/*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" using namespace cv; using namespace cv::cuda; #if !defined HAVE_CUDA || defined(CUDA_DISABLER) 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(); } 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(); } void cv::cuda::rotate(InputArray, OutputArray, Size, double, double, double, int, Stream&) { throw_no_cuda(); } #else // HAVE_CUDA namespace cv { namespace cuda { namespace device { namespace imgproc { void buildWarpAffineMaps_gpu(float coeffs[2 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream); template void warpAffine_gpu(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); void buildWarpPerspectiveMaps_gpu(float coeffs[3 * 3], PtrStepSzf xmap, PtrStepSzf ymap, cudaStream_t stream); template void warpPerspective_gpu(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, float coeffs[3 * 3], PtrStepSzb dst, int interpolation, int borderMode, const float* borderValue, cudaStream_t stream, bool cc20); } }}} void cv::cuda::buildWarpAffineMaps(InputArray _M, bool inverse, Size dsize, OutputArray _xmap, OutputArray _ymap, Stream& stream) { using namespace cv::cuda::device::imgproc; Mat M = _M.getMat(); 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(); 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)); } void cv::cuda::buildWarpPerspectiveMaps(InputArray _M, bool inverse, Size dsize, OutputArray _xmap, OutputArray _ymap, Stream& stream) { using namespace cv::cuda::device::imgproc; Mat M = _M.getMat(); CV_Assert( M.rows == 3 && M.cols == 3 ); _xmap.create(dsize, CV_32FC1); _ymap.create(dsize, CV_32FC1); GpuMat xmap = _xmap.getGpuMat(); GpuMat ymap = _ymap.getGpuMat(); 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 struct NppWarpFunc { typedef typename NPPTypeTraits::npp_type npp_type; typedef NppStatus (*func_t)(const npp_type* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, npp_type* pDst, int dstStep, NppiRect dstRoi, const double coeffs[][3], int interpolation); }; template ::func_t func> struct NppWarp { typedef typename NppWarpFunc::npp_type npp_type; static void call(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int interpolation, cudaStream_t stream) { static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC}; NppiSize srcsz; srcsz.height = src.rows; srcsz.width = src.cols; NppiRect srcroi; srcroi.x = 0; srcroi.y = 0; srcroi.height = src.rows; srcroi.width = src.cols; NppiRect dstroi; dstroi.x = 0; dstroi.y = 0; dstroi.height = dst.rows; dstroi.width = dst.cols; cv::cuda::NppStreamHandler h(stream); nppSafeCall( func(src.ptr(), srcsz, static_cast(src.step), srcroi, dst.ptr(), static_cast(dst.step), dstroi, coeffs, npp_inter[interpolation]) ); if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } }; } void cv::cuda::warpAffine(InputArray _src, OutputArray _dst, InputArray _M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream) { GpuMat src = _src.getGpuMat(); Mat M = _M.getMat(); CV_Assert( M.rows == 2 && M.cols == 3 ); const int interpolation = flags & INTER_MAX; 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(); 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} } }; bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation]; // NPP bug on float data useNpp = useNpp && src.depth() != CV_32F; if (useNpp) { typedef void (*func_t)(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream); static const func_t funcs[2][6][4] = { { {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {NppWarp::call, 0, NppWarp::call, NppWarp::call} }, { {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {NppWarp::call, 0, NppWarp::call, NppWarp::call} } }; dst.setTo(borderValue, stream); 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); func(src, dst, coeffs, interpolation, StreamAccessor::getStream(stream)); } else { using namespace cv::cuda::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] = { {warpAffine_gpu , 0 /*warpAffine_gpu*/ , warpAffine_gpu , warpAffine_gpu }, {0 /*warpAffine_gpu*/, 0 /*warpAffine_gpu*/ , 0 /*warpAffine_gpu*/, 0 /*warpAffine_gpu*/}, {warpAffine_gpu , 0 /*warpAffine_gpu*/, warpAffine_gpu , warpAffine_gpu }, {warpAffine_gpu , 0 /*warpAffine_gpu*/ , warpAffine_gpu , warpAffine_gpu }, {0 /*warpAffine_gpu*/ , 0 /*warpAffine_gpu*/ , 0 /*warpAffine_gpu*/ , 0 /*warpAffine_gpu*/ }, {warpAffine_gpu , 0 /*warpAffine_gpu*/ , warpAffine_gpu , warpAffine_gpu } }; 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_ borderValueFloat; borderValueFloat = borderValue; func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs, dst, interpolation, borderMode, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20)); } } void cv::cuda::warpPerspective(InputArray _src, OutputArray _dst, InputArray _M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& stream) { GpuMat src = _src.getGpuMat(); Mat M = _M.getMat(); CV_Assert( M.rows == 3 && M.cols == 3 ); const int interpolation = flags & INTER_MAX; 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(); 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} } }; bool useNpp = borderMode == BORDER_CONSTANT && ofs.x == 0 && ofs.y == 0 && useNppTab[src.depth()][src.channels() - 1][interpolation]; // NPP bug on float data useNpp = useNpp && src.depth() != CV_32F; if (useNpp) { typedef void (*func_t)(const cv::cuda::GpuMat& src, cv::cuda::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream); static const func_t funcs[2][6][4] = { { {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {NppWarp::call, 0, NppWarp::call, NppWarp::call} }, { {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {0, 0, 0, 0}, {NppWarp::call, 0, NppWarp::call, NppWarp::call}, {NppWarp::call, 0, NppWarp::call, NppWarp::call} } }; dst.setTo(borderValue, stream); 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); func(src, dst, coeffs, interpolation, StreamAccessor::getStream(stream)); } else { using namespace cv::cuda::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 , 0 /*warpPerspective_gpu*/ , warpPerspective_gpu , warpPerspective_gpu }, {0 /*warpPerspective_gpu*/, 0 /*warpPerspective_gpu*/ , 0 /*warpPerspective_gpu*/, 0 /*warpPerspective_gpu*/}, {warpPerspective_gpu , 0 /*warpPerspective_gpu*/, warpPerspective_gpu , warpPerspective_gpu }, {warpPerspective_gpu , 0 /*warpPerspective_gpu*/ , warpPerspective_gpu , warpPerspective_gpu }, {0 /*warpPerspective_gpu*/ , 0 /*warpPerspective_gpu*/ , 0 /*warpPerspective_gpu*/ , 0 /*warpPerspective_gpu*/ }, {warpPerspective_gpu , 0 /*warpPerspective_gpu*/ , warpPerspective_gpu , warpPerspective_gpu } }; 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_ borderValueFloat; borderValueFloat = borderValue; func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs, dst, interpolation, borderMode, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20)); } } //////////////////////////////////////////////////////////////////////// // rotate namespace { template struct NppRotateFunc { typedef typename NPPTypeTraits::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 ::func_t func> struct NppRotate { typedef typename NppRotateFunc::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(), srcsz, static_cast(src.step), srcroi, dst.ptr(), static_cast(dst.step), dstroi, angle, xShift, yShift, npp_inter[interpolation]) ); if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } }; } void cv::cuda::rotate(InputArray _src, OutputArray _dst, Size dsize, double angle, double xShift, double yShift, int interpolation, Stream& stream) { 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::call, 0, NppRotate::call, NppRotate::call}, {0,0,0,0}, {NppRotate::call, 0, NppRotate::call, NppRotate::call}, {0,0,0,0}, {0,0,0,0}, {NppRotate::call, 0, NppRotate::call, NppRotate::call} }; 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(); dst.setTo(Scalar::all(0), stream); funcs[src.depth()][src.channels() - 1](src, dst, dsize, angle, xShift, yShift, interpolation, StreamAccessor::getStream(stream)); } #endif // HAVE_CUDA