Vladislav Vinogradov
12 years ago
parent
2153a14872
commit
db1178b5df
7 changed files with 1377 additions and 1303 deletions
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/*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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// Copyright (C) 2013, OpenCV Foundation, 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/core/cuda/common.hpp" |
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namespace cv { namespace gpu { namespace cudev |
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{ |
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void copyWithMask(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool multiChannelMask, cudaStream_t stream); |
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template <typename T> |
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void set(PtrStepSz<T> mat, const T* scalar, int channels, cudaStream_t stream); |
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template <typename T> |
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void set(PtrStepSz<T> mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); |
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void convert(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream); |
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}}} |
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/*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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// Copyright (C) 2013, OpenCV Foundation, 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 "precomp.hpp" |
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using namespace cv; |
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using namespace cv::gpu; |
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/////////////////////////// matrix operations /////////////////////////
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#ifdef HAVE_CUDA |
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// CUDA implementation
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#include "cuda/matrix_operations.hpp" |
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namespace |
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{ |
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template <typename T> void cudaSet_(GpuMat& src, Scalar s, cudaStream_t stream) |
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{ |
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Scalar_<T> sf = s; |
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cudev::set<T>(PtrStepSz<T>(src), sf.val, src.channels(), stream); |
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} |
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void cudaSet(GpuMat& src, Scalar s, cudaStream_t stream) |
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{ |
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typedef void (*func_t)(GpuMat& src, Scalar s, cudaStream_t stream); |
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static const func_t funcs[] = |
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{ |
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cudaSet_<uchar>, |
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cudaSet_<schar>, |
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cudaSet_<ushort>, |
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cudaSet_<short>, |
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cudaSet_<int>, |
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cudaSet_<float>, |
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cudaSet_<double> |
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}; |
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funcs[src.depth()](src, s, stream); |
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} |
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template <typename T> void cudaSet_(GpuMat& src, Scalar s, PtrStepSzb mask, cudaStream_t stream) |
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{ |
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Scalar_<T> sf = s; |
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cudev::set<T>(PtrStepSz<T>(src), sf.val, mask, src.channels(), stream); |
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} |
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void cudaSet(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream) |
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{ |
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typedef void (*func_t)(GpuMat& src, Scalar s, PtrStepSzb mask, cudaStream_t stream); |
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static const func_t funcs[] = |
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{ |
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cudaSet_<uchar>, |
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cudaSet_<schar>, |
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cudaSet_<ushort>, |
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cudaSet_<short>, |
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cudaSet_<int>, |
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cudaSet_<float>, |
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cudaSet_<double> |
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}; |
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funcs[src.depth()](src, s, mask, stream); |
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} |
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void cudaCopyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
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{ |
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cudev::copyWithMask(src.reshape(1), dst.reshape(1), src.elemSize1(), src.channels(), mask.reshape(1), mask.channels() != 1, stream); |
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} |
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void cudaConvert(const GpuMat& src, GpuMat& dst, cudaStream_t stream) |
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{ |
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cudev::convert(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), 1.0, 0.0, stream); |
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} |
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void cudaConvert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream) |
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{ |
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cudev::convert(src.reshape(1), src.depth(), dst.reshape(1), dst.depth(), alpha, beta, stream); |
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} |
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} |
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// NPP implementation
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namespace |
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{ |
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//////////////////////////////////////////////////////////////////////////
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// Convert
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template<int SDEPTH, int DDEPTH> struct NppConvertFunc |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
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typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI); |
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}; |
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template<int DDEPTH> struct NppConvertFunc<CV_32F, DDEPTH> |
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{ |
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typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
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typedef NppStatus (*func_ptr)(const Npp32f* pSrc, int nSrcStep, dst_t* pDst, int nDstStep, NppiSize oSizeROI, NppRoundMode eRoundMode); |
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}; |
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template<int SDEPTH, int DDEPTH, typename NppConvertFunc<SDEPTH, DDEPTH>::func_ptr func> struct NppCvt |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
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static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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template<int DDEPTH, typename NppConvertFunc<CV_32F, DDEPTH>::func_ptr func> struct NppCvt<CV_32F, DDEPTH, func> |
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{ |
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typedef typename NPPTypeTraits<DDEPTH>::npp_type dst_t; |
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static void call(const GpuMat& src, GpuMat& dst, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(src.ptr<Npp32f>(), static_cast<int>(src.step), dst.ptr<dst_t>(), static_cast<int>(dst.step), sz, NPP_RND_NEAR) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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//////////////////////////////////////////////////////////////////////////
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// Set
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template<int SDEPTH, int SCN> struct NppSetFunc |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI); |
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}; |
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template<int SDEPTH> struct NppSetFunc<SDEPTH, 1> |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI); |
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}; |
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template<int SCN> struct NppSetFunc<CV_8S, SCN> |
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{ |
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typedef NppStatus (*func_ptr)(Npp8s values[], Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI); |
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}; |
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template<> struct NppSetFunc<CV_8S, 1> |
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{ |
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typedef NppStatus (*func_ptr)(Npp8s val, Npp8s* pSrc, int nSrcStep, NppiSize oSizeROI); |
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}; |
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template<int SDEPTH, int SCN, typename NppSetFunc<SDEPTH, SCN>::func_ptr func> struct NppSet |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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static void call(GpuMat& src, Scalar s, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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Scalar_<src_t> nppS = s; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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template<int SDEPTH, typename NppSetFunc<SDEPTH, 1>::func_ptr func> struct NppSet<SDEPTH, 1, func> |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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static void call(GpuMat& src, Scalar s, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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Scalar_<src_t> nppS = s; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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template<int SDEPTH, int SCN> struct NppSetMaskFunc |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef NppStatus (*func_ptr)(const src_t values[], src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep); |
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}; |
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template<int SDEPTH> struct NppSetMaskFunc<SDEPTH, 1> |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef NppStatus (*func_ptr)(src_t val, src_t* pSrc, int nSrcStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep); |
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}; |
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template<int SDEPTH, int SCN, typename NppSetMaskFunc<SDEPTH, SCN>::func_ptr func> struct NppSetMask |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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static void call(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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Scalar_<src_t> nppS = s; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(nppS.val, src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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template<int SDEPTH, typename NppSetMaskFunc<SDEPTH, 1>::func_ptr func> struct NppSetMask<SDEPTH, 1, func> |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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static void call(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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Scalar_<src_t> nppS = s; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(nppS[0], src.ptr<src_t>(), static_cast<int>(src.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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//////////////////////////////////////////////////////////////////////////
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// CopyMasked
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template<int SDEPTH> struct NppCopyWithMaskFunc |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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typedef NppStatus (*func_ptr)(const src_t* pSrc, int nSrcStep, src_t* pDst, int nDstStep, NppiSize oSizeROI, const Npp8u* pMask, int nMaskStep); |
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}; |
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template<int SDEPTH, typename NppCopyWithMaskFunc<SDEPTH>::func_ptr func> struct NppCopyWithMask |
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{ |
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typedef typename NPPTypeTraits<SDEPTH>::npp_type src_t; |
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static void call(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
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{ |
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NppiSize sz; |
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sz.width = src.cols; |
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sz.height = src.rows; |
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NppStreamHandler h(stream); |
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nppSafeCall( func(src.ptr<src_t>(), static_cast<int>(src.step), dst.ptr<src_t>(), static_cast<int>(dst.step), sz, mask.ptr<Npp8u>(), static_cast<int>(mask.step)) ); |
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if (stream == 0) |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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}; |
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} |
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// Dispatcher
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namespace cv { namespace gpu |
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{ |
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void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream = 0); |
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void convert(const GpuMat& src, GpuMat& dst, cudaStream_t stream = 0); |
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void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream = 0); |
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void set(GpuMat& m, Scalar s, cudaStream_t stream = 0); |
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void set(GpuMat& m, Scalar s, const GpuMat& mask, cudaStream_t stream = 0); |
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}} |
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namespace cv { namespace gpu |
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{ |
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void copyWithMask(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
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{ |
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CV_DbgAssert( src.size() == dst.size() && src.type() == dst.type() ); |
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CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 ); |
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CV_Assert( src.size() == mask.size() && mask.depth() == CV_8U && (mask.channels() == 1 || mask.channels() == src.channels()) ); |
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if (src.depth() == CV_64F) |
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{ |
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CV_Assert( deviceSupports(NATIVE_DOUBLE) ); |
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} |
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typedef void (*func_t)(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream); |
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static const func_t funcs[7][4] = |
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{ |
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/* 8U */ {NppCopyWithMask<CV_8U , nppiCopy_8u_C1MR >::call, cudaCopyWithMask, NppCopyWithMask<CV_8U , nppiCopy_8u_C3MR >::call, NppCopyWithMask<CV_8U , nppiCopy_8u_C4MR >::call}, |
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/* 8S */ {cudaCopyWithMask , cudaCopyWithMask, cudaCopyWithMask , cudaCopyWithMask }, |
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/* 16U */ {NppCopyWithMask<CV_16U, nppiCopy_16u_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_16U, nppiCopy_16u_C3MR>::call, NppCopyWithMask<CV_16U, nppiCopy_16u_C4MR>::call}, |
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/* 16S */ {NppCopyWithMask<CV_16S, nppiCopy_16s_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_16S, nppiCopy_16s_C3MR>::call, NppCopyWithMask<CV_16S, nppiCopy_16s_C4MR>::call}, |
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/* 32S */ {NppCopyWithMask<CV_32S, nppiCopy_32s_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_32S, nppiCopy_32s_C3MR>::call, NppCopyWithMask<CV_32S, nppiCopy_32s_C4MR>::call}, |
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/* 32F */ {NppCopyWithMask<CV_32F, nppiCopy_32f_C1MR>::call, cudaCopyWithMask, NppCopyWithMask<CV_32F, nppiCopy_32f_C3MR>::call, NppCopyWithMask<CV_32F, nppiCopy_32f_C4MR>::call}, |
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/* 64F */ {cudaCopyWithMask , cudaCopyWithMask, cudaCopyWithMask , cudaCopyWithMask } |
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}; |
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const func_t func = mask.channels() == src.channels() ? funcs[src.depth()][src.channels() - 1] : cudaCopyWithMask; |
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func(src, dst, mask, stream); |
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} |
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void convert(const GpuMat& src, GpuMat& dst, cudaStream_t stream) |
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{ |
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CV_DbgAssert( src.size() == dst.size() && src.channels() == dst.channels() ); |
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CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 ); |
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CV_Assert( dst.depth() <= CV_64F ); |
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if (src.depth() == CV_64F || dst.depth() == CV_64F) |
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{ |
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) ); |
||||
} |
||||
|
||||
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, cudaStream_t stream); |
||||
static const func_t funcs[7][7][4] = |
||||
{ |
||||
{ |
||||
/* 8U -> 8U */ {0, 0, 0, 0}, |
||||
/* 8U -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 8U -> 16U */ {NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C1R>::call, cudaConvert, cudaConvert, NppCvt<CV_8U, CV_16U, nppiConvert_8u16u_C4R>::call}, |
||||
/* 8U -> 16S */ {NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C1R>::call, cudaConvert, cudaConvert, NppCvt<CV_8U, CV_16S, nppiConvert_8u16s_C4R>::call}, |
||||
/* 8U -> 32S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 8U -> 32F */ {NppCvt<CV_8U, CV_32F, nppiConvert_8u32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 8U -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert } |
||||
}, |
||||
{ |
||||
/* 8S -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 8S -> 8S */ {0,0,0,0}, |
||||
/* 8S -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 8S -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 8S -> 32S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 8S -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 8S -> 64F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert} |
||||
}, |
||||
{ |
||||
/* 16U -> 8U */ {NppCvt<CV_16U, CV_8U , nppiConvert_16u8u_C1R >::call, cudaConvert, cudaConvert, NppCvt<CV_16U, CV_8U, nppiConvert_16u8u_C4R>::call}, |
||||
/* 16U -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16U -> 16U */ {0,0,0,0}, |
||||
/* 16U -> 16S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16U -> 32S */ {NppCvt<CV_16U, CV_32S, nppiConvert_16u32s_C1R>::call, cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16U -> 32F */ {NppCvt<CV_16U, CV_32F, nppiConvert_16u32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16U -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert } |
||||
}, |
||||
{ |
||||
/* 16S -> 8U */ {NppCvt<CV_16S, CV_8U , nppiConvert_16s8u_C1R >::call, cudaConvert, cudaConvert, NppCvt<CV_16S, CV_8U, nppiConvert_16s8u_C4R>::call}, |
||||
/* 16S -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16S -> 16U */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16S -> 16S */ {0,0,0,0}, |
||||
/* 16S -> 32S */ {NppCvt<CV_16S, CV_32S, nppiConvert_16s32s_C1R>::call, cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16S -> 32F */ {NppCvt<CV_16S, CV_32F, nppiConvert_16s32f_C1R>::call, cudaConvert, cudaConvert, cudaConvert }, |
||||
/* 16S -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert } |
||||
}, |
||||
{ |
||||
/* 32S -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32S -> 8S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32S -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32S -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32S -> 32S */ {0,0,0,0}, |
||||
/* 32S -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32S -> 64F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert} |
||||
}, |
||||
{ |
||||
/* 32F -> 8U */ {NppCvt<CV_32F, CV_8U , nppiConvert_32f8u_C1R >::call, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32F -> 8S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32F -> 16U */ {NppCvt<CV_32F, CV_16U, nppiConvert_32f16u_C1R>::call, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32F -> 16S */ {NppCvt<CV_32F, CV_16S, nppiConvert_32f16s_C1R>::call, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32F -> 32S */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 32F -> 32F */ {0,0,0,0}, |
||||
/* 32F -> 64F */ {cudaConvert , cudaConvert, cudaConvert, cudaConvert} |
||||
}, |
||||
{ |
||||
/* 64F -> 8U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 8S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 16U */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 16S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 32S */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 32F */ {cudaConvert, cudaConvert, cudaConvert, cudaConvert}, |
||||
/* 64F -> 64F */ {0,0,0,0} |
||||
} |
||||
}; |
||||
|
||||
const bool aligned = isAligned(src.data, 16) && isAligned(dst.data, 16); |
||||
if (!aligned) |
||||
{ |
||||
cudaConvert(src, dst, stream); |
||||
return; |
||||
} |
||||
|
||||
const func_t func = funcs[src.depth()][dst.depth()][src.channels() - 1]; |
||||
CV_DbgAssert( func != 0 ); |
||||
|
||||
func(src, dst, stream); |
||||
} |
||||
|
||||
void convert(const GpuMat& src, GpuMat& dst, double alpha, double beta, cudaStream_t stream) |
||||
{ |
||||
CV_DbgAssert( src.size() == dst.size() && src.channels() == dst.channels() ); |
||||
|
||||
CV_Assert( src.depth() <= CV_64F && src.channels() <= 4 ); |
||||
CV_Assert( dst.depth() <= CV_64F ); |
||||
|
||||
if (src.depth() == CV_64F || dst.depth() == CV_64F) |
||||
{ |
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) ); |
||||
} |
||||
|
||||
cudaConvert(src, dst, alpha, beta, stream); |
||||
} |
||||
|
||||
void set(GpuMat& m, Scalar s, cudaStream_t stream) |
||||
{ |
||||
if (s[0] == 0.0 && s[1] == 0.0 && s[2] == 0.0 && s[3] == 0.0) |
||||
{ |
||||
if (stream) |
||||
cudaSafeCall( cudaMemset2DAsync(m.data, m.step, 0, m.cols * m.elemSize(), m.rows, stream) ); |
||||
else |
||||
cudaSafeCall( cudaMemset2D(m.data, m.step, 0, m.cols * m.elemSize(), m.rows) ); |
||||
return; |
||||
} |
||||
|
||||
if (m.depth() == CV_8U) |
||||
{ |
||||
int cn = m.channels(); |
||||
|
||||
if (cn == 1 || (cn == 2 && s[0] == s[1]) || (cn == 3 && s[0] == s[1] && s[0] == s[2]) || (cn == 4 && s[0] == s[1] && s[0] == s[2] && s[0] == s[3])) |
||||
{ |
||||
int val = saturate_cast<uchar>(s[0]); |
||||
if (stream) |
||||
cudaSafeCall( cudaMemset2DAsync(m.data, m.step, val, m.cols * m.elemSize(), m.rows, stream) ); |
||||
else |
||||
cudaSafeCall( cudaMemset2D(m.data, m.step, val, m.cols * m.elemSize(), m.rows) ); |
||||
return; |
||||
} |
||||
} |
||||
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, cudaStream_t stream); |
||||
static const func_t funcs[7][4] = |
||||
{ |
||||
{NppSet<CV_8U , 1, nppiSet_8u_C1R >::call, cudaSet , cudaSet , NppSet<CV_8U , 4, nppiSet_8u_C4R >::call}, |
||||
{NppSet<CV_8S , 1, nppiSet_8s_C1R >::call, NppSet<CV_8S , 2, nppiSet_8s_C2R >::call, NppSet<CV_8S, 3, nppiSet_8s_C3R>::call, NppSet<CV_8S , 4, nppiSet_8s_C4R >::call}, |
||||
{NppSet<CV_16U, 1, nppiSet_16u_C1R>::call, NppSet<CV_16U, 2, nppiSet_16u_C2R>::call, cudaSet , NppSet<CV_16U, 4, nppiSet_16u_C4R>::call}, |
||||
{NppSet<CV_16S, 1, nppiSet_16s_C1R>::call, NppSet<CV_16S, 2, nppiSet_16s_C2R>::call, cudaSet , NppSet<CV_16S, 4, nppiSet_16s_C4R>::call}, |
||||
{NppSet<CV_32S, 1, nppiSet_32s_C1R>::call, cudaSet , cudaSet , NppSet<CV_32S, 4, nppiSet_32s_C4R>::call}, |
||||
{NppSet<CV_32F, 1, nppiSet_32f_C1R>::call, cudaSet , cudaSet , NppSet<CV_32F, 4, nppiSet_32f_C4R>::call}, |
||||
{cudaSet , cudaSet , cudaSet , cudaSet } |
||||
}; |
||||
|
||||
CV_Assert( m.depth() <= CV_64F && m.channels() <= 4 ); |
||||
|
||||
if (m.depth() == CV_64F) |
||||
{ |
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) ); |
||||
} |
||||
|
||||
funcs[m.depth()][m.channels() - 1](m, s, stream); |
||||
} |
||||
|
||||
void set(GpuMat& m, Scalar s, const GpuMat& mask, cudaStream_t stream) |
||||
{ |
||||
CV_DbgAssert( !mask.empty() ); |
||||
|
||||
CV_Assert( m.depth() <= CV_64F && m.channels() <= 4 ); |
||||
|
||||
if (m.depth() == CV_64F) |
||||
{ |
||||
CV_Assert( deviceSupports(NATIVE_DOUBLE) ); |
||||
} |
||||
|
||||
typedef void (*func_t)(GpuMat& src, Scalar s, const GpuMat& mask, cudaStream_t stream); |
||||
static const func_t funcs[7][4] = |
||||
{ |
||||
{NppSetMask<CV_8U , 1, nppiSet_8u_C1MR >::call, cudaSet, cudaSet, NppSetMask<CV_8U , 4, nppiSet_8u_C4MR >::call}, |
||||
{cudaSet , cudaSet, cudaSet, cudaSet }, |
||||
{NppSetMask<CV_16U, 1, nppiSet_16u_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_16U, 4, nppiSet_16u_C4MR>::call}, |
||||
{NppSetMask<CV_16S, 1, nppiSet_16s_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_16S, 4, nppiSet_16s_C4MR>::call}, |
||||
{NppSetMask<CV_32S, 1, nppiSet_32s_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_32S, 4, nppiSet_32s_C4MR>::call}, |
||||
{NppSetMask<CV_32F, 1, nppiSet_32f_C1MR>::call, cudaSet, cudaSet, NppSetMask<CV_32F, 4, nppiSet_32f_C4MR>::call}, |
||||
{cudaSet , cudaSet, cudaSet, cudaSet } |
||||
}; |
||||
|
||||
funcs[m.depth()][m.channels() - 1](m, s, mask, stream); |
||||
} |
||||
}} |
||||
|
||||
#endif // HAVE_CUDA
|
||||
|
||||
cv::gpu::GpuMat::GpuMat(int rows_, int cols_, int type_, void* data_, size_t step_) : |
||||
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(rows_), cols(cols_), |
||||
step(step_), data((uchar*)data_), refcount(0), |
||||
datastart((uchar*)data_), dataend((uchar*)data_) |
||||
{ |
||||
size_t minstep = cols * elemSize(); |
||||
|
||||
if (step == Mat::AUTO_STEP) |
||||
{ |
||||
step = minstep; |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
} |
||||
else |
||||
{ |
||||
if (rows == 1) |
||||
step = minstep; |
||||
|
||||
CV_DbgAssert( step >= minstep ); |
||||
|
||||
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; |
||||
} |
||||
|
||||
dataend += step * (rows - 1) + minstep; |
||||
} |
||||
|
||||
cv::gpu::GpuMat::GpuMat(Size size_, int type_, void* data_, size_t step_) : |
||||
flags(Mat::MAGIC_VAL + (type_ & Mat::TYPE_MASK)), rows(size_.height), cols(size_.width), |
||||
step(step_), data((uchar*)data_), refcount(0), |
||||
datastart((uchar*)data_), dataend((uchar*)data_) |
||||
{ |
||||
size_t minstep = cols * elemSize(); |
||||
|
||||
if (step == Mat::AUTO_STEP) |
||||
{ |
||||
step = minstep; |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
} |
||||
else |
||||
{ |
||||
if (rows == 1) |
||||
step = minstep; |
||||
|
||||
CV_DbgAssert( step >= minstep ); |
||||
|
||||
flags |= step == minstep ? Mat::CONTINUOUS_FLAG : 0; |
||||
} |
||||
dataend += step * (rows - 1) + minstep; |
||||
} |
||||
|
||||
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Range rowRange_, Range colRange_) |
||||
{ |
||||
flags = m.flags; |
||||
step = m.step; refcount = m.refcount; |
||||
data = m.data; datastart = m.datastart; dataend = m.dataend; |
||||
|
||||
if (rowRange_ == Range::all()) |
||||
{ |
||||
rows = m.rows; |
||||
} |
||||
else |
||||
{ |
||||
CV_Assert( 0 <= rowRange_.start && rowRange_.start <= rowRange_.end && rowRange_.end <= m.rows ); |
||||
|
||||
rows = rowRange_.size(); |
||||
data += step*rowRange_.start; |
||||
} |
||||
|
||||
if (colRange_ == Range::all()) |
||||
{ |
||||
cols = m.cols; |
||||
} |
||||
else |
||||
{ |
||||
CV_Assert( 0 <= colRange_.start && colRange_.start <= colRange_.end && colRange_.end <= m.cols ); |
||||
|
||||
cols = colRange_.size(); |
||||
data += colRange_.start*elemSize(); |
||||
flags &= cols < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; |
||||
} |
||||
|
||||
if (rows == 1) |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
|
||||
if (refcount) |
||||
CV_XADD(refcount, 1); |
||||
|
||||
if (rows <= 0 || cols <= 0) |
||||
rows = cols = 0; |
||||
} |
||||
|
||||
cv::gpu::GpuMat::GpuMat(const GpuMat& m, Rect roi) : |
||||
flags(m.flags), rows(roi.height), cols(roi.width), |
||||
step(m.step), data(m.data + roi.y*step), refcount(m.refcount), |
||||
datastart(m.datastart), dataend(m.dataend) |
||||
{ |
||||
flags &= roi.width < m.cols ? ~Mat::CONTINUOUS_FLAG : -1; |
||||
data += roi.x * elemSize(); |
||||
|
||||
CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows ); |
||||
|
||||
if (refcount) |
||||
CV_XADD(refcount, 1); |
||||
|
||||
if (rows <= 0 || cols <= 0) |
||||
rows = cols = 0; |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::create(int _rows, int _cols, int _type) |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) _rows; |
||||
(void) _cols; |
||||
(void) _type; |
||||
throw_no_cuda(); |
||||
#else |
||||
_type &= Mat::TYPE_MASK; |
||||
|
||||
if (rows == _rows && cols == _cols && type() == _type && data) |
||||
return; |
||||
|
||||
if (data) |
||||
release(); |
||||
|
||||
CV_DbgAssert( _rows >= 0 && _cols >= 0 ); |
||||
|
||||
if (_rows > 0 && _cols > 0) |
||||
{ |
||||
flags = Mat::MAGIC_VAL + _type; |
||||
rows = _rows; |
||||
cols = _cols; |
||||
|
||||
size_t esz = elemSize(); |
||||
|
||||
void* devPtr; |
||||
cudaSafeCall( cudaMallocPitch(&devPtr, &step, esz * cols, rows) ); |
||||
|
||||
// Single row must be continuous
|
||||
if (rows == 1) |
||||
step = esz * cols; |
||||
|
||||
if (esz * cols == step) |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
|
||||
int64 _nettosize = static_cast<int64>(step) * rows; |
||||
size_t nettosize = static_cast<size_t>(_nettosize); |
||||
|
||||
datastart = data = static_cast<uchar*>(devPtr); |
||||
dataend = data + nettosize; |
||||
|
||||
refcount = static_cast<int*>(fastMalloc(sizeof(*refcount))); |
||||
*refcount = 1; |
||||
} |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::release() |
||||
{ |
||||
#ifdef HAVE_CUDA |
||||
if (refcount && CV_XADD(refcount, -1) == 1) |
||||
{ |
||||
cudaFree(datastart); |
||||
fastFree(refcount); |
||||
} |
||||
|
||||
data = datastart = dataend = 0; |
||||
step = rows = cols = 0; |
||||
refcount = 0; |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::upload(const Mat& m) |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) m; |
||||
throw_no_cuda(); |
||||
#else |
||||
CV_DbgAssert( !m.empty() ); |
||||
|
||||
create(m.size(), m.type()); |
||||
|
||||
cudaSafeCall( cudaMemcpy2D(data, step, m.data, m.step, cols * elemSize(), rows, cudaMemcpyHostToDevice) ); |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::download(Mat& m) const |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) m; |
||||
throw_no_cuda(); |
||||
#else |
||||
CV_DbgAssert( !empty() ); |
||||
|
||||
m.create(size(), type()); |
||||
|
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToHost) ); |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& m) const |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) m; |
||||
throw_no_cuda(); |
||||
#else |
||||
CV_DbgAssert( !empty() ); |
||||
|
||||
m.create(size(), type()); |
||||
|
||||
cudaSafeCall( cudaMemcpy2D(m.data, m.step, data, step, cols * elemSize(), rows, cudaMemcpyDeviceToDevice) ); |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::copyTo(GpuMat& mat, const GpuMat& mask) const |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) mat; |
||||
(void) mask; |
||||
throw_no_cuda(); |
||||
#else |
||||
CV_DbgAssert( !empty() ); |
||||
|
||||
if (mask.empty()) |
||||
{ |
||||
copyTo(mat); |
||||
} |
||||
else |
||||
{ |
||||
mat.create(size(), type()); |
||||
|
||||
copyWithMask(*this, mat, mask); |
||||
} |
||||
#endif |
||||
} |
||||
|
||||
GpuMat& cv::gpu::GpuMat::setTo(Scalar s, const GpuMat& mask) |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) s; |
||||
(void) mask; |
||||
throw_no_cuda(); |
||||
return *this; |
||||
#else |
||||
CV_DbgAssert( !empty() ); |
||||
|
||||
if (mask.empty()) |
||||
set(*this, s); |
||||
else |
||||
set(*this, s, mask); |
||||
|
||||
return *this; |
||||
#endif |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::convertTo(GpuMat& dst, int rtype, double alpha, double beta) const |
||||
{ |
||||
#ifndef HAVE_CUDA |
||||
(void) dst; |
||||
(void) rtype; |
||||
(void) alpha; |
||||
(void) beta; |
||||
throw_no_cuda(); |
||||
#else |
||||
bool noScale = fabs(alpha - 1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon(); |
||||
|
||||
if (rtype < 0) |
||||
rtype = type(); |
||||
else |
||||
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), channels()); |
||||
|
||||
int sdepth = depth(); |
||||
int ddepth = CV_MAT_DEPTH(rtype); |
||||
if (sdepth == ddepth && noScale) |
||||
{ |
||||
copyTo(dst); |
||||
return; |
||||
} |
||||
|
||||
GpuMat temp; |
||||
const GpuMat* psrc = this; |
||||
if (sdepth != ddepth && psrc == &dst) |
||||
{ |
||||
temp = *this; |
||||
psrc = &temp; |
||||
} |
||||
|
||||
dst.create(size(), rtype); |
||||
|
||||
if (noScale) |
||||
convert(*psrc, dst); |
||||
else |
||||
convert(*psrc, dst, alpha, beta); |
||||
#endif |
||||
} |
||||
|
||||
GpuMat cv::gpu::GpuMat::reshape(int new_cn, int new_rows) const |
||||
{ |
||||
GpuMat hdr = *this; |
||||
|
||||
int cn = channels(); |
||||
if (new_cn == 0) |
||||
new_cn = cn; |
||||
|
||||
int total_width = cols * cn; |
||||
|
||||
if ((new_cn > total_width || total_width % new_cn != 0) && new_rows == 0) |
||||
new_rows = rows * total_width / new_cn; |
||||
|
||||
if (new_rows != 0 && new_rows != rows) |
||||
{ |
||||
int total_size = total_width * rows; |
||||
|
||||
if (!isContinuous()) |
||||
CV_Error(cv::Error::BadStep, "The matrix is not continuous, thus its number of rows can not be changed"); |
||||
|
||||
if ((unsigned)new_rows > (unsigned)total_size) |
||||
CV_Error(cv::Error::StsOutOfRange, "Bad new number of rows"); |
||||
|
||||
total_width = total_size / new_rows; |
||||
|
||||
if (total_width * new_rows != total_size) |
||||
CV_Error(cv::Error::StsBadArg, "The total number of matrix elements is not divisible by the new number of rows"); |
||||
|
||||
hdr.rows = new_rows; |
||||
hdr.step = total_width * elemSize1(); |
||||
} |
||||
|
||||
int new_width = total_width / new_cn; |
||||
|
||||
if (new_width * new_cn != total_width) |
||||
CV_Error(cv::Error::BadNumChannels, "The total width is not divisible by the new number of channels"); |
||||
|
||||
hdr.cols = new_width; |
||||
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn - 1) << CV_CN_SHIFT); |
||||
|
||||
return hdr; |
||||
} |
||||
|
||||
void cv::gpu::GpuMat::locateROI(Size& wholeSize, Point& ofs) const |
||||
{ |
||||
CV_DbgAssert( step > 0 ); |
||||
|
||||
size_t esz = elemSize(); |
||||
ptrdiff_t delta1 = data - datastart; |
||||
ptrdiff_t delta2 = dataend - datastart; |
||||
|
||||
if (delta1 == 0) |
||||
{ |
||||
ofs.x = ofs.y = 0; |
||||
} |
||||
else |
||||
{ |
||||
ofs.y = static_cast<int>(delta1 / step); |
||||
ofs.x = static_cast<int>((delta1 - step * ofs.y) / esz); |
||||
|
||||
CV_DbgAssert( data == datastart + ofs.y * step + ofs.x * esz ); |
||||
} |
||||
|
||||
size_t minstep = (ofs.x + cols) * esz; |
||||
|
||||
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / step + 1), ofs.y + rows); |
||||
wholeSize.width = std::max(static_cast<int>((delta2 - step * (wholeSize.height - 1)) / esz), ofs.x + cols); |
||||
} |
||||
|
||||
GpuMat& cv::gpu::GpuMat::adjustROI(int dtop, int dbottom, int dleft, int dright) |
||||
{ |
||||
Size wholeSize; |
||||
Point ofs; |
||||
locateROI(wholeSize, ofs); |
||||
|
||||
size_t esz = elemSize(); |
||||
|
||||
int row1 = std::max(ofs.y - dtop, 0); |
||||
int row2 = std::min(ofs.y + rows + dbottom, wholeSize.height); |
||||
|
||||
int col1 = std::max(ofs.x - dleft, 0); |
||||
int col2 = std::min(ofs.x + cols + dright, wholeSize.width); |
||||
|
||||
data += (row1 - ofs.y) * step + (col1 - ofs.x) * esz; |
||||
rows = row2 - row1; |
||||
cols = col2 - col1; |
||||
|
||||
if (esz * cols == step || rows == 1) |
||||
flags |= Mat::CONTINUOUS_FLAG; |
||||
else |
||||
flags &= ~Mat::CONTINUOUS_FLAG; |
||||
|
||||
return *this; |
||||
} |
||||
|
||||
void cv::gpu::createContinuous(int rows, int cols, int type, GpuMat& m) |
||||
{ |
||||
const int area = rows * cols; |
||||
|
||||
if (m.empty() || m.type() != type || !m.isContinuous() || m.size().area() < area) |
||||
m.create(1, area, type); |
||||
|
||||
m.cols = cols; |
||||
m.rows = rows; |
||||
m.step = m.elemSize() * cols; |
||||
m.flags |= Mat::CONTINUOUS_FLAG; |
||||
} |
||||
|
||||
void cv::gpu::ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m) |
||||
{ |
||||
if (m.empty() || m.type() != type || m.data != m.datastart) |
||||
{ |
||||
m.create(rows, cols, type); |
||||
} |
||||
else |
||||
{ |
||||
const size_t esz = m.elemSize(); |
||||
const ptrdiff_t delta2 = m.dataend - m.datastart; |
||||
|
||||
const size_t minstep = m.cols * esz; |
||||
|
||||
Size wholeSize; |
||||
wholeSize.height = std::max(static_cast<int>((delta2 - minstep) / m.step + 1), m.rows); |
||||
wholeSize.width = std::max(static_cast<int>((delta2 - m.step * (wholeSize.height - 1)) / esz), m.cols); |
||||
|
||||
if (wholeSize.height < rows || wholeSize.width < cols) |
||||
{ |
||||
m.create(rows, cols, type); |
||||
} |
||||
else |
||||
{ |
||||
m.cols = cols; |
||||
m.rows = rows; |
||||
} |
||||
} |
||||
} |
||||
|
||||
GpuMat cv::gpu::allocMatFromBuf(int rows, int cols, int type, GpuMat& mat) |
||||
{ |
||||
if (!mat.empty() && mat.type() == type && mat.rows >= rows && mat.cols >= cols) |
||||
return mat(Rect(0, 0, cols, rows)); |
||||
|
||||
return mat = GpuMat(rows, cols, type); |
||||
} |
||||
Loading…
Reference in new issue