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opencv/modules/gpu/src/arithm.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;
using namespace std;
#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::transpose(const GpuMat&, 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::meanStdDev(const GpuMat&, Scalar&, Scalar&) { throw_nogpu(); }
double cv::gpu::norm(const GpuMat&, int) { throw_nogpu(); return 0.0; }
double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return 0.0; }
void cv::gpu::flip(const GpuMat&, GpuMat&, int) { throw_nogpu(); }
Scalar cv::gpu::sum(const GpuMat&) { throw_nogpu(); return Scalar(); }
void cv::gpu::minMax(const GpuMat&, double*, double*) { throw_nogpu(); }
void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::exp(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::log(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitudeSqr(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::magnitudeSqr(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitudeSqr(const GpuMat&, const GpuMat&, GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::phase(const GpuMat&, const GpuMat&, GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::phase(const GpuMat&, const GpuMat&, GpuMat&, bool, const Stream&) { throw_nogpu(); }
void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, const Stream&) { throw_nogpu(); }
void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, const Stream&) { 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 /* !defined (HAVE_CUDA) */
#define NPP_VERSION (10 * NPP_VERSION_MAJOR + NPP_VERSION_MINOR)
#if (defined(_WIN32) || defined(_WIN64)) && (NPP_VERSION >= 32)
# define NPP_HAVE_COMPLEX_TYPE
#endif
////////////////////////////////////////////////////////////////////////
// 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());
#if NPP_VERSION >= 32
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
#else
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32FC1);
#endif
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;
#if NPP_VERSION >= 32
case CV_32SC1:
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
#endif
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);
};
#ifdef NPP_HAVE_COMPLEX_TYPE
template<> struct NppArithmScalarFunc<2>
{
typedef NppStatus (*func_ptr)(const Npp32fc *pSrc, int nSrcStep, Npp32fc nValue, Npp32fc *pDst,
int nDstStep, NppiSize oSizeROI);
};
#endif
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) );
}
};
#ifdef NPP_HAVE_COMPLEX_TYPE
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) );
}
};
#endif
}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
#if NPP_VERSION >= 32
nppArithmCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, nppiAdd_32s_C1R, nppiAdd_32f_C1R);
#else
nppArithmCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, 0, nppiAdd_32f_C1R);
#endif
}
void cv::gpu::subtract(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
#if NPP_VERSION >= 32
nppArithmCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, nppiSub_32s_C1R, nppiSub_32f_C1R);
#else
nppArithmCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, 0, nppiSub_32f_C1R);
#endif
}
void cv::gpu::multiply(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
#if NPP_VERSION >= 32
nppArithmCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, nppiMul_32s_C1R, nppiMul_32f_C1R);
#else
nppArithmCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, 0, nppiMul_32f_C1R);
#endif
}
void cv::gpu::divide(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
#if NPP_VERSION >= 32
nppArithmCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, nppiDiv_32s_C1R, nppiDiv_32f_C1R);
#else
nppArithmCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, 0, nppiDiv_32f_C1R);
#endif
}
void cv::gpu::add(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
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);
#else
# if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
NppArithmScalar<1, nppiAddC_32f_C1R>::calc(src, sc, dst);
# else
CV_Assert(!"This function doesn't supported");
# endif
#endif
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
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);
#else
# if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
NppArithmScalar<1, nppiSubC_32f_C1R>::calc(src, sc, dst);
# else
CV_Assert(!"This function doesn't supported");
# endif
#endif
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
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);
#else
# if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
NppArithmScalar<1, nppiMulC_32f_C1R>::calc(src, sc, dst);
# else
CV_Assert(!"This function doesn't supported");
# endif
#endif
}
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
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);
#else
# if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
NppArithmScalar<1, nppiDivC_32f_C1R>::calc(src, sc, dst);
# else
CV_Assert(!"This function doesn't supported");
# endif
#endif
}
////////////////////////////////////////////////////////////////////////
// transpose
void cv::gpu::transpose(const GpuMat& src, GpuMat& dst)
{
CV_Assert(src.type() == CV_8UC1);
dst.create( src.cols, src.rows, src.type() );
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz) );
}
////////////////////////////////////////////////////////////////////////
// absdiff
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
#if NPP_VERSION >= 32
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
#else
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32FC1);
#endif
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;
#if NPP_VERSION >= 32
case CV_32SC1:
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
#endif
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)
{
#if NPP_VERSION >= 32
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]) );
#else
CV_Assert(!"This function doesn't supported");
#endif
}
////////////////////////////////////////////////////////////////////////
// compare
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);
}
}
}
////////////////////////////////////////////////////////////////////////
// meanStdDev
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
{
CV_Assert(src.type() == CV_8UC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), src.step, sz, mean.val, stddev.val) );
}
////////////////////////////////////////////////////////////////////////
// norm
double cv::gpu::norm(const GpuMat& src1, int normType)
{
return norm(src1, GpuMat(src1.size(), src1.type(), Scalar::all(0.0)), normType);
}
double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1);
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
typedef NppStatus (*npp_norm_diff_func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2,
NppiSize oSizeROI, Npp64f* pRetVal);
static const npp_norm_diff_func_t npp_norm_diff_func[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
int funcIdx = normType >> 1;
double retVal;
nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
sz, &retVal) );
return retVal;
}
////////////////////////////////////////////////////////////////////////
// flip
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
dst.create( src.size(), src.type() );
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiMirror_8u_C1R(src.ptr<Npp8u>(), src.step,
dst.ptr<Npp8u>(), dst.step, sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
else
{
nppSafeCall( nppiMirror_8u_C4R(src.ptr<Npp8u>(), src.step,
dst.ptr<Npp8u>(), dst.step, sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
}
}
////////////////////////////////////////////////////////////////////////
// sum
Scalar cv::gpu::sum(const GpuMat& src)
{
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Scalar res;
#if NPP_VERSION >= 32
CV_Assert(!"disabled until fix crash");
int bufsz;
if (src.type() == CV_8UC1)
{
nppiReductionGetBufferHostSize_8u_C1R(sz, &bufsz);
GpuMat buf(1, bufsz, CV_32S);
nppSafeCall( nppiSum_8u_C1R(src.ptr<Npp8u>(), src.step, sz, buf.ptr<Npp32s>(), res.val) );
}
else
{
nppiReductionGetBufferHostSize_8u_C4R(sz, &bufsz);
GpuMat buf(1, bufsz, CV_32S);
nppSafeCall( nppiSum_8u_C4R(src.ptr<Npp8u>(), src.step, sz, buf.ptr<Npp32s>(), res.val) );
}
#else
if (src.type() == CV_8UC1)
nppSafeCall( nppiSum_8u_C1R(src.ptr<Npp8u>(), src.step, sz, res.val) );
else
nppSafeCall( nppiSum_8u_C4R(src.ptr<Npp8u>(), src.step, sz, res.val) );
#endif
return res;
}
////////////////////////////////////////////////////////////////////////
// minMax
namespace
{
void minMax_c1(const GpuMat& src, double* minVal, double* maxVal)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp8u min_res, max_res;
nppSafeCall( nppiMinMax_8u_C1R(src.ptr<Npp8u>(), src.step, sz, &min_res, &max_res) );
if (minVal)
*minVal = min_res;
if (maxVal)
*maxVal = max_res;
}
void minMax_c4(const GpuMat& src, double* minVal, double* maxVal)
{
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp8u* cuMem;
#if NPP_VERSION >= 32
cuMem = nppsMalloc_8u(8);
#else
cudaSafeCall( cudaMalloc((void**)&cuMem, 8 * sizeof(Npp8u)) );
#endif
nppSafeCall( nppiMinMax_8u_C4R(src.ptr<Npp8u>(), src.step, sz, cuMem, cuMem + 4) );
if (minVal)
cudaMemcpy(minVal, cuMem, 4 * sizeof(Npp8u), cudaMemcpyDeviceToHost);
if (maxVal)
cudaMemcpy(maxVal, cuMem + 4, 4 * sizeof(Npp8u), cudaMemcpyDeviceToHost);
#if NPP_VERSION >= 32
nppsFree(cuMem);
#else
cudaSafeCall( cudaFree(cuMem) );
#endif
}
}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal)
{
typedef void (*minMax_t)(const GpuMat& src, double* minVal, double* maxVal);
static const minMax_t minMax_callers[] = {0, minMax_c1, 0, 0, minMax_c4};
CV_Assert(!"disabled until fix npp bug");
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
minMax_callers[src.channels()](src, minVal, maxVal);
}
////////////////////////////////////////////////////////////////////////
// LUT
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst)
{
class LevelsInit
{
public:
Npp32s pLevels[256];
const Npp32s* pLevels3[3];
int nValues3[3];
LevelsInit()
{
nValues3[0] = nValues3[1] = nValues3[2] = 256;
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
}
};
static LevelsInit lvls;
int cn = src.channels();
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3);
CV_Assert(lut.depth() == CV_8U && (lut.channels() == 1 || lut.channels() == cn) && lut.rows * lut.cols == 256 && lut.isContinuous());
dst.create(src.size(), CV_MAKETYPE(lut.depth(), cn));
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
Mat nppLut;
lut.convertTo(nppLut, CV_32S);
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz,
nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
}
else
{
Mat nppLut3[3];
const Npp32s* pValues3[3];
if (nppLut.channels() == 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>();
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz,
pValues3, lvls.pLevels3, lvls.nValues3) );
}
}
////////////////////////////////////////////////////////////////////////
// exp
void cv::gpu::exp(const GpuMat& src, GpuMat& dst)
{
#if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiExp_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
#else
CV_Assert(!"This function doesn't supported");
#endif
}
////////////////////////////////////////////////////////////////////////
// log
void cv::gpu::log(const GpuMat& src, GpuMat& dst)
{
#if NPP_VERSION >= 32
CV_Assert(src.type() == CV_32FC1);
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiLn_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
#else
CV_Assert(!"This function doesn't supported");
#endif
}
////////////////////////////////////////////////////////////////////////
// NPP magnitide
#ifdef NPP_HAVE_COMPLEX_TYPE
namespace
{
typedef NppStatus (*nppMagnitude_t)(const Npp32fc* pSrc, int nSrcStep, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
inline void npp_magnitude(const GpuMat& src, GpuMat& dst, nppMagnitude_t func)
{
CV_Assert(src.type() == CV_32FC2);
dst.create(src.size(), CV_32FC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
}
}
#endif
void cv::gpu::magnitude(const GpuMat& src, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
::npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R);
#else
CV_Assert(!"This function doesn't supported");
#endif
}
void cv::gpu::magnitudeSqr(const GpuMat& src, GpuMat& dst)
{
#ifdef NPP_HAVE_COMPLEX_TYPE
::npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R);
#else
CV_Assert(!"This function doesn't supported");
#endif
}
////////////////////////////////////////////////////////////////////////
// Polar <-> Cart
namespace cv { namespace gpu { namespace mathfunc
{
void cartToPolar_gpu(const DevMem2Df& x, const DevMem2Df& y, const DevMem2Df& mag, bool magSqr, const DevMem2Df& angle, bool angleInDegrees, cudaStream_t stream);
void polarToCart_gpu(const DevMem2Df& mag, const DevMem2Df& angle, const DevMem2Df& x, const DevMem2Df& y, bool angleInDegrees, cudaStream_t stream);
}}}
namespace
{
inline void cartToPolar_caller(const GpuMat& x, const GpuMat& y, GpuMat* mag, bool magSqr, GpuMat* angle, bool angleInDegrees, cudaStream_t stream)
{
CV_DbgAssert(x.size() == y.size() && x.type() == y.type());
CV_Assert(x.depth() == CV_32F);
if (mag)
mag->create(x.size(), x.type());
if (angle)
angle->create(x.size(), x.type());
GpuMat x1cn = x.reshape(1);
GpuMat y1cn = y.reshape(1);
GpuMat mag1cn = mag ? mag->reshape(1) : GpuMat();
GpuMat angle1cn = angle ? angle->reshape(1) : GpuMat();
mathfunc::cartToPolar_gpu(x1cn, y1cn, mag1cn, magSqr, angle1cn, angleInDegrees, stream);
}
inline void polarToCart_caller(const GpuMat& mag, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, cudaStream_t stream)
{
CV_DbgAssert((mag.empty() || mag.size() == angle.size()) && mag.type() == angle.type());
CV_Assert(mag.depth() == CV_32F);
x.create(mag.size(), mag.type());
y.create(mag.size(), mag.type());
GpuMat mag1cn = mag.reshape(1);
GpuMat angle1cn = angle.reshape(1);
GpuMat x1cn = x.reshape(1);
GpuMat y1cn = y.reshape(1);
mathfunc::polarToCart_gpu(mag1cn, angle1cn, x1cn, y1cn, angleInDegrees, stream);
}
}
void cv::gpu::magnitude(const GpuMat& x, const GpuMat& y, GpuMat& dst)
{
::cartToPolar_caller(x, y, &dst, false, 0, false, 0);
}
void cv::gpu::magnitude(const GpuMat& x, const GpuMat& y, GpuMat& dst, const Stream& stream)
{
::cartToPolar_caller(x, y, &dst, false, 0, false, StreamAccessor::getStream(stream));
}
void cv::gpu::magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& dst)
{
::cartToPolar_caller(x, y, &dst, true, 0, false, 0);
}
void cv::gpu::magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& dst, const Stream& stream)
{
::cartToPolar_caller(x, y, &dst, true, 0, false, StreamAccessor::getStream(stream));
}
void cv::gpu::phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees)
{
::cartToPolar_caller(x, y, 0, false, &angle, angleInDegrees, 0);
}
void cv::gpu::phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees, const Stream& stream)
{
::cartToPolar_caller(x, y, 0, false, &angle, angleInDegrees, StreamAccessor::getStream(stream));
}
void cv::gpu::cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& mag, GpuMat& angle, bool angleInDegrees)
{
::cartToPolar_caller(x, y, &mag, false, &angle, angleInDegrees, 0);
}
void cv::gpu::cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& mag, GpuMat& angle, bool angleInDegrees, const Stream& stream)
{
::cartToPolar_caller(x, y, &mag, false, &angle, angleInDegrees, StreamAccessor::getStream(stream));
}
void cv::gpu::polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees)
{
::polarToCart_caller(magnitude, angle, x, y, angleInDegrees, 0);
}
void cv::gpu::polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, const Stream& stream)
{
::polarToCart_caller(magnitude, angle, x, y, angleInDegrees, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// Per-element bit-wise logical matrix operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwise_not_caller(int rows, int cols, const PtrStep src, int elemSize, PtrStep dst, cudaStream_t stream);
void bitwise_not_caller(int rows, int cols, const PtrStep src, int elemSize, PtrStep dst, const PtrStep mask, cudaStream_t stream);
void bitwise_or_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, cudaStream_t stream);
void bitwise_or_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, const PtrStep mask, cudaStream_t stream);
void bitwise_and_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, cudaStream_t stream);
void bitwise_and_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, const PtrStep mask, cudaStream_t stream);
void bitwise_xor_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, cudaStream_t stream);
void bitwise_xor_caller(int rows, int cols, const PtrStep src1, const PtrStep src2, int elemSize, PtrStep dst, const PtrStep mask, cudaStream_t stream);
template <int opid, typename Mask>
void bitwise_bin_op(int rows, int cols, const PtrStep src1, const PtrStep src2, PtrStep dst, int elem_size, Mask mask, cudaStream_t stream);
}}}
namespace
{
void bitwise_not_caller(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
{
dst.create(src.size(), src.type());
mathfunc::bitwise_not_caller(src.rows, src.cols, src, src.elemSize(), dst, stream);
}
void bitwise_not_caller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
dst.create(src.size(), src.type());
mathfunc::bitwise_not_caller(src.rows, src.cols, src, src.elemSize(), dst, mask, stream);
}
void bitwise_or_caller(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());
mathfunc::bitwise_or_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, stream);
}
void bitwise_or_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
mathfunc::bitwise_or_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, mask, stream);
}
void bitwise_and_caller(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());
mathfunc::bitwise_and_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, stream);
}
void bitwise_and_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
mathfunc::bitwise_and_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, mask, stream);
}
void bitwise_xor_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size());
CV_Assert(src1.type() == src2.type());
dst.create(src1.size(), src1.type());
mathfunc::bitwise_xor_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, stream);
}
void bitwise_xor_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
mathfunc::bitwise_xor_caller(dst.rows, dst.cols, src1, src2, dst.elemSize(), dst, mask, stream);
}
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_not_caller(src, dst, 0);
else
::bitwise_not_caller(src, dst, mask, 0);
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwise_not_caller(src, dst, StreamAccessor::getStream(stream));
else
::bitwise_not_caller(src, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_or_caller(src1, src2, dst, 0);
else
::bitwise_or_caller(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())
::bitwise_or_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_or_caller(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())
::bitwise_and_caller(src1, src2, dst, 0);
else
::bitwise_and_caller(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())
::bitwise_and_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_and_caller(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())
::bitwise_xor_caller(src1, src2, dst, 0);
else
::bitwise_xor_caller(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())
::bitwise_xor_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_xor_caller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat& src)
{
GpuMat dst;
bitwise_not(src, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_or(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_and(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_xor(src1, src2, dst);
return dst;
}
#endif /* !defined (HAVE_CUDA) */