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455 lines
19 KiB
C++
455 lines
19 KiB
C++
/*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 "precomp.hpp"
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#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
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void cv::gpu::warpAffine(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); }
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void cv::gpu::buildWarpAffineMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::warpPerspective(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); }
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void cv::gpu::buildWarpPerspectiveMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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#else // HAVE_CUDA
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namespace cv { namespace gpu { namespace device
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{
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namespace imgproc
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{
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void buildWarpAffineMaps_gpu(float coeffs[2 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream);
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template <typename T>
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void warpAffine_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
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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);
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template <typename T>
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void warpPerspective_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation,
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int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
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}
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}}}
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void cv::gpu::buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream)
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{
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using namespace cv::gpu::device::imgproc;
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CV_Assert(M.rows == 2 && M.cols == 3);
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xmap.create(dsize, CV_32FC1);
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ymap.create(dsize, CV_32FC1);
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float coeffs[2 * 3];
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Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);
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if (inverse)
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M.convertTo(coeffsMat, coeffsMat.type());
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else
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{
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cv::Mat iM;
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invertAffineTransform(M, iM);
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iM.convertTo(coeffsMat, coeffsMat.type());
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}
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buildWarpAffineMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
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}
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void cv::gpu::buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream)
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{
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using namespace cv::gpu::device::imgproc;
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CV_Assert(M.rows == 3 && M.cols == 3);
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xmap.create(dsize, CV_32FC1);
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ymap.create(dsize, CV_32FC1);
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float coeffs[3 * 3];
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Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);
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if (inverse)
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M.convertTo(coeffsMat, coeffsMat.type());
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else
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{
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cv::Mat iM;
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invert(M, iM);
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iM.convertTo(coeffsMat, coeffsMat.type());
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}
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buildWarpPerspectiveMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
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}
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namespace
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{
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template<int DEPTH> struct NppTypeTraits;
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template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
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template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
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template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
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template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; typedef Npp16sc npp_complex_type; };
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template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; typedef Npp32sc npp_complex_type; };
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template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; typedef Npp32fc npp_complex_type; };
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template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; typedef Npp64fc npp_complex_type; };
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template <int DEPTH> struct NppWarpFunc
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{
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typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
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typedef NppStatus (*func_t)(const npp_t* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, npp_t* pDst,
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int dstStep, NppiRect dstRoi, const double coeffs[][3],
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int interpolation);
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};
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template <int DEPTH, typename NppWarpFunc<DEPTH>::func_t func> struct NppWarp
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{
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typedef typename NppWarpFunc<DEPTH>::npp_t npp_t;
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static void call(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int interpolation, cudaStream_t stream)
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{
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static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};
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NppiSize srcsz;
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srcsz.height = src.rows;
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srcsz.width = src.cols;
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NppiRect srcroi;
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srcroi.x = 0;
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srcroi.y = 0;
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srcroi.height = src.rows;
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srcroi.width = src.cols;
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NppiRect dstroi;
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dstroi.x = 0;
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dstroi.y = 0;
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dstroi.height = dst.rows;
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dstroi.width = dst.cols;
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cv::gpu::NppStreamHandler h(stream);
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nppSafeCall( func(src.ptr<npp_t>(), srcsz, static_cast<int>(src.step), srcroi,
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dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi,
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coeffs, npp_inter[interpolation]) );
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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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void cv::gpu::warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s)
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{
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CV_Assert(M.rows == 2 && M.cols == 3);
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int interpolation = flags & INTER_MAX;
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CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
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CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
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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());
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Size wholeSize;
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Point ofs;
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src.locateROI(wholeSize, ofs);
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static const bool useNppTab[6][4][3] =
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{
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{
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{false, false, true},
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{false, false, false},
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{false, true, true},
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{false, false, false}
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},
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{
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{false, false, false},
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{false, false, false},
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{false, false, false},
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{false, false, false}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, false}
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},
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{
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{false, false, false},
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{false, false, false},
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{false, false, false},
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{false, false, false}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, true}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, true}
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}
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};
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bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
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// NPP bug on float data
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useNpp = useNpp && src.depth() != CV_32F;
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if (useNpp)
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{
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typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
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static const func_t funcs[2][6][4] =
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{
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{
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{NppWarp<CV_8U, nppiWarpAffine_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffine_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffine_8u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_16U, nppiWarpAffine_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffine_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffine_16u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_32S, nppiWarpAffine_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffine_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffine_32s_C4R>::call},
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{NppWarp<CV_32F, nppiWarpAffine_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpAffine_32f_C3R>::call, NppWarp<CV_32F, nppiWarpAffine_32f_C4R>::call}
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},
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{
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{NppWarp<CV_8U, nppiWarpAffineBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffineBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffineBack_8u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_16U, nppiWarpAffineBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffineBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffineBack_16u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_32S, nppiWarpAffineBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffineBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffineBack_32s_C4R>::call},
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{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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}
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};
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dst.setTo(borderValue);
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double coeffs[2][3];
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Mat coeffsMat(2, 3, CV_64F, (void*)coeffs);
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M.convertTo(coeffsMat, coeffsMat.type());
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const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
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CV_Assert(func != 0);
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func(src, dst, coeffs, interpolation, StreamAccessor::getStream(s));
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}
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else
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{
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using namespace cv::gpu::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,
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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] =
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{
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{warpAffine_gpu<uchar> , 0 /*warpAffine_gpu<uchar2>*/ , warpAffine_gpu<uchar3> , warpAffine_gpu<uchar4> },
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{0 /*warpAffine_gpu<schar>*/, 0 /*warpAffine_gpu<char2>*/ , 0 /*warpAffine_gpu<char3>*/, 0 /*warpAffine_gpu<char4>*/},
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{warpAffine_gpu<ushort> , 0 /*warpAffine_gpu<ushort2>*/, warpAffine_gpu<ushort3> , warpAffine_gpu<ushort4> },
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{warpAffine_gpu<short> , 0 /*warpAffine_gpu<short2>*/ , warpAffine_gpu<short3> , warpAffine_gpu<short4> },
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{0 /*warpAffine_gpu<int>*/ , 0 /*warpAffine_gpu<int2>*/ , 0 /*warpAffine_gpu<int3>*/ , 0 /*warpAffine_gpu<int4>*/ },
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{warpAffine_gpu<float> , 0 /*warpAffine_gpu<float2>*/ , warpAffine_gpu<float3> , warpAffine_gpu<float4> }
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};
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const func_t func = funcs[src.depth()][src.channels() - 1];
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CV_Assert(func != 0);
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int gpuBorderType;
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CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
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float coeffs[2 * 3];
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Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);
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if (flags & WARP_INVERSE_MAP)
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M.convertTo(coeffsMat, coeffsMat.type());
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else
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{
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cv::Mat iM;
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invertAffineTransform(M, iM);
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iM.convertTo(coeffsMat, coeffsMat.type());
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}
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Scalar_<float> borderValueFloat;
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borderValueFloat = borderValue;
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func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
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dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), deviceSupports(FEATURE_SET_COMPUTE_20));
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}
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}
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void cv::gpu::warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s)
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{
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CV_Assert(M.rows == 3 && M.cols == 3);
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int interpolation = flags & INTER_MAX;
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CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
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CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
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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());
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Size wholeSize;
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Point ofs;
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src.locateROI(wholeSize, ofs);
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static const bool useNppTab[6][4][3] =
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{
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{
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{false, false, true},
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{false, false, false},
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{false, true, true},
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{false, false, false}
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},
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{
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{false, false, false},
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{false, false, false},
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{false, false, false},
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{false, false, false}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, false}
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},
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{
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{false, false, false},
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{false, false, false},
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{false, false, false},
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{false, false, false}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, true}
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},
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{
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{false, true, true},
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{false, false, false},
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{false, true, true},
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{false, false, true}
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}
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};
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bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation];
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// NPP bug on float data
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useNpp = useNpp && src.depth() != CV_32F;
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if (useNpp)
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{
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typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream);
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static const func_t funcs[2][6][4] =
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{
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{
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{NppWarp<CV_8U, nppiWarpPerspective_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspective_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspective_8u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_16U, nppiWarpPerspective_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspective_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspective_16u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_32S, nppiWarpPerspective_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspective_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspective_32s_C4R>::call},
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{NppWarp<CV_32F, nppiWarpPerspective_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspective_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspective_32f_C4R>::call}
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},
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{
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{NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C4R>::call},
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{0, 0, 0, 0},
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{NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C4R>::call},
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{0, 0, 0, 0},
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|
{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}
|
|
}
|
|
};
|
|
|
|
dst.setTo(borderValue);
|
|
|
|
double coeffs[3][3];
|
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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);
|
|
|
|
func(src, dst, coeffs, interpolation, StreamAccessor::getStream(s));
|
|
}
|
|
else
|
|
{
|
|
using namespace cv::gpu::device::imgproc;
|
|
|
|
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[2 * 3], PtrStepSzb dst, int interpolation,
|
|
int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
|
|
|
|
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);
|
|
|
|
int gpuBorderType;
|
|
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
|
|
|
|
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,
|
|
dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), deviceSupports(FEATURE_SET_COMPUTE_20));
|
|
}
|
|
}
|
|
|
|
#endif // HAVE_CUDA
|