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Open Source Computer Vision Library
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463 lines
20 KiB
463 lines
20 KiB
/*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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#ifndef HAVE_CUDA |
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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], DevMem2Df xmap, DevMem2Df ymap, cudaStream_t stream); |
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template <typename T> |
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void warpAffine_gpu(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation, |
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int borderMode, const float* borderValue, cudaStream_t stream, int cc); |
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void buildWarpPerspectiveMaps_gpu(float coeffs[3 * 3], DevMem2Df xmap, DevMem2Df ymap, cudaStream_t stream); |
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template <typename T> |
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void warpPerspective_gpu(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[3 * 3], DevMem2Db dst, int interpolation, |
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int borderMode, const float* borderValue, cudaStream_t stream, int cc); |
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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::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, |
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double coeffs[][3], cv::Size dsize, 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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dst.create(dsize, src.type()); |
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dst.setTo(cv::Scalar::all(0)); |
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NppiSize srcsz; |
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srcsz.height = wholeSize.height; |
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srcsz.width = wholeSize.width; |
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NppiRect srcroi; |
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srcroi.x = ofs.x; |
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srcroi.y = ofs.y; |
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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 = 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((npp_t*)src.datastart, srcsz, static_cast<int>(src.step), srcroi, |
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dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi, 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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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; |
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useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation]; |
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#ifdef linux |
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// NPP bug on float data |
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useNpp = useNpp && src.depth() != CV_32F; |
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#endif |
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if (useNpp) |
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{ |
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typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, 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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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, wholeSize, ofs, dst, coeffs, dsize, 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)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation, |
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int borderMode, const float* borderValue, cudaStream_t stream, int cc); |
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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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dst.create(dsize, src.type()); |
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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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DeviceInfo info; |
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int cc = info.majorVersion() * 10 + info.minorVersion(); |
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func(src, DevMem2Db(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), cc); |
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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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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; |
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useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation]; |
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#ifdef linux |
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// NPP bug on float data |
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useNpp = useNpp && src.depth() != CV_32F; |
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#endif |
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if (useNpp) |
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{ |
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typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, 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}, |
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{NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C4R>::call} |
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} |
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}; |
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double coeffs[3][3]; |
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Mat coeffsMat(3, 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, wholeSize, ofs, dst, coeffs, dsize, 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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|
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typedef void (*func_t)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation, |
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int borderMode, const float* borderValue, cudaStream_t stream, int cc); |
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static const func_t funcs[6][4] = |
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{ |
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{warpPerspective_gpu<uchar> , 0 /*warpPerspective_gpu<uchar2>*/ , warpPerspective_gpu<uchar3> , warpPerspective_gpu<uchar4> }, |
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{0 /*warpPerspective_gpu<schar>*/, 0 /*warpPerspective_gpu<char2>*/ , 0 /*warpPerspective_gpu<char3>*/, 0 /*warpPerspective_gpu<char4>*/}, |
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{warpPerspective_gpu<ushort> , 0 /*warpPerspective_gpu<ushort2>*/, warpPerspective_gpu<ushort3> , warpPerspective_gpu<ushort4> }, |
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{warpPerspective_gpu<short> , 0 /*warpPerspective_gpu<short2>*/ , warpPerspective_gpu<short3> , warpPerspective_gpu<short4> }, |
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{0 /*warpPerspective_gpu<int>*/ , 0 /*warpPerspective_gpu<int2>*/ , 0 /*warpPerspective_gpu<int3>*/ , 0 /*warpPerspective_gpu<int4>*/ }, |
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{warpPerspective_gpu<float> , 0 /*warpPerspective_gpu<float2>*/ , warpPerspective_gpu<float3> , warpPerspective_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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dst.create(dsize, src.type()); |
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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 (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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invert(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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DeviceInfo info; |
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int cc = info.majorVersion() * 10 + info.minorVersion(); |
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func(src, DevMem2Db(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), cc); |
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} |
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} |
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#endif // HAVE_CUDA
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