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9260ad4d70
opencv/modules/imgproc/src/smooth.cpp
Andrey Kamaev 9260ad4d70 Fixed Tegra optimization stub for gaussian
2012-01-11 13:16:00 +00:00

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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"
/*
* This file includes the code, contributed by Simon Perreault
* (the function icvMedianBlur_8u_O1)
*
* Constant-time median filtering -- http://nomis80.org/ctmf.html
* Copyright (C) 2006 Simon Perreault
*
* Contact:
* Laboratoire de vision et systemes numeriques
* Pavillon Adrien-Pouliot
* Universite Laval
* Sainte-Foy, Quebec, Canada
* G1K 7P4
*
* perreaul@gel.ulaval.ca
*/
namespace cv
{
/****************************************************************************************\
Box Filter
\****************************************************************************************/
template<typename T, typename ST> struct RowSum : public BaseRowFilter
{
RowSum( int _ksize, int _anchor )
{
ksize = _ksize;
anchor = _anchor;
}
void operator()(const uchar* src, uchar* dst, int width, int cn)
{
const T* S = (const T*)src;
ST* D = (ST*)dst;
int i = 0, k, ksz_cn = ksize*cn;
width = (width - 1)*cn;
for( k = 0; k < cn; k++, S++, D++ )
{
ST s = 0;
for( i = 0; i < ksz_cn; i += cn )
s += S[i];
D[0] = s;
for( i = 0; i < width; i += cn )
{
s += S[i + ksz_cn] - S[i];
D[i+cn] = s;
}
}
}
};
template<typename ST, typename T> struct ColumnSum : public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale )
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
void reset() { sumCount = 0; }
void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int i;
ST* SUM;
bool haveScale = scale != 1;
double _scale = scale;
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
for( i = 0; i < width; i++ )
SUM[i] = 0;
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const ST* Sp = (const ST*)src[0];
for( i = 0; i <= width - 2; i += 2 )
{
ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
SUM[i] = s0; SUM[i+1] = s1;
}
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const ST* Sp = (const ST*)src[0];
const ST* Sm = (const ST*)src[1-ksize];
T* D = (T*)dst;
if( haveScale )
{
for( i = 0; i <= width - 2; i += 2 )
{
ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
D[i] = saturate_cast<T>(s0*_scale);
D[i+1] = saturate_cast<T>(s1*_scale);
s0 -= Sm[i]; s1 -= Sm[i+1];
SUM[i] = s0; SUM[i+1] = s1;
}
for( ; i < width; i++ )
{
ST s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<T>(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
for( i = 0; i <= width - 2; i += 2 )
{
ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
D[i] = saturate_cast<T>(s0);
D[i+1] = saturate_cast<T>(s1);
s0 -= Sm[i]; s1 -= Sm[i+1];
SUM[i] = s0; SUM[i+1] = s1;
}
for( ; i < width; i++ )
{
ST s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<T>(s0);
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
vector<ST> sum;
};
}
cv::Ptr<cv::BaseRowFilter> cv::getRowSumFilter(int srcType, int sumType, int ksize, int anchor)
{
int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType);
CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(srcType) );
if( anchor < 0 )
anchor = ksize/2;
if( sdepth == CV_8U && ddepth == CV_32S )
return Ptr<BaseRowFilter>(new RowSum<uchar, int>(ksize, anchor));
if( sdepth == CV_8U && ddepth == CV_64F )
return Ptr<BaseRowFilter>(new RowSum<uchar, double>(ksize, anchor));
if( sdepth == CV_16U && ddepth == CV_32S )
return Ptr<BaseRowFilter>(new RowSum<ushort, int>(ksize, anchor));
if( sdepth == CV_16U && ddepth == CV_64F )
return Ptr<BaseRowFilter>(new RowSum<ushort, double>(ksize, anchor));
if( sdepth == CV_16S && ddepth == CV_32S )
return Ptr<BaseRowFilter>(new RowSum<short, int>(ksize, anchor));
if( sdepth == CV_32S && ddepth == CV_32S )
return Ptr<BaseRowFilter>(new RowSum<int, int>(ksize, anchor));
if( sdepth == CV_16S && ddepth == CV_64F )
return Ptr<BaseRowFilter>(new RowSum<short, double>(ksize, anchor));
if( sdepth == CV_32F && ddepth == CV_64F )
return Ptr<BaseRowFilter>(new RowSum<float, double>(ksize, anchor));
if( sdepth == CV_64F && ddepth == CV_64F )
return Ptr<BaseRowFilter>(new RowSum<double, double>(ksize, anchor));
CV_Error_( CV_StsNotImplemented,
("Unsupported combination of source format (=%d), and buffer format (=%d)",
srcType, sumType));
return Ptr<BaseRowFilter>(0);
}
cv::Ptr<cv::BaseColumnFilter> cv::getColumnSumFilter(int sumType, int dstType, int ksize,
int anchor, double scale)
{
int sdepth = CV_MAT_DEPTH(sumType), ddepth = CV_MAT_DEPTH(dstType);
CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(dstType) );
if( anchor < 0 )
anchor = ksize/2;
if( ddepth == CV_8U && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, uchar>(ksize, anchor, scale));
if( ddepth == CV_8U && sdepth == CV_64F )
return Ptr<BaseColumnFilter>(new ColumnSum<double, uchar>(ksize, anchor, scale));
if( ddepth == CV_16U && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, ushort>(ksize, anchor, scale));
if( ddepth == CV_16U && sdepth == CV_64F )
return Ptr<BaseColumnFilter>(new ColumnSum<double, ushort>(ksize, anchor, scale));
if( ddepth == CV_16S && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, short>(ksize, anchor, scale));
if( ddepth == CV_16S && sdepth == CV_64F )
return Ptr<BaseColumnFilter>(new ColumnSum<double, short>(ksize, anchor, scale));
if( ddepth == CV_32S && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, int>(ksize, anchor, scale));
if( ddepth == CV_32F && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, float>(ksize, anchor, scale));
if( ddepth == CV_32F && sdepth == CV_64F )
return Ptr<BaseColumnFilter>(new ColumnSum<double, float>(ksize, anchor, scale));
if( ddepth == CV_64F && sdepth == CV_32S )
return Ptr<BaseColumnFilter>(new ColumnSum<int, double>(ksize, anchor, scale));
if( ddepth == CV_64F && sdepth == CV_64F )
return Ptr<BaseColumnFilter>(new ColumnSum<double, double>(ksize, anchor, scale));
CV_Error_( CV_StsNotImplemented,
("Unsupported combination of sum format (=%d), and destination format (=%d)",
sumType, dstType));
return Ptr<BaseColumnFilter>(0);
}
cv::Ptr<cv::FilterEngine> cv::createBoxFilter( int srcType, int dstType, Size ksize,
Point anchor, bool normalize, int borderType )
{
int sdepth = CV_MAT_DEPTH(srcType);
int cn = CV_MAT_CN(srcType), sumType = CV_64F;
if( sdepth < CV_32S && (!normalize ||
ksize.width*ksize.height <= (sdepth == CV_8U ? (1<<23) :
sdepth == CV_16U ? (1 << 15) : (1 << 16))) )
sumType = CV_32S;
sumType = CV_MAKETYPE( sumType, cn );
Ptr<BaseRowFilter> rowFilter = getRowSumFilter(srcType, sumType, ksize.width, anchor.x );
Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType,
dstType, ksize.height, anchor.y, normalize ? 1./(ksize.width*ksize.height) : 1);
return Ptr<FilterEngine>(new FilterEngine(Ptr<BaseFilter>(0), rowFilter, columnFilter,
srcType, dstType, sumType, borderType ));
}
void cv::boxFilter( InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor,
bool normalize, int borderType )
{
Mat src = _src.getMat();
int sdepth = src.depth(), cn = src.channels();
if( ddepth < 0 )
ddepth = sdepth;
_dst.create( src.size(), CV_MAKETYPE(ddepth, cn) );
Mat dst = _dst.getMat();
if( borderType != BORDER_CONSTANT && normalize )
{
if( src.rows == 1 )
ksize.height = 1;
if( src.cols == 1 )
ksize.width = 1;
}
Ptr<FilterEngine> f = createBoxFilter( src.type(), dst.type(),
ksize, anchor, normalize, borderType );
f->apply( src, dst );
}
void cv::blur( InputArray src, OutputArray dst,
Size ksize, Point anchor, int borderType )
{
boxFilter( src, dst, -1, ksize, anchor, true, borderType );
}
/****************************************************************************************\
Gaussian Blur
\****************************************************************************************/
cv::Mat cv::getGaussianKernel( int n, double sigma, int ktype )
{
const int SMALL_GAUSSIAN_SIZE = 7;
static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
{
{1.f},
{0.25f, 0.5f, 0.25f},
{0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
{0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
};
const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
small_gaussian_tab[n>>1] : 0;
CV_Assert( ktype == CV_32F || ktype == CV_64F );
Mat kernel(n, 1, ktype);
float* cf = (float*)kernel.data;
double* cd = (double*)kernel.data;
double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
double scale2X = -0.5/(sigmaX*sigmaX);
double sum = 0;
int i;
for( i = 0; i < n; i++ )
{
double x = i - (n-1)*0.5;
double t = fixed_kernel ? (double)fixed_kernel[i] : std::exp(scale2X*x*x);
if( ktype == CV_32F )
{
cf[i] = (float)t;
sum += cf[i];
}
else
{
cd[i] = t;
sum += cd[i];
}
}
sum = 1./sum;
for( i = 0; i < n; i++ )
{
if( ktype == CV_32F )
cf[i] = (float)(cf[i]*sum);
else
cd[i] *= sum;
}
return kernel;
}
cv::Ptr<cv::FilterEngine> cv::createGaussianFilter( int type, Size ksize,
double sigma1, double sigma2,
int borderType )
{
int depth = CV_MAT_DEPTH(type);
if( sigma2 <= 0 )
sigma2 = sigma1;
// automatic detection of kernel size from sigma
if( ksize.width <= 0 && sigma1 > 0 )
ksize.width = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
if( ksize.height <= 0 && sigma2 > 0 )
ksize.height = cvRound(sigma2*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
CV_Assert( ksize.width > 0 && ksize.width % 2 == 1 &&
ksize.height > 0 && ksize.height % 2 == 1 );
sigma1 = std::max( sigma1, 0. );
sigma2 = std::max( sigma2, 0. );
Mat kx = getGaussianKernel( ksize.width, sigma1, std::max(depth, CV_32F) );
Mat ky;
if( ksize.height == ksize.width && std::abs(sigma1 - sigma2) < DBL_EPSILON )
ky = kx;
else
ky = getGaussianKernel( ksize.height, sigma2, std::max(depth, CV_32F) );
return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType );
}
void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2,
int borderType )
{
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if( borderType != BORDER_CONSTANT )
{
if( src.rows == 1 )
ksize.height = 1;
if( src.cols == 1 )
ksize.width = 1;
}
if( ksize.width == 1 && ksize.height == 1 )
{
src.copyTo(dst);
return;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if(sigma1 == 0 && sigma2 == 0 && tegra::gaussian(src, dst, ksize, borderType))
return;
#endif
Ptr<FilterEngine> f = createGaussianFilter( src.type(), ksize, sigma1, sigma2, borderType );
f->apply( src, dst );
}
/****************************************************************************************\
Median Filter
\****************************************************************************************/
namespace cv
{
#if _MSC_VER >= 1200
#pragma warning( disable: 4244 )
#endif
typedef ushort HT;
/**
* This structure represents a two-tier histogram. The first tier (known as the
* "coarse" level) is 4 bit wide and the second tier (known as the "fine" level)
* is 8 bit wide. Pixels inserted in the fine level also get inserted into the
* coarse bucket designated by the 4 MSBs of the fine bucket value.
*
* The structure is aligned on 16 bits, which is a prerequisite for SIMD
* instructions. Each bucket is 16 bit wide, which means that extra care must be
* taken to prevent overflow.
*/
typedef struct
{
HT coarse[16];
HT fine[16][16];
} Histogram;
#if CV_SSE2
#define MEDIAN_HAVE_SIMD 1
static inline void histogram_add_simd( const HT x[16], HT y[16] )
{
const __m128i* rx = (const __m128i*)x;
__m128i* ry = (__m128i*)y;
__m128i r0 = _mm_add_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
__m128i r1 = _mm_add_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
_mm_store_si128(ry+0, r0);
_mm_store_si128(ry+1, r1);
}
static inline void histogram_sub_simd( const HT x[16], HT y[16] )
{
const __m128i* rx = (const __m128i*)x;
__m128i* ry = (__m128i*)y;
__m128i r0 = _mm_sub_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
__m128i r1 = _mm_sub_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
_mm_store_si128(ry+0, r0);
_mm_store_si128(ry+1, r1);
}
#else
#define MEDIAN_HAVE_SIMD 0
#endif
static inline void histogram_add( const HT x[16], HT y[16] )
{
int i;
for( i = 0; i < 16; ++i )
y[i] = (HT)(y[i] + x[i]);
}
static inline void histogram_sub( const HT x[16], HT y[16] )
{
int i;
for( i = 0; i < 16; ++i )
y[i] = (HT)(y[i] - x[i]);
}
static inline void histogram_muladd( int a, const HT x[16],
HT y[16] )
{
for( int i = 0; i < 16; ++i )
y[i] = (HT)(y[i] + a * x[i]);
}
static void
medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
{
/**
* HOP is short for Histogram OPeration. This macro makes an operation \a op on
* histogram \a h for pixel value \a x. It takes care of handling both levels.
*/
#define HOP(h,x,op) \
h.coarse[x>>4] op, \
*((HT*)h.fine + x) op
#define COP(c,j,x,op) \
h_coarse[ 16*(n*c+j) + (x>>4) ] op, \
h_fine[ 16 * (n*(16*c+(x>>4)) + j) + (x & 0xF) ] op
int cn = _dst.channels(), m = _dst.rows, r = (ksize-1)/2;
size_t sstep = _src.step, dstep = _dst.step;
Histogram CV_DECL_ALIGNED(16) H[4];
HT CV_DECL_ALIGNED(16) luc[4][16];
int STRIPE_SIZE = std::min( _dst.cols, 512/cn );
vector<HT> _h_coarse(1 * 16 * (STRIPE_SIZE + 2*r) * cn + 16);
vector<HT> _h_fine(16 * 16 * (STRIPE_SIZE + 2*r) * cn + 16);
HT* h_coarse = alignPtr(&_h_coarse[0], 16);
HT* h_fine = alignPtr(&_h_fine[0], 16);
#if MEDIAN_HAVE_SIMD
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
for( int x = 0; x < _dst.cols; x += STRIPE_SIZE )
{
int i, j, k, c, n = std::min(_dst.cols - x, STRIPE_SIZE) + r*2;
const uchar* src = _src.data + x*cn;
uchar* dst = _dst.data + (x - r)*cn;
memset( h_coarse, 0, 16*n*cn*sizeof(h_coarse[0]) );
memset( h_fine, 0, 16*16*n*cn*sizeof(h_fine[0]) );
// First row initialization
for( c = 0; c < cn; c++ )
{
for( j = 0; j < n; j++ )
COP( c, j, src[cn*j+c], += r+2 );
for( i = 1; i < r; i++ )
{
const uchar* p = src + sstep*std::min(i, m-1);
for ( j = 0; j < n; j++ )
COP( c, j, p[cn*j+c], ++ );
}
}
for( i = 0; i < m; i++ )
{
const uchar* p0 = src + sstep * std::max( 0, i-r-1 );
const uchar* p1 = src + sstep * std::min( m-1, i+r );
memset( H, 0, cn*sizeof(H[0]) );
memset( luc, 0, cn*sizeof(luc[0]) );
for( c = 0; c < cn; c++ )
{
// Update column histograms for the entire row.
for( j = 0; j < n; j++ )
{
COP( c, j, p0[j*cn + c], -- );
COP( c, j, p1[j*cn + c], ++ );
}
// First column initialization
for( k = 0; k < 16; ++k )
histogram_muladd( 2*r+1, &h_fine[16*n*(16*c+k)], &H[c].fine[k][0] );
#if MEDIAN_HAVE_SIMD
if( useSIMD )
{
for( j = 0; j < 2*r; ++j )
histogram_add_simd( &h_coarse[16*(n*c+j)], H[c].coarse );
for( j = r; j < n-r; j++ )
{
int t = 2*r*r + 2*r, b, sum = 0;
HT* segment;
histogram_add_simd( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse );
// Find median at coarse level
for ( k = 0; k < 16 ; ++k )
{
sum += H[c].coarse[k];
if ( sum > t )
{
sum -= H[c].coarse[k];
break;
}
}
assert( k < 16 );
/* Update corresponding histogram segment */
if ( luc[c][k] <= j-r )
{
memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
histogram_add_simd( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
if ( luc[c][k] < j+r+1 )
{
histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
luc[c][k] = (HT)(j+r+1);
}
}
else
{
for ( ; luc[c][k] < j+r+1; ++luc[c][k] )
{
histogram_sub_simd( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] );
histogram_add_simd( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] );
}
}
histogram_sub_simd( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse );
/* Find median in segment */
segment = H[c].fine[k];
for ( b = 0; b < 16 ; b++ )
{
sum += segment[b];
if ( sum > t )
{
dst[dstep*i+cn*j+c] = (uchar)(16*k + b);
break;
}
}
assert( b < 16 );
}
}
else
#endif
{
for( j = 0; j < 2*r; ++j )
histogram_add( &h_coarse[16*(n*c+j)], H[c].coarse );
for( j = r; j < n-r; j++ )
{
int t = 2*r*r + 2*r, b, sum = 0;
HT* segment;
histogram_add( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse );
// Find median at coarse level
for ( k = 0; k < 16 ; ++k )
{
sum += H[c].coarse[k];
if ( sum > t )
{
sum -= H[c].coarse[k];
break;
}
}
assert( k < 16 );
/* Update corresponding histogram segment */
if ( luc[c][k] <= j-r )
{
memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
if ( luc[c][k] < j+r+1 )
{
histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
luc[c][k] = (HT)(j+r+1);
}
}
else
{
for ( ; luc[c][k] < j+r+1; ++luc[c][k] )
{
histogram_sub( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] );
histogram_add( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] );
}
}
histogram_sub( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse );
/* Find median in segment */
segment = H[c].fine[k];
for ( b = 0; b < 16 ; b++ )
{
sum += segment[b];
if ( sum > t )
{
dst[dstep*i+cn*j+c] = (uchar)(16*k + b);
break;
}
}
assert( b < 16 );
}
}
}
}
}
#undef HOP
#undef COP
}
#if _MSC_VER >= 1200
#pragma warning( default: 4244 )
#endif
static void
medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m )
{
#define N 16
int zone0[4][N];
int zone1[4][N*N];
int x, y;
int n2 = m*m/2;
Size size = _dst.size();
const uchar* src = _src.data;
uchar* dst = _dst.data;
int src_step = (int)_src.step, dst_step = (int)_dst.step;
int cn = _src.channels();
const uchar* src_max = src + size.height*src_step;
#define UPDATE_ACC01( pix, cn, op ) \
{ \
int p = (pix); \
zone1[cn][p] op; \
zone0[cn][p >> 4] op; \
}
//CV_Assert( size.height >= nx && size.width >= nx );
for( x = 0; x < size.width; x++, src += cn, dst += cn )
{
uchar* dst_cur = dst;
const uchar* src_top = src;
const uchar* src_bottom = src;
int k, c;
int src_step1 = src_step, dst_step1 = dst_step;
if( x % 2 != 0 )
{
src_bottom = src_top += src_step*(size.height-1);
dst_cur += dst_step*(size.height-1);
src_step1 = -src_step1;
dst_step1 = -dst_step1;
}
// init accumulator
memset( zone0, 0, sizeof(zone0[0])*cn );
memset( zone1, 0, sizeof(zone1[0])*cn );
for( y = 0; y <= m/2; y++ )
{
for( c = 0; c < cn; c++ )
{
if( y > 0 )
{
for( k = 0; k < m*cn; k += cn )
UPDATE_ACC01( src_bottom[k+c], c, ++ );
}
else
{
for( k = 0; k < m*cn; k += cn )
UPDATE_ACC01( src_bottom[k+c], c, += m/2+1 );
}
}
if( (src_step1 > 0 && y < size.height-1) ||
(src_step1 < 0 && size.height-y-1 > 0) )
src_bottom += src_step1;
}
for( y = 0; y < size.height; y++, dst_cur += dst_step1 )
{
// find median
for( c = 0; c < cn; c++ )
{
int s = 0;
for( k = 0; ; k++ )
{
int t = s + zone0[c][k];
if( t > n2 ) break;
s = t;
}
for( k *= N; ;k++ )
{
s += zone1[c][k];
if( s > n2 ) break;
}
dst_cur[c] = (uchar)k;
}
if( y+1 == size.height )
break;
if( cn == 1 )
{
for( k = 0; k < m; k++ )
{
int p = src_top[k];
int q = src_bottom[k];
zone1[0][p]--;
zone0[0][p>>4]--;
zone1[0][q]++;
zone0[0][q>>4]++;
}
}
else if( cn == 3 )
{
for( k = 0; k < m*3; k += 3 )
{
UPDATE_ACC01( src_top[k], 0, -- );
UPDATE_ACC01( src_top[k+1], 1, -- );
UPDATE_ACC01( src_top[k+2], 2, -- );
UPDATE_ACC01( src_bottom[k], 0, ++ );
UPDATE_ACC01( src_bottom[k+1], 1, ++ );
UPDATE_ACC01( src_bottom[k+2], 2, ++ );
}
}
else
{
assert( cn == 4 );
for( k = 0; k < m*4; k += 4 )
{
UPDATE_ACC01( src_top[k], 0, -- );
UPDATE_ACC01( src_top[k+1], 1, -- );
UPDATE_ACC01( src_top[k+2], 2, -- );
UPDATE_ACC01( src_top[k+3], 3, -- );
UPDATE_ACC01( src_bottom[k], 0, ++ );
UPDATE_ACC01( src_bottom[k+1], 1, ++ );
UPDATE_ACC01( src_bottom[k+2], 2, ++ );
UPDATE_ACC01( src_bottom[k+3], 3, ++ );
}
}
if( (src_step1 > 0 && src_bottom + src_step1 < src_max) ||
(src_step1 < 0 && src_bottom + src_step1 >= src) )
src_bottom += src_step1;
if( y >= m/2 )
src_top += src_step1;
}
}
#undef N
#undef UPDATE_ACC
}
struct MinMax8u
{
typedef uchar value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const uchar* ptr) { return *ptr; }
void store(uchar* ptr, arg_type val) { *ptr = (uchar)val; }
void operator()(arg_type& a, arg_type& b) const
{
int t = CV_FAST_CAST_8U(a - b);
b += t; a -= t;
}
};
struct MinMax16u
{
typedef ushort value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const ushort* ptr) { return *ptr; }
void store(ushort* ptr, arg_type val) { *ptr = (ushort)val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
struct MinMax16s
{
typedef short value_type;
typedef int arg_type;
enum { SIZE = 1 };
arg_type load(const short* ptr) { return *ptr; }
void store(short* ptr, arg_type val) { *ptr = (short)val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
struct MinMax32f
{
typedef float value_type;
typedef float arg_type;
enum { SIZE = 1 };
arg_type load(const float* ptr) { return *ptr; }
void store(float* ptr, arg_type val) { *ptr = val; }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = std::min(a, b);
b = std::max(b, t);
}
};
#if CV_SSE2
struct MinMaxVec8u
{
typedef uchar value_type;
typedef __m128i arg_type;
enum { SIZE = 16 };
arg_type load(const uchar* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
void store(uchar* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = _mm_min_epu8(a, b);
b = _mm_max_epu8(b, t);
}
};
struct MinMaxVec16u
{
typedef ushort value_type;
typedef __m128i arg_type;
enum { SIZE = 8 };
arg_type load(const ushort* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
void store(ushort* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = _mm_subs_epu16(a, b);
a = _mm_subs_epu16(a, t);
b = _mm_adds_epu16(b, t);
}
};
struct MinMaxVec16s
{
typedef short value_type;
typedef __m128i arg_type;
enum { SIZE = 8 };
arg_type load(const short* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
void store(short* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = _mm_min_epi16(a, b);
b = _mm_max_epi16(b, t);
}
};
struct MinMaxVec32f
{
typedef float value_type;
typedef __m128 arg_type;
enum { SIZE = 4 };
arg_type load(const float* ptr) { return _mm_loadu_ps(ptr); }
void store(float* ptr, arg_type val) { _mm_storeu_ps(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = _mm_min_ps(a, b);
b = _mm_max_ps(b, t);
}
};
#else
typedef MinMax8u MinMaxVec8u;
typedef MinMax16u MinMaxVec16u;
typedef MinMax16s MinMaxVec16s;
typedef MinMax32f MinMaxVec32f;
#endif
template<class Op, class VecOp>
static void
medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
{
typedef typename Op::value_type T;
typedef typename Op::arg_type WT;
typedef typename VecOp::arg_type VT;
const T* src = (const T*)_src.data;
T* dst = (T*)_dst.data;
int sstep = (int)(_src.step/sizeof(T));
int dstep = (int)(_dst.step/sizeof(T));
Size size = _dst.size();
int i, j, k, cn = _src.channels();
Op op;
VecOp vop;
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
if( m == 3 )
{
if( size.width == 1 || size.height == 1 )
{
int len = size.width + size.height - 1;
int sdelta = size.height == 1 ? cn : sstep;
int sdelta0 = size.height == 1 ? 0 : sstep - cn;
int ddelta = size.height == 1 ? cn : dstep;
for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
for( j = 0; j < cn; j++, src++ )
{
WT p0 = src[i > 0 ? -sdelta : 0];
WT p1 = src[0];
WT p2 = src[i < len - 1 ? sdelta : 0];
op(p0, p1); op(p1, p2); op(p0, p1);
dst[j] = (T)p1;
}
return;
}
size.width *= cn;
for( i = 0; i < size.height; i++, dst += dstep )
{
const T* row0 = src + std::max(i - 1, 0)*sstep;
const T* row1 = src + i*sstep;
const T* row2 = src + std::min(i + 1, size.height-1)*sstep;
int limit = useSIMD ? cn : size.width;
for(j = 0;; )
{
for( ; j < limit; j++ )
{
int j0 = j >= cn ? j - cn : j;
int j2 = j < size.width - cn ? j + cn : j;
WT p0 = row0[j0], p1 = row0[j], p2 = row0[j2];
WT p3 = row1[j0], p4 = row1[j], p5 = row1[j2];
WT p6 = row2[j0], p7 = row2[j], p8 = row2[j2];
op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1);
op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5);
op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7);
op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7);
op(p4, p2); op(p6, p4); op(p4, p2);
dst[j] = (T)p4;
}
if( limit == size.width )
break;
for( ; j <= size.width - VecOp::SIZE - cn; j += VecOp::SIZE )
{
VT p0 = vop.load(row0+j-cn), p1 = vop.load(row0+j), p2 = vop.load(row0+j+cn);
VT p3 = vop.load(row1+j-cn), p4 = vop.load(row1+j), p5 = vop.load(row1+j+cn);
VT p6 = vop.load(row2+j-cn), p7 = vop.load(row2+j), p8 = vop.load(row2+j+cn);
vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p1);
vop(p3, p4); vop(p6, p7); vop(p1, p2); vop(p4, p5);
vop(p7, p8); vop(p0, p3); vop(p5, p8); vop(p4, p7);
vop(p3, p6); vop(p1, p4); vop(p2, p5); vop(p4, p7);
vop(p4, p2); vop(p6, p4); vop(p4, p2);
vop.store(dst+j, p4);
}
limit = size.width;
}
}
}
else if( m == 5 )
{
if( size.width == 1 || size.height == 1 )
{
int len = size.width + size.height - 1;
int sdelta = size.height == 1 ? cn : sstep;
int sdelta0 = size.height == 1 ? 0 : sstep - cn;
int ddelta = size.height == 1 ? cn : dstep;
for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
for( j = 0; j < cn; j++, src++ )
{
int i1 = i > 0 ? -sdelta : 0;
int i0 = i > 1 ? -sdelta*2 : i1;
int i3 = i < len-1 ? sdelta : 0;
int i4 = i < len-2 ? sdelta*2 : i3;
WT p0 = src[i0], p1 = src[i1], p2 = src[0], p3 = src[i3], p4 = src[i4];
op(p0, p1); op(p3, p4); op(p2, p3); op(p3, p4); op(p0, p2);
op(p2, p4); op(p1, p3); op(p1, p2);
dst[j] = (T)p2;
}
return;
}
size.width *= cn;
for( i = 0; i < size.height; i++, dst += dstep )
{
const T* row[5];
row[0] = src + std::max(i - 2, 0)*sstep;
row[1] = src + std::max(i - 1, 0)*sstep;
row[2] = src + i*sstep;
row[3] = src + std::min(i + 1, size.height-1)*sstep;
row[4] = src + std::min(i + 2, size.height-1)*sstep;
int limit = useSIMD ? cn*2 : size.width;
for(j = 0;; )
{
for( ; j < limit; j++ )
{
WT p[25];
int j1 = j >= cn ? j - cn : j;
int j0 = j >= cn*2 ? j - cn*2 : j1;
int j3 = j < size.width - cn ? j + cn : j;
int j4 = j < size.width - cn*2 ? j + cn*2 : j3;
for( k = 0; k < 5; k++ )
{
const T* rowk = row[k];
p[k*5] = rowk[j0]; p[k*5+1] = rowk[j1];
p[k*5+2] = rowk[j]; p[k*5+3] = rowk[j3];
p[k*5+4] = rowk[j4];
}
op(p[1], p[2]); op(p[0], p[1]); op(p[1], p[2]); op(p[4], p[5]); op(p[3], p[4]);
op(p[4], p[5]); op(p[0], p[3]); op(p[2], p[5]); op(p[2], p[3]); op(p[1], p[4]);
op(p[1], p[2]); op(p[3], p[4]); op(p[7], p[8]); op(p[6], p[7]); op(p[7], p[8]);
op(p[10], p[11]); op(p[9], p[10]); op(p[10], p[11]); op(p[6], p[9]); op(p[8], p[11]);
op(p[8], p[9]); op(p[7], p[10]); op(p[7], p[8]); op(p[9], p[10]); op(p[0], p[6]);
op(p[4], p[10]); op(p[4], p[6]); op(p[2], p[8]); op(p[2], p[4]); op(p[6], p[8]);
op(p[1], p[7]); op(p[5], p[11]); op(p[5], p[7]); op(p[3], p[9]); op(p[3], p[5]);
op(p[7], p[9]); op(p[1], p[2]); op(p[3], p[4]); op(p[5], p[6]); op(p[7], p[8]);
op(p[9], p[10]); op(p[13], p[14]); op(p[12], p[13]); op(p[13], p[14]); op(p[16], p[17]);
op(p[15], p[16]); op(p[16], p[17]); op(p[12], p[15]); op(p[14], p[17]); op(p[14], p[15]);
op(p[13], p[16]); op(p[13], p[14]); op(p[15], p[16]); op(p[19], p[20]); op(p[18], p[19]);
op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[21], p[23]); op(p[22], p[24]);
op(p[22], p[23]); op(p[18], p[21]); op(p[20], p[23]); op(p[20], p[21]); op(p[19], p[22]);
op(p[22], p[24]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[12], p[18]);
op(p[16], p[22]); op(p[16], p[18]); op(p[14], p[20]); op(p[20], p[24]); op(p[14], p[16]);
op(p[18], p[20]); op(p[22], p[24]); op(p[13], p[19]); op(p[17], p[23]); op(p[17], p[19]);
op(p[15], p[21]); op(p[15], p[17]); op(p[19], p[21]); op(p[13], p[14]); op(p[15], p[16]);
op(p[17], p[18]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[0], p[12]);
op(p[8], p[20]); op(p[8], p[12]); op(p[4], p[16]); op(p[16], p[24]); op(p[12], p[16]);
op(p[2], p[14]); op(p[10], p[22]); op(p[10], p[14]); op(p[6], p[18]); op(p[6], p[10]);
op(p[10], p[12]); op(p[1], p[13]); op(p[9], p[21]); op(p[9], p[13]); op(p[5], p[17]);
op(p[13], p[17]); op(p[3], p[15]); op(p[11], p[23]); op(p[11], p[15]); op(p[7], p[19]);
op(p[7], p[11]); op(p[11], p[13]); op(p[11], p[12]);
dst[j] = (T)p[12];
}
if( limit == size.width )
break;
for( ; j <= size.width - VecOp::SIZE - cn*2; j += VecOp::SIZE )
{
VT p[25];
for( k = 0; k < 5; k++ )
{
const T* rowk = row[k];
p[k*5] = vop.load(rowk+j-cn*2); p[k*5+1] = vop.load(rowk+j-cn);
p[k*5+2] = vop.load(rowk+j); p[k*5+3] = vop.load(rowk+j+cn);
p[k*5+4] = vop.load(rowk+j+cn*2);
}
vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]);
vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]);
vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]);
vop(p[10], p[11]); vop(p[9], p[10]); vop(p[10], p[11]); vop(p[6], p[9]); vop(p[8], p[11]);
vop(p[8], p[9]); vop(p[7], p[10]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[0], p[6]);
vop(p[4], p[10]); vop(p[4], p[6]); vop(p[2], p[8]); vop(p[2], p[4]); vop(p[6], p[8]);
vop(p[1], p[7]); vop(p[5], p[11]); vop(p[5], p[7]); vop(p[3], p[9]); vop(p[3], p[5]);
vop(p[7], p[9]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[5], p[6]); vop(p[7], p[8]);
vop(p[9], p[10]); vop(p[13], p[14]); vop(p[12], p[13]); vop(p[13], p[14]); vop(p[16], p[17]);
vop(p[15], p[16]); vop(p[16], p[17]); vop(p[12], p[15]); vop(p[14], p[17]); vop(p[14], p[15]);
vop(p[13], p[16]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[19], p[20]); vop(p[18], p[19]);
vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[21], p[23]); vop(p[22], p[24]);
vop(p[22], p[23]); vop(p[18], p[21]); vop(p[20], p[23]); vop(p[20], p[21]); vop(p[19], p[22]);
vop(p[22], p[24]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[12], p[18]);
vop(p[16], p[22]); vop(p[16], p[18]); vop(p[14], p[20]); vop(p[20], p[24]); vop(p[14], p[16]);
vop(p[18], p[20]); vop(p[22], p[24]); vop(p[13], p[19]); vop(p[17], p[23]); vop(p[17], p[19]);
vop(p[15], p[21]); vop(p[15], p[17]); vop(p[19], p[21]); vop(p[13], p[14]); vop(p[15], p[16]);
vop(p[17], p[18]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[0], p[12]);
vop(p[8], p[20]); vop(p[8], p[12]); vop(p[4], p[16]); vop(p[16], p[24]); vop(p[12], p[16]);
vop(p[2], p[14]); vop(p[10], p[22]); vop(p[10], p[14]); vop(p[6], p[18]); vop(p[6], p[10]);
vop(p[10], p[12]); vop(p[1], p[13]); vop(p[9], p[21]); vop(p[9], p[13]); vop(p[5], p[17]);
vop(p[13], p[17]); vop(p[3], p[15]); vop(p[11], p[23]); vop(p[11], p[15]); vop(p[7], p[19]);
vop(p[7], p[11]); vop(p[11], p[13]); vop(p[11], p[12]);
vop.store(dst+j, p[12]);
}
limit = size.width;
}
}
}
}
}
void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
{
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
if( ksize <= 1 )
{
src0.copyTo(dst);
return;
}
CV_Assert( ksize % 2 == 1 );
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::medianBlur(src0, dst, ksize))
return;
#endif
bool useSortNet = ksize == 3 || (ksize == 5
#if !CV_SSE2
&& src0.depth() > CV_8U
#endif
);
Mat src;
if( useSortNet )
{
if( dst.data != src0.data )
src = src0;
else
src0.copyTo(src);
if( src.depth() == CV_8U )
medianBlur_SortNet<MinMax8u, MinMaxVec8u>( src, dst, ksize );
else if( src.depth() == CV_16U )
medianBlur_SortNet<MinMax16u, MinMaxVec16u>( src, dst, ksize );
else if( src.depth() == CV_16S )
medianBlur_SortNet<MinMax16s, MinMaxVec16s>( src, dst, ksize );
else if( src.depth() == CV_32F )
medianBlur_SortNet<MinMax32f, MinMaxVec32f>( src, dst, ksize );
else
CV_Error(CV_StsUnsupportedFormat, "");
return;
}
else
{
cv::copyMakeBorder( src0, src, 0, 0, ksize/2, ksize/2, BORDER_REPLICATE );
int cn = src0.channels();
CV_Assert( src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4) );
double img_size_mp = (double)(src0.total())/(1 << 20);
if( ksize <= 3 + (img_size_mp < 1 ? 12 : img_size_mp < 4 ? 6 : 2)*(MEDIAN_HAVE_SIMD && checkHardwareSupport(CV_CPU_SSE2) ? 1 : 3))
medianBlur_8u_Om( src, dst, ksize );
else
medianBlur_8u_O1( src, dst, ksize );
}
}
/****************************************************************************************\
Bilateral Filtering
\****************************************************************************************/
namespace cv
{
static void
bilateralFilter_8u( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
{
int cn = src.channels();
int i, j, k, maxk, radius;
Size size = src.size();
CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) &&
src.type() == dst.type() && src.size() == dst.size() &&
src.data != dst.data );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
if( d <= 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
vector<float> _color_weight(cn*256);
vector<float> _space_weight(d*d);
vector<int> _space_ofs(d*d);
float* color_weight = &_color_weight[0];
float* space_weight = &_space_weight[0];
int* space_ofs = &_space_ofs[0];
// initialize color-related bilateral filter coefficients
for( i = 0; i < 256*cn; i++ )
color_weight[i] = (float)std::exp(i*i*gauss_color_coeff);
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = (int)(i*temp.step + j*cn);
}
for( i = 0; i < size.height; i++ )
{
const uchar* sptr = temp.data + (i+radius)*temp.step + radius*cn;
uchar* dptr = dst.data + i*dst.step;
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
int val0 = sptr[j];
for( k = 0; k < maxk; k++ )
{
int val = sptr[j + space_ofs[k]];
float w = space_weight[k]*color_weight[std::abs(val - val0)];
sum += val*w;
wsum += w;
}
// overflow is not possible here => there is no need to use CV_CAST_8U
dptr[j] = (uchar)cvRound(sum/wsum);
}
}
else
{
assert( cn == 3 );
for( j = 0; j < size.width*3; j += 3 )
{
float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
int b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
for( k = 0; k < maxk; k++ )
{
const uchar* sptr_k = sptr + j + space_ofs[k];
int b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float w = space_weight[k]*color_weight[std::abs(b - b0) +
std::abs(g - g0) + std::abs(r - r0)];
sum_b += b*w; sum_g += g*w; sum_r += r*w;
wsum += w;
}
wsum = 1.f/wsum;
b0 = cvRound(sum_b*wsum);
g0 = cvRound(sum_g*wsum);
r0 = cvRound(sum_r*wsum);
dptr[j] = (uchar)b0; dptr[j+1] = (uchar)g0; dptr[j+2] = (uchar)r0;
}
}
}
}
static void
bilateralFilter_32f( const Mat& src, Mat& dst, int d,
double sigma_color, double sigma_space,
int borderType )
{
int cn = src.channels();
int i, j, k, maxk, radius;
double minValSrc=-1, maxValSrc=1;
const int kExpNumBinsPerChannel = 1 << 12;
int kExpNumBins = 0;
float lastExpVal = 1.f;
float len, scale_index;
Size size = src.size();
CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) &&
src.type() == dst.type() && src.size() == dst.size() &&
src.data != dst.data );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
if( d <= 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
// compute the min/max range for the input image (even if multichannel)
minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc );
// temporary copy of the image with borders for easy processing
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
// allocate lookup tables
vector<float> _space_weight(d*d);
vector<int> _space_ofs(d*d);
float* space_weight = &_space_weight[0];
int* space_ofs = &_space_ofs[0];
// assign a length which is slightly more than needed
len = (float)(maxValSrc - minValSrc) * cn;
kExpNumBins = kExpNumBinsPerChannel * cn;
vector<float> _expLUT(kExpNumBins+2);
float* expLUT = &_expLUT[0];
scale_index = kExpNumBins/len;
// initialize the exp LUT
for( i = 0; i < kExpNumBins+2; i++ )
{
if( lastExpVal > 0.f )
{
double val = i / scale_index;
expLUT[i] = (float)std::exp(val * val * gauss_color_coeff);
lastExpVal = expLUT[i];
}
else
expLUT[i] = 0.f;
}
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = (int)(i*(temp.step/sizeof(float)) + j*cn);
}
for( i = 0; i < size.height; i++ )
{
const float* sptr = (const float*)(temp.data + (i+radius)*temp.step) + radius*cn;
float* dptr = (float*)(dst.data + i*dst.step);
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
float val0 = sptr[j];
for( k = 0; k < maxk; k++ )
{
float val = sptr[j + space_ofs[k]];
float alpha = (float)(std::abs(val - val0)*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum += val*w;
wsum += w;
}
dptr[j] = (float)(sum/wsum);
}
}
else
{
assert( cn == 3 );
for( j = 0; j < size.width*3; j += 3 )
{
float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
for( k = 0; k < maxk; k++ )
{
const float* sptr_k = sptr + j + space_ofs[k];
float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float alpha = (float)((std::abs(b - b0) +
std::abs(g - g0) + std::abs(r - r0))*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum_b += b*w; sum_g += g*w; sum_r += r*w;
wsum += w;
}
wsum = 1.f/wsum;
b0 = sum_b*wsum;
g0 = sum_g*wsum;
r0 = sum_r*wsum;
dptr[j] = b0; dptr[j+1] = g0; dptr[j+2] = r0;
}
}
}
}
}
void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
double sigmaColor, double sigmaSpace,
int borderType )
{
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if( src.depth() == CV_8U )
bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType );
else if( src.depth() == CV_32F )
bilateralFilter_32f( src, dst, d, sigmaColor, sigmaSpace, borderType );
else
CV_Error( CV_StsUnsupportedFormat,
"Bilateral filtering is only implemented for 8u and 32f images" );
}
//////////////////////////////////////////////////////////////////////////////////////////
CV_IMPL void
cvSmooth( const void* srcarr, void* dstarr, int smooth_type,
int param1, int param2, double param3, double param4 )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0;
CV_Assert( dst.size() == src.size() &&
(smooth_type == CV_BLUR_NO_SCALE || dst.type() == src.type()) );
if( param2 <= 0 )
param2 = param1;
if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE )
cv::boxFilter( src, dst, dst.depth(), cv::Size(param1, param2), cv::Point(-1,-1),
smooth_type == CV_BLUR, cv::BORDER_REPLICATE );
else if( smooth_type == CV_GAUSSIAN )
cv::GaussianBlur( src, dst, cv::Size(param1, param2), param3, param4, cv::BORDER_REPLICATE );
else if( smooth_type == CV_MEDIAN )
cv::medianBlur( src, dst, param1 );
else
cv::bilateralFilter( src, dst, param1, param3, param4, cv::BORDER_REPLICATE );
if( dst.data != dst0.data )
CV_Error( CV_StsUnmatchedFormats, "The destination image does not have the proper type" );
}
/* End of file. */
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