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arithm.cpp
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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) 2009-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2014-2015, Itseez 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*/
/* ////////////////////////////////////////////////////////////////////
//
// Arithmetic and logical operations: +, -, *, /, &, |, ^, ~, abs ...
//
// */
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
namespace cv
{
/****************************************************************************************\
* logical operations *
\****************************************************************************************/
void convertAndUnrollScalar( const Mat& sc, int buftype, uchar* scbuf, size_t blocksize )
{
int scn = (int)sc.total(), cn = CV_MAT_CN(buftype);
size_t esz = CV_ELEM_SIZE(buftype);
getConvertFunc(sc.depth(), buftype)(sc.ptr(), 1, 0, 1, scbuf, 1, Size(std::min(cn, scn), 1), 0);
// unroll the scalar
if( scn < cn )
{
CV_Assert( scn == 1 );
size_t esz1 = CV_ELEM_SIZE1(buftype);
for( size_t i = esz1; i < esz; i++ )
scbuf[i] = scbuf[i - esz1];
}
for( size_t i = esz; i < blocksize*esz; i++ )
scbuf[i] = scbuf[i - esz];
}
enum { OCL_OP_ADD=0, OCL_OP_SUB=1, OCL_OP_RSUB=2, OCL_OP_ABSDIFF=3, OCL_OP_MUL=4,
OCL_OP_MUL_SCALE=5, OCL_OP_DIV_SCALE=6, OCL_OP_RECIP_SCALE=7, OCL_OP_ADDW=8,
OCL_OP_AND=9, OCL_OP_OR=10, OCL_OP_XOR=11, OCL_OP_NOT=12, OCL_OP_MIN=13, OCL_OP_MAX=14,
OCL_OP_RDIV_SCALE=15 };
#ifdef HAVE_OPENCL
static const char* oclop2str[] = { "OP_ADD", "OP_SUB", "OP_RSUB", "OP_ABSDIFF",
"OP_MUL", "OP_MUL_SCALE", "OP_DIV_SCALE", "OP_RECIP_SCALE",
"OP_ADDW", "OP_AND", "OP_OR", "OP_XOR", "OP_NOT", "OP_MIN", "OP_MAX", "OP_RDIV_SCALE", 0 };
static bool ocl_binary_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, bool bitwise, int oclop, bool haveScalar )
{
bool haveMask = !_mask.empty();
int srctype = _src1.type();
int srcdepth = CV_MAT_DEPTH(srctype);
int cn = CV_MAT_CN(srctype);
const ocl::Device d = ocl::Device::getDefault();
bool doubleSupport = d.doubleFPConfig() > 0;
if( oclop < 0 || ((haveMask || haveScalar) && cn > 4) ||
(!doubleSupport && srcdepth == CV_64F && !bitwise))
return false;
char opts[1024];
int kercn = haveMask || haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst);
int scalarcn = kercn == 3 ? 4 : kercn;
int rowsPerWI = d.isIntel() ? 4 : 1;
sprintf(opts, "-D %s%s -D %s -D dstT=%s%s -D dstT_C1=%s -D workST=%s -D cn=%d -D rowsPerWI=%d",
haveMask ? "MASK_" : "", haveScalar ? "UNARY_OP" : "BINARY_OP", oclop2str[oclop],
bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, kercn)) :
ocl::typeToStr(CV_MAKETYPE(srcdepth, kercn)), doubleSupport ? " -D DOUBLE_SUPPORT" : "",
bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, 1)) :
ocl::typeToStr(CV_MAKETYPE(srcdepth, 1)),
bitwise ? ocl::memopTypeToStr(CV_MAKETYPE(srcdepth, scalarcn)) :
ocl::typeToStr(CV_MAKETYPE(srcdepth, scalarcn)),
kercn, rowsPerWI);
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, opts);
if (k.empty())
return false;
UMat src1 = _src1.getUMat(), src2;
UMat dst = _dst.getUMat(), mask = _mask.getUMat();
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1, cn, kercn);
ocl::KernelArg dstarg = haveMask ? ocl::KernelArg::ReadWrite(dst, cn, kercn) :
ocl::KernelArg::WriteOnly(dst, cn, kercn);
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask, 1);
if( haveScalar )
{
size_t esz = CV_ELEM_SIZE1(srctype)*scalarcn;
double buf[4] = {0,0,0,0};
if( oclop != OCL_OP_NOT )
{
Mat src2sc = _src2.getMat();
convertAndUnrollScalar(src2sc, srctype, (uchar*)buf, 1);
}
ocl::KernelArg scalararg = ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, esz);
if( !haveMask )
k.args(src1arg, dstarg, scalararg);
else
k.args(src1arg, maskarg, dstarg, scalararg);
}
else
{
src2 = _src2.getUMat();
ocl::KernelArg src2arg = ocl::KernelArg::ReadOnlyNoSize(src2, cn, kercn);
if( !haveMask )
k.args(src1arg, src2arg, dstarg);
else
k.args(src1arg, src2arg, maskarg, dstarg);
}
size_t globalsize[] = { (size_t)src1.cols * cn / kercn, ((size_t)src1.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, 0, false);
}
#endif
static void binary_op( InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, const BinaryFuncC* tab,
bool bitwise, int oclop )
{
const _InputArray *psrc1 = &_src1, *psrc2 = &_src2;
int kind1 = psrc1->kind(), kind2 = psrc2->kind();
int type1 = psrc1->type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
int type2 = psrc2->type(), depth2 = CV_MAT_DEPTH(type2), cn2 = CV_MAT_CN(type2);
int dims1 = psrc1->dims(), dims2 = psrc2->dims();
Size sz1 = dims1 <= 2 ? psrc1->size() : Size();
Size sz2 = dims2 <= 2 ? psrc2->size() : Size();
#ifdef HAVE_OPENCL
bool use_opencl = (kind1 == _InputArray::UMAT || kind2 == _InputArray::UMAT) &&
dims1 <= 2 && dims2 <= 2;
#endif
bool haveMask = !_mask.empty(), haveScalar = false;
BinaryFuncC func;
if( dims1 <= 2 && dims2 <= 2 && kind1 == kind2 && sz1 == sz2 && type1 == type2 && !haveMask )
{
_dst.create(sz1, type1);
CV_OCL_RUN(use_opencl,
ocl_binary_op(*psrc1, *psrc2, _dst, _mask, bitwise, oclop, false))
if( bitwise )
{
func = *tab;
cn = (int)CV_ELEM_SIZE(type1);
}
else
func = tab[depth1];
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat();
Size sz = getContinuousSize(src1, src2, dst);
size_t len = sz.width*(size_t)cn;
if( len == (size_t)(int)len )
{
sz.width = (int)len;
func(src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, 0);
return;
}
}
if( oclop == OCL_OP_NOT )
haveScalar = true;
else if( (kind1 == _InputArray::MATX) + (kind2 == _InputArray::MATX) == 1 ||
!psrc1->sameSize(*psrc2) || type1 != type2 )
{
if( checkScalar(*psrc1, type2, kind1, kind2) )
{
// src1 is a scalar; swap it with src2
swap(psrc1, psrc2);
swap(type1, type2);
swap(depth1, depth2);
swap(cn, cn2);
swap(sz1, sz2);
}
else if( !checkScalar(*psrc2, type1, kind2, kind1) )
CV_Error( CV_StsUnmatchedSizes,
"The operation is neither 'array op array' (where arrays have the same size and type), "
"nor 'array op scalar', nor 'scalar op array'" );
haveScalar = true;
}
else
{
CV_Assert( psrc1->sameSize(*psrc2) && type1 == type2 );
}
size_t esz = CV_ELEM_SIZE(type1);
size_t blocksize0 = (BLOCK_SIZE + esz-1)/esz;
BinaryFunc copymask = 0;
bool reallocate = false;
if( haveMask )
{
int mtype = _mask.type();
CV_Assert( (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1));
copymask = getCopyMaskFunc(esz);
reallocate = !_dst.sameSize(*psrc1) || _dst.type() != type1;
}
AutoBuffer<uchar> _buf;
uchar *scbuf = 0, *maskbuf = 0;
_dst.createSameSize(*psrc1, type1);
// if this is mask operation and dst has been reallocated,
// we have to clear the destination
if( haveMask && reallocate )
_dst.setTo(0.);
CV_OCL_RUN(use_opencl,
ocl_binary_op(*psrc1, *psrc2, _dst, _mask, bitwise, oclop, haveScalar))
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat();
Mat dst = _dst.getMat(), mask = _mask.getMat();
if( bitwise )
{
func = *tab;
cn = (int)esz;
}
else
func = tab[depth1];
if( !haveScalar )
{
const Mat* arrays[] = { &src1, &src2, &dst, &mask, 0 };
uchar* ptrs[4];
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = total;
if( blocksize*cn > INT_MAX )
blocksize = INT_MAX/cn;
if( haveMask )
{
blocksize = std::min(blocksize, blocksize0);
_buf.allocate(blocksize*esz);
maskbuf = _buf;
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
func( ptrs[0], 0, ptrs[1], 0, haveMask ? maskbuf : ptrs[2], 0, bsz*cn, 1, 0 );
if( haveMask )
{
copymask( maskbuf, 0, ptrs[3], 0, ptrs[2], 0, Size(bsz, 1), &esz );
ptrs[3] += bsz;
}
bsz *= (int)esz;
ptrs[0] += bsz; ptrs[1] += bsz; ptrs[2] += bsz;
}
}
}
else
{
const Mat* arrays[] = { &src1, &dst, &mask, 0 };
uchar* ptrs[3];
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
_buf.allocate(blocksize*(haveMask ? 2 : 1)*esz + 32);
scbuf = _buf;
maskbuf = alignPtr(scbuf + blocksize*esz, 16);
convertAndUnrollScalar( src2, src1.type(), scbuf, blocksize);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
func( ptrs[0], 0, scbuf, 0, haveMask ? maskbuf : ptrs[1], 0, bsz*cn, 1, 0 );
if( haveMask )
{
copymask( maskbuf, 0, ptrs[2], 0, ptrs[1], 0, Size(bsz, 1), &esz );
ptrs[2] += bsz;
}
bsz *= (int)esz;
ptrs[0] += bsz; ptrs[1] += bsz;
}
}
}
}
static BinaryFuncC* getMaxTab()
{
static BinaryFuncC maxTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::max8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::max16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::max32f), (BinaryFuncC)cv::hal::max64f,
0
};
return maxTab;
}
static BinaryFuncC* getMinTab()
{
static BinaryFuncC minTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::min8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::min16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::min32f), (BinaryFuncC)cv::hal::min64f,
0
};
return minTab;
}
}
void cv::bitwise_and(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION()
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::and8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_AND);
}
void cv::bitwise_or(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION()
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::or8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_OR);
}
void cv::bitwise_xor(InputArray a, InputArray b, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION()
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::xor8u);
binary_op(a, b, c, mask, &f, true, OCL_OP_XOR);
}
void cv::bitwise_not(InputArray a, OutputArray c, InputArray mask)
{
CV_INSTRUMENT_REGION()
BinaryFuncC f = (BinaryFuncC)GET_OPTIMIZED(cv::hal::not8u);
binary_op(a, a, c, mask, &f, true, OCL_OP_NOT);
}
void cv::max( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION()
binary_op(src1, src2, dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION()
binary_op(src1, src2, dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
void cv::max(const Mat& src1, const Mat& src2, Mat& dst)
{
CV_INSTRUMENT_REGION()
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min(const Mat& src1, const Mat& src2, Mat& dst)
{
CV_INSTRUMENT_REGION()
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
void cv::max(const UMat& src1, const UMat& src2, UMat& dst)
{
CV_INSTRUMENT_REGION()
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMaxTab(), false, OCL_OP_MAX );
}
void cv::min(const UMat& src1, const UMat& src2, UMat& dst)
{
CV_INSTRUMENT_REGION()
OutputArray _dst(dst);
binary_op(src1, src2, _dst, noArray(), getMinTab(), false, OCL_OP_MIN );
}
/****************************************************************************************\
* add/subtract *
\****************************************************************************************/
namespace cv
{
static int actualScalarDepth(const double* data, int len)
{
int i = 0, minval = INT_MAX, maxval = INT_MIN;
for(; i < len; ++i)
{
int ival = cvRound(data[i]);
if( ival != data[i] )
break;
minval = MIN(minval, ival);
maxval = MAX(maxval, ival);
}
return i < len ? CV_64F :
minval >= 0 && maxval <= (int)UCHAR_MAX ? CV_8U :
minval >= (int)SCHAR_MIN && maxval <= (int)SCHAR_MAX ? CV_8S :
minval >= 0 && maxval <= (int)USHRT_MAX ? CV_16U :
minval >= (int)SHRT_MIN && maxval <= (int)SHRT_MAX ? CV_16S :
CV_32S;
}
#ifdef HAVE_OPENCL
static bool ocl_arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, int wtype,
void* usrdata, int oclop,
bool haveScalar )
{
const ocl::Device d = ocl::Device::getDefault();
bool doubleSupport = d.doubleFPConfig() > 0;
int type1 = _src1.type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
bool haveMask = !_mask.empty();
if ( (haveMask || haveScalar) && cn > 4 )
return false;
int dtype = _dst.type(), ddepth = CV_MAT_DEPTH(dtype), wdepth = std::max(CV_32S, CV_MAT_DEPTH(wtype));
if (!doubleSupport)
wdepth = std::min(wdepth, CV_32F);
wtype = CV_MAKETYPE(wdepth, cn);
int type2 = haveScalar ? wtype : _src2.type(), depth2 = CV_MAT_DEPTH(type2);
if (!doubleSupport && (depth2 == CV_64F || depth1 == CV_64F))
return false;
int kercn = haveMask || haveScalar ? cn : ocl::predictOptimalVectorWidth(_src1, _src2, _dst);
int scalarcn = kercn == 3 ? 4 : kercn, rowsPerWI = d.isIntel() ? 4 : 1;
char cvtstr[4][32], opts[1024];
sprintf(opts, "-D %s%s -D %s -D srcT1=%s -D srcT1_C1=%s -D srcT2=%s -D srcT2_C1=%s "
"-D dstT=%s -D dstT_C1=%s -D workT=%s -D workST=%s -D scaleT=%s -D wdepth=%d -D convertToWT1=%s "
"-D convertToWT2=%s -D convertToDT=%s%s -D cn=%d -D rowsPerWI=%d -D convertFromU=%s",
(haveMask ? "MASK_" : ""), (haveScalar ? "UNARY_OP" : "BINARY_OP"),
oclop2str[oclop], ocl::typeToStr(CV_MAKETYPE(depth1, kercn)),
ocl::typeToStr(depth1), ocl::typeToStr(CV_MAKETYPE(depth2, kercn)),
ocl::typeToStr(depth2), ocl::typeToStr(CV_MAKETYPE(ddepth, kercn)),
ocl::typeToStr(ddepth), ocl::typeToStr(CV_MAKETYPE(wdepth, kercn)),
ocl::typeToStr(CV_MAKETYPE(wdepth, scalarcn)),
ocl::typeToStr(wdepth), wdepth,
ocl::convertTypeStr(depth1, wdepth, kercn, cvtstr[0]),
ocl::convertTypeStr(depth2, wdepth, kercn, cvtstr[1]),
ocl::convertTypeStr(wdepth, ddepth, kercn, cvtstr[2]),
doubleSupport ? " -D DOUBLE_SUPPORT" : "", kercn, rowsPerWI,
oclop == OCL_OP_ABSDIFF && wdepth == CV_32S && ddepth == wdepth ?
ocl::convertTypeStr(CV_8U, ddepth, kercn, cvtstr[3]) : "noconvert");
size_t usrdata_esz = CV_ELEM_SIZE(wdepth);
const uchar* usrdata_p = (const uchar*)usrdata;
const double* usrdata_d = (const double*)usrdata;
float usrdata_f[3];
int i, n = oclop == OCL_OP_MUL_SCALE || oclop == OCL_OP_DIV_SCALE ||
oclop == OCL_OP_RDIV_SCALE || oclop == OCL_OP_RECIP_SCALE ? 1 : oclop == OCL_OP_ADDW ? 3 : 0;
if( usrdata && n > 0 && wdepth == CV_32F )
{
for( i = 0; i < n; i++ )
usrdata_f[i] = (float)usrdata_d[i];
usrdata_p = (const uchar*)usrdata_f;
}
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, opts);
if (k.empty())
return false;
UMat src1 = _src1.getUMat(), src2;
UMat dst = _dst.getUMat(), mask = _mask.getUMat();
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1, cn, kercn);
ocl::KernelArg dstarg = haveMask ? ocl::KernelArg::ReadWrite(dst, cn, kercn) :
ocl::KernelArg::WriteOnly(dst, cn, kercn);
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask, 1);
if( haveScalar )
{
size_t esz = CV_ELEM_SIZE1(wtype)*scalarcn;
double buf[4]={0,0,0,0};
Mat src2sc = _src2.getMat();
if( !src2sc.empty() )
convertAndUnrollScalar(src2sc, wtype, (uchar*)buf, 1);
ocl::KernelArg scalararg = ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, esz);
if( !haveMask )
{
if(n == 0)
k.args(src1arg, dstarg, scalararg);
else if(n == 1)
k.args(src1arg, dstarg, scalararg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz));
else
CV_Error(Error::StsNotImplemented, "unsupported number of extra parameters");
}
else
k.args(src1arg, maskarg, dstarg, scalararg);
}
else
{
src2 = _src2.getUMat();
ocl::KernelArg src2arg = ocl::KernelArg::ReadOnlyNoSize(src2, cn, kercn);
if( !haveMask )
{
if (n == 0)
k.args(src1arg, src2arg, dstarg);
else if (n == 1)
k.args(src1arg, src2arg, dstarg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz));
else if (n == 3)
k.args(src1arg, src2arg, dstarg,
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p, usrdata_esz),
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p + usrdata_esz, usrdata_esz),
ocl::KernelArg(ocl::KernelArg::CONSTANT, 0, 0, 0, usrdata_p + usrdata_esz*2, usrdata_esz));
else
CV_Error(Error::StsNotImplemented, "unsupported number of extra parameters");
}
else
k.args(src1arg, src2arg, maskarg, dstarg);
}
size_t globalsize[] = { (size_t)src1.cols * cn / kercn, ((size_t)src1.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
#endif
static void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray _mask, int dtype, BinaryFuncC* tab, bool muldiv=false,
void* usrdata=0, int oclop=-1 )
{
const _InputArray *psrc1 = &_src1, *psrc2 = &_src2;
int kind1 = psrc1->kind(), kind2 = psrc2->kind();
bool haveMask = !_mask.empty();
bool reallocate = false;
int type1 = psrc1->type(), depth1 = CV_MAT_DEPTH(type1), cn = CV_MAT_CN(type1);
int type2 = psrc2->type(), depth2 = CV_MAT_DEPTH(type2), cn2 = CV_MAT_CN(type2);
int wtype, dims1 = psrc1->dims(), dims2 = psrc2->dims();
Size sz1 = dims1 <= 2 ? psrc1->size() : Size();
Size sz2 = dims2 <= 2 ? psrc2->size() : Size();
#ifdef HAVE_OPENCL
bool use_opencl = OCL_PERFORMANCE_CHECK(_dst.isUMat()) && dims1 <= 2 && dims2 <= 2;
#endif
bool src1Scalar = checkScalar(*psrc1, type2, kind1, kind2);
bool src2Scalar = checkScalar(*psrc2, type1, kind2, kind1);
if( (kind1 == kind2 || cn == 1) && sz1 == sz2 && dims1 <= 2 && dims2 <= 2 && type1 == type2 &&
!haveMask && ((!_dst.fixedType() && (dtype < 0 || CV_MAT_DEPTH(dtype) == depth1)) ||
(_dst.fixedType() && _dst.type() == type1)) &&
((src1Scalar && src2Scalar) || (!src1Scalar && !src2Scalar)) )
{
_dst.createSameSize(*psrc1, type1);
CV_OCL_RUN(use_opencl,
ocl_arithm_op(*psrc1, *psrc2, _dst, _mask,
(!usrdata ? type1 : std::max(depth1, CV_32F)),
usrdata, oclop, false))
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat();
Size sz = getContinuousSize(src1, src2, dst, src1.channels());
tab[depth1](src1.ptr(), src1.step, src2.ptr(), src2.step, dst.ptr(), dst.step, sz.width, sz.height, usrdata);
return;
}
bool haveScalar = false, swapped12 = false;
if( dims1 != dims2 || sz1 != sz2 || cn != cn2 ||
(kind1 == _InputArray::MATX && (sz1 == Size(1,4) || sz1 == Size(1,1))) ||
(kind2 == _InputArray::MATX && (sz2 == Size(1,4) || sz2 == Size(1,1))) )
{
if( checkScalar(*psrc1, type2, kind1, kind2) )
{
// src1 is a scalar; swap it with src2
swap(psrc1, psrc2);
swap(sz1, sz2);
swap(type1, type2);
swap(depth1, depth2);
swap(cn, cn2);
swap(dims1, dims2);
swapped12 = true;
if( oclop == OCL_OP_SUB )
oclop = OCL_OP_RSUB;
if ( oclop == OCL_OP_DIV_SCALE )
oclop = OCL_OP_RDIV_SCALE;
}
else if( !checkScalar(*psrc2, type1, kind2, kind1) )
CV_Error( CV_StsUnmatchedSizes,
"The operation is neither 'array op array' "
"(where arrays have the same size and the same number of channels), "
"nor 'array op scalar', nor 'scalar op array'" );
haveScalar = true;
CV_Assert(type2 == CV_64F && (sz2.height == 1 || sz2.height == 4));
if (!muldiv)
{
Mat sc = psrc2->getMat();
depth2 = actualScalarDepth(sc.ptr<double>(), sz2 == Size(1, 1) ? cn2 : cn);
if( depth2 == CV_64F && (depth1 < CV_32S || depth1 == CV_32F) )
depth2 = CV_32F;
}
else
depth2 = CV_64F;
}
if( dtype < 0 )
{
if( _dst.fixedType() )
dtype = _dst.type();
else
{
if( !haveScalar && type1 != type2 )
CV_Error(CV_StsBadArg,
"When the input arrays in add/subtract/multiply/divide functions have different types, "
"the output array type must be explicitly specified");
dtype = type1;
}
}
dtype = CV_MAT_DEPTH(dtype);
if( depth1 == depth2 && dtype == depth1 )
wtype = dtype;
else if( !muldiv )
{
wtype = depth1 <= CV_8S && depth2 <= CV_8S ? CV_16S :
depth1 <= CV_32S && depth2 <= CV_32S ? CV_32S : std::max(depth1, depth2);
wtype = std::max(wtype, dtype);
// when the result of addition should be converted to an integer type,
// and just one of the input arrays is floating-point, it makes sense to convert that input to integer type before the operation,
// instead of converting the other input to floating-point and then converting the operation result back to integers.
if( dtype < CV_32F && (depth1 < CV_32F || depth2 < CV_32F) )
wtype = CV_32S;
}
else
{
wtype = std::max(depth1, std::max(depth2, CV_32F));
wtype = std::max(wtype, dtype);
}
dtype = CV_MAKETYPE(dtype, cn);
wtype = CV_MAKETYPE(wtype, cn);
if( haveMask )
{
int mtype = _mask.type();
CV_Assert( (mtype == CV_8UC1 || mtype == CV_8SC1) && _mask.sameSize(*psrc1) );
reallocate = !_dst.sameSize(*psrc1) || _dst.type() != dtype;
}
_dst.createSameSize(*psrc1, dtype);
if( reallocate )
_dst.setTo(0.);
CV_OCL_RUN(use_opencl,
ocl_arithm_op(*psrc1, *psrc2, _dst, _mask, wtype,
usrdata, oclop, haveScalar))
BinaryFunc cvtsrc1 = type1 == wtype ? 0 : getConvertFunc(type1, wtype);
BinaryFunc cvtsrc2 = type2 == type1 ? cvtsrc1 : type2 == wtype ? 0 : getConvertFunc(type2, wtype);
BinaryFunc cvtdst = dtype == wtype ? 0 : getConvertFunc(wtype, dtype);
size_t esz1 = CV_ELEM_SIZE(type1), esz2 = CV_ELEM_SIZE(type2);
size_t dsz = CV_ELEM_SIZE(dtype), wsz = CV_ELEM_SIZE(wtype);
size_t blocksize0 = (size_t)(BLOCK_SIZE + wsz-1)/wsz;
BinaryFunc copymask = getCopyMaskFunc(dsz);
Mat src1 = psrc1->getMat(), src2 = psrc2->getMat(), dst = _dst.getMat(), mask = _mask.getMat();
AutoBuffer<uchar> _buf;
uchar *buf, *maskbuf = 0, *buf1 = 0, *buf2 = 0, *wbuf = 0;
size_t bufesz = (cvtsrc1 ? wsz : 0) +
(cvtsrc2 || haveScalar ? wsz : 0) +
(cvtdst ? wsz : 0) +
(haveMask ? dsz : 0);
BinaryFuncC func = tab[CV_MAT_DEPTH(wtype)];
if( !haveScalar )
{
const Mat* arrays[] = { &src1, &src2, &dst, &mask, 0 };
uchar* ptrs[4];
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = total;
if( haveMask || cvtsrc1 || cvtsrc2 || cvtdst )
blocksize = std::min(blocksize, blocksize0);
_buf.allocate(bufesz*blocksize + 64);
buf = _buf;
if( cvtsrc1 )
buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
if( cvtsrc2 )
buf2 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
wbuf = maskbuf = buf;
if( cvtdst )
buf = alignPtr(buf + blocksize*wsz, 16);
if( haveMask )
maskbuf = buf;
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
Size bszn(bsz*cn, 1);
const uchar *sptr1 = ptrs[0], *sptr2 = ptrs[1];
uchar* dptr = ptrs[2];
if( cvtsrc1 )
{
cvtsrc1( sptr1, 1, 0, 1, buf1, 1, bszn, 0 );
sptr1 = buf1;
}
if( ptrs[0] == ptrs[1] )
sptr2 = sptr1;
else if( cvtsrc2 )
{
cvtsrc2( sptr2, 1, 0, 1, buf2, 1, bszn, 0 );
sptr2 = buf2;
}
if( !haveMask && !cvtdst )
func( sptr1, 1, sptr2, 1, dptr, 1, bszn.width, bszn.height, usrdata );
else
{
func( sptr1, 1, sptr2, 1, wbuf, 0, bszn.width, bszn.height, usrdata );
if( !haveMask )
cvtdst( wbuf, 1, 0, 1, dptr, 1, bszn, 0 );
else if( !cvtdst )
{
copymask( wbuf, 1, ptrs[3], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[3] += bsz;
}
else
{
cvtdst( wbuf, 1, 0, 1, maskbuf, 1, bszn, 0 );
copymask( maskbuf, 1, ptrs[3], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[3] += bsz;
}
}
ptrs[0] += bsz*esz1; ptrs[1] += bsz*esz2; ptrs[2] += bsz*dsz;
}
}
}
else
{
const Mat* arrays[] = { &src1, &dst, &mask, 0 };
uchar* ptrs[3];
NAryMatIterator it(arrays, ptrs);
size_t total = it.size, blocksize = std::min(total, blocksize0);
_buf.allocate(bufesz*blocksize + 64);
buf = _buf;
if( cvtsrc1 )
buf1 = buf, buf = alignPtr(buf + blocksize*wsz, 16);
buf2 = buf; buf = alignPtr(buf + blocksize*wsz, 16);
wbuf = maskbuf = buf;
if( cvtdst )
buf = alignPtr(buf + blocksize*wsz, 16);
if( haveMask )
maskbuf = buf;
convertAndUnrollScalar( src2, wtype, buf2, blocksize);
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( size_t j = 0; j < total; j += blocksize )
{
int bsz = (int)MIN(total - j, blocksize);
Size bszn(bsz*cn, 1);
const uchar *sptr1 = ptrs[0];
const uchar* sptr2 = buf2;
uchar* dptr = ptrs[1];
if( cvtsrc1 )
{
cvtsrc1( sptr1, 1, 0, 1, buf1, 1, bszn, 0 );
sptr1 = buf1;
}
if( swapped12 )
std::swap(sptr1, sptr2);
if( !haveMask && !cvtdst )
func( sptr1, 1, sptr2, 1, dptr, 1, bszn.width, bszn.height, usrdata );
else
{
func( sptr1, 1, sptr2, 1, wbuf, 1, bszn.width, bszn.height, usrdata );
if( !haveMask )
cvtdst( wbuf, 1, 0, 1, dptr, 1, bszn, 0 );
else if( !cvtdst )
{
copymask( wbuf, 1, ptrs[2], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[2] += bsz;
}
else
{
cvtdst( wbuf, 1, 0, 1, maskbuf, 1, bszn, 0 );
copymask( maskbuf, 1, ptrs[2], 1, dptr, 1, Size(bsz, 1), &dsz );
ptrs[2] += bsz;
}
}
ptrs[0] += bsz*esz1; ptrs[1] += bsz*dsz;
}
}
}
}
static BinaryFuncC* getAddTab()
{
static BinaryFuncC addTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::add8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::add16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::add32f), (BinaryFuncC)cv::hal::add64f,
0
};
return addTab;
}
static BinaryFuncC* getSubTab()
{
static BinaryFuncC subTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::sub8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::sub16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::sub32f), (BinaryFuncC)cv::hal::sub64f,
0
};
return subTab;
}
static BinaryFuncC* getAbsDiffTab()
{
static BinaryFuncC absDiffTab[] =
{
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff8u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff8s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff16u), (BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff16s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff32s),
(BinaryFuncC)GET_OPTIMIZED(cv::hal::absdiff32f), (BinaryFuncC)cv::hal::absdiff64f,
0
};
return absDiffTab;
}
}
void cv::add( InputArray src1, InputArray src2, OutputArray dst,
InputArray mask, int dtype )
{
CV_INSTRUMENT_REGION()
arithm_op(src1, src2, dst, mask, dtype, getAddTab(), false, 0, OCL_OP_ADD );
}
void cv::subtract( InputArray _src1, InputArray _src2, OutputArray _dst,
InputArray mask, int dtype )
{
CV_INSTRUMENT_REGION()
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra())
{
int kind1 = _src1.kind(), kind2 = _src2.kind();
Mat src1 = _src1.getMat(), src2 = _src2.getMat();
bool src1Scalar = checkScalar(src1, _src2.type(), kind1, kind2);
bool src2Scalar = checkScalar(src2, _src1.type(), kind2, kind1);
if (!src1Scalar && !src2Scalar &&
src1.depth() == CV_8U && src2.type() == src1.type() &&
src1.dims == 2 && src2.size() == src1.size() &&
mask.empty())
{
if (dtype < 0)
{
if (_dst.fixedType())
{
dtype = _dst.depth();
}
else
{
dtype = src1.depth();
}
}
dtype = CV_MAT_DEPTH(dtype);
if (!_dst.fixedType() || dtype == _dst.depth())
{
_dst.create(src1.size(), CV_MAKE_TYPE(dtype, src1.channels()));
if (dtype == CV_16S)
{
Mat dst = _dst.getMat();
if(tegra::subtract_8u8u16s(src1, src2, dst))
return;
}
else if (dtype == CV_32F)
{
Mat dst = _dst.getMat();
if(tegra::subtract_8u8u32f(src1, src2, dst))
return;
}
else if (dtype == CV_8S)
{
Mat dst = _dst.getMat();
if(tegra::subtract_8u8u8s(src1, src2, dst))
return;
}
}
}
}
#endif
arithm_op(_src1, _src2, _dst, mask, dtype, getSubTab(), false, 0, OCL_OP_SUB );
}
void cv::absdiff( InputArray src1, InputArray src2, OutputArray dst )
{
CV_INSTRUMENT_REGION()
arithm_op(src1, src2, dst, noArray(), -1, getAbsDiffTab(), false, 0, OCL_OP_ABSDIFF);
}
/****************************************************************************************\
* multiply/divide *
\****************************************************************************************/