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opencv/modules/imgproc/src/deriv.cpp
Andrey Kamaev 2a6fb2867e Remove all using directives for STL namespace and members 
Made all STL usages explicit to be able automatically find all usages of
particular class or function.
2013-02-25 15:04:17 +04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
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// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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
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// (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
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//
//M*/
#include "precomp.hpp"
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
static IppStatus sts = ippInit();
#endif
/****************************************************************************************\
Sobel & Scharr Derivative Filters
\****************************************************************************************/
namespace cv
{
static void getScharrKernels( OutputArray _kx, OutputArray _ky,
int dx, int dy, bool normalize, int ktype )
{
const int ksize = 3;
CV_Assert( ktype == CV_32F || ktype == CV_64F );
_kx.create(ksize, 1, ktype, -1, true);
_ky.create(ksize, 1, ktype, -1, true);
Mat kx = _kx.getMat();
Mat ky = _ky.getMat();
CV_Assert( dx >= 0 && dy >= 0 && dx+dy == 1 );
for( int k = 0; k < 2; k++ )
{
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
int kerI[3];
if( order == 0 )
kerI[0] = 3, kerI[1] = 10, kerI[2] = 3;
else if( order == 1 )
kerI[0] = -1, kerI[1] = 0, kerI[2] = 1;
Mat temp(kernel->rows, kernel->cols, CV_32S, &kerI[0]);
double scale = !normalize || order == 1 ? 1. : 1./32;
temp.convertTo(*kernel, ktype, scale);
}
}
static void getSobelKernels( OutputArray _kx, OutputArray _ky,
int dx, int dy, int _ksize, bool normalize, int ktype )
{
int i, j, ksizeX = _ksize, ksizeY = _ksize;
if( ksizeX == 1 && dx > 0 )
ksizeX = 3;
if( ksizeY == 1 && dy > 0 )
ksizeY = 3;
CV_Assert( ktype == CV_32F || ktype == CV_64F );
_kx.create(ksizeX, 1, ktype, -1, true);
_ky.create(ksizeY, 1, ktype, -1, true);
Mat kx = _kx.getMat();
Mat ky = _ky.getMat();
if( _ksize % 2 == 0 || _ksize > 31 )
CV_Error( CV_StsOutOfRange, "The kernel size must be odd and not larger than 31" );
std::vector<int> kerI(std::max(ksizeX, ksizeY) + 1);
CV_Assert( dx >= 0 && dy >= 0 && dx+dy > 0 );
for( int k = 0; k < 2; k++ )
{
Mat* kernel = k == 0 ? &kx : &ky;
int order = k == 0 ? dx : dy;
int ksize = k == 0 ? ksizeX : ksizeY;
CV_Assert( ksize > order );
if( ksize == 1 )
kerI[0] = 1;
else if( ksize == 3 )
{
if( order == 0 )
kerI[0] = 1, kerI[1] = 2, kerI[2] = 1;
else if( order == 1 )
kerI[0] = -1, kerI[1] = 0, kerI[2] = 1;
else
kerI[0] = 1, kerI[1] = -2, kerI[2] = 1;
}
else
{
int oldval, newval;
kerI[0] = 1;
for( i = 0; i < ksize; i++ )
kerI[i+1] = 0;
for( i = 0; i < ksize - order - 1; i++ )
{
oldval = kerI[0];
for( j = 1; j <= ksize; j++ )
{
newval = kerI[j]+kerI[j-1];
kerI[j-1] = oldval;
oldval = newval;
}
}
for( i = 0; i < order; i++ )
{
oldval = -kerI[0];
for( j = 1; j <= ksize; j++ )
{
newval = kerI[j-1] - kerI[j];
kerI[j-1] = oldval;
oldval = newval;
}
}
}
Mat temp(kernel->rows, kernel->cols, CV_32S, &kerI[0]);
double scale = !normalize ? 1. : 1./(1 << (ksize-order-1));
temp.convertTo(*kernel, ktype, scale);
}
}
}
void cv::getDerivKernels( OutputArray kx, OutputArray ky, int dx, int dy,
int ksize, bool normalize, int ktype )
{
if( ksize <= 0 )
getScharrKernels( kx, ky, dx, dy, normalize, ktype );
else
getSobelKernels( kx, ky, dx, dy, ksize, normalize, ktype );
}
cv::Ptr<cv::FilterEngine> cv::createDerivFilter(int srcType, int dstType,
int dx, int dy, int ksize, int borderType )
{
Mat kx, ky;
getDerivKernels( kx, ky, dx, dy, ksize, false, CV_32F );
return createSeparableLinearFilter(srcType, dstType,
kx, ky, Point(-1,-1), 0, borderType );
}
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
namespace cv
{
static bool IPPDerivScharr(const Mat& src, Mat& dst, int ddepth, int dx, int dy, double scale)
{
int bufSize = 0;
cv::AutoBuffer<char> buffer;
IppiSize roi = ippiSize(src.cols, src.rows);
if( ddepth < 0 )
ddepth = src.depth();
dst.create( src.size(), CV_MAKETYPE(ddepth, src.channels()) );
switch(src.type())
{
case CV_8U:
{
if(scale != 1)
return false;
switch(dst.type())
{
case CV_16S:
{
if((dx == 1) && (dy == 0))
{
ippiFilterScharrVertGetBufferSize_8u16s_C1R(roi,&bufSize);
buffer.allocate(bufSize);
ippiFilterScharrVertBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
if((dx == 0) && (dy == 1))
{
ippiFilterScharrHorizGetBufferSize_8u16s_C1R(roi,&bufSize);
buffer.allocate(bufSize);
ippiFilterScharrHorizBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, roi, ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
}
default:
return false;
}
}
case CV_32F:
{
switch(dst.type())
{
case CV_32F:
if((dx == 1) && (dy == 0))
{
ippiFilterScharrVertGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows),&bufSize);
buffer.allocate(bufSize);
ippiFilterScharrVertBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
/* IPP is fast, so MulC produce very little perf degradation */
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f*)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
if((dx == 0) && (dy == 1))
{
ippiFilterScharrHorizGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows),&bufSize);
buffer.allocate(bufSize);
ippiFilterScharrHorizBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f *)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
default:
return false;
}
}
default:
return false;
}
}
static bool IPPDeriv(const Mat& src, Mat& dst, int ddepth, int dx, int dy, int ksize, double scale)
{
int bufSize = 0;
cv::AutoBuffer<char> buffer;
if(ksize == 3 || ksize == 5)
{
if( ddepth < 0 )
ddepth = src.depth();
if(src.type() == CV_8U && dst.type() == CV_16S && scale == 1)
{
if((dx == 1) && (dy == 0))
{
ippiFilterSobelNegVertGetBufferSize_8u16s_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelNegVertBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
if((dx == 0) && (dy == 1))
{
ippiFilterSobelHorizGetBufferSize_8u16s_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelHorizBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
if((dx == 2) && (dy == 0))
{
ippiFilterSobelVertSecondGetBufferSize_8u16s_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelVertSecondBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
if((dx == 0) && (dy == 2))
{
ippiFilterSobelHorizSecondGetBufferSize_8u16s_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelHorizSecondBorder_8u16s_C1R((const Ipp8u*)src.data, src.step,
(Ipp16s*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
return true;
}
}
if(src.type() == CV_32F && dst.type() == CV_32F)
{
if((dx == 1) && (dy == 0))
{
ippiFilterSobelNegVertGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelNegVertBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f *)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
if((dx == 0) && (dy == 1))
{
ippiFilterSobelHorizGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelHorizBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f *)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
if((dx == 2) && (dy == 0))
{
ippiFilterSobelVertSecondGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelVertSecondBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f *)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
if((dx == 0) && (dy == 2))
{
ippiFilterSobelHorizSecondGetBufferSize_32f_C1R(ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),&bufSize);
buffer.allocate(bufSize);
ippiFilterSobelHorizSecondBorder_32f_C1R((const Ipp32f*)src.data, src.step,
(Ipp32f*)dst.data, dst.step, ippiSize(src.cols, src.rows), (IppiMaskSize)(ksize*10+ksize),
ippBorderRepl, 0, (Ipp8u*)(char*)buffer);
if(scale != 1)
ippiMulC_32f_C1IR((Ipp32f)scale,(Ipp32f *)dst.data,dst.step,ippiSize(dst.cols*dst.channels(),dst.rows));
return true;
}
}
}
if(ksize <= 0)
return IPPDerivScharr(src, dst, ddepth, dx, dy, scale);
return false;
}
}
#endif
void cv::Sobel( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,
int ksize, double scale, double delta, int borderType )
{
Mat src = _src.getMat();
if (ddepth < 0)
ddepth = src.depth();
_dst.create( src.size(), CV_MAKETYPE(ddepth, src.channels()) );
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if (scale == 1.0 && delta == 0)
{
if (ksize == 3 && tegra::sobel3x3(src, dst, dx, dy, borderType))
return;
if (ksize == -1 && tegra::scharr(src, dst, dx, dy, borderType))
return;
}
#endif
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
if(dx < 3 && dy < 3 && src.channels() == 1 && borderType == 1)
{
if(IPPDeriv(src, dst, ddepth, dx, dy, ksize,scale))
return;
}
#endif
int ktype = std::max(CV_32F, std::max(ddepth, src.depth()));
Mat kx, ky;
getDerivKernels( kx, ky, dx, dy, ksize, false, ktype );
if( scale != 1 )
{
// usually the smoothing part is the slowest to compute,
// so try to scale it instead of the faster differenciating part
if( dx == 0 )
kx *= scale;
else
ky *= scale;
}
sepFilter2D( src, dst, ddepth, kx, ky, Point(-1,-1), delta, borderType );
}
void cv::Scharr( InputArray _src, OutputArray _dst, int ddepth, int dx, int dy,
double scale, double delta, int borderType )
{
Mat src = _src.getMat();
if (ddepth < 0)
ddepth = src.depth();
_dst.create( src.size(), CV_MAKETYPE(ddepth, src.channels()) );
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if (scale == 1.0 && delta == 0)
if (tegra::scharr(src, dst, dx, dy, borderType))
return;
#endif
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
if(dx < 2 && dy < 2 && src.channels() == 1 && borderType == 1)
{
if(IPPDerivScharr(src, dst, ddepth, dx, dy, scale))
return;
}
#endif
int ktype = std::max(CV_32F, std::max(ddepth, src.depth()));
Mat kx, ky;
getScharrKernels( kx, ky, dx, dy, false, ktype );
if( scale != 1 )
{
// usually the smoothing part is the slowest to compute,
// so try to scale it instead of the faster differenciating part
if( dx == 0 )
kx *= scale;
else
ky *= scale;
}
sepFilter2D( src, dst, ddepth, kx, ky, Point(-1,-1), delta, borderType );
}
void cv::Laplacian( InputArray _src, OutputArray _dst, int ddepth, int ksize,
double scale, double delta, int borderType )
{
Mat src = _src.getMat();
if (ddepth < 0)
ddepth = src.depth();
_dst.create( src.size(), CV_MAKETYPE(ddepth, src.channels()) );
Mat dst = _dst.getMat();
#ifdef HAVE_TEGRA_OPTIMIZATION
if (scale == 1.0 && delta == 0)
{
if (ksize == 1 && tegra::laplace1(src, dst, borderType))
return;
if (ksize == 3 && tegra::laplace3(src, dst, borderType))
return;
if (ksize == 5 && tegra::laplace5(src, dst, borderType))
return;
}
#endif
if( ksize == 1 || ksize == 3 )
{
float K[2][9] =
{{0, 1, 0, 1, -4, 1, 0, 1, 0},
{2, 0, 2, 0, -8, 0, 2, 0, 2}};
Mat kernel(3, 3, CV_32F, K[ksize == 3]);
if( scale != 1 )
kernel *= scale;
filter2D( src, dst, ddepth, kernel, Point(-1,-1), delta, borderType );
}
else
{
const size_t STRIPE_SIZE = 1 << 14;
int depth = src.depth();
int ktype = std::max(CV_32F, std::max(ddepth, depth));
int wdepth = depth == CV_8U && ksize <= 5 ? CV_16S : depth <= CV_32F ? CV_32F : CV_64F;
int wtype = CV_MAKETYPE(wdepth, src.channels());
Mat kd, ks;
getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
if( ddepth < 0 )
ddepth = src.depth();
int dtype = CV_MAKETYPE(ddepth, src.channels());
int dy0 = std::min(std::max((int)(STRIPE_SIZE/(getElemSize(src.type())*src.cols)), 1), src.rows);
Ptr<FilterEngine> fx = createSeparableLinearFilter(src.type(),
wtype, kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
Ptr<FilterEngine> fy = createSeparableLinearFilter(src.type(),
wtype, ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
int y = fx->start(src), dsty = 0, dy = 0;
fy->start(src);
const uchar* sptr = src.data + y*src.step;
Mat d2x( dy0 + kd.rows - 1, src.cols, wtype );
Mat d2y( dy0 + kd.rows - 1, src.cols, wtype );
for( ; dsty < src.rows; sptr += dy0*src.step, dsty += dy )
{
fx->proceed( sptr, (int)src.step, dy0, d2x.data, (int)d2x.step );
dy = fy->proceed( sptr, (int)src.step, dy0, d2y.data, (int)d2y.step );
if( dy > 0 )
{
Mat dstripe = dst.rowRange(dsty, dsty + dy);
d2x.rows = d2y.rows = dy; // modify the headers, which should work
d2x += d2y;
d2x.convertTo( dstripe, dtype, scale, delta );
}
}
}
}
/////////////////////////////////////////////////////////////////////////////////////////
CV_IMPL void
cvSobel( const void* srcarr, void* dstarr, int dx, int dy, int aperture_size )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size() == dst.size() && src.channels() == dst.channels() );
cv::Sobel( src, dst, dst.depth(), dx, dy, aperture_size, 1, 0, cv::BORDER_REPLICATE );
if( CV_IS_IMAGE(srcarr) && ((IplImage*)srcarr)->origin && dy % 2 != 0 )
dst *= -1;
}
CV_IMPL void
cvLaplace( const void* srcarr, void* dstarr, int aperture_size )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr);
CV_Assert( src.size() == dst.size() && src.channels() == dst.channels() );
cv::Laplacian( src, dst, dst.depth(), aperture_size, 1, 0, cv::BORDER_REPLICATE );
}
/* End of file. */
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