/*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 "opencv2/gpu/device/saturate_cast.hpp" #include "opencv2/gpu/device/transform.hpp" #include "opencv2/gpu/device/functional.hpp" #include "opencv2/gpu/device/type_traits.hpp" #include "opencv2/gpu/device/vec_traits.hpp" namespace cv { namespace gpu { namespace device { void writeScalar(const uchar*); void writeScalar(const schar*); void writeScalar(const ushort*); void writeScalar(const short int*); void writeScalar(const int*); void writeScalar(const float*); void writeScalar(const double*); void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream); void convert_gpu(PtrStepSzb, int, PtrStepSzb, int, double, double, cudaStream_t); }}} namespace cv { namespace gpu { namespace device { template <typename T> struct shift_and_sizeof; template <> struct shift_and_sizeof<signed char> { enum { shift = 0 }; }; template <> struct shift_and_sizeof<unsigned char> { enum { shift = 0 }; }; template <> struct shift_and_sizeof<short> { enum { shift = 1 }; }; template <> struct shift_and_sizeof<unsigned short> { enum { shift = 1 }; }; template <> struct shift_and_sizeof<int> { enum { shift = 2 }; }; template <> struct shift_and_sizeof<float> { enum { shift = 2 }; }; template <> struct shift_and_sizeof<double> { enum { shift = 3 }; }; /////////////////////////////////////////////////////////////////////////// ////////////////////////////////// CopyTo ///////////////////////////////// /////////////////////////////////////////////////////////////////////////// template <typename T> void copyToWithMask(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream) { if (colorMask) cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, identity<T>(), SingleMask(mask), stream); else cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<T>)dst, identity<T>(), SingleMaskChannels(mask, cn), stream); } void copyToWithMask_gpu(PtrStepSzb src, PtrStepSzb dst, size_t elemSize1, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream) { typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, int cn, PtrStepSzb mask, bool colorMask, cudaStream_t stream); static func_t tab[] = { 0, copyToWithMask<unsigned char>, copyToWithMask<unsigned short>, 0, copyToWithMask<int>, 0, 0, 0, copyToWithMask<double> }; tab[elemSize1](src, dst, cn, mask, colorMask, stream); } /////////////////////////////////////////////////////////////////////////// ////////////////////////////////// SetTo ////////////////////////////////// /////////////////////////////////////////////////////////////////////////// template<typename T> __global__ void set_to_without_mask(PtrStepSz<T> mat, typename TypeVec<T, 4>::vec_type val, int channels) { const int y = blockIdx.x * blockDim.y + threadIdx.y; if (y < mat.rows) { const T vals[] = { val.x, val.y, val.z, val.w }; T* row = mat.ptr(y); for (int x = threadIdx.x; x < mat.cols * channels; x += 32) { row[x] = vals[x % channels]; } } } template<typename T> __global__ void set_to_with_mask(PtrStepSz<T> mat, const PtrStepb mask, typename TypeVec<T, 4>::vec_type val, int channels) { const int y = blockIdx.x * blockDim.y + threadIdx.y; if (y < mat.rows) { const T vals[] = { val.x, val.y, val.z, val.w }; T* row = mat.ptr(y); const uchar* mask_row = mask.ptr(y); for (int x = threadIdx.x; x < mat.cols * channels; x += 32) { if (mask_row[x / channels]) { row[x] = vals[x % channels]; } } } } template <typename T> void set_to_gpu(PtrStepSzb mat, const T* scalar, PtrStepSzb mask, int channels, cudaStream_t stream) { typedef typename TypeVec<T, 4>::vec_type vec_type; dim3 block(32, 8); dim3 grid(divUp(mat.rows, block.y)); set_to_with_mask<T><<<grid, block, 0, stream>>>(PtrStepSz<T>(mat), mask, VecTraits<vec_type>::make(scalar), channels); cudaSafeCall( cudaGetLastError() ); if (stream == 0) cudaSafeCall ( cudaDeviceSynchronize() ); } template void set_to_gpu<uchar >(PtrStepSzb mat, const uchar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<schar >(PtrStepSzb mat, const schar* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<ushort>(PtrStepSzb mat, const ushort* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<short >(PtrStepSzb mat, const short* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<int >(PtrStepSzb mat, const int* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<float >(PtrStepSzb mat, const float* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template void set_to_gpu<double>(PtrStepSzb mat, const double* scalar, PtrStepSzb mask, int channels, cudaStream_t stream); template <typename T> void set_to_gpu(PtrStepSzb mat, const T* scalar, int channels, cudaStream_t stream) { typedef typename TypeVec<T, 4>::vec_type vec_type; dim3 block(32, 8); dim3 grid(divUp(mat.rows, block.y)); set_to_without_mask<T><<<grid, block, 0, stream>>>(PtrStepSz<T>(mat), VecTraits<vec_type>::make(scalar), channels); cudaSafeCall( cudaGetLastError() ); if (stream == 0) cudaSafeCall ( cudaDeviceSynchronize() ); } template void set_to_gpu<uchar >(PtrStepSzb mat, const uchar* scalar, int channels, cudaStream_t stream); template void set_to_gpu<schar >(PtrStepSzb mat, const schar* scalar, int channels, cudaStream_t stream); template void set_to_gpu<ushort>(PtrStepSzb mat, const ushort* scalar, int channels, cudaStream_t stream); template void set_to_gpu<short >(PtrStepSzb mat, const short* scalar, int channels, cudaStream_t stream); template void set_to_gpu<int >(PtrStepSzb mat, const int* scalar, int channels, cudaStream_t stream); template void set_to_gpu<float >(PtrStepSzb mat, const float* scalar, int channels, cudaStream_t stream); template void set_to_gpu<double>(PtrStepSzb mat, const double* scalar, int channels, cudaStream_t stream); /////////////////////////////////////////////////////////////////////////// //////////////////////////////// ConvertTo //////////////////////////////// /////////////////////////////////////////////////////////////////////////// template <typename T, typename D, typename S> struct Convertor : unary_function<T, D> { Convertor(S alpha_, S beta_) : alpha(alpha_), beta(beta_) {} __device__ __forceinline__ D operator()(typename TypeTraits<T>::ParameterType src) const { return saturate_cast<D>(alpha * src + beta); } S alpha, beta; }; namespace detail { template <size_t src_size, size_t dst_size, typename F> struct ConvertTraitsDispatcher : DefaultTransformFunctorTraits<F> { }; template <typename F> struct ConvertTraitsDispatcher<1, 1, F> : DefaultTransformFunctorTraits<F> { enum { smart_shift = 8 }; }; template <typename F> struct ConvertTraitsDispatcher<1, 2, F> : DefaultTransformFunctorTraits<F> { enum { smart_shift = 4 }; }; template <typename F> struct ConvertTraitsDispatcher<1, 4, F> : DefaultTransformFunctorTraits<F> { enum { smart_block_dim_y = 8 }; enum { smart_shift = 4 }; }; template <typename F> struct ConvertTraitsDispatcher<2, 2, F> : DefaultTransformFunctorTraits<F> { enum { smart_shift = 4 }; }; template <typename F> struct ConvertTraitsDispatcher<2, 4, F> : DefaultTransformFunctorTraits<F> { enum { smart_shift = 2 }; }; template <typename F> struct ConvertTraitsDispatcher<4, 2, F> : DefaultTransformFunctorTraits<F> { enum { smart_block_dim_y = 8 }; enum { smart_shift = 4 }; }; template <typename F> struct ConvertTraitsDispatcher<4, 4, F> : DefaultTransformFunctorTraits<F> { enum { smart_block_dim_y = 8 }; enum { smart_shift = 2 }; }; template <typename F> struct ConvertTraits : ConvertTraitsDispatcher<sizeof(typename F::argument_type), sizeof(typename F::result_type), F> { }; } template <typename T, typename D, typename S> struct TransformFunctorTraits< Convertor<T, D, S> > : detail::ConvertTraits< Convertor<T, D, S> > { }; template<typename T, typename D, typename S> void cvt_(PtrStepSzb src, PtrStepSzb dst, double alpha, double beta, cudaStream_t stream) { cudaSafeCall( cudaSetDoubleForDevice(&alpha) ); cudaSafeCall( cudaSetDoubleForDevice(&beta) ); Convertor<T, D, S> op(static_cast<S>(alpha), static_cast<S>(beta)); cv::gpu::device::transform((PtrStepSz<T>)src, (PtrStepSz<D>)dst, op, WithOutMask(), stream); } #if defined __clang__ # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wmissing-declarations" #endif void convert_gpu(PtrStepSzb src, int sdepth, PtrStepSzb dst, int ddepth, double alpha, double beta, cudaStream_t stream) { typedef void (*caller_t)(PtrStepSzb src, PtrStepSzb dst, double alpha, double beta, cudaStream_t stream); static const caller_t tab[7][7] = { { cvt_<uchar, uchar, float>, cvt_<uchar, schar, float>, cvt_<uchar, ushort, float>, cvt_<uchar, short, float>, cvt_<uchar, int, float>, cvt_<uchar, float, float>, cvt_<uchar, double, double> }, { cvt_<schar, uchar, float>, cvt_<schar, schar, float>, cvt_<schar, ushort, float>, cvt_<schar, short, float>, cvt_<schar, int, float>, cvt_<schar, float, float>, cvt_<schar, double, double> }, { cvt_<ushort, uchar, float>, cvt_<ushort, schar, float>, cvt_<ushort, ushort, float>, cvt_<ushort, short, float>, cvt_<ushort, int, float>, cvt_<ushort, float, float>, cvt_<ushort, double, double> }, { cvt_<short, uchar, float>, cvt_<short, schar, float>, cvt_<short, ushort, float>, cvt_<short, short, float>, cvt_<short, int, float>, cvt_<short, float, float>, cvt_<short, double, double> }, { cvt_<int, uchar, float>, cvt_<int, schar, float>, cvt_<int, ushort, float>, cvt_<int, short, float>, cvt_<int, int, double>, cvt_<int, float, double>, cvt_<int, double, double> }, { cvt_<float, uchar, float>, cvt_<float, schar, float>, cvt_<float, ushort, float>, cvt_<float, short, float>, cvt_<float, int, float>, cvt_<float, float, float>, cvt_<float, double, double> }, { cvt_<double, uchar, double>, cvt_<double, schar, double>, cvt_<double, ushort, double>, cvt_<double, short, double>, cvt_<double, int, double>, cvt_<double, float, double>, cvt_<double, double, double> } }; caller_t func = tab[sdepth][ddepth]; func(src, dst, alpha, beta, stream); } #if defined __clang__ # pragma clang diagnostic pop #endif }}} // namespace cv { namespace gpu { namespace device