/*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::gpu; #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) void cv::gpu::merge(const GpuMat*, size_t, OutputArray, Stream&) { throw_no_cuda(); } void cv::gpu::merge(const std::vector<GpuMat>&, OutputArray, Stream&) { throw_no_cuda(); } void cv::gpu::split(InputArray, GpuMat*, Stream&) { throw_no_cuda(); } void cv::gpu::split(InputArray, std::vector<GpuMat>&, Stream&) { throw_no_cuda(); } void cv::gpu::transpose(InputArray, OutputArray, Stream&) { throw_no_cuda(); } void cv::gpu::flip(InputArray, OutputArray, int, Stream&) { throw_no_cuda(); } Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray) { throw_no_cuda(); return Ptr<LookUpTable>(); } void cv::gpu::copyMakeBorder(InputArray, OutputArray, int, int, int, int, int, Scalar, Stream&) { throw_no_cuda(); } #else /* !defined (HAVE_CUDA) */ //////////////////////////////////////////////////////////////////////// // merge/split namespace cv { namespace gpu { namespace cudev { namespace split_merge { void merge(const PtrStepSzb* src, PtrStepSzb& dst, int total_channels, size_t elem_size, const cudaStream_t& stream); void split(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream); } }}} namespace { void merge_caller(const GpuMat* src, size_t n, OutputArray _dst, Stream& stream) { CV_Assert( src != 0 ); CV_Assert( n > 0 && n <= 4 ); const int depth = src[0].depth(); const Size size = src[0].size(); for (size_t i = 0; i < n; ++i) { CV_Assert( src[i].size() == size ); CV_Assert( src[i].depth() == depth ); CV_Assert( src[i].channels() == 1 ); } if (depth == CV_64F) { if (!deviceSupports(NATIVE_DOUBLE)) CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double"); } if (n == 1) { src[0].copyTo(_dst, stream); } else { _dst.create(size, CV_MAKE_TYPE(depth, (int)n)); GpuMat dst = _dst.getGpuMat(); PtrStepSzb src_as_devmem[4]; for(size_t i = 0; i < n; ++i) src_as_devmem[i] = src[i]; PtrStepSzb dst_as_devmem(dst); cv::gpu::cudev::split_merge::merge(src_as_devmem, dst_as_devmem, (int)n, CV_ELEM_SIZE(depth), StreamAccessor::getStream(stream)); } } void split_caller(const GpuMat& src, GpuMat* dst, Stream& stream) { CV_Assert( dst != 0 ); const int depth = src.depth(); const int num_channels = src.channels(); CV_Assert( num_channels <= 4 ); if (depth == CV_64F) { if (!deviceSupports(NATIVE_DOUBLE)) CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double"); } if (num_channels == 1) { src.copyTo(dst[0], stream); return; } for (int i = 0; i < num_channels; ++i) dst[i].create(src.size(), depth); PtrStepSzb dst_as_devmem[4]; for (int i = 0; i < num_channels; ++i) dst_as_devmem[i] = dst[i]; PtrStepSzb src_as_devmem(src); cv::gpu::cudev::split_merge::split(src_as_devmem, dst_as_devmem, num_channels, src.elemSize1(), StreamAccessor::getStream(stream)); } } void cv::gpu::merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream) { merge_caller(src, n, dst, stream); } void cv::gpu::merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream) { merge_caller(&src[0], src.size(), dst, stream); } void cv::gpu::split(InputArray _src, GpuMat* dst, Stream& stream) { GpuMat src = _src.getGpuMat(); split_caller(src, dst, stream); } void cv::gpu::split(InputArray _src, std::vector<GpuMat>& dst, Stream& stream) { GpuMat src = _src.getGpuMat(); dst.resize(src.channels()); if(src.channels() > 0) split_caller(src, &dst[0], stream); } //////////////////////////////////////////////////////////////////////// // transpose namespace arithm { template <typename T> void transpose(PtrStepSz<T> src, PtrStepSz<T> dst, cudaStream_t stream); } void cv::gpu::transpose(InputArray _src, OutputArray _dst, Stream& _stream) { GpuMat src = _src.getGpuMat(); CV_Assert( src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8 ); _dst.create( src.cols, src.rows, src.type() ); GpuMat dst = _dst.getGpuMat(); cudaStream_t stream = StreamAccessor::getStream(_stream); if (src.elemSize() == 1) { NppStreamHandler h(stream); NppiSize sz; sz.width = src.cols; sz.height = src.rows; nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) ); if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } else if (src.elemSize() == 4) { arithm::transpose<int>(src, dst, stream); } else // if (src.elemSize() == 8) { if (!deviceSupports(NATIVE_DOUBLE)) CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double"); arithm::transpose<double>(src, dst, stream); } } //////////////////////////////////////////////////////////////////////// // flip 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; }; template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; }; template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; }; template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; }; template <int DEPTH> struct NppMirrorFunc { typedef typename NppTypeTraits<DEPTH>::npp_t npp_t; typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip); }; template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror { typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t; static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream) { NppStreamHandler h(stream); NppiSize sz; sz.width = src.cols; sz.height = src.rows; nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), dst.ptr<npp_t>(), static_cast<int>(dst.step), sz, (flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) ); if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } }; } void cv::gpu::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream) { typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream); static const func_t funcs[6][4] = { {NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call}, {0,0,0,0}, {NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call}, {0,0,0,0}, {NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call}, {NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call} }; GpuMat src = _src.getGpuMat(); CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F); CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4); _dst.create(src.size(), src.type()); GpuMat dst = _dst.getGpuMat(); funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream)); } //////////////////////////////////////////////////////////////////////// // LUT #if (CUDA_VERSION >= 5000) namespace { class LookUpTableImpl : public LookUpTable { public: LookUpTableImpl(InputArray lut); void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()); private: int lut_cn; int nValues3[3]; const Npp32s* pValues3[3]; const Npp32s* pLevels3[3]; GpuMat d_pLevels; GpuMat d_nppLut; GpuMat d_nppLut3[3]; }; LookUpTableImpl::LookUpTableImpl(InputArray _lut) { nValues3[0] = nValues3[1] = nValues3[2] = 256; Npp32s pLevels[256]; for (int i = 0; i < 256; ++i) pLevels[i] = i; d_pLevels.upload(Mat(1, 256, CV_32S, pLevels)); pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>(); GpuMat lut; if (_lut.kind() == _InputArray::GPU_MAT) { lut = _lut.getGpuMat(); } else { Mat hLut = _lut.getMat(); CV_Assert( hLut.total() == 256 && hLut.isContinuous() ); lut.upload(Mat(1, 256, hLut.type(), hLut.data)); } lut_cn = lut.channels(); CV_Assert( lut.depth() == CV_8U ); CV_Assert( lut.rows == 1 && lut.cols == 256 ); lut.convertTo(d_nppLut, CV_32S); if (lut_cn == 1) { pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>(); } else { gpu::split(d_nppLut, d_nppLut3); pValues3[0] = d_nppLut3[0].ptr<Npp32s>(); pValues3[1] = d_nppLut3[1].ptr<Npp32s>(); pValues3[2] = d_nppLut3[2].ptr<Npp32s>(); } } void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream) { GpuMat src = _src.getGpuMat(); const int cn = src.channels(); CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 ); CV_Assert( lut_cn == 1 || lut_cn == cn ); _dst.create(src.size(), src.type()); GpuMat dst = _dst.getGpuMat(); cudaStream_t stream = StreamAccessor::getStream(_stream); NppStreamHandler h(stream); NppiSize sz; sz.height = src.rows; sz.width = src.cols; if (src.type() == CV_8UC1) { nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), d_pLevels.ptr<Npp32s>(), 256) ); } else { nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) ); } if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } } #else // (CUDA_VERSION >= 5000) namespace { class LookUpTableImpl : public LookUpTable { public: LookUpTableImpl(InputArray lut); void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()); private: int lut_cn; Npp32s pLevels[256]; int nValues3[3]; const Npp32s* pValues3[3]; const Npp32s* pLevels3[3]; Mat nppLut; Mat nppLut3[3]; }; LookUpTableImpl::LookUpTableImpl(InputArray _lut) { nValues3[0] = nValues3[1] = nValues3[2] = 256; for (int i = 0; i < 256; ++i) pLevels[i] = i; pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels; Mat lut; if (_lut.kind() == _InputArray::GPU_MAT) { lut = Mat(_lut.getGpuMat()); } else { Mat hLut = _lut.getMat(); CV_Assert( hLut.total() == 256 && hLut.isContinuous() ); lut = hLut; } lut_cn = lut.channels(); CV_Assert( lut.depth() == CV_8U ); CV_Assert( lut.rows == 1 && lut.cols == 256 ); lut.convertTo(nppLut, CV_32S); if (lut_cn == 1) { pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>(); } else { cv::split(nppLut, nppLut3); pValues3[0] = nppLut3[0].ptr<Npp32s>(); pValues3[1] = nppLut3[1].ptr<Npp32s>(); pValues3[2] = nppLut3[2].ptr<Npp32s>(); } } void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream) { GpuMat src = _src.getGpuMat(); const int cn = src.channels(); CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 ); CV_Assert( lut_cn == 1 || lut_cn == cn ); _dst.create(src.size(), src.type()); GpuMat dst = _dst.getGpuMat(); cudaStream_t stream = StreamAccessor::getStream(_stream); NppStreamHandler h(stream); NppiSize sz; sz.height = src.rows; sz.width = src.cols; if (src.type() == CV_8UC1) { nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), pLevels, 256) ); } else { nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step), dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) ); } if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } } #endif // (CUDA_VERSION >= 5000) Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray lut) { return makePtr<LookUpTableImpl>(lut); } //////////////////////////////////////////////////////////////////////// // copyMakeBorder namespace cv { namespace gpu { namespace cudev { namespace imgproc { template <typename T, int cn> void copyMakeBorder_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const T* borderValue, cudaStream_t stream); } }}} namespace { template <typename T, int cn> void copyMakeBorder_caller(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream) { using namespace ::cv::gpu::cudev::imgproc; Scalar_<T> val(saturate_cast<T>(value[0]), saturate_cast<T>(value[1]), saturate_cast<T>(value[2]), saturate_cast<T>(value[3])); copyMakeBorder_gpu<T, cn>(src, dst, top, left, borderType, val.val, stream); } } #if defined __GNUC__ && __GNUC__ > 2 && __GNUC_MINOR__ > 4 typedef Npp32s __attribute__((__may_alias__)) Npp32s_a; #else typedef Npp32s Npp32s_a; #endif void cv::gpu::copyMakeBorder(InputArray _src, OutputArray _dst, int top, int bottom, int left, int right, int borderType, Scalar value, Stream& _stream) { GpuMat src = _src.getGpuMat(); CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 ); CV_Assert( borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP ); _dst.create(src.rows + top + bottom, src.cols + left + right, src.type()); GpuMat dst = _dst.getGpuMat(); cudaStream_t stream = StreamAccessor::getStream(_stream); if (borderType == BORDER_CONSTANT && (src.type() == CV_8UC1 || src.type() == CV_8UC4 || src.type() == CV_32SC1 || src.type() == CV_32FC1)) { NppiSize srcsz; srcsz.width = src.cols; srcsz.height = src.rows; NppiSize dstsz; dstsz.width = dst.cols; dstsz.height = dst.rows; NppStreamHandler h(stream); switch (src.type()) { case CV_8UC1: { Npp8u nVal = saturate_cast<Npp8u>(value[0]); nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz, dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) ); break; } case CV_8UC4: { Npp8u nVal[] = {saturate_cast<Npp8u>(value[0]), saturate_cast<Npp8u>(value[1]), saturate_cast<Npp8u>(value[2]), saturate_cast<Npp8u>(value[3])}; nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz, dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) ); break; } case CV_32SC1: { Npp32s nVal = saturate_cast<Npp32s>(value[0]); nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz, dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) ); break; } case CV_32FC1: { Npp32f val = saturate_cast<Npp32f>(value[0]); Npp32s nVal = *(reinterpret_cast<Npp32s_a*>(&val)); nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz, dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) ); break; } } if (stream == 0) cudaSafeCall( cudaDeviceSynchronize() ); } else { typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream); static const caller_t callers[6][4] = { { copyMakeBorder_caller<uchar, 1> , copyMakeBorder_caller<uchar, 2> , copyMakeBorder_caller<uchar, 3> , copyMakeBorder_caller<uchar, 4>}, {0/*copyMakeBorder_caller<schar, 1>*/, 0/*copyMakeBorder_caller<schar, 2>*/ , 0/*copyMakeBorder_caller<schar, 3>*/, 0/*copyMakeBorder_caller<schar, 4>*/}, { copyMakeBorder_caller<ushort, 1> , 0/*copyMakeBorder_caller<ushort, 2>*/, copyMakeBorder_caller<ushort, 3> , copyMakeBorder_caller<ushort, 4>}, { copyMakeBorder_caller<short, 1> , 0/*copyMakeBorder_caller<short, 2>*/ , copyMakeBorder_caller<short, 3> , copyMakeBorder_caller<short, 4>}, {0/*copyMakeBorder_caller<int, 1>*/, 0/*copyMakeBorder_caller<int, 2>*/ , 0/*copyMakeBorder_caller<int, 3>*/, 0/*copyMakeBorder_caller<int , 4>*/}, { copyMakeBorder_caller<float, 1> , 0/*copyMakeBorder_caller<float, 2>*/ , copyMakeBorder_caller<float, 3> , copyMakeBorder_caller<float ,4>} }; caller_t func = callers[src.depth()][src.channels() - 1]; CV_Assert(func != 0); func(src, dst, top, left, borderType, value, stream); } } #endif /* !defined (HAVE_CUDA) */