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Open Source Computer Vision Library https://opencv.org/
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opencv/modules/gpu/src/element_operations.cpp

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/*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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
// 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)
void cv::gpu::add(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::add(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::subtract(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::subtract(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::multiply(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::multiply(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::divide(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::divide(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::absdiff(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::absdiff(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::compare(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_nogpu(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
#else
////////////////////////////////////////////////////////////////////////
// Basic arithmetical operations (add subtract multiply divide)
namespace
{
typedef NppStatus (*npp_arithm_8u_t)(const Npp8u* pSrc1, int nSrc1Step, const Npp8u* pSrc2, int nSrc2Step, Npp8u* pDst, int nDstStep,
NppiSize oSizeROI, int nScaleFactor);
typedef NppStatus (*npp_arithm_32s_t)(const Npp32s* pSrc1, int nSrc1Step, const Npp32s* pSrc2, int nSrc2Step, Npp32s* pDst,
int nDstStep, NppiSize oSizeROI);
typedef NppStatus (*npp_arithm_32f_t)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f* pSrc2, int nSrc2Step, Npp32f* pDst,
int nDstStep, NppiSize oSizeROI);
void nppArithmCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst,
npp_arithm_8u_t npp_func_8uc1, npp_arithm_8u_t npp_func_8uc4,
npp_arithm_32s_t npp_func_32sc1, npp_arithm_32f_t npp_func_32fc1)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, 0) );
break;
case CV_8UC4:
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, 0) );
break;
case CV_32SC1:
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
case CV_32FC1:
nppSafeCall( npp_func_32fc1(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp32f>(), dst.step, sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
}
template<int SCN> struct NppArithmScalarFunc;
template<> struct NppArithmScalarFunc<1>
{
typedef NppStatus (*func_ptr)(const Npp32f *pSrc, int nSrcStep, Npp32f nValue, Npp32f *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<> struct NppArithmScalarFunc<2>
{
typedef NppStatus (*func_ptr)(const Npp32fc *pSrc, int nSrcStep, Npp32fc nValue, Npp32fc *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<int SCN, typename NppArithmScalarFunc<SCN>::func_ptr func> struct NppArithmScalar;
template<typename NppArithmScalarFunc<1>::func_ptr func> struct NppArithmScalar<1, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
}
};
template<typename NppArithmScalarFunc<2>::func_ptr func> struct NppArithmScalar<2, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp32fc nValue;
nValue.re = (Npp32f)sc[0];
nValue.im = (Npp32f)sc[1];
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, nValue, dst.ptr<Npp32fc>(), dst.step, sz) );
}
};
}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppArithmCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, nppiAdd_32s_C1R, nppiAdd_32f_C1R);
}
void cv::gpu::subtract(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppArithmCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, nppiSub_32s_C1R, nppiSub_32f_C1R);
}
void cv::gpu::multiply(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppArithmCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, nppiMul_32s_C1R, nppiMul_32f_C1R);
}
void cv::gpu::divide(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppArithmCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, nppiDiv_32s_C1R, nppiDiv_32f_C1R);
}
void cv::gpu::add(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiAddC_32f_C1R>::calc, NppArithmScalar<2, nppiAddC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiSubC_32f_C1R>::calc, NppArithmScalar<2, nppiSubC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiMulC_32f_C1R>::calc, NppArithmScalar<2, nppiMulC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiDivC_32f_C1R>::calc, NppArithmScalar<2, nppiDivC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
//////////////////////////////////////////////////////////////////////////////
// Absolute difference
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz) );
break;
case CV_8UC4:
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz) );
break;
case CV_32SC1:
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
case CV_32FC1:
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp32f>(), dst.step, sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
}
void cv::gpu::absdiff(const GpuMat& src, const Scalar& s, GpuMat& dst)
{
CV_Assert(src.type() == CV_32FC1);
dst.create( src.size(), src.type() );
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiAbsDiffC_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz, (Npp32f)s[0]) );
}
//////////////////////////////////////////////////////////////////////////////
// Comparison of two matrixes
namespace cv { namespace gpu { namespace mathfunc
{
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
}}}
void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC4 || src1.type() == CV_32FC1);
dst.create( src1.size(), CV_8UC1 );
static const NppCmpOp nppCmpOp[] = { NPP_CMP_EQ, NPP_CMP_GREATER, NPP_CMP_GREATER_EQ, NPP_CMP_LESS, NPP_CMP_LESS_EQ };
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
if (src1.type() == CV_8UC4)
{
if (cmpop != CMP_NE)
{
nppSafeCall( nppiCompare_8u_C4R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
}
else
{
mathfunc::compare_ne_8uc4(src1, src2, dst);
}
}
else
{
if (cmpop != CMP_NE)
{
nppSafeCall( nppiCompare_32f_C1R(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
}
else
{
mathfunc::compare_ne_32f(src1, src2, dst);
}
}
}
//////////////////////////////////////////////////////////////////////////////
// Unary bitwise logical operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream);
}}}
namespace
{
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
{
dst.create(src.size(), src.type());
cv::gpu::mathfunc::bitwiseNotCaller(src.rows, src.cols, src.elemSize1(),
dst.channels(), src, dst, stream);
}
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskNotCaller<unsigned char>, mathfunc::bitwiseMaskNotCaller<unsigned char>,
mathfunc::bitwiseMaskNotCaller<unsigned short>, mathfunc::bitwiseMaskNotCaller<unsigned short>,
mathfunc::bitwiseMaskNotCaller<unsigned int>, mathfunc::bitwiseMaskNotCaller<unsigned int>,
mathfunc::bitwiseMaskNotCaller<unsigned int>};
CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
dst.create(src.size(), src.type());
Caller caller = callers[src.depth()];
CV_Assert(caller);
int cn = src.depth() != CV_64F ? src.channels() : src.channels() * (sizeof(double) / sizeof(unsigned int));
caller(src.rows, src.cols, cn, src, mask, dst, stream);
}
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseNotCaller(src, dst, 0);
else
::bitwiseNotCaller(src, dst, mask, 0);
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseNotCaller(src, dst, StreamAccessor::getStream(stream));
else
::bitwiseNotCaller(src, dst, mask, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
}}}
namespace
{
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseOrCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskOrCaller<unsigned char>, mathfunc::bitwiseMaskOrCaller<unsigned char>,
mathfunc::bitwiseMaskOrCaller<unsigned short>, mathfunc::bitwiseMaskOrCaller<unsigned short>,
mathfunc::bitwiseMaskOrCaller<unsigned int>, mathfunc::bitwiseMaskOrCaller<unsigned int>,
mathfunc::bitwiseMaskOrCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseAndCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskAndCaller<unsigned char>, mathfunc::bitwiseMaskAndCaller<unsigned char>,
mathfunc::bitwiseMaskAndCaller<unsigned short>, mathfunc::bitwiseMaskAndCaller<unsigned short>,
mathfunc::bitwiseMaskAndCaller<unsigned int>, mathfunc::bitwiseMaskAndCaller<unsigned int>,
mathfunc::bitwiseMaskAndCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseXorCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskXorCaller<unsigned char>, mathfunc::bitwiseMaskXorCaller<unsigned char>,
mathfunc::bitwiseMaskXorCaller<unsigned short>, mathfunc::bitwiseMaskXorCaller<unsigned short>,
mathfunc::bitwiseMaskXorCaller<unsigned int>, mathfunc::bitwiseMaskXorCaller<unsigned int>,
mathfunc::bitwiseMaskXorCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseOrCaller(src1, src2, dst, 0);
else
::bitwiseOrCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseOrCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseOrCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseAndCaller(src1, src2, dst, 0);
else
::bitwiseAndCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseAndCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseAndCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseXorCaller(src1, src2, dst, 0);
else
::bitwiseXorCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseXorCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseXorCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
#endif