/*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" #if !defined HAVE_CUDA || defined(CUDA_DISABLER) void cv::gpu::warpAffine(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); } void cv::gpu::buildWarpAffineMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); } void cv::gpu::warpPerspective(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); } void cv::gpu::buildWarpPerspectiveMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); } #else // HAVE_CUDA namespace cv { namespace gpu { namespace device { 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, 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 <typename T> 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::gpu::buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream) { using namespace cv::gpu::device::imgproc; CV_Assert(M.rows == 2 && M.cols == 3); xmap.create(dsize, CV_32FC1); ymap.create(dsize, CV_32FC1); 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::gpu::buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream) { using namespace cv::gpu::device::imgproc; CV_Assert(M.rows == 3 && M.cols == 3); xmap.create(dsize, CV_32FC1); ymap.create(dsize, CV_32FC1); 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 NppTypeTraits; template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; }; template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; }; template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; }; template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; typedef Npp16sc npp_complex_type; }; template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; typedef Npp32sc npp_complex_type; }; template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; typedef Npp32fc npp_complex_type; }; template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; typedef Npp64fc npp_complex_type; }; template <int DEPTH> struct NppWarpFunc { typedef typename NppTypeTraits<DEPTH>::npp_t npp_t; typedef NppStatus (*func_t)(const npp_t* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, npp_t* pDst, int dstStep, NppiRect dstRoi, const double coeffs[][3], int interpolation); }; template <int DEPTH, typename NppWarpFunc<DEPTH>::func_t func> struct NppWarp { typedef typename NppWarpFunc<DEPTH>::npp_t npp_t; static void call(const cv::gpu::GpuMat& src, cv::gpu::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::gpu::NppStreamHandler h(stream); nppSafeCall( func(src.ptr<npp_t>(), srcsz, static_cast<int>(src.step), srcroi, dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) ); if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } }; } void cv::gpu::warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s) { CV_Assert(M.rows == 2 && M.cols == 3); 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()); 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::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream); 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} } }; dst.setTo(borderValue); 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(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); #ifdef OPENCV_TINY_GPU_MODULE static const func_t funcs[6][4] = { {warpAffine_gpu<uchar> , 0 /*warpAffine_gpu<uchar2>*/ , warpAffine_gpu<uchar3> , warpAffine_gpu<uchar4> }, {0, 0, 0, 0}, {0, 0, 0, 0}, {0, 0, 0, 0}, {0, 0, 0, 0}, {warpAffine_gpu<float> , 0 /*warpAffine_gpu<float2>*/ , warpAffine_gpu<float3> , warpAffine_gpu<float4> } }; #else 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> } }; #endif const func_t func = funcs[src.depth()][src.channels() - 1]; CV_Assert(func != 0); int gpuBorderType; CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType)); 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, dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), deviceSupports(FEATURE_SET_COMPUTE_20)); } } void cv::gpu::warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s) { CV_Assert(M.rows == 3 && M.cols == 3); 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()); 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::gpu::GpuMat& src, cv::gpu::GpuMat& dst, double coeffs[][3], int flags, cudaStream_t stream); 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} } }; dst.setTo(borderValue); 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(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); #ifdef OPENCV_TINY_GPU_MODULE static const func_t funcs[6][4] = { {warpPerspective_gpu<uchar> , 0 /*warpPerspective_gpu<uchar2>*/ , warpPerspective_gpu<uchar3> , warpPerspective_gpu<uchar4> }, {0, 0, 0, 0}, {0, 0, 0, 0}, {0, 0, 0, 0}, {0, 0, 0, 0}, {warpPerspective_gpu<float> , 0 /*warpPerspective_gpu<float2>*/ , warpPerspective_gpu<float3> , warpPerspective_gpu<float4> } }; #else 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> } }; #endif 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