tab[sdepth][ddepth];
if (!func)
cv::gpu::error("Unsupported convert operation", __FILE__, __LINE__);
func(src, dst, alpha, beta, stream);
}
}}}