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opencv/modules/cudawarping/src/warp.cpp

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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 "precomp.hpp"
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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using namespace cv;
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using namespace cv::cuda;
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Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
Revert "Merge pull request #836 from jet47:gpu-modules" This reverts commit fba72cb60d27905cce9f1390250adf13d5355c03, reversing changes made to 02131ffb620f349617a65da334591eca5d3cb23b.
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renamed gpu namespace -> cuda
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void cv::cuda::warpAffine(InputArray, OutputArray, InputArray, Size, int, int, Scalar, Stream&) { throw_no_cuda(); }
void cv::cuda::buildWarpAffineMaps(InputArray, bool, Size, OutputArray, OutputArray, Stream&) { throw_no_cuda(); }
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renamed gpu namespace -> cuda
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void cv::cuda::warpPerspective(InputArray, OutputArray, InputArray, Size, int, int, Scalar, Stream&) { throw_no_cuda(); }
void cv::cuda::buildWarpPerspectiveMaps(InputArray, bool, Size, OutputArray, OutputArray, Stream&) { throw_no_cuda(); }
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renamed gpu namespace -> cuda
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void cv::cuda::rotate(InputArray, OutputArray, Size, double, double, double, int, Stream&) { throw_no_cuda(); }
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
2013-06-04 17:32:35 +08:00
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#else // HAVE_CUDA
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namespace cv { namespace cuda { namespace device
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{
namespace imgproc
{
void buildWarpAffineMaps_gpu(float coeffs[2 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream);
template <typename T>
void warpAffine_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
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void buildWarpPerspectiveMaps_gpu(float coeffs[3 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream);
template <typename T>
void warpPerspective_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation,
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
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}
}}}
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void cv::cuda::buildWarpAffineMaps(InputArray _M, bool inverse, Size dsize, OutputArray _xmap, OutputArray _ymap, Stream& stream)
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{
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using namespace cv::cuda::device::imgproc;
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Mat M = _M.getMat();
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CV_Assert( M.rows == 2 && M.cols == 3 );
_xmap.create(dsize, CV_32FC1);
_ymap.create(dsize, CV_32FC1);
GpuMat xmap = _xmap.getGpuMat();
GpuMat ymap = _ymap.getGpuMat();
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float coeffs[2 * 3];
Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);
if (inverse)
M.convertTo(coeffsMat, coeffsMat.type());
else
{
cv::Mat iM;
invertAffineTransform(M, iM);
iM.convertTo(coeffsMat, coeffsMat.type());
}
buildWarpAffineMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
}
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void cv::cuda::buildWarpPerspectiveMaps(InputArray _M, bool inverse, Size dsize, OutputArray _xmap, OutputArray _ymap, Stream& stream)
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{
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using namespace cv::cuda::device::imgproc;
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Mat M = _M.getMat();
CV_Assert( M.rows == 3 && M.cols == 3 );
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_xmap.create(dsize, CV_32FC1);
_ymap.create(dsize, CV_32FC1);
GpuMat xmap = _xmap.getGpuMat();
GpuMat ymap = _ymap.getGpuMat();
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float coeffs[3 * 3];
Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);
if (inverse)
M.convertTo(coeffsMat, coeffsMat.type());
else
{
cv::Mat iM;
invert(M, iM);
iM.convertTo(coeffsMat, coeffsMat.type());
}
buildWarpPerspectiveMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
}
namespace
{
template <int DEPTH> struct NppWarpFunc
{
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
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Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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typedef NppStatus (*func_t)(const npp_type* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, npp_type* pDst,
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int dstStep, NppiRect dstRoi, const double coeffs[][3],
int interpolation);
};
template <int DEPTH, typename NppWarpFunc<DEPTH>::func_t func> struct NppWarp
{
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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typedef typename NppWarpFunc<DEPTH>::npp_type npp_type;
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renamed gpu namespace -> cuda
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static void call(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int interpolation, cudaStream_t stream)
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{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
NppiSize srcsz;
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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srcsz.height = src.rows;
srcsz.width = src.cols;
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NppiRect srcroi;
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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srcroi.x = 0;
srcroi.y = 0;
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srcroi.height = src.rows;
srcroi.width = src.cols;
NppiRect dstroi;
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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dstroi.x = 0;
dstroi.y = 0;
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dstroi.height = dst.rows;
dstroi.width = dst.cols;
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cv::cuda::NppStreamHandler h(stream);
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Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
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nppSafeCall( func(src.ptr<npp_type>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<npp_type>(), static_cast<int>(dst.step), dstroi,
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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coeffs, npp_inter[interpolation]) );
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if (stream == 0)
restore cudaSafeCall
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cudaSafeCall( cudaDeviceSynchronize() );
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}
};
}
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void cv::cuda::warpAffine(InputArray _src, OutputArray _dst, InputArray _M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream)
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{
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GpuMat src = _src.getGpuMat();
Mat M = _M.getMat();
CV_Assert( M.rows == 2 && M.cols == 3 );
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const int interpolation = flags & INTER_MAX;
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CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC );
CV_Assert( borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP );
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_dst.create(dsize, src.type());
GpuMat dst = _dst.getGpuMat();
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
2013-01-23 18:43:36 +08:00
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Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
static const bool useNppTab[6][4][3] =
{
{
{false, false, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
}
};
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
refactored gpu perf tests and fixed sanity tests
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// NPP bug on float data
useNpp = useNpp && src.depth() != CV_32F;
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if (useNpp)
{
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typedef void (*func_t)(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
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static const func_t funcs[2][6][4] =
{
{
{NppWarp<CV_8U, nppiWarpAffine_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffine_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffine_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpAffine_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffine_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffine_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpAffine_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffine_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffine_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpAffine_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpAffine_32f_C3R>::call, NppWarp<CV_32F, nppiWarpAffine_32f_C4R>::call}
},
{
{NppWarp<CV_8U, nppiWarpAffineBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffineBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffineBack_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpAffineBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffineBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffineBack_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpAffineBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffineBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffineBack_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpAffineBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpAffineBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpAffineBack_32f_C4R>::call}
}
};
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dst.setTo(borderValue, stream);
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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double coeffs[2][3];
Mat coeffsMat(2, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
CV_Assert(func != 0);
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func(src, dst, coeffs, interpolation, StreamAccessor::getStream(stream));
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}
else
{
renamed gpu::cudev namespace -> cuda::device cudev is used for new device layer
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using namespace cv::cuda::device::imgproc;
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typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
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static const func_t funcs[6][4] =
{
{warpAffine_gpu<uchar> , 0 /*warpAffine_gpu<uchar2>*/ , warpAffine_gpu<uchar3> , warpAffine_gpu<uchar4> },
{0 /*warpAffine_gpu<schar>*/, 0 /*warpAffine_gpu<char2>*/ , 0 /*warpAffine_gpu<char3>*/, 0 /*warpAffine_gpu<char4>*/},
{warpAffine_gpu<ushort> , 0 /*warpAffine_gpu<ushort2>*/, warpAffine_gpu<ushort3> , warpAffine_gpu<ushort4> },
{warpAffine_gpu<short> , 0 /*warpAffine_gpu<short2>*/ , warpAffine_gpu<short3> , warpAffine_gpu<short4> },
{0 /*warpAffine_gpu<int>*/ , 0 /*warpAffine_gpu<int2>*/ , 0 /*warpAffine_gpu<int3>*/ , 0 /*warpAffine_gpu<int4>*/ },
{warpAffine_gpu<float> , 0 /*warpAffine_gpu<float2>*/ , warpAffine_gpu<float3> , warpAffine_gpu<float4> }
};
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
float coeffs[2 * 3];
Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);
if (flags & WARP_INVERSE_MAP)
M.convertTo(coeffsMat, coeffsMat.type());
else
{
cv::Mat iM;
invertAffineTransform(M, iM);
iM.convertTo(coeffsMat, coeffsMat.type());
}
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
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dst, interpolation, borderMode, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20));
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}
}
renamed gpu namespace -> cuda
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void cv::cuda::warpPerspective(InputArray _src, OutputArray _dst, InputArray _M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream)
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{
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GpuMat src = _src.getGpuMat();
Mat M = _M.getMat();
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CV_Assert( M.rows == 3 && M.cols == 3 );
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const int interpolation = flags & INTER_MAX;
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CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC );
CV_Assert( borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP) ;
_dst.create(dsize, src.type());
GpuMat dst = _dst.getGpuMat();
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
2013-01-23 18:43:36 +08:00
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Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
static const bool useNppTab[6][4][3] =
{
{
{false, false, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
}
};
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
refactored gpu perf tests and fixed sanity tests
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// NPP bug on float data
useNpp = useNpp && src.depth() != CV_32F;
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if (useNpp)
{
renamed gpu namespace -> cuda
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typedef void (*func_t)(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
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static const func_t funcs[2][6][4] =
{
{
{NppWarp<CV_8U, nppiWarpPerspective_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspective_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspective_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpPerspective_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspective_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspective_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpPerspective_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspective_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspective_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpPerspective_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspective_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspective_32f_C4R>::call}
},
{
{NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C4R>::call}
}
};
switched to Input/Output Array in gpuwarping functions
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dst.setTo(borderValue, stream);
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
2013-01-23 18:43:36 +08:00
Normalize line endings and whitespace
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double coeffs[3][3];
Mat coeffsMat(3, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
CV_Assert(func != 0);
switched to Input/Output Array in gpuwarping functions
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func(src, dst, coeffs, interpolation, StreamAccessor::getStream(stream));
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}
else
{
renamed gpu::cudev namespace -> cuda::device cudev is used for new device layer
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using namespace cv::cuda::device::imgproc;
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typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
optimizations: - new reduce implementation (with kepler optimizations) - saturate_cast via asm command - video SIMD instructions in element operations - float arithmetics instead of double - new deviceSupports function
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int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
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static const func_t funcs[6][4] =
{
{warpPerspective_gpu<uchar> , 0 /*warpPerspective_gpu<uchar2>*/ , warpPerspective_gpu<uchar3> , warpPerspective_gpu<uchar4> },
{0 /*warpPerspective_gpu<schar>*/, 0 /*warpPerspective_gpu<char2>*/ , 0 /*warpPerspective_gpu<char3>*/, 0 /*warpPerspective_gpu<char4>*/},
{warpPerspective_gpu<ushort> , 0 /*warpPerspective_gpu<ushort2>*/, warpPerspective_gpu<ushort3> , warpPerspective_gpu<ushort4> },
{warpPerspective_gpu<short> , 0 /*warpPerspective_gpu<short2>*/ , warpPerspective_gpu<short3> , warpPerspective_gpu<short4> },
{0 /*warpPerspective_gpu<int>*/ , 0 /*warpPerspective_gpu<int2>*/ , 0 /*warpPerspective_gpu<int3>*/ , 0 /*warpPerspective_gpu<int4>*/ },
{warpPerspective_gpu<float> , 0 /*warpPerspective_gpu<float2>*/ , warpPerspective_gpu<float3> , warpPerspective_gpu<float4> }
};
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
float coeffs[3 * 3];
Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);
if (flags & WARP_INVERSE_MAP)
M.convertTo(coeffsMat, coeffsMat.type());
else
{
cv::Mat iM;
invert(M, iM);
iM.convertTo(coeffsMat, coeffsMat.type());
}
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
switched to Input/Output Array in gpuwarping functions
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dst, interpolation, borderMode, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20));
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
2013-06-04 17:32:35 +08:00
}
}
////////////////////////////////////////////////////////////////////////
// rotate
namespace
{
template <int DEPTH> struct NppRotateFunc
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_type* pSrc, NppiSize oSrcSize, int nSrcStep, NppiRect oSrcROI,
npp_type* pDst, int nDstStep, NppiRect oDstROI,
double nAngle, double nShiftX, double nShiftY, int eInterpolation);
};
template <int DEPTH, typename NppRotateFunc<DEPTH>::func_t func> struct NppRotate
{
typedef typename NppRotateFunc<DEPTH>::npp_type npp_type;
static void call(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream)
{
(void)dsize;
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
NppStreamHandler h(stream);
NppiSize srcsz;
srcsz.height = src.rows;
srcsz.width = src.cols;
NppiRect srcroi;
srcroi.x = srcroi.y = 0;
srcroi.height = src.rows;
srcroi.width = src.cols;
NppiRect dstroi;
dstroi.x = dstroi.y = 0;
dstroi.height = dst.rows;
dstroi.width = dst.cols;
nppSafeCall( func(src.ptr<npp_type>(), srcsz, static_cast<int>(src.step), srcroi,
dst.ptr<npp_type>(), static_cast<int>(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
renamed gpu namespace -> cuda
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void cv::cuda::rotate(InputArray _src, OutputArray _dst, Size dsize, double angle, double xShift, double yShift, int interpolation, Stream& stream)
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
2013-06-04 17:32:35 +08:00
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{NppRotate<CV_8U, nppiRotate_8u_C1R>::call, 0, NppRotate<CV_8U, nppiRotate_8u_C3R>::call, NppRotate<CV_8U, nppiRotate_8u_C4R>::call},
{0,0,0,0},
{NppRotate<CV_16U, nppiRotate_16u_C1R>::call, 0, NppRotate<CV_16U, nppiRotate_16u_C3R>::call, NppRotate<CV_16U, nppiRotate_16u_C4R>::call},
{0,0,0,0},
{0,0,0,0},
{NppRotate<CV_32F, nppiRotate_32f_C1R>::call, 0, NppRotate<CV_32F, nppiRotate_32f_C3R>::call, NppRotate<CV_32F, nppiRotate_32f_C4R>::call}
};
switched to Input/Output Array in gpuwarping functions
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GpuMat src = _src.getGpuMat();
CV_Assert( src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32F );
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC );
_dst.create(dsize, src.type());
GpuMat dst = _dst.getGpuMat();
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
2013-06-04 17:32:35 +08:00
switched to Input/Output Array in gpuwarping functions
2013-04-26 21:05:59 +08:00
dst.setTo(Scalar::all(0), stream);
Revert "Revert "Merge pull request #836 from jet47:gpu-modules""
2013-06-04 17:32:35 +08:00
funcs[src.depth()][src.channels() - 1](src, dst, dsize, angle, xShift, yShift, interpolation, StreamAccessor::getStream(stream));
gpuwarping module for image warping
2013-04-18 14:45:48 +08:00
}
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#endif // HAVE_CUDA
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