322 lines
15 KiB
Plaintext
322 lines
15 KiB
Plaintext
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "opencv2/gpu/device/saturate_cast.hpp"
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#include "opencv2/gpu/device/transform.hpp"
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#include "opencv2/gpu/device/functional.hpp"
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namespace cv { namespace gpu { namespace device
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{
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template <typename T> struct shift_and_sizeof;
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template <> struct shift_and_sizeof<signed char> { enum { shift = 0 }; };
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template <> struct shift_and_sizeof<unsigned char> { enum { shift = 0 }; };
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template <> struct shift_and_sizeof<short> { enum { shift = 1 }; };
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template <> struct shift_and_sizeof<unsigned short> { enum { shift = 1 }; };
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template <> struct shift_and_sizeof<int> { enum { shift = 2 }; };
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template <> struct shift_and_sizeof<float> { enum { shift = 2 }; };
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template <> struct shift_and_sizeof<double> { enum { shift = 3 }; };
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///////////////////////////////////////////////////////////////////////////
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////////////////////////////////// CopyTo /////////////////////////////////
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///////////////////////////////////////////////////////////////////////////
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template <typename T> void copyToWithMask(DevMem2Db src, DevMem2Db dst, int cn, DevMem2Db mask, bool colorMask, cudaStream_t stream)
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{
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if (colorMask)
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cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<T>)dst, identity<T>(), SingleMask(mask), stream);
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else
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cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<T>)dst, identity<T>(), SingleMaskChannels(mask, cn), stream);
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}
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void copyToWithMask_gpu(DevMem2Db src, DevMem2Db dst, size_t elemSize1, int cn, DevMem2Db mask, bool colorMask, cudaStream_t stream)
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{
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typedef void (*func_t)(DevMem2Db src, DevMem2Db dst, int cn, DevMem2Db mask, bool colorMask, cudaStream_t stream);
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static func_t tab[] =
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{
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0,
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copyToWithMask<unsigned char>,
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copyToWithMask<unsigned short>,
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0,
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copyToWithMask<int>,
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0,
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0,
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0,
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copyToWithMask<double>
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};
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tab[elemSize1](src, dst, cn, mask, colorMask, stream);
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}
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///////////////////////////////////////////////////////////////////////////
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////////////////////////////////// SetTo //////////////////////////////////
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///////////////////////////////////////////////////////////////////////////
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__constant__ uchar scalar_8u[4];
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__constant__ schar scalar_8s[4];
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__constant__ ushort scalar_16u[4];
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__constant__ short scalar_16s[4];
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__constant__ int scalar_32s[4];
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__constant__ float scalar_32f[4];
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__constant__ double scalar_64f[4];
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template <typename T> __device__ __forceinline__ T readScalar(int i);
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template <> __device__ __forceinline__ uchar readScalar<uchar>(int i) {return scalar_8u[i];}
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template <> __device__ __forceinline__ schar readScalar<schar>(int i) {return scalar_8s[i];}
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template <> __device__ __forceinline__ ushort readScalar<ushort>(int i) {return scalar_16u[i];}
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template <> __device__ __forceinline__ short readScalar<short>(int i) {return scalar_16s[i];}
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template <> __device__ __forceinline__ int readScalar<int>(int i) {return scalar_32s[i];}
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template <> __device__ __forceinline__ float readScalar<float>(int i) {return scalar_32f[i];}
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template <> __device__ __forceinline__ double readScalar<double>(int i) {return scalar_64f[i];}
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void writeScalar(const uchar* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_8u, vals, sizeof(uchar) * 4) );
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}
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void writeScalar(const schar* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_8s, vals, sizeof(schar) * 4) );
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}
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void writeScalar(const ushort* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_16u, vals, sizeof(ushort) * 4) );
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}
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void writeScalar(const short* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_16s, vals, sizeof(short) * 4) );
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}
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void writeScalar(const int* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_32s, vals, sizeof(int) * 4) );
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}
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void writeScalar(const float* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_32f, vals, sizeof(float) * 4) );
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}
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void writeScalar(const double* vals)
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{
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cudaSafeCall( cudaMemcpyToSymbol(scalar_64f, vals, sizeof(double) * 4) );
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}
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template<typename T>
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__global__ void set_to_without_mask(T* mat, int cols, int rows, size_t step, int channels)
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{
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size_t x = blockIdx.x * blockDim.x + threadIdx.x;
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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 >> shift_and_sizeof<T>::shift ) + x;
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mat[idx] = readScalar<T>(x % channels);
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}
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}
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template<typename T>
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__global__ void set_to_with_mask(T* mat, const uchar* mask, int cols, int rows, size_t step, int channels, size_t step_mask)
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{
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size_t x = blockIdx.x * blockDim.x + threadIdx.x;
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size_t y = blockIdx.y * blockDim.y + threadIdx.y;
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if ((x < cols * channels ) && (y < rows))
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if (mask[y * step_mask + x / channels] != 0)
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{
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size_t idx = y * ( step >> shift_and_sizeof<T>::shift ) + x;
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mat[idx] = readScalar<T>(x % channels);
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}
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}
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template <typename T>
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void set_to_gpu(DevMem2Db mat, const T* scalar, DevMem2Db mask, int channels, cudaStream_t stream)
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{
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writeScalar(scalar);
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dim3 threadsPerBlock(32, 8, 1);
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dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
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set_to_with_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, (uchar*)mask.data, mat.cols, mat.rows, mat.step, channels, mask.step);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall ( cudaDeviceSynchronize() );
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}
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template void set_to_gpu<uchar >(DevMem2Db mat, const uchar* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<schar >(DevMem2Db mat, const schar* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<ushort>(DevMem2Db mat, const ushort* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<short >(DevMem2Db mat, const short* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<int >(DevMem2Db mat, const int* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<float >(DevMem2Db mat, const float* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template void set_to_gpu<double>(DevMem2Db mat, const double* scalar, DevMem2Db mask, int channels, cudaStream_t stream);
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template <typename T>
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void set_to_gpu(DevMem2Db mat, const T* scalar, int channels, cudaStream_t stream)
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{
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writeScalar(scalar);
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dim3 threadsPerBlock(32, 8, 1);
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dim3 numBlocks (mat.cols * channels / threadsPerBlock.x + 1, mat.rows / threadsPerBlock.y + 1, 1);
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set_to_without_mask<T><<<numBlocks, threadsPerBlock, 0, stream>>>((T*)mat.data, mat.cols, mat.rows, mat.step, channels);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall ( cudaDeviceSynchronize() );
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}
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template void set_to_gpu<uchar >(DevMem2Db mat, const uchar* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<schar >(DevMem2Db mat, const schar* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<ushort>(DevMem2Db mat, const ushort* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<short >(DevMem2Db mat, const short* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<int >(DevMem2Db mat, const int* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<float >(DevMem2Db mat, const float* scalar, int channels, cudaStream_t stream);
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template void set_to_gpu<double>(DevMem2Db mat, const double* scalar, int channels, cudaStream_t stream);
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///////////////////////////////////////////////////////////////////////////
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//////////////////////////////// ConvertTo ////////////////////////////////
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///////////////////////////////////////////////////////////////////////////
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template <typename T, typename D> struct Convertor : unary_function<T, D>
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{
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Convertor(double alpha_, double beta_) : alpha(alpha_), beta(beta_) {}
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__device__ __forceinline__ D operator()(const T& src) const
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{
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return saturate_cast<D>(alpha * src + beta);
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}
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double alpha, beta;
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};
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namespace detail
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{
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template <size_t src_size, size_t dst_size, typename F> struct ConvertTraitsDispatcher : DefaultTransformFunctorTraits<F>
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{
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};
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template <typename F> struct ConvertTraitsDispatcher<1, 1, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_shift = 8 };
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};
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template <typename F> struct ConvertTraitsDispatcher<1, 2, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_shift = 4 };
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};
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template <typename F> struct ConvertTraitsDispatcher<1, 4, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_block_dim_y = 8 };
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enum { smart_shift = 4 };
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};
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template <typename F> struct ConvertTraitsDispatcher<2, 2, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_shift = 4 };
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};
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template <typename F> struct ConvertTraitsDispatcher<2, 4, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_shift = 2 };
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};
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template <typename F> struct ConvertTraitsDispatcher<4, 2, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_block_dim_y = 8 };
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enum { smart_shift = 4 };
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};
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template <typename F> struct ConvertTraitsDispatcher<4, 4, F> : DefaultTransformFunctorTraits<F>
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{
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enum { smart_block_dim_y = 8 };
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enum { smart_shift = 2 };
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};
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template <typename F> struct ConvertTraits : ConvertTraitsDispatcher<sizeof(typename F::argument_type), sizeof(typename F::result_type), F>
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{
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};
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}
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template <typename T, typename D> struct TransformFunctorTraits< Convertor<T, D> > : detail::ConvertTraits< Convertor<T, D> >
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{
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};
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template<typename T, typename D>
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void cvt_(DevMem2Db src, DevMem2Db dst, double alpha, double beta, cudaStream_t stream)
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{
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cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
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cudaSafeCall( cudaSetDoubleForDevice(&beta) );
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Convertor<T, D> op(alpha, beta);
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cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<D>)dst, op, WithOutMask(), stream);
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}
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void convert_gpu(DevMem2Db src, int sdepth, DevMem2Db dst, int ddepth, double alpha, double beta, cudaStream_t stream)
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{
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typedef void (*caller_t)(DevMem2Db src, DevMem2Db dst, double alpha, double beta, cudaStream_t stream);
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static const caller_t tab[8][8] =
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{
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{cvt_<uchar, uchar>, cvt_<uchar, schar>, cvt_<uchar, ushort>, cvt_<uchar, short>,
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cvt_<uchar, int>, cvt_<uchar, float>, cvt_<uchar, double>, 0},
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{cvt_<schar, uchar>, cvt_<schar, schar>, cvt_<schar, ushort>, cvt_<schar, short>,
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cvt_<schar, int>, cvt_<schar, float>, cvt_<schar, double>, 0},
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{cvt_<ushort, uchar>, cvt_<ushort, schar>, cvt_<ushort, ushort>, cvt_<ushort, short>,
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cvt_<ushort, int>, cvt_<ushort, float>, cvt_<ushort, double>, 0},
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{cvt_<short, uchar>, cvt_<short, schar>, cvt_<short, ushort>, cvt_<short, short>,
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cvt_<short, int>, cvt_<short, float>, cvt_<short, double>, 0},
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{cvt_<int, uchar>, cvt_<int, schar>, cvt_<int, ushort>,
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cvt_<int, short>, cvt_<int, int>, cvt_<int, float>, cvt_<int, double>, 0},
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{cvt_<float, uchar>, cvt_<float, schar>, cvt_<float, ushort>,
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cvt_<float, short>, cvt_<float, int>, cvt_<float, float>, cvt_<float, double>, 0},
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{cvt_<double, uchar>, cvt_<double, schar>, cvt_<double, ushort>,
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cvt_<double, short>, cvt_<double, int>, cvt_<double, float>, cvt_<double, double>, 0},
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{0,0,0,0,0,0,0,0}
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};
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caller_t func = tab[sdepth][ddepth];
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if (!func)
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cv::gpu::error("Unsupported convert operation", __FILE__, __LINE__, "convert_gpu");
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func(src, dst, alpha, beta, stream);
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}
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}}} // namespace cv { namespace gpu { namespace device
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