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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 materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::gemm(const GpuMat&, const GpuMat&, double, const GpuMat&, double, GpuMat&, int, Stream&) { throw_no_cuda(); }
void cv::gpu::transpose(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::flip(const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); }
void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::magnitude(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::magnitudeSqr(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::magnitudeSqr(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::phase(const GpuMat&, const GpuMat&, GpuMat&, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&) { throw_no_cuda(); }
void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&, GpuMat&, GpuMat&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
////////////////////////////////////////////////////////////////////////
// gemm
void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
(void)src1;
(void)src2;
(void)alpha;
(void)src3;
(void)beta;
(void)dst;
(void)flags;
(void)stream;
CV_Error(cv::Error::StsNotImplemented, "The library was build without CUBLAS");
#else
// CUBLAS works with column-major matrices
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
if (src1.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
bool tr1 = (flags & GEMM_1_T) != 0;
bool tr2 = (flags & GEMM_2_T) != 0;
bool tr3 = (flags & GEMM_3_T) != 0;
if (src1.type() == CV_64FC2)
{
if (tr1 || tr2 || tr3)
CV_Error(cv::Error::StsNotImplemented, "transpose operation doesn't implemented for CV_64FC2 type");
}
Size src1Size = tr1 ? Size(src1.rows, src1.cols) : src1.size();
Size src2Size = tr2 ? Size(src2.rows, src2.cols) : src2.size();
Size src3Size = tr3 ? Size(src3.rows, src3.cols) : src3.size();
Size dstSize(src2Size.width, src1Size.height);
CV_Assert(src1Size.width == src2Size.height);
CV_Assert(src3.empty() || src3Size == dstSize);
dst.create(dstSize, src1.type());
if (beta != 0)
{
if (src3.empty())
{
if (stream)
stream.enqueueMemSet(dst, Scalar::all(0));
else
dst.setTo(Scalar::all(0));
}
else
{
if (tr3)
{
transpose(src3, dst, stream);
}
else
{
if (stream)
stream.enqueueCopy(src3, dst);
else
src3.copyTo(dst);
}
}
}
cublasHandle_t handle;
cublasSafeCall( cublasCreate_v2(&handle) );
cublasSafeCall( cublasSetStream_v2(handle, StreamAccessor::getStream(stream)) );
cublasSafeCall( cublasSetPointerMode_v2(handle, CUBLAS_POINTER_MODE_HOST) );
const float alphaf = static_cast<float>(alpha);
const float betaf = static_cast<float>(beta);
const cuComplex alphacf = make_cuComplex(alphaf, 0);
const cuComplex betacf = make_cuComplex(betaf, 0);
const cuDoubleComplex alphac = make_cuDoubleComplex(alpha, 0);
const cuDoubleComplex betac = make_cuDoubleComplex(beta, 0);
cublasOperation_t transa = tr2 ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t transb = tr1 ? CUBLAS_OP_T : CUBLAS_OP_N;
switch (src1.type())
{
case CV_32FC1:
cublasSafeCall( cublasSgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphaf,
src2.ptr<float>(), static_cast<int>(src2.step / sizeof(float)),
src1.ptr<float>(), static_cast<int>(src1.step / sizeof(float)),
&betaf,
dst.ptr<float>(), static_cast<int>(dst.step / sizeof(float))) );
break;
case CV_64FC1:
cublasSafeCall( cublasDgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alpha,
src2.ptr<double>(), static_cast<int>(src2.step / sizeof(double)),
src1.ptr<double>(), static_cast<int>(src1.step / sizeof(double)),
&beta,
dst.ptr<double>(), static_cast<int>(dst.step / sizeof(double))) );
break;
case CV_32FC2:
cublasSafeCall( cublasCgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphacf,
src2.ptr<cuComplex>(), static_cast<int>(src2.step / sizeof(cuComplex)),
src1.ptr<cuComplex>(), static_cast<int>(src1.step / sizeof(cuComplex)),
&betacf,
dst.ptr<cuComplex>(), static_cast<int>(dst.step / sizeof(cuComplex))) );
break;
case CV_64FC2:
cublasSafeCall( cublasZgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphac,
src2.ptr<cuDoubleComplex>(), static_cast<int>(src2.step / sizeof(cuDoubleComplex)),
src1.ptr<cuDoubleComplex>(), static_cast<int>(src1.step / sizeof(cuDoubleComplex)),
&betac,
dst.ptr<cuDoubleComplex>(), static_cast<int>(dst.step / sizeof(cuDoubleComplex))) );
break;
}
cublasSafeCall( cublasDestroy_v2(handle) );
#endif
}
////////////////////////////////////////////////////////////////////////
// transpose
void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
{
CV_Assert(src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8);
dst.create( src.cols, src.rows, src.type() );
cudaStream_t stream = StreamAccessor::getStream(s);
if (src.elemSize() == 1)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
}
else if (src.elemSize() == 4)
{
NppStStreamHandler h(stream);
NcvSize32u sz;
sz.width = src.cols;
sz.height = src.rows;
ncvSafeCall( nppiStTranspose_32u_C1R(const_cast<Ncv32u*>(src.ptr<Ncv32u>()), static_cast<int>(src.step),
dst.ptr<Ncv32u>(), static_cast<int>(dst.step), sz) );
}
else // if (src.elemSize() == 8)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
NppStStreamHandler h(stream);
NcvSize32u sz;
sz.width = src.cols;
sz.height = src.rows;
ncvSafeCall( nppiStTranspose_64u_C1R(const_cast<Ncv64u*>(src.ptr<Ncv64u>()), static_cast<int>(src.step),
dst.ptr<Ncv64u>(), static_cast<int>(dst.step), sz) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// flip
namespace
{
template<int DEPTH> struct NppTypeTraits;
template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; };
template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };
template <int DEPTH> struct NppMirrorFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip);
};
template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror
{
typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t;
static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step),
dst.ptr<npp_t>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
};
CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
dst.create(src.size(), src.type());
funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// LUT
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
{
const int cn = src.channels();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
CV_Assert( lut.depth() == CV_8U );
CV_Assert( lut.channels() == 1 || lut.channels() == cn );
CV_Assert( lut.rows * lut.cols == 256 && lut.isContinuous() );
dst.create(src.size(), CV_MAKE_TYPE(lut.depth(), cn));
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
Mat nppLut;
lut.convertTo(nppLut, CV_32S);
int nValues3[] = {256, 256, 256};
Npp32s pLevels[256];
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
const Npp32s* pLevels3[3];
#if (CUDA_VERSION <= 4020)
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
#else
GpuMat d_pLevels;
d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();
#endif
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
if (src.type() == CV_8UC1)
{
#if (CUDA_VERSION <= 4020)
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), pLevels, 256) );
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), d_pLevels.ptr<Npp32s>(), 256) );
#endif
}
else
{
const Npp32s* pValues3[3];
Mat nppLut3[3];
if (nppLut.channels() == 1)
{
#if (CUDA_VERSION <= 4020)
pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>();
#endif
}
else
{
cv::split(nppLut, nppLut3);
#if (CUDA_VERSION <= 4020)
pValues3[0] = nppLut3[0].ptr<Npp32s>();
pValues3[1] = nppLut3[1].ptr<Npp32s>();
pValues3[2] = nppLut3[2].ptr<Npp32s>();
#else
GpuMat d_nppLut0(Mat(1, 256, CV_32S, nppLut3[0].data));
GpuMat d_nppLut1(Mat(1, 256, CV_32S, nppLut3[1].data));
GpuMat d_nppLut2(Mat(1, 256, CV_32S, nppLut3[2].data));
pValues3[0] = d_nppLut0.ptr<Npp32s>();
pValues3[1] = d_nppLut1.ptr<Npp32s>();
pValues3[2] = d_nppLut2.ptr<Npp32s>();
#endif
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
////////////////////////////////////////////////////////////////////////
// NPP magnitide
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, cudaStream_t stream)
{
CV_Assert(src.type() == CV_32FC2);
dst.create(src.size(), CV_32FC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp32fc>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
void cv::gpu::magnitude(const GpuMat& src, GpuMat& dst, Stream& stream)
{
npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R, StreamAccessor::getStream(stream));
}
void cv::gpu::magnitudeSqr(const GpuMat& src, GpuMat& dst, Stream& stream)
{
npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// Polar <-> Cart
namespace cv { namespace gpu { namespace cudev
{
namespace mathfunc
{
void cartToPolar_gpu(PtrStepSzf x, PtrStepSzf y, PtrStepSzf mag, bool magSqr, PtrStepSzf angle, bool angleInDegrees, cudaStream_t stream);
void polarToCart_gpu(PtrStepSzf mag, PtrStepSzf angle, PtrStepSzf x, PtrStepSzf 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)
{
using namespace ::cv::gpu::cudev::mathfunc;
CV_Assert(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();
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)
{
using namespace ::cv::gpu::cudev::mathfunc;
CV_Assert((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);
polarToCart_gpu(mag1cn, angle1cn, x1cn, y1cn, angleInDegrees, stream);
}
}
void cv::gpu::magnitude(const GpuMat& x, const GpuMat& y, GpuMat& dst, 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, 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, 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, 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, Stream& stream)
{
polarToCart_caller(magnitude, angle, x, y, angleInDegrees, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
// normalize
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask)
{
GpuMat norm_buf;
GpuMat cvt_buf;
normalize(src, dst, a, b, norm_type, dtype, mask, norm_buf, cvt_buf);
}
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf)
{
double scale = 1, shift = 0;
if (norm_type == NORM_MINMAX)
{
double smin = 0, smax = 0;
double dmin = std::min(a, b), dmax = std::max(a, b);
minMax(src, &smin, &smax, mask, norm_buf);
scale = (dmax - dmin) * (smax - smin > std::numeric_limits<double>::epsilon() ? 1.0 / (smax - smin) : 0.0);
shift = dmin - smin * scale;
}
else if (norm_type == NORM_L2 || norm_type == NORM_L1 || norm_type == NORM_INF)
{
scale = norm(src, norm_type, mask, norm_buf);
scale = scale > std::numeric_limits<double>::epsilon() ? a / scale : 0.0;
shift = 0;
}
else
{
CV_Error(cv::Error::StsBadArg, "Unknown/unsupported norm type");
}
if (mask.empty())
{
src.convertTo(dst, dtype, scale, shift);
}
else
{
src.convertTo(cvt_buf, dtype, scale, shift);
cvt_buf.copyTo(dst, mask);
}
}
#endif /* !defined (HAVE_CUDA) */