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Commit b11cccaa authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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switched to new device layer in bitwize operations

parent fdfffa52
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Showing with 244 additions and 518 deletions
modules/cudaarithm/src/cuda/bitwise_mat.cu
View file @ b11cccaa
......@@ -40,87 +40,124 @@
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/opencv_modules.hpp"
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/transform.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
#include "opencv2/core/cuda/simd_functions.hpp"
#ifndef HAVE_OPENCV_CUDEV
#include "arithm_func_traits.hpp"
#error "opencv_cudev is required"
using namespace cv::cuda;
using namespace cv::cuda::device;
#else
namespace cv { namespace cuda { namespace device
{
template <typename T> struct TransformFunctorTraits< bit_not<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
template <typename T> struct TransformFunctorTraits< bit_and<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
using namespace cv::cudev;
template <typename T> struct TransformFunctorTraits< bit_or<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
template <typename T> struct TransformFunctorTraits< bit_xor<T> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
{
};
}}}
//////////////////////////////////////////////////////////////////////////////
/// bitwise_not
namespace arithm
namespace
{
template <typename T> void bitMatNot(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
template <typename T>
void bitMatNot(const GpuMat& src, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
GlobPtrSz<T> vsrc = globPtr((T*) src.data, src.step, src.rows, src.cols * src.channels());
GlobPtrSz<T> vdst = globPtr((T*) dst.data, dst.step, src.rows, src.cols * src.channels());
if (mask.data)
device::transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, bit_not<T>(), mask, stream);
gridTransformUnary(vsrc, vdst, bit_not<T>(), singleMaskChannels(globPtr<uchar>(mask), src.channels()), stream);
else
device::transform((PtrStepSz<T>) src, (PtrStepSz<T>) dst, bit_not<T>(), WithOutMask(), stream);
gridTransformUnary(vsrc, vdst, bit_not<T>(), stream);
}
}
template <typename T> void bitMatAnd(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
void cv::cuda::bitwise_not(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
const int depth = src.depth();
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
if (depth == CV_32F || depth == CV_32S)
{
if (mask.data)
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_and<T>(), mask, stream);
else
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_and<T>(), WithOutMask(), stream);
bitMatNot<uint>(src, dst, mask, stream);
}
template <typename T> void bitMatOr(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
else if (depth == CV_16S || depth == CV_16U)
{
if (mask.data)
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_or<T>(), mask, stream);
else
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_or<T>(), WithOutMask(), stream);
bitMatNot<ushort>(src, dst, mask, stream);
}
else
{
bitMatNot<uchar>(src, dst, mask, stream);
}
}
template <typename T> void bitMatXor(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
//////////////////////////////////////////////////////////////////////////////
/// Binary bitwise logical operations
namespace
{
template <template <typename> class Op, typename T>
void bitMatOp(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
GlobPtrSz<T> vsrc1 = globPtr((T*) src1.data, src1.step, src1.rows, src1.cols * src1.channels());
GlobPtrSz<T> vsrc2 = globPtr((T*) src2.data, src2.step, src1.rows, src1.cols * src1.channels());
GlobPtrSz<T> vdst = globPtr((T*) dst.data, dst.step, src1.rows, src1.cols * src1.channels());
if (mask.data)
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_xor<T>(), mask, stream);
gridTransformBinary(vsrc1, vsrc2, vdst, Op<T>(), singleMaskChannels(globPtr<uchar>(mask), src1.channels()), stream);
else
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) src2, (PtrStepSz<T>) dst, bit_xor<T>(), WithOutMask(), stream);
gridTransformBinary(vsrc1, vsrc2, vdst, Op<T>(), stream);
}
}
template void bitMatNot<uchar>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatNot<ushort>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatNot<uint>(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs32[] =
{
bitMatOp<bit_and, uint>,
bitMatOp<bit_or, uint>,
bitMatOp<bit_xor, uint>
};
static const func_t funcs16[] =
{
bitMatOp<bit_and, ushort>,
bitMatOp<bit_or, ushort>,
bitMatOp<bit_xor, ushort>
};
static const func_t funcs8[] =
{
bitMatOp<bit_and, uchar>,
bitMatOp<bit_or, uchar>,
bitMatOp<bit_xor, uchar>
};
template void bitMatAnd<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatAnd<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatAnd<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
const int depth = src1.depth();
template void bitMatOr<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatOr<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatOr<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( op >= 0 && op < 3 );
template void bitMatXor<uchar>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatXor<ushort>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void bitMatXor<uint>(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
if (depth == CV_32F || depth == CV_32S)
{
funcs32[op](src1, src2, dst, mask, stream);
}
else if (depth == CV_16S || depth == CV_16U)
{
funcs16[op](src1, src2, dst, mask, stream);
}
else
{
funcs8[op](src1, src2, dst, mask, stream);
}
}
#endif // CUDA_DISABLER
#endif
modules/cudaarithm/src/cuda/bitwise_scalar.cu
View file @ b11cccaa
......@@ -40,65 +40,132 @@
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/opencv_modules.hpp"
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/transform.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
#include "opencv2/core/cuda/simd_functions.hpp"
#ifndef HAVE_OPENCV_CUDEV
#include "arithm_func_traits.hpp"
#error "opencv_cudev is required"
using namespace cv::cuda;
using namespace cv::cuda::device;
#else
namespace cv { namespace cuda { namespace device
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv::cudev;
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
namespace
{
template <typename T> struct TransformFunctorTraits< binder2nd< bit_and<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
template <template <typename> class Op, typename T>
void bitScalarOp(const GpuMat& src, uint value, GpuMat& dst, Stream& stream)
{
};
gridTransformUnary(globPtr<T>(src), globPtr<T>(dst), bind2nd(Op<T>(), value), stream);
}
typedef void (*bit_scalar_func_t)(const GpuMat& src, uint value, GpuMat& dst, Stream& stream);
template <typename T> struct TransformFunctorTraits< binder2nd< bit_or<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
template <typename T, bit_scalar_func_t func> struct BitScalar
{
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
func(src, cv::saturate_cast<T>(value[0]), dst, stream);
}
};
template <typename T> struct TransformFunctorTraits< binder2nd< bit_xor<T> > > : arithm::ArithmFuncTraits<sizeof(T), sizeof(T)>
template <bit_scalar_func_t func> struct BitScalar4
{
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
uint packedVal = 0;
packedVal |= cv::saturate_cast<uchar>(value[0]);
packedVal |= cv::saturate_cast<uchar>(value[1]) << 8;
packedVal |= cv::saturate_cast<uchar>(value[2]) << 16;
packedVal |= cv::saturate_cast<uchar>(value[3]) << 24;
func(src, packedVal, dst, stream);
}
};
}}}
namespace arithm
{
template <typename T> void bitScalarAnd(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
template <int DEPTH, int cn> struct NppBitwiseCFunc
{
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::cuda::device::bind2nd(bit_and<T>(), src2), WithOutMask(), stream);
}
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const npp_type* pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <typename T> void bitScalarOr(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
template <int DEPTH, int cn, typename NppBitwiseCFunc<DEPTH, cn>::func_t func> struct NppBitwiseC
{
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::cuda::device::bind2nd(bit_or<T>(), src2), WithOutMask(), stream);
}
typedef typename NppBitwiseCFunc<DEPTH, cn>::npp_type npp_type;
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& _stream)
{
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
const npp_type pConstants[] =
{
cv::saturate_cast<npp_type>(value[0]),
cv::saturate_cast<npp_type>(value[1]),
cv::saturate_cast<npp_type>(value[2]),
cv::saturate_cast<npp_type>(value[3])
};
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), pConstants, dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
};
}
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
(void) mask;
template <typename T> void bitScalarXor(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream)
typedef void (*func_t)(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream);
static const func_t funcs[3][6][4] =
{
device::transform((PtrStepSz<T>) src1, (PtrStepSz<T>) dst, cv::cuda::device::bind2nd(bit_xor<T>(), src2), WithOutMask(), stream);
}
{
{BitScalar<uchar, bitScalarOp<bit_and, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_and, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_and, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_and, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_and, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_and, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_and, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_and, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
},
{
{BitScalar<uchar, bitScalarOp<bit_or, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_or, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_or, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_or, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_or, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_or, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_or, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_or, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
},
{
{BitScalar<uchar, bitScalarOp<bit_xor, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_xor, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_xor, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_xor, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_xor, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_xor, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_xor, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_xor, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
}
};
template void bitScalarAnd<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarAnd<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarAnd<int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarAnd<unsigned int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
const int depth = src.depth();
const int cn = src.channels();
template void bitScalarOr<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarOr<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarOr<int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarOr<unsigned int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( cn == 1 || cn == 3 || cn == 4 );
CV_DbgAssert( mask.empty() );
CV_DbgAssert( op >= 0 && op < 3 );
template void bitScalarXor<uchar>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarXor<ushort>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarXor<int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
template void bitScalarXor<unsigned int>(PtrStepSzb src1, uint src2, PtrStepSzb dst, cudaStream_t stream);
funcs[op][depth][cn - 1](src, value, dst, stream);
}
#endif // CUDA_DISABLER
#endif
modules/cudaarithm/src/element_operations.cpp
View file @ b11cccaa
......@@ -159,180 +159,6 @@ namespace
}
}
////////////////////////////////////////////////////////////////////////
// Basic arithmetical operations (add subtract multiply divide)
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, int cn> struct NppArithmScalarFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_ptr)(const npp_t* pSrc1, int nSrc1Step, const npp_t* pConstants,
npp_t* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
};
template<int DEPTH> struct NppArithmScalarFunc<DEPTH, 1>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_ptr)(const npp_t* pSrc1, int nSrc1Step, const npp_t pConstants,
npp_t* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
};
template<int DEPTH> struct NppArithmScalarFunc<DEPTH, 2>
{
typedef typename NppTypeTraits<DEPTH>::npp_complex_type npp_complex_type;
typedef NppStatus (*func_ptr)(const npp_complex_type* pSrc1, int nSrc1Step, const npp_complex_type pConstants,
npp_complex_type* pDst, int nDstStep, NppiSize oSizeROI, int nScaleFactor);
};
template<int cn> struct NppArithmScalarFunc<CV_32F, cn>
{
typedef NppStatus (*func_ptr)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f* pConstants, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
};
template<> struct NppArithmScalarFunc<CV_32F, 1>
{
typedef NppStatus (*func_ptr)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f pConstants, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
};
template<> struct NppArithmScalarFunc<CV_32F, 2>
{
typedef NppStatus (*func_ptr)(const Npp32fc* pSrc1, int nSrc1Step, const Npp32fc pConstants, Npp32fc* pDst, int nDstStep, NppiSize oSizeROI);
};
template<int DEPTH, int cn, typename NppArithmScalarFunc<DEPTH, cn>::func_ptr func> struct NppArithmScalar
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
const npp_t pConstants[] = { saturate_cast<npp_t>(sc.val[0]), saturate_cast<npp_t>(sc.val[1]), saturate_cast<npp_t>(sc.val[2]), saturate_cast<npp_t>(sc.val[3]) };
nppSafeCall( func((const npp_t*)src.data, static_cast<int>(src.step), pConstants, (npp_t*)dst.data, static_cast<int>(dst.step), sz, 0) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int DEPTH, typename NppArithmScalarFunc<DEPTH, 1>::func_ptr func> struct NppArithmScalar<DEPTH, 1, func>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func((const npp_t*)src.data, static_cast<int>(src.step), saturate_cast<npp_t>(sc.val[0]), (npp_t*)dst.data, static_cast<int>(dst.step), sz, 0) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int DEPTH, typename NppArithmScalarFunc<DEPTH, 2>::func_ptr func> struct NppArithmScalar<DEPTH, 2, func>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NppTypeTraits<DEPTH>::npp_complex_type npp_complex_type;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
npp_complex_type nConstant;
nConstant.re = saturate_cast<npp_t>(sc.val[0]);
nConstant.im = saturate_cast<npp_t>(sc.val[1]);
nppSafeCall( func((const npp_complex_type*)src.data, static_cast<int>(src.step), nConstant,
(npp_complex_type*)dst.data, static_cast<int>(dst.step), sz, 0) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<int cn, typename NppArithmScalarFunc<CV_32F, cn>::func_ptr func> struct NppArithmScalar<CV_32F, cn, func>
{
typedef typename NppTypeTraits<CV_32F>::npp_t npp_t;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
const Npp32f pConstants[] = { saturate_cast<Npp32f>(sc.val[0]), saturate_cast<Npp32f>(sc.val[1]), saturate_cast<Npp32f>(sc.val[2]), saturate_cast<Npp32f>(sc.val[3]) };
nppSafeCall( func((const npp_t*)src.data, static_cast<int>(src.step), pConstants, (npp_t*)dst.data, static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<typename NppArithmScalarFunc<CV_32F, 1>::func_ptr func> struct NppArithmScalar<CV_32F, 1, func>
{
typedef typename NppTypeTraits<CV_32F>::npp_t npp_t;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func((const npp_t*)src.data, static_cast<int>(src.step), saturate_cast<Npp32f>(sc.val[0]), (npp_t*)dst.data, static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<typename NppArithmScalarFunc<CV_32F, 2>::func_ptr func> struct NppArithmScalar<CV_32F, 2, func>
{
typedef typename NppTypeTraits<CV_32F>::npp_t npp_t;
typedef typename NppTypeTraits<CV_32F>::npp_complex_type npp_complex_type;
static void call(const PtrStepSzb src, Scalar sc, PtrStepb dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp32fc nConstant;
nConstant.re = saturate_cast<Npp32f>(sc.val[0]);
nConstant.im = saturate_cast<Npp32f>(sc.val[1]);
nppSafeCall( func((const npp_complex_type*)src.data, static_cast<int>(src.step), nConstant, (npp_complex_type*)dst.data, static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
////////////////////////////////////////////////////////////////////////
// add
......@@ -463,60 +289,6 @@ void cv::cuda::compare(InputArray src1, InputArray src2, OutputArray dst, int cm
arithm_op(src1, src2, dst, noArray(), 1.0, CV_8U, stream, cmpMat, cmpScalar, cmpop);
}
//////////////////////////////////////////////////////////////////////////////
// bitwise_not
namespace arithm
{
template <typename T> void bitMatNot(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void cv::cuda::bitwise_not(InputArray _src, OutputArray _dst, InputArray _mask, Stream& _stream)
{
using namespace arithm;
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
const int depth = src.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(_stream);
const int bcols = (int) (src.cols * src.elemSize());
if ((bcols & 3) == 0)
{
const int vcols = bcols >> 2;
bitMatNot<unsigned int>(
PtrStepSzb(src.rows, vcols, src.data, src.step),
PtrStepSzb(src.rows, vcols, dst.data, dst.step),
mask, stream);
}
else if ((bcols & 1) == 0)
{
const int vcols = bcols >> 1;
bitMatNot<unsigned short>(
PtrStepSzb(src.rows, vcols, src.data, src.step),
PtrStepSzb(src.rows, vcols, dst.data, dst.step),
mask, stream);
}
else
{
bitMatNot<unsigned char>(
PtrStepSzb(src.rows, bcols, src.data, src.step),
PtrStepSzb(src.rows, bcols, dst.data, dst.step),
mask, stream);
}
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
......@@ -530,195 +302,9 @@ namespace
};
}
namespace arithm
{
template <typename T> void bitMatAnd(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template <typename T> void bitMatOr(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template <typename T> void bitMatXor(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
static void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int op)
{
using namespace arithm;
typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs32[] =
{
bitMatAnd<uint>,
bitMatOr<uint>,
bitMatXor<uint>
};
static const func_t funcs16[] =
{
bitMatAnd<ushort>,
bitMatOr<ushort>,
bitMatXor<ushort>
};
static const func_t funcs8[] =
{
bitMatAnd<uchar>,
bitMatOr<uchar>,
bitMatXor<uchar>
};
cudaStream_t stream = StreamAccessor::getStream(_stream);
const int bcols = (int) (src1.cols * src1.elemSize());
if ((bcols & 3) == 0)
{
const int vcols = bcols >> 2;
funcs32[op](PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else if ((bcols & 1) == 0)
{
const int vcols = bcols >> 1;
funcs16[op](PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else
{
funcs8[op](PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
mask, stream);
}
}
namespace arithm
{
template <typename T> void bitScalarAnd(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T> void bitScalarOr(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T> void bitScalarXor(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
}
namespace
{
typedef void (*bit_scalar_func_t)(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
template <typename T, bit_scalar_func_t func> struct BitScalar
{
static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
{
func(src, saturate_cast<T>(sc.val[0]), dst, stream);
}
};
template <bit_scalar_func_t func> struct BitScalar4
{
static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
{
unsigned int packedVal = 0;
packedVal |= (saturate_cast<unsigned char>(sc.val[0]) & 0xffff);
packedVal |= (saturate_cast<unsigned char>(sc.val[1]) & 0xffff) << 8;
packedVal |= (saturate_cast<unsigned char>(sc.val[2]) & 0xffff) << 16;
packedVal |= (saturate_cast<unsigned char>(sc.val[3]) & 0xffff) << 24;
func(src, packedVal, dst, stream);
}
};
template <int DEPTH, int cn> struct NppBitwiseCFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const npp_t* pConstants, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH> struct NppBitwiseCFunc<DEPTH, 1>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const npp_t pConstant, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH, int cn, typename NppBitwiseCFunc<DEPTH, cn>::func_t func> struct NppBitwiseC
{
typedef typename NppBitwiseCFunc<DEPTH, cn>::npp_t npp_t;
static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
const npp_t pConstants[] = {saturate_cast<npp_t>(sc.val[0]), saturate_cast<npp_t>(sc.val[1]), saturate_cast<npp_t>(sc.val[2]), saturate_cast<npp_t>(sc.val[3])};
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), pConstants, dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <int DEPTH, typename NppBitwiseCFunc<DEPTH, 1>::func_t func> struct NppBitwiseC<DEPTH, 1, func>
{
typedef typename NppBitwiseCFunc<DEPTH, 1>::npp_t npp_t;
static void call(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), saturate_cast<npp_t>(sc.val[0]), dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
static void bitScalar(const GpuMat& src, Scalar val, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
using namespace arithm;
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[3][5][4] =
{
{
{BitScalar<unsigned char, bitScalarAnd<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarAnd<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarAnd<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarAnd<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
},
{
{BitScalar<unsigned char, bitScalarOr<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOr<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarOr<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarOr<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
},
{
{BitScalar<unsigned char, bitScalarXor<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarXor<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarXor<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarXor<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
}
};
const int depth = src.depth();
const int cn = src.channels();
CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
CV_Assert( cn == 1 || cn == 3 || cn == 4 );
CV_Assert( mask.empty() );
funcs[op][depth][cn - 1](src, val, dst, StreamAccessor::getStream(stream));
}
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
void cv::cuda::bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
......@@ -742,20 +328,20 @@ namespace
{
template <int DEPTH, int cn> struct NppShiftFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const Npp32u* pConstants, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const Npp32u* pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH> struct NppShiftFunc<DEPTH, 1>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, const Npp32u pConstants, npp_t* pDst, int nDstStep, NppiSize oSizeROI);
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const Npp32u pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH, int cn, typename NppShiftFunc<DEPTH, cn>::func_t func> struct NppShift
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
static void call(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream)
{
......@@ -765,7 +351,7 @@ namespace
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), sc.val, dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), sc.val, dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......@@ -773,7 +359,7 @@ namespace
};
template <int DEPTH, typename NppShiftFunc<DEPTH, 1>::func_t func> struct NppShift<DEPTH, 1, func>
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
static void call(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream)
{
......@@ -783,7 +369,7 @@ namespace
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step), sc.val[0], dst.ptr<npp_t>(), static_cast<int>(dst.step), oSizeROI) );
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), sc.val[0], dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
......
modules/cudev/include/opencv2/cudev/ptr2d/mask.hpp
View file @ b11cccaa
......@@ -62,6 +62,42 @@ struct WithOutMask
}
};
template <class MaskPtr> struct SingleMaskChannels
{
typedef typename PtrTraits<MaskPtr>::value_type value_type;
typedef typename PtrTraits<MaskPtr>::index_type index_type;
MaskPtr mask;
int channels;
__device__ __forceinline__ value_type operator()(index_type y, index_type x) const
{
return mask(y, x / channels);
}
};
template <class MaskPtr> struct SingleMaskChannelsSz : SingleMaskChannels<MaskPtr>
{
int rows, cols;
};
template <class MaskPtr>
__host__ SingleMaskChannelsSz<typename PtrTraits<MaskPtr>::ptr_type>
singleMaskChannels(const MaskPtr& mask, int channels)
{
SingleMaskChannelsSz<typename PtrTraits<MaskPtr>::ptr_type> ptr;
ptr.mask = shrinkPtr(mask);
ptr.channels = channels;
ptr.rows = getRows(mask);
ptr.cols = getCols(mask) * channels;
return ptr;
}
template <class MaskPtr> struct PtrTraits< SingleMaskChannelsSz<MaskPtr> > : PtrTraitsBase<SingleMaskChannelsSz<MaskPtr>, SingleMaskChannels<MaskPtr> >
{
};
}}
#endif
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