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opencv/modules/imgproc/src/smooth.cpp
elenagvo 5d0a8d2aaf fix the parameters order
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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.
// 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*/
#include "precomp.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_imgproc.hpp"
#include "opencv2/core/openvx/ovx_defs.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 ) :
BaseRowFilter()
{
ksize = _ksize;
anchor = _anchor;
}
virtual 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;
if( ksize == 3 )
{
for( i = 0; i < width + cn; i++ )
{
D[i] = (ST)S[i] + (ST)S[i+cn] + (ST)S[i+cn*2];
}
}
else if( ksize == 5 )
{
for( i = 0; i < width + cn; i++ )
{
D[i] = (ST)S[i] + (ST)S[i+cn] + (ST)S[i+cn*2] + (ST)S[i + cn*3] + (ST)S[i + cn*4];
}
}
else if( cn == 1 )
{
ST s = 0;
for( i = 0; i < ksz_cn; i++ )
s += (ST)S[i];
D[0] = s;
for( i = 0; i < width; i++ )
{
s += (ST)S[i + ksz_cn] - (ST)S[i];
D[i+1] = s;
}
}
else if( cn == 3 )
{
ST s0 = 0, s1 = 0, s2 = 0;
for( i = 0; i < ksz_cn; i += 3 )
{
s0 += (ST)S[i];
s1 += (ST)S[i+1];
s2 += (ST)S[i+2];
}
D[0] = s0;
D[1] = s1;
D[2] = s2;
for( i = 0; i < width; i += 3 )
{
s0 += (ST)S[i + ksz_cn] - (ST)S[i];
s1 += (ST)S[i + ksz_cn + 1] - (ST)S[i + 1];
s2 += (ST)S[i + ksz_cn + 2] - (ST)S[i + 2];
D[i+3] = s0;
D[i+4] = s1;
D[i+5] = s2;
}
}
else if( cn == 4 )
{
ST s0 = 0, s1 = 0, s2 = 0, s3 = 0;
for( i = 0; i < ksz_cn; i += 4 )
{
s0 += (ST)S[i];
s1 += (ST)S[i+1];
s2 += (ST)S[i+2];
s3 += (ST)S[i+3];
}
D[0] = s0;
D[1] = s1;
D[2] = s2;
D[3] = s3;
for( i = 0; i < width; i += 4 )
{
s0 += (ST)S[i + ksz_cn] - (ST)S[i];
s1 += (ST)S[i + ksz_cn + 1] - (ST)S[i + 1];
s2 += (ST)S[i + ksz_cn + 2] - (ST)S[i + 2];
s3 += (ST)S[i + ksz_cn + 3] - (ST)S[i + 3];
D[i+4] = s0;
D[i+5] = s1;
D[i+6] = s2;
D[i+7] = s3;
}
}
else
for( k = 0; k < cn; k++, S++, D++ )
{
ST s = 0;
for( i = 0; i < ksz_cn; i += cn )
s += (ST)S[i];
D[0] = s;
for( i = 0; i < width; i += cn )
{
s += (ST)S[i + ksz_cn] - (ST)S[i];
D[i+cn] = s;
}
}
}
};
template<typename ST, typename T>
struct ColumnSum :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual 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 )
{
memset((void*)SUM, 0, width*sizeof(ST));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const ST* Sp = (const ST*)src[0];
for( i = 0; 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;
std::vector<ST> sum;
};
template<>
struct ColumnSum<int, uchar> :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int* SUM;
bool haveScale = scale != 1;
double _scale = scale;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(int));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const int* Sp = (const int*)src[0];
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for (; i <= width - 4; i += 4)
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const int* Sp = (const int*)src[0];
const int* Sm = (const int*)src[1-ksize];
uchar* D = (uchar*)dst;
if( haveScale )
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 v_scale = v_setall_f32((float)_scale);
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_uint32x4 v_s0d = v_reinterpret_as_u32(v_round(v_cvt_f32(v_s0) * v_scale));
v_uint32x4 v_s01d = v_reinterpret_as_u32(v_round(v_cvt_f32(v_s01) * v_scale));
v_uint16x8 v_dst = v_pack(v_s0d, v_s01d);
v_pack_store(D + i, v_dst);
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<uchar>(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_uint16x8 v_dst = v_pack(v_reinterpret_as_u32(v_s0), v_reinterpret_as_u32(v_s01));
v_pack_store(D + i, v_dst);
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<uchar>(s0);
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
std::vector<int> sum;
};
template<>
struct ColumnSum<ushort, uchar> :
public BaseColumnFilter
{
enum { SHIFT = 23 };
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
divDelta = 0;
divScale = 1;
if( scale != 1 )
{
int d = cvRound(1./scale);
double scalef = ((double)(1 << SHIFT))/d;
divScale = cvFloor(scalef);
scalef -= divScale;
divDelta = d/2;
if( scalef < 0.5 )
divDelta++;
else
divScale++;
}
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
const int ds = divScale;
const int dd = divDelta;
ushort* SUM;
const bool haveScale = scale != 1;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(SUM[0]));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const ushort* Sp = (const ushort*)src[0];
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width - 8; i += 8 )
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const ushort* Sp = (const ushort*)src[0];
const ushort* Sm = (const ushort*)src[1-ksize];
uchar* D = (uchar*)dst;
if( haveScale )
{
int i = 0;
#if CV_SIMD128
v_uint32x4 ds4 = v_setall_u32((unsigned)ds);
v_uint16x8 dd8 = v_setall_u16((ushort)dd);
for( ; i <= width-16; i+=16 )
{
v_uint16x8 _sm0 = v_load(Sm + i);
v_uint16x8 _sm1 = v_load(Sm + i + 8);
v_uint16x8 _s0 = v_add_wrap(v_load(SUM + i), v_load(Sp + i));
v_uint16x8 _s1 = v_add_wrap(v_load(SUM + i + 8), v_load(Sp + i + 8));
v_uint32x4 _s00, _s01, _s10, _s11;
v_expand(_s0 + dd8, _s00, _s01);
v_expand(_s1 + dd8, _s10, _s11);
_s00 = v_shr<SHIFT>(_s00*ds4);
_s01 = v_shr<SHIFT>(_s01*ds4);
_s10 = v_shr<SHIFT>(_s10*ds4);
_s11 = v_shr<SHIFT>(_s11*ds4);
v_int16x8 r0 = v_pack(v_reinterpret_as_s32(_s00), v_reinterpret_as_s32(_s01));
v_int16x8 r1 = v_pack(v_reinterpret_as_s32(_s10), v_reinterpret_as_s32(_s11));
_s0 = v_sub_wrap(_s0, _sm0);
_s1 = v_sub_wrap(_s1, _sm1);
v_store(D + i, v_pack_u(r0, r1));
v_store(SUM + i, _s0);
v_store(SUM + i + 8, _s1);
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = (uchar)((s0 + dd)*ds >> SHIFT);
SUM[i] = (ushort)(s0 - Sm[i]);
}
}
else
{
int i = 0;
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<uchar>(s0);
SUM[i] = (ushort)(s0 - Sm[i]);
}
}
dst += dststep;
}
}
double scale;
int sumCount;
int divDelta;
int divScale;
std::vector<ushort> sum;
};
template<>
struct ColumnSum<int, short> :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int i;
int* SUM;
bool haveScale = scale != 1;
double _scale = scale;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(int));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const int* Sp = (const int*)src[0];
i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width - 4; i+=4 )
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const int* Sp = (const int*)src[0];
const int* Sm = (const int*)src[1-ksize];
short* D = (short*)dst;
if( haveScale )
{
i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 v_scale = v_setall_f32((float)_scale);
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_int32x4 v_s0d = v_round(v_cvt_f32(v_s0) * v_scale);
v_int32x4 v_s01d = v_round(v_cvt_f32(v_s01) * v_scale);
v_store(D + i, v_pack(v_s0d, v_s01d));
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<short>(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_store(D + i, v_pack(v_s0, v_s01));
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<short>(s0);
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
std::vector<int> sum;
};
template<>
struct ColumnSum<int, ushort> :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int* SUM;
bool haveScale = scale != 1;
double _scale = scale;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(int));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const int* Sp = (const int*)src[0];
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for (; i <= width - 4; i += 4)
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const int* Sp = (const int*)src[0];
const int* Sm = (const int*)src[1-ksize];
ushort* D = (ushort*)dst;
if( haveScale )
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 v_scale = v_setall_f32((float)_scale);
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_uint32x4 v_s0d = v_reinterpret_as_u32(v_round(v_cvt_f32(v_s0) * v_scale));
v_uint32x4 v_s01d = v_reinterpret_as_u32(v_round(v_cvt_f32(v_s01) * v_scale));
v_store(D + i, v_pack(v_s0d, v_s01d));
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<ushort>(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_store(D + i, v_pack(v_reinterpret_as_u32(v_s0), v_reinterpret_as_u32(v_s01)));
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<ushort>(s0);
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
std::vector<int> sum;
};
template<>
struct ColumnSum<int, int> :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int* SUM;
bool haveScale = scale != 1;
double _scale = scale;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(int));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const int* Sp = (const int*)src[0];
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width - 4; i+=4 )
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const int* Sp = (const int*)src[0];
const int* Sm = (const int*)src[1-ksize];
int* D = (int*)dst;
if( haveScale )
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 v_scale = v_setall_f32((float)_scale);
for( ; i <= width-4; i+=4 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s0d = v_round(v_cvt_f32(v_s0) * v_scale);
v_store(D + i, v_s0d);
v_store(SUM + i, v_s0 - v_load(Sm + i));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = saturate_cast<int>(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width-4; i+=4 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_store(D + i, v_s0);
v_store(SUM + i, v_s0 - v_load(Sm + i));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = s0;
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
std::vector<int> sum;
};
template<>
struct ColumnSum<int, float> :
public BaseColumnFilter
{
ColumnSum( int _ksize, int _anchor, double _scale ) :
BaseColumnFilter()
{
ksize = _ksize;
anchor = _anchor;
scale = _scale;
sumCount = 0;
}
virtual void reset() { sumCount = 0; }
virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
{
int* SUM;
bool haveScale = scale != 1;
double _scale = scale;
#if CV_SIMD128
bool haveSIMD128 = hasSIMD128();
#endif
if( width != (int)sum.size() )
{
sum.resize(width);
sumCount = 0;
}
SUM = &sum[0];
if( sumCount == 0 )
{
memset((void*)SUM, 0, width*sizeof(int));
for( ; sumCount < ksize - 1; sumCount++, src++ )
{
const int* Sp = (const int*)src[0];
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width - 4; i+=4 )
{
v_store(SUM + i, v_load(SUM + i) + v_load(Sp + i));
}
}
#endif
for( ; i < width; i++ )
SUM[i] += Sp[i];
}
}
else
{
CV_Assert( sumCount == ksize-1 );
src += ksize-1;
}
for( ; count--; src++ )
{
const int * Sp = (const int*)src[0];
const int * Sm = (const int*)src[1-ksize];
float* D = (float*)dst;
if( haveScale )
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 v_scale = v_setall_f32((float)_scale);
for (; i <= width - 8; i += 8)
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_store(D + i, v_cvt_f32(v_s0) * v_scale);
v_store(D + i + 4, v_cvt_f32(v_s01) * v_scale);
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = (float)(s0*_scale);
SUM[i] = s0 - Sm[i];
}
}
else
{
int i = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
for( ; i <= width-8; i+=8 )
{
v_int32x4 v_s0 = v_load(SUM + i) + v_load(Sp + i);
v_int32x4 v_s01 = v_load(SUM + i + 4) + v_load(Sp + i + 4);
v_store(D + i, v_cvt_f32(v_s0));
v_store(D + i + 4, v_cvt_f32(v_s01));
v_store(SUM + i, v_s0 - v_load(Sm + i));
v_store(SUM + i + 4, v_s01 - v_load(Sm + i + 4));
}
}
#endif
for( ; i < width; i++ )
{
int s0 = SUM[i] + Sp[i];
D[i] = (float)(s0);
SUM[i] = s0 - Sm[i];
}
}
dst += dststep;
}
}
double scale;
int sumCount;
std::vector<int> sum;
};
#ifdef HAVE_OPENCL
static bool ocl_boxFilter3x3_8UC1( InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor, int borderType, bool normalize )
{
const ocl::Device & dev = ocl::Device::getDefault();
int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if (ddepth < 0)
ddepth = sdepth;
if (anchor.x < 0)
anchor.x = ksize.width / 2;
if (anchor.y < 0)
anchor.y = ksize.height / 2;
if ( !(dev.isIntel() && (type == CV_8UC1) &&
(_src.offset() == 0) && (_src.step() % 4 == 0) &&
(_src.cols() % 16 == 0) && (_src.rows() % 2 == 0) &&
(anchor.x == 1) && (anchor.y == 1) &&
(ksize.width == 3) && (ksize.height == 3)) )
return false;
float alpha = 1.0f / (ksize.height * ksize.width);
Size size = _src.size();
size_t globalsize[2] = { 0, 0 };
size_t localsize[2] = { 0, 0 };
const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };
globalsize[0] = size.width / 16;
globalsize[1] = size.height / 2;
char build_opts[1024];
sprintf(build_opts, "-D %s %s", borderMap[borderType], normalize ? "-D NORMALIZE" : "");
ocl::Kernel kernel("boxFilter3x3_8UC1_cols16_rows2", cv::ocl::imgproc::boxFilter3x3_oclsrc, build_opts);
if (kernel.empty())
return false;
UMat src = _src.getUMat();
_dst.create(size, CV_MAKETYPE(ddepth, cn));
if (!(_dst.offset() == 0 && _dst.step() % 4 == 0))
return false;
UMat dst = _dst.getUMat();
int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
idxArg = kernel.set(idxArg, (int)src.step);
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dst));
idxArg = kernel.set(idxArg, (int)dst.step);
idxArg = kernel.set(idxArg, (int)dst.rows);
idxArg = kernel.set(idxArg, (int)dst.cols);
if (normalize)
idxArg = kernel.set(idxArg, (float)alpha);
return kernel.run(2, globalsize, (localsize[0] == 0) ? NULL : localsize, false);
}
#define DIVUP(total, grain) ((total + grain - 1) / (grain))
#define ROUNDUP(sz, n) ((sz) + (n) - 1 - (((sz) + (n) - 1) % (n)))
static bool ocl_boxFilter( InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor, int borderType, bool normalize, bool sqr = false )
{
const ocl::Device & dev = ocl::Device::getDefault();
int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), esz = CV_ELEM_SIZE(type);
bool doubleSupport = dev.doubleFPConfig() > 0;
if (ddepth < 0)
ddepth = sdepth;
if (cn > 4 || (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F)) ||
_src.offset() % esz != 0 || _src.step() % esz != 0)
return false;
if (anchor.x < 0)
anchor.x = ksize.width / 2;
if (anchor.y < 0)
anchor.y = ksize.height / 2;
int computeUnits = ocl::Device::getDefault().maxComputeUnits();
float alpha = 1.0f / (ksize.height * ksize.width);
Size size = _src.size(), wholeSize;
bool isolated = (borderType & BORDER_ISOLATED) != 0;
borderType &= ~BORDER_ISOLATED;
int wdepth = std::max(CV_32F, std::max(ddepth, sdepth)),
wtype = CV_MAKE_TYPE(wdepth, cn), dtype = CV_MAKE_TYPE(ddepth, cn);
const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };
size_t globalsize[2] = { (size_t)size.width, (size_t)size.height };
size_t localsize_general[2] = { 0, 1 }, * localsize = NULL;
UMat src = _src.getUMat();
if (!isolated)
{
Point ofs;
src.locateROI(wholeSize, ofs);
}
int h = isolated ? size.height : wholeSize.height;
int w = isolated ? size.width : wholeSize.width;
size_t maxWorkItemSizes[32];
ocl::Device::getDefault().maxWorkItemSizes(maxWorkItemSizes);
int tryWorkItems = (int)maxWorkItemSizes[0];
ocl::Kernel kernel;
if (dev.isIntel() && !(dev.type() & ocl::Device::TYPE_CPU) &&
((ksize.width < 5 && ksize.height < 5 && esz <= 4) ||
(ksize.width == 5 && ksize.height == 5 && cn == 1)))
{
if (w < ksize.width || h < ksize.height)
return false;
// Figure out what vector size to use for loading the pixels.
int pxLoadNumPixels = cn != 1 || size.width % 4 ? 1 : 4;
int pxLoadVecSize = cn * pxLoadNumPixels;
// Figure out how many pixels per work item to compute in X and Y
// directions. Too many and we run out of registers.
int pxPerWorkItemX = 1, pxPerWorkItemY = 1;
if (cn <= 2 && ksize.width <= 4 && ksize.height <= 4)
{
pxPerWorkItemX = size.width % 8 ? size.width % 4 ? size.width % 2 ? 1 : 2 : 4 : 8;
pxPerWorkItemY = size.height % 2 ? 1 : 2;
}
else if (cn < 4 || (ksize.width <= 4 && ksize.height <= 4))
{
pxPerWorkItemX = size.width % 2 ? 1 : 2;
pxPerWorkItemY = size.height % 2 ? 1 : 2;
}
globalsize[0] = size.width / pxPerWorkItemX;
globalsize[1] = size.height / pxPerWorkItemY;
// Need some padding in the private array for pixels
int privDataWidth = ROUNDUP(pxPerWorkItemX + ksize.width - 1, pxLoadNumPixels);
// Make the global size a nice round number so the runtime can pick
// from reasonable choices for the workgroup size
const int wgRound = 256;
globalsize[0] = ROUNDUP(globalsize[0], wgRound);
char build_options[1024], cvt[2][40];
sprintf(build_options, "-D cn=%d "
"-D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d "
"-D PX_LOAD_VEC_SIZE=%d -D PX_LOAD_NUM_PX=%d "
"-D PX_PER_WI_X=%d -D PX_PER_WI_Y=%d -D PRIV_DATA_WIDTH=%d -D %s -D %s "
"-D PX_LOAD_X_ITERATIONS=%d -D PX_LOAD_Y_ITERATIONS=%d "
"-D srcT=%s -D srcT1=%s -D dstT=%s -D dstT1=%s -D WT=%s -D WT1=%s "
"-D convertToWT=%s -D convertToDstT=%s%s%s -D PX_LOAD_FLOAT_VEC_CONV=convert_%s -D OP_BOX_FILTER",
cn, anchor.x, anchor.y, ksize.width, ksize.height,
pxLoadVecSize, pxLoadNumPixels,
pxPerWorkItemX, pxPerWorkItemY, privDataWidth, borderMap[borderType],
isolated ? "BORDER_ISOLATED" : "NO_BORDER_ISOLATED",
privDataWidth / pxLoadNumPixels, pxPerWorkItemY + ksize.height - 1,
ocl::typeToStr(type), ocl::typeToStr(sdepth), ocl::typeToStr(dtype),
ocl::typeToStr(ddepth), ocl::typeToStr(wtype), ocl::typeToStr(wdepth),
ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]),
ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]),
normalize ? " -D NORMALIZE" : "", sqr ? " -D SQR" : "",
ocl::typeToStr(CV_MAKE_TYPE(wdepth, pxLoadVecSize)) //PX_LOAD_FLOAT_VEC_CONV
);
if (!kernel.create("filterSmall", cv::ocl::imgproc::filterSmall_oclsrc, build_options))
return false;
}
else
{
localsize = localsize_general;
for ( ; ; )
{
int BLOCK_SIZE_X = tryWorkItems, BLOCK_SIZE_Y = std::min(ksize.height * 10, size.height);
while (BLOCK_SIZE_X > 32 && BLOCK_SIZE_X >= ksize.width * 2 && BLOCK_SIZE_X > size.width * 2)
BLOCK_SIZE_X /= 2;
while (BLOCK_SIZE_Y < BLOCK_SIZE_X / 8 && BLOCK_SIZE_Y * computeUnits * 32 < size.height)
BLOCK_SIZE_Y *= 2;
if (ksize.width > BLOCK_SIZE_X || w < ksize.width || h < ksize.height)
return false;
char cvt[2][50];
String opts = format("-D LOCAL_SIZE_X=%d -D BLOCK_SIZE_Y=%d -D ST=%s -D DT=%s -D WT=%s -D convertToDT=%s -D convertToWT=%s"
" -D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d -D %s%s%s%s%s"
" -D ST1=%s -D DT1=%s -D cn=%d",
BLOCK_SIZE_X, BLOCK_SIZE_Y, ocl::typeToStr(type), ocl::typeToStr(CV_MAKE_TYPE(ddepth, cn)),
ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)),
ocl::convertTypeStr(wdepth, ddepth, cn, cvt[0]),
ocl::convertTypeStr(sdepth, wdepth, cn, cvt[1]),
anchor.x, anchor.y, ksize.width, ksize.height, borderMap[borderType],
isolated ? " -D BORDER_ISOLATED" : "", doubleSupport ? " -D DOUBLE_SUPPORT" : "",
normalize ? " -D NORMALIZE" : "", sqr ? " -D SQR" : "",
ocl::typeToStr(sdepth), ocl::typeToStr(ddepth), cn);
localsize[0] = BLOCK_SIZE_X;
globalsize[0] = DIVUP(size.width, BLOCK_SIZE_X - (ksize.width - 1)) * BLOCK_SIZE_X;
globalsize[1] = DIVUP(size.height, BLOCK_SIZE_Y);
kernel.create("boxFilter", cv::ocl::imgproc::boxFilter_oclsrc, opts);
if (kernel.empty())
return false;
size_t kernelWorkGroupSize = kernel.workGroupSize();
if (localsize[0] <= kernelWorkGroupSize)
break;
if (BLOCK_SIZE_X < (int)kernelWorkGroupSize)
return false;
tryWorkItems = (int)kernelWorkGroupSize;
}
}
_dst.create(size, CV_MAKETYPE(ddepth, cn));
UMat dst = _dst.getUMat();
int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
idxArg = kernel.set(idxArg, (int)src.step);
int srcOffsetX = (int)((src.offset % src.step) / src.elemSize());
int srcOffsetY = (int)(src.offset / src.step);
int srcEndX = isolated ? srcOffsetX + size.width : wholeSize.width;
int srcEndY = isolated ? srcOffsetY + size.height : wholeSize.height;
idxArg = kernel.set(idxArg, srcOffsetX);
idxArg = kernel.set(idxArg, srcOffsetY);
idxArg = kernel.set(idxArg, srcEndX);
idxArg = kernel.set(idxArg, srcEndY);
idxArg = kernel.set(idxArg, ocl::KernelArg::WriteOnly(dst));
if (normalize)
idxArg = kernel.set(idxArg, (float)alpha);
return kernel.run(2, globalsize, localsize, false);
}
#undef ROUNDUP
#endif
}
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 makePtr<RowSum<uchar, int> >(ksize, anchor);
if( sdepth == CV_8U && ddepth == CV_16U )
return makePtr<RowSum<uchar, ushort> >(ksize, anchor);
if( sdepth == CV_8U && ddepth == CV_64F )
return makePtr<RowSum<uchar, double> >(ksize, anchor);
if( sdepth == CV_16U && ddepth == CV_32S )
return makePtr<RowSum<ushort, int> >(ksize, anchor);
if( sdepth == CV_16U && ddepth == CV_64F )
return makePtr<RowSum<ushort, double> >(ksize, anchor);
if( sdepth == CV_16S && ddepth == CV_32S )
return makePtr<RowSum<short, int> >(ksize, anchor);
if( sdepth == CV_32S && ddepth == CV_32S )
return makePtr<RowSum<int, int> >(ksize, anchor);
if( sdepth == CV_16S && ddepth == CV_64F )
return makePtr<RowSum<short, double> >(ksize, anchor);
if( sdepth == CV_32F && ddepth == CV_64F )
return makePtr<RowSum<float, double> >(ksize, anchor);
if( sdepth == CV_64F && ddepth == CV_64F )
return makePtr<RowSum<double, double> >(ksize, anchor);
CV_Error_( CV_StsNotImplemented,
("Unsupported combination of source format (=%d), and buffer format (=%d)",
srcType, sumType));
return Ptr<BaseRowFilter>();
}
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 makePtr<ColumnSum<int, uchar> >(ksize, anchor, scale);
if( ddepth == CV_8U && sdepth == CV_16U )
return makePtr<ColumnSum<ushort, uchar> >(ksize, anchor, scale);
if( ddepth == CV_8U && sdepth == CV_64F )
return makePtr<ColumnSum<double, uchar> >(ksize, anchor, scale);
if( ddepth == CV_16U && sdepth == CV_32S )
return makePtr<ColumnSum<int, ushort> >(ksize, anchor, scale);
if( ddepth == CV_16U && sdepth == CV_64F )
return makePtr<ColumnSum<double, ushort> >(ksize, anchor, scale);
if( ddepth == CV_16S && sdepth == CV_32S )
return makePtr<ColumnSum<int, short> >(ksize, anchor, scale);
if( ddepth == CV_16S && sdepth == CV_64F )
return makePtr<ColumnSum<double, short> >(ksize, anchor, scale);
if( ddepth == CV_32S && sdepth == CV_32S )
return makePtr<ColumnSum<int, int> >(ksize, anchor, scale);
if( ddepth == CV_32F && sdepth == CV_32S )
return makePtr<ColumnSum<int, float> >(ksize, anchor, scale);
if( ddepth == CV_32F && sdepth == CV_64F )
return makePtr<ColumnSum<double, float> >(ksize, anchor, scale);
if( ddepth == CV_64F && sdepth == CV_32S )
return makePtr<ColumnSum<int, double> >(ksize, anchor, scale);
if( ddepth == CV_64F && sdepth == CV_64F )
return makePtr<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>();
}
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_8U && CV_MAT_DEPTH(dstType) == CV_8U &&
ksize.width*ksize.height <= 256 )
sumType = CV_16U;
else 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 makePtr<FilterEngine>(Ptr<BaseFilter>(), rowFilter, columnFilter,
srcType, dstType, sumType, borderType );
}
#ifdef HAVE_OPENVX
namespace cv
{
namespace ovx {
template <> inline bool skipSmallImages<VX_KERNEL_BOX_3x3>(int w, int h) { return w*h < 640 * 480; }
}
static bool openvx_boxfilter(InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor,
bool normalize, int borderType)
{
if (ddepth < 0)
ddepth = CV_8UC1;
if (_src.type() != CV_8UC1 || ddepth != CV_8U || !normalize ||
_src.cols() < 3 || _src.rows() < 3 ||
ksize.width != 3 || ksize.height != 3 ||
(anchor.x >= 0 && anchor.x != 1) ||
(anchor.y >= 0 && anchor.y != 1) ||
ovx::skipSmallImages<VX_KERNEL_BOX_3x3>(_src.cols(), _src.rows()))
return false;
Mat src = _src.getMat();
if ((borderType & BORDER_ISOLATED) == 0 && src.isSubmatrix())
return false; //Process isolated borders only
vx_enum border;
switch (borderType & ~BORDER_ISOLATED)
{
case BORDER_CONSTANT:
border = VX_BORDER_CONSTANT;
break;
case BORDER_REPLICATE:
border = VX_BORDER_REPLICATE;
break;
default:
return false;
}
_dst.create(src.size(), CV_8UC1);
Mat dst = _dst.getMat();
try
{
ivx::Context ctx = ovx::getOpenVXContext();
Mat a;
if (dst.data != src.data)
a = src;
else
src.copyTo(a);
ivx::Image
ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),
ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols, dst.rows, 1, (vx_int32)(dst.step)), dst.data);
//ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments
//since OpenVX standart says nothing about thread-safety for now
ivx::border_t prevBorder = ctx.immediateBorder();
ctx.setImmediateBorder(border, (vx_uint8)(0));
ivx::IVX_CHECK_STATUS(vxuBox3x3(ctx, ia, ib));
ctx.setImmediateBorder(prevBorder);
}
catch (ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
}
#endif
#if defined(HAVE_IPP)
namespace cv
{
static bool ipp_boxfilter(Mat &src, Mat &dst, Size ksize, Point anchor, bool normalize, int borderType)
{
#ifdef HAVE_IPP_IW
CV_INSTRUMENT_REGION_IPP()
#if IPP_VERSION_X100 < 201801
// Problem with SSE42 optimization for 16s and some 8u modes
if(ipp::getIppTopFeatures() == ippCPUID_SSE42 && (((src.depth() == CV_16S || src.depth() == CV_16U) && (src.channels() == 3 || src.channels() == 4)) || (src.depth() == CV_8U && src.channels() == 3 && (ksize.width > 5 || ksize.height > 5))))
return false;
// Other optimizations has some degradations too
if((((src.depth() == CV_16S || src.depth() == CV_16U) && (src.channels() == 4)) || (src.depth() == CV_8U && src.channels() == 1 && (ksize.width > 5 || ksize.height > 5))))
return false;
#endif
if(!normalize)
return false;
if(!ippiCheckAnchor(anchor, ksize))
return false;
try
{
::ipp::IwiImage iwSrc = ippiGetImage(src);
::ipp::IwiImage iwDst = ippiGetImage(dst);
::ipp::IwiSize iwKSize = ippiGetSize(ksize);
::ipp::IwiBorderSize borderSize(iwKSize);
::ipp::IwiBorderType ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));
if(!ippBorder)
return false;
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterBox, iwSrc, iwDst, iwKSize, ::ipp::IwDefault(), ippBorder);
}
catch (::ipp::IwException)
{
return false;
}
return true;
#else
CV_UNUSED(src); CV_UNUSED(dst); CV_UNUSED(ksize); CV_UNUSED(anchor); CV_UNUSED(normalize); CV_UNUSED(borderType);
return false;
#endif
}
}
#endif
void cv::boxFilter( InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor,
bool normalize, int borderType )
{
CV_INSTRUMENT_REGION()
CV_OCL_RUN(_dst.isUMat() &&
(borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT ||
borderType == BORDER_REFLECT || borderType == BORDER_REFLECT_101),
ocl_boxFilter3x3_8UC1(_src, _dst, ddepth, ksize, anchor, borderType, normalize))
CV_OCL_RUN(_dst.isUMat(), ocl_boxFilter(_src, _dst, ddepth, ksize, anchor, borderType, normalize))
CV_OVX_RUN(true,
openvx_boxfilter(_src, _dst, ddepth, ksize, anchor, normalize, borderType))
Mat src = _src.getMat();
int stype = src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);
if( ddepth < 0 )
ddepth = sdepth;
_dst.create( src.size(), CV_MAKETYPE(ddepth, cn) );
Mat dst = _dst.getMat();
if( borderType != BORDER_CONSTANT && normalize && (borderType & BORDER_ISOLATED) != 0 )
{
if( src.rows == 1 )
ksize.height = 1;
if( src.cols == 1 )
ksize.width = 1;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if ( tegra::useTegra() && tegra::box(src, dst, ksize, anchor, normalize, borderType) )
return;
#endif
CV_IPP_RUN_FAST(ipp_boxfilter(src, dst, ksize, anchor, normalize, borderType));
Point ofs;
Size wsz(src.cols, src.rows);
if(!(borderType&BORDER_ISOLATED))
src.locateROI( wsz, ofs );
borderType = (borderType&~BORDER_ISOLATED);
Ptr<FilterEngine> f = createBoxFilter( src.type(), dst.type(),
ksize, anchor, normalize, borderType );
f->apply( src, dst, wsz, ofs );
}
void cv::blur( InputArray src, OutputArray dst,
Size ksize, Point anchor, int borderType )
{
CV_INSTRUMENT_REGION()
boxFilter( src, dst, -1, ksize, anchor, true, borderType );
}
/****************************************************************************************\
Squared Box Filter
\****************************************************************************************/
namespace cv
{
template<typename T, typename ST>
struct SqrRowSum :
public BaseRowFilter
{
SqrRowSum( int _ksize, int _anchor ) :
BaseRowFilter()
{
ksize = _ksize;
anchor = _anchor;
}
virtual 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 )
{
ST val = (ST)S[i];
s += val*val;
}
D[0] = s;
for( i = 0; i < width; i += cn )
{
ST val0 = (ST)S[i], val1 = (ST)S[i + ksz_cn];
s += val1*val1 - val0*val0;
D[i+cn] = s;
}
}
}
};
static Ptr<BaseRowFilter> getSqrRowSumFilter(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 makePtr<SqrRowSum<uchar, int> >(ksize, anchor);
if( sdepth == CV_8U && ddepth == CV_64F )
return makePtr<SqrRowSum<uchar, double> >(ksize, anchor);
if( sdepth == CV_16U && ddepth == CV_64F )
return makePtr<SqrRowSum<ushort, double> >(ksize, anchor);
if( sdepth == CV_16S && ddepth == CV_64F )
return makePtr<SqrRowSum<short, double> >(ksize, anchor);
if( sdepth == CV_32F && ddepth == CV_64F )
return makePtr<SqrRowSum<float, double> >(ksize, anchor);
if( sdepth == CV_64F && ddepth == CV_64F )
return makePtr<SqrRowSum<double, double> >(ksize, anchor);
CV_Error_( CV_StsNotImplemented,
("Unsupported combination of source format (=%d), and buffer format (=%d)",
srcType, sumType));
return Ptr<BaseRowFilter>();
}
}
void cv::sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
Size ksize, Point anchor,
bool normalize, int borderType )
{
CV_INSTRUMENT_REGION()
int srcType = _src.type(), sdepth = CV_MAT_DEPTH(srcType), cn = CV_MAT_CN(srcType);
Size size = _src.size();
if( ddepth < 0 )
ddepth = sdepth < CV_32F ? CV_32F : CV_64F;
if( borderType != BORDER_CONSTANT && normalize )
{
if( size.height == 1 )
ksize.height = 1;
if( size.width == 1 )
ksize.width = 1;
}
CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2,
ocl_boxFilter(_src, _dst, ddepth, ksize, anchor, borderType, normalize, true))
int sumDepth = CV_64F;
if( sdepth == CV_8U )
sumDepth = CV_32S;
int sumType = CV_MAKETYPE( sumDepth, cn ), dstType = CV_MAKETYPE(ddepth, cn);
Mat src = _src.getMat();
_dst.create( size, dstType );
Mat dst = _dst.getMat();
Ptr<BaseRowFilter> rowFilter = getSqrRowSumFilter(srcType, sumType, ksize.width, anchor.x );
Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType,
dstType, ksize.height, anchor.y,
normalize ? 1./(ksize.width*ksize.height) : 1);
Ptr<FilterEngine> f = makePtr<FilterEngine>(Ptr<BaseFilter>(), rowFilter, columnFilter,
srcType, dstType, sumType, borderType );
Point ofs;
Size wsz(src.cols, src.rows);
src.locateROI( wsz, ofs );
f->apply( src, dst, wsz, ofs );
}
/****************************************************************************************\
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 = kernel.ptr<float>();
double* cd = kernel.ptr<double>();
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;
}
namespace cv {
static void createGaussianKernels( Mat & kx, Mat & ky, int type, Size ksize,
double sigma1, double sigma2 )
{
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. );
kx = getGaussianKernel( ksize.width, sigma1, std::max(depth, CV_32F) );
if( ksize.height == ksize.width && std::abs(sigma1 - sigma2) < DBL_EPSILON )
ky = kx;
else
ky = getGaussianKernel( ksize.height, sigma2, std::max(depth, CV_32F) );
}
}
cv::Ptr<cv::FilterEngine> cv::createGaussianFilter( int type, Size ksize,
double sigma1, double sigma2,
int borderType )
{
Mat kx, ky;
createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType );
}
namespace cv
{
#ifdef HAVE_OPENCL
static bool ocl_GaussianBlur_8UC1(InputArray _src, OutputArray _dst, Size ksize, int ddepth,
InputArray _kernelX, InputArray _kernelY, int borderType)
{
const ocl::Device & dev = ocl::Device::getDefault();
int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if ( !(dev.isIntel() && (type == CV_8UC1) &&
(_src.offset() == 0) && (_src.step() % 4 == 0) &&
((ksize.width == 5 && (_src.cols() % 4 == 0)) ||
(ksize.width == 3 && (_src.cols() % 16 == 0) && (_src.rows() % 2 == 0)))) )
return false;
Mat kernelX = _kernelX.getMat().reshape(1, 1);
if (kernelX.cols % 2 != 1)
return false;
Mat kernelY = _kernelY.getMat().reshape(1, 1);
if (kernelY.cols % 2 != 1)
return false;
if (ddepth < 0)
ddepth = sdepth;
Size size = _src.size();
size_t globalsize[2] = { 0, 0 };
size_t localsize[2] = { 0, 0 };
if (ksize.width == 3)
{
globalsize[0] = size.width / 16;
globalsize[1] = size.height / 2;
}
else if (ksize.width == 5)
{
globalsize[0] = size.width / 4;
globalsize[1] = size.height / 1;
}
const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };
char build_opts[1024];
sprintf(build_opts, "-D %s %s%s", borderMap[borderType],
ocl::kernelToStr(kernelX, CV_32F, "KERNEL_MATRIX_X").c_str(),
ocl::kernelToStr(kernelY, CV_32F, "KERNEL_MATRIX_Y").c_str());
ocl::Kernel kernel;
if (ksize.width == 3)
kernel.create("gaussianBlur3x3_8UC1_cols16_rows2", cv::ocl::imgproc::gaussianBlur3x3_oclsrc, build_opts);
else if (ksize.width == 5)
kernel.create("gaussianBlur5x5_8UC1_cols4", cv::ocl::imgproc::gaussianBlur5x5_oclsrc, build_opts);
if (kernel.empty())
return false;
UMat src = _src.getUMat();
_dst.create(size, CV_MAKETYPE(ddepth, cn));
if (!(_dst.offset() == 0 && _dst.step() % 4 == 0))
return false;
UMat dst = _dst.getUMat();
int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
idxArg = kernel.set(idxArg, (int)src.step);
idxArg = kernel.set(idxArg, ocl::KernelArg::PtrWriteOnly(dst));
idxArg = kernel.set(idxArg, (int)dst.step);
idxArg = kernel.set(idxArg, (int)dst.rows);
idxArg = kernel.set(idxArg, (int)dst.cols);
return kernel.run(2, globalsize, (localsize[0] == 0) ? NULL : localsize, false);
}
#endif
#ifdef HAVE_OPENVX
namespace ovx {
template <> inline bool skipSmallImages<VX_KERNEL_GAUSSIAN_3x3>(int w, int h) { return w*h < 320 * 240; }
}
static bool openvx_gaussianBlur(InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2, int borderType)
{
if (sigma2 <= 0)
sigma2 = sigma1;
// automatic detection of kernel size from sigma
if (ksize.width <= 0 && sigma1 > 0)
ksize.width = cvRound(sigma1*6 + 1) | 1;
if (ksize.height <= 0 && sigma2 > 0)
ksize.height = cvRound(sigma2*6 + 1) | 1;
if (_src.type() != CV_8UC1 ||
_src.cols() < 3 || _src.rows() < 3 ||
ksize.width != 3 || ksize.height != 3)
return false;
sigma1 = std::max(sigma1, 0.);
sigma2 = std::max(sigma2, 0.);
if (!(sigma1 == 0.0 || (sigma1 - 0.8) < DBL_EPSILON) || !(sigma2 == 0.0 || (sigma2 - 0.8) < DBL_EPSILON) ||
ovx::skipSmallImages<VX_KERNEL_GAUSSIAN_3x3>(_src.cols(), _src.rows()))
return false;
Mat src = _src.getMat();
Mat dst = _dst.getMat();
if ((borderType & BORDER_ISOLATED) == 0 && src.isSubmatrix())
return false; //Process isolated borders only
vx_enum border;
switch (borderType & ~BORDER_ISOLATED)
{
case BORDER_CONSTANT:
border = VX_BORDER_CONSTANT;
break;
case BORDER_REPLICATE:
border = VX_BORDER_REPLICATE;
break;
default:
return false;
}
try
{
ivx::Context ctx = ovx::getOpenVXContext();
Mat a;
if (dst.data != src.data)
a = src;
else
src.copyTo(a);
ivx::Image
ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),
ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols, dst.rows, 1, (vx_int32)(dst.step)), dst.data);
//ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments
//since OpenVX standart says nothing about thread-safety for now
ivx::border_t prevBorder = ctx.immediateBorder();
ctx.setImmediateBorder(border, (vx_uint8)(0));
ivx::IVX_CHECK_STATUS(vxuGaussian3x3(ctx, ia, ib));
ctx.setImmediateBorder(prevBorder);
}
catch (ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
#endif
#ifdef HAVE_IPP
#if IPP_VERSION_X100 == 201702 // IW 2017u2 has bug which doesn't allow use of partial inMem with tiling
#define IPP_GAUSSIANBLUR_PARALLEL 0
#else
#define IPP_GAUSSIANBLUR_PARALLEL 1
#endif
#ifdef HAVE_IPP_IW
class ipp_gaussianBlurParallel: public ParallelLoopBody
{
public:
ipp_gaussianBlurParallel(::ipp::IwiImage &src, ::ipp::IwiImage &dst, int kernelSize, float sigma, ::ipp::IwiBorderType &border, bool *pOk):
m_src(src), m_dst(dst), m_kernelSize(kernelSize), m_sigma(sigma), m_border(border), m_pOk(pOk) {
*m_pOk = true;
}
~ipp_gaussianBlurParallel()
{
}
virtual void operator() (const Range& range) const
{
CV_INSTRUMENT_REGION_IPP()
if(!*m_pOk)
return;
try
{
::ipp::IwiTile tile = ::ipp::IwiRoi(0, range.start, m_dst.m_size.width, range.end - range.start);
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterGaussian, m_src, m_dst, m_kernelSize, m_sigma, ::ipp::IwDefault(), m_border, tile);
}
catch(::ipp::IwException e)
{
*m_pOk = false;
return;
}
}
private:
::ipp::IwiImage &m_src;
::ipp::IwiImage &m_dst;
int m_kernelSize;
float m_sigma;
::ipp::IwiBorderType &m_border;
volatile bool *m_pOk;
const ipp_gaussianBlurParallel& operator= (const ipp_gaussianBlurParallel&);
};
#endif
static bool ipp_GaussianBlur(InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2, int borderType )
{
#ifdef HAVE_IPP_IW
CV_INSTRUMENT_REGION_IPP()
#if IPP_VERSION_X100 < 201800 && ((defined _MSC_VER && defined _M_IX86) || (defined __GNUC__ && defined __i386__))
CV_UNUSED(_src); CV_UNUSED(_dst); CV_UNUSED(ksize); CV_UNUSED(sigma1); CV_UNUSED(sigma2); CV_UNUSED(borderType);
return false; // bug on ia32
#else
if(sigma1 != sigma2)
return false;
if(sigma1 < FLT_EPSILON)
return false;
if(ksize.width != ksize.height)
return false;
// Acquire data and begin processing
try
{
Mat src = _src.getMat();
Mat dst = _dst.getMat();
::ipp::IwiImage iwSrc = ippiGetImage(src);
::ipp::IwiImage iwDst = ippiGetImage(dst);
::ipp::IwiBorderSize borderSize = ::ipp::iwiSizeToBorderSize(ippiGetSize(ksize));
::ipp::IwiBorderType ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));
if(!ippBorder)
return false;
const int threads = ippiSuggestThreadsNum(iwDst, 2);
if(IPP_GAUSSIANBLUR_PARALLEL && threads > 1) {
bool ok;
ipp_gaussianBlurParallel invoker(iwSrc, iwDst, ksize.width, (float) sigma1, ippBorder, &ok);
if(!ok)
return false;
const Range range(0, (int) iwDst.m_size.height);
parallel_for_(range, invoker, threads*4);
if(!ok)
return false;
} else {
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterGaussian, iwSrc, iwDst, ksize.width, sigma1, ::ipp::IwDefault(), ippBorder);
}
}
catch (::ipp::IwException ex)
{
return false;
}
return true;
#endif
#else
CV_UNUSED(_src); CV_UNUSED(_dst); CV_UNUSED(ksize); CV_UNUSED(sigma1); CV_UNUSED(sigma2); CV_UNUSED(borderType);
return false;
#endif
}
#endif
}
void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
double sigma1, double sigma2,
int borderType )
{
CV_INSTRUMENT_REGION()
int type = _src.type();
Size size = _src.size();
_dst.create( size, type );
if( borderType != BORDER_CONSTANT && (borderType & BORDER_ISOLATED) != 0 )
{
if( size.height == 1 )
ksize.height = 1;
if( size.width == 1 )
ksize.width = 1;
}
if( ksize.width == 1 && ksize.height == 1 )
{
_src.copyTo(_dst);
return;
}
CV_OVX_RUN(true,
openvx_gaussianBlur(_src, _dst, ksize, sigma1, sigma2, borderType))
#ifdef HAVE_TEGRA_OPTIMIZATION
Mat src = _src.getMat();
Mat dst = _dst.getMat();
if(sigma1 == 0 && sigma2 == 0 && tegra::useTegra() && tegra::gaussian(src, dst, ksize, borderType))
return;
#endif
bool useOpenCL = (ocl::useOpenCL() && _dst.isUMat() && _src.dims() <= 2 &&
((ksize.width == 3 && ksize.height == 3) ||
(ksize.width == 5 && ksize.height == 5)) &&
_src.rows() > ksize.height && _src.cols() > ksize.width);
(void)useOpenCL;
CV_IPP_RUN(!useOpenCL, ipp_GaussianBlur( _src, _dst, ksize, sigma1, sigma2, borderType));
Mat kx, ky;
createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
CV_OCL_RUN(useOpenCL, ocl_GaussianBlur_8UC1(_src, _dst, ksize, CV_MAT_DEPTH(type), kx, ky, borderType));
sepFilter2D(_src, _dst, CV_MAT_DEPTH(type), kx, ky, Point(-1,-1), 0, borderType );
}
/****************************************************************************************\
Median Filter
\****************************************************************************************/
namespace cv
{
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_SIMD128
static inline void histogram_add_simd( const HT x[16], HT y[16] )
{
v_store(y, v_load(x) + v_load(y));
v_store(y + 8, v_load(x + 8) + v_load(y + 8));
}
static inline void histogram_sub_simd( const HT x[16], HT y[16] )
{
v_store(y, v_load(y) - v_load(x));
v_store(y + 8, v_load(y + 8) - v_load(x + 8));
}
#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;
CV_Assert(cn > 0 && cn <= 4);
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 );
std::vector<HT> _h_coarse(1 * 16 * (STRIPE_SIZE + 2*r) * cn + 16);
std::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 CV_SIMD128
volatile bool useSIMD = hasSIMD128();
#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.ptr() + x*cn;
uchar* dst = _dst.ptr() + (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], += (cv::HT)(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 CV_SIMD128
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;
}
}
CV_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] = cv::HT(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;
}
}
CV_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;
}
}
CV_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] = cv::HT(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;
}
}
CV_Assert( b < 16 );
}
}
}
}
}
#undef HOP
#undef COP
}
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.ptr();
uchar* dst = _dst.ptr();
int src_step = (int)_src.step, dst_step = (int)_dst.step;
int cn = _src.channels();
const uchar* src_max = src + size.height*src_step;
CV_Assert(cn > 0 && cn <= 4);
#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_SIMD128
struct MinMaxVec8u
{
typedef uchar value_type;
typedef v_uint8x16 arg_type;
enum { SIZE = 16 };
arg_type load(const uchar* ptr) { return v_load(ptr); }
void store(uchar* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
};
struct MinMaxVec16u
{
typedef ushort value_type;
typedef v_uint16x8 arg_type;
enum { SIZE = 8 };
arg_type load(const ushort* ptr) { return v_load(ptr); }
void store(ushort* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
};
struct MinMaxVec16s
{
typedef short value_type;
typedef v_int16x8 arg_type;
enum { SIZE = 8 };
arg_type load(const short* ptr) { return v_load(ptr); }
void store(short* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(b, t);
}
};
struct MinMaxVec32f
{
typedef float value_type;
typedef v_float32x4 arg_type;
enum { SIZE = 4 };
arg_type load(const float* ptr) { return v_load(ptr); }
void store(float* ptr, const arg_type &val) { v_store(ptr, val); }
void operator()(arg_type& a, arg_type& b) const
{
arg_type t = a;
a = v_min(a, b);
b = v_max(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 = _src.ptr<T>();
T* dst = _dst.ptr<T>();
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 = hasSIMD128();
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;
}
}
}
}
#ifdef HAVE_OPENCL
static bool ocl_medianFilter(InputArray _src, OutputArray _dst, int m)
{
size_t localsize[2] = { 16, 16 };
size_t globalsize[2];
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
if ( !((depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F) && cn <= 4 && (m == 3 || m == 5)) )
return false;
Size imgSize = _src.size();
bool useOptimized = (1 == cn) &&
(size_t)imgSize.width >= localsize[0] * 8 &&
(size_t)imgSize.height >= localsize[1] * 8 &&
imgSize.width % 4 == 0 &&
imgSize.height % 4 == 0 &&
(ocl::Device::getDefault().isIntel());
cv::String kname = format( useOptimized ? "medianFilter%d_u" : "medianFilter%d", m) ;
cv::String kdefs = useOptimized ?
format("-D T=%s -D T1=%s -D T4=%s%d -D cn=%d -D USE_4OPT", ocl::typeToStr(type),
ocl::typeToStr(depth), ocl::typeToStr(depth), cn*4, cn)
:
format("-D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn) ;
ocl::Kernel k(kname.c_str(), ocl::imgproc::medianFilter_oclsrc, kdefs.c_str() );
if (k.empty())
return false;
UMat src = _src.getUMat();
_dst.create(src.size(), type);
UMat dst = _dst.getUMat();
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst));
if( useOptimized )
{
globalsize[0] = DIVUP(src.cols / 4, localsize[0]) * localsize[0];
globalsize[1] = DIVUP(src.rows / 4, localsize[1]) * localsize[1];
}
else
{
globalsize[0] = (src.cols + localsize[0] + 2) / localsize[0] * localsize[0];
globalsize[1] = (src.rows + localsize[1] - 1) / localsize[1] * localsize[1];
}
return k.run(2, globalsize, localsize, false);
}
#endif
}
#ifdef HAVE_OPENVX
namespace cv
{
namespace ovx {
template <> inline bool skipSmallImages<VX_KERNEL_MEDIAN_3x3>(int w, int h) { return w*h < 1280 * 720; }
}
static bool openvx_medianFilter(InputArray _src, OutputArray _dst, int ksize)
{
if (_src.type() != CV_8UC1 || _dst.type() != CV_8U
#ifndef VX_VERSION_1_1
|| ksize != 3
#endif
)
return false;
Mat src = _src.getMat();
Mat dst = _dst.getMat();
if (
#ifdef VX_VERSION_1_1
ksize != 3 ? ovx::skipSmallImages<VX_KERNEL_NON_LINEAR_FILTER>(src.cols, src.rows) :
#endif
ovx::skipSmallImages<VX_KERNEL_MEDIAN_3x3>(src.cols, src.rows)
)
return false;
try
{
ivx::Context ctx = ovx::getOpenVXContext();
#ifdef VX_VERSION_1_1
if ((vx_size)ksize > ctx.nonlinearMaxDimension())
return false;
#endif
Mat a;
if (dst.data != src.data)
a = src;
else
src.copyTo(a);
ivx::Image
ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(a.cols, a.rows, 1, (vx_int32)(a.step)), a.data),
ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols, dst.rows, 1, (vx_int32)(dst.step)), dst.data);
//ATTENTION: VX_CONTEXT_IMMEDIATE_BORDER attribute change could lead to strange issues in multi-threaded environments
//since OpenVX standart says nothing about thread-safety for now
ivx::border_t prevBorder = ctx.immediateBorder();
ctx.setImmediateBorder(VX_BORDER_REPLICATE);
#ifdef VX_VERSION_1_1
if (ksize == 3)
#endif
{
ivx::IVX_CHECK_STATUS(vxuMedian3x3(ctx, ia, ib));
}
#ifdef VX_VERSION_1_1
else
{
ivx::Matrix mtx;
if(ksize == 5)
mtx = ivx::Matrix::createFromPattern(ctx, VX_PATTERN_BOX, ksize, ksize);
else
{
vx_size supportedSize;
ivx::IVX_CHECK_STATUS(vxQueryContext(ctx, VX_CONTEXT_NONLINEAR_MAX_DIMENSION, &supportedSize, sizeof(supportedSize)));
if ((vx_size)ksize > supportedSize)
{
ctx.setImmediateBorder(prevBorder);
return false;
}
Mat mask(ksize, ksize, CV_8UC1, Scalar(255));
mtx = ivx::Matrix::create(ctx, VX_TYPE_UINT8, ksize, ksize);
mtx.copyFrom(mask);
}
ivx::IVX_CHECK_STATUS(vxuNonLinearFilter(ctx, VX_NONLINEAR_FILTER_MEDIAN, ia, mtx, ib));
}
#endif
ctx.setImmediateBorder(prevBorder);
}
catch (ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
}
#endif
#ifdef HAVE_IPP
namespace cv
{
static bool ipp_medianFilter(Mat &src0, Mat &dst, int ksize)
{
CV_INSTRUMENT_REGION_IPP()
#if IPP_VERSION_X100 < 201801
// Degradations for big kernel
if(ksize > 7)
return false;
#endif
{
int bufSize;
IppiSize dstRoiSize = ippiSize(dst.cols, dst.rows), maskSize = ippiSize(ksize, ksize);
IppDataType ippType = ippiGetDataType(src0.type());
int channels = src0.channels();
IppAutoBuffer<Ipp8u> buffer;
if(src0.isSubmatrix())
return false;
Mat src;
if(dst.data != src0.data)
src = src0;
else
src0.copyTo(src);
if(ippiFilterMedianBorderGetBufferSize(dstRoiSize, maskSize, ippType, channels, &bufSize) < 0)
return false;
buffer.allocate(bufSize);
switch(ippType)
{
case ipp8u:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C1R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C3R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_8u_C4R, src.ptr<Ipp8u>(), (int)src.step, dst.ptr<Ipp8u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp16u:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C1R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C3R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16u_C4R, src.ptr<Ipp16u>(), (int)src.step, dst.ptr<Ipp16u>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp16s:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C1R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 3)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C3R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else if(channels == 4)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_16s_C4R, src.ptr<Ipp16s>(), (int)src.step, dst.ptr<Ipp16s>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
case ipp32f:
if(channels == 1)
return CV_INSTRUMENT_FUN_IPP(ippiFilterMedianBorder_32f_C1R, src.ptr<Ipp32f>(), (int)src.step, dst.ptr<Ipp32f>(), (int)dst.step, dstRoiSize, maskSize, ippBorderRepl, 0, buffer) >= 0;
else
return false;
default:
return false;
}
}
}
}
#endif
void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
{
CV_INSTRUMENT_REGION()
CV_Assert( (ksize % 2 == 1) && (_src0.dims() <= 2 ));
if( ksize <= 1 || _src0.empty() )
{
_src0.copyTo(_dst);
return;
}
CV_OCL_RUN(_dst.isUMat(),
ocl_medianFilter(_src0,_dst, ksize))
Mat src0 = _src0.getMat();
_dst.create( src0.size(), src0.type() );
Mat dst = _dst.getMat();
CALL_HAL(medianBlur, cv_hal_medianBlur, src0.data, src0.step, dst.data, dst.step, src0.cols, src0.rows, src0.depth(),
src0.channels(), ksize);
CV_OVX_RUN(true,
openvx_medianFilter(_src0, _dst, ksize))
CV_IPP_RUN_FAST(ipp_medianFilter(src0, dst, ksize));
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra() && tegra::medianBlur(src0, dst, ksize))
return;
#endif
bool useSortNet = ksize == 3 || (ksize == 5
#if !(CV_SIMD128)
&& ( src0.depth() > CV_8U || src0.channels() == 2 || src0.channels() > 4 )
#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|BORDER_ISOLATED);
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)*
(CV_SIMD128 && hasSIMD128() ? 1 : 3))
medianBlur_8u_Om( src, dst, ksize );
else
medianBlur_8u_O1( src, dst, ksize );
}
}
/****************************************************************************************\
Bilateral Filtering
\****************************************************************************************/
namespace cv
{
class BilateralFilter_8u_Invoker :
public ParallelLoopBody
{
public:
BilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, int _radius, int _maxk,
int* _space_ofs, float *_space_weight, float *_color_weight) :
temp(&_temp), dest(&_dest), radius(_radius),
maxk(_maxk), space_ofs(_space_ofs), space_weight(_space_weight), color_weight(_color_weight)
{
}
virtual void operator() (const Range& range) const
{
int i, j, cn = dest->channels(), k;
Size size = dest->size();
#if CV_SIMD128
int CV_DECL_ALIGNED(16) buf[4];
bool haveSIMD128 = hasSIMD128();
#endif
for( i = range.start; i < range.end; i++ )
{
const uchar* sptr = temp->ptr(i+radius) + radius*cn;
uchar* dptr = dest->ptr(i);
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
int val0 = sptr[j];
k = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 _val0 = v_setall_f32(static_cast<float>(val0));
v_float32x4 vsumw = v_setzero_f32();
v_float32x4 vsumc = v_setzero_f32();
for( ; k <= maxk - 4; k += 4 )
{
v_float32x4 _valF = v_float32x4(sptr[j + space_ofs[k]],
sptr[j + space_ofs[k + 1]],
sptr[j + space_ofs[k + 2]],
sptr[j + space_ofs[k + 3]]);
v_float32x4 _val = v_abs(_valF - _val0);
v_store(buf, v_round(_val));
v_float32x4 _cw = v_float32x4(color_weight[buf[0]],
color_weight[buf[1]],
color_weight[buf[2]],
color_weight[buf[3]]);
v_float32x4 _sw = v_load(space_weight+k);
v_float32x4 _w = _cw * _sw;
_cw = _w * _valF;
vsumw += _w;
vsumc += _cw;
}
float *bufFloat = (float*)buf;
v_float32x4 sum4 = v_reduce_sum4(vsumw, vsumc, vsumw, vsumc);
v_store(bufFloat, sum4);
sum += bufFloat[1];
wsum += bufFloat[0];
}
#endif
for( ; 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::saturate_cast
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];
k = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 vsumw = v_setzero_f32();
v_float32x4 vsumb = v_setzero_f32();
v_float32x4 vsumg = v_setzero_f32();
v_float32x4 vsumr = v_setzero_f32();
const v_float32x4 _b0 = v_setall_f32(static_cast<float>(b0));
const v_float32x4 _g0 = v_setall_f32(static_cast<float>(g0));
const v_float32x4 _r0 = v_setall_f32(static_cast<float>(r0));
for( ; k <= maxk - 4; k += 4 )
{
const uchar* const sptr_k0 = sptr + j + space_ofs[k];
const uchar* const sptr_k1 = sptr + j + space_ofs[k+1];
const uchar* const sptr_k2 = sptr + j + space_ofs[k+2];
const uchar* const sptr_k3 = sptr + j + space_ofs[k+3];
v_float32x4 __b = v_cvt_f32(v_reinterpret_as_s32(v_load_expand_q(sptr_k0)));
v_float32x4 __g = v_cvt_f32(v_reinterpret_as_s32(v_load_expand_q(sptr_k1)));
v_float32x4 __r = v_cvt_f32(v_reinterpret_as_s32(v_load_expand_q(sptr_k2)));
v_float32x4 __z = v_cvt_f32(v_reinterpret_as_s32(v_load_expand_q(sptr_k3)));
v_float32x4 _b, _g, _r, _z;
v_transpose4x4(__b, __g, __r, __z, _b, _g, _r, _z);
v_float32x4 bt = v_abs(_b -_b0);
v_float32x4 gt = v_abs(_g -_g0);
v_float32x4 rt = v_abs(_r -_r0);
bt = rt + bt + gt;
v_store(buf, v_round(bt));
v_float32x4 _w = v_float32x4(color_weight[buf[0]],color_weight[buf[1]],
color_weight[buf[2]],color_weight[buf[3]]);
v_float32x4 _sw = v_load(space_weight+k);
_w *= _sw;
_b *= _w;
_g *= _w;
_r *= _w;
vsumw += _w;
vsumb += _b;
vsumg += _g;
vsumr += _r;
}
float *bufFloat = (float*)buf;
v_float32x4 sum4 = v_reduce_sum4(vsumw, vsumb, vsumg, vsumr);
v_store(bufFloat, sum4);
wsum += bufFloat[0];
sum_b += bufFloat[1];
sum_g += bufFloat[2];
sum_r += bufFloat[3];
}
#endif
for( ; 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;
}
}
}
}
private:
const Mat *temp;
Mat *dest;
int radius, maxk, *space_ofs;
float *space_weight, *color_weight;
};
#ifdef HAVE_OPENCL
static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d,
double sigma_color, double sigma_space,
int borderType)
{
#ifdef __ANDROID__
if (ocl::Device::getDefault().isNVidia())
return false;
#endif
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
int i, j, maxk, radius;
if (depth != CV_8U || cn > 4)
return false;
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;
UMat src = _src.getUMat(), dst = _dst.getUMat(), temp;
if (src.u == dst.u)
return false;
copyMakeBorder(src, temp, radius, radius, radius, radius, borderType);
std::vector<float> _space_weight(d * d);
std::vector<int> _space_ofs(d * d);
float * const space_weight = &_space_weight[0];
int * const space_ofs = &_space_ofs[0];
// 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);
}
char cvt[3][40];
String cnstr = cn > 1 ? format("%d", cn) : "";
String kernelName("bilateral");
size_t sizeDiv = 1;
if ((ocl::Device::getDefault().isIntel()) &&
(ocl::Device::getDefault().type() == ocl::Device::TYPE_GPU))
{
//Intel GPU
if (dst.cols % 4 == 0 && cn == 1) // For single channel x4 sized images.
{
kernelName = "bilateral_float4";
sizeDiv = 4;
}
}
ocl::Kernel k(kernelName.c_str(), ocl::imgproc::bilateral_oclsrc,
format("-D radius=%d -D maxk=%d -D cn=%d -D int_t=%s -D uint_t=uint%s -D convert_int_t=%s"
" -D uchar_t=%s -D float_t=%s -D convert_float_t=%s -D convert_uchar_t=%s -D gauss_color_coeff=(float)%f",
radius, maxk, cn, ocl::typeToStr(CV_32SC(cn)), cnstr.c_str(),
ocl::convertTypeStr(CV_8U, CV_32S, cn, cvt[0]),
ocl::typeToStr(type), ocl::typeToStr(CV_32FC(cn)),
ocl::convertTypeStr(CV_32S, CV_32F, cn, cvt[1]),
ocl::convertTypeStr(CV_32F, CV_8U, cn, cvt[2]), gauss_color_coeff));
if (k.empty())
return false;
Mat mspace_weight(1, d * d, CV_32FC1, space_weight);
Mat mspace_ofs(1, d * d, CV_32SC1, space_ofs);
UMat ucolor_weight, uspace_weight, uspace_ofs;
mspace_weight.copyTo(uspace_weight);
mspace_ofs.copyTo(uspace_ofs);
k.args(ocl::KernelArg::ReadOnlyNoSize(temp), ocl::KernelArg::WriteOnly(dst),
ocl::KernelArg::PtrReadOnly(uspace_weight),
ocl::KernelArg::PtrReadOnly(uspace_ofs));
size_t globalsize[2] = { (size_t)dst.cols / sizeDiv, (size_t)dst.rows };
return k.run(2, globalsize, NULL, false);
}
#endif
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, maxk, radius;
Size size = src.size();
CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && 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 );
std::vector<float> _color_weight(cn*256);
std::vector<float> _space_weight(d*d);
std::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++ )
{
j = -radius;
for( ; 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);
}
}
BilateralFilter_8u_Invoker body(dst, temp, radius, maxk, space_ofs, space_weight, color_weight);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
}
class BilateralFilter_32f_Invoker :
public ParallelLoopBody
{
public:
BilateralFilter_32f_Invoker(int _cn, int _radius, int _maxk, int *_space_ofs,
const Mat& _temp, Mat& _dest, float _scale_index, float *_space_weight, float *_expLUT) :
cn(_cn), radius(_radius), maxk(_maxk), space_ofs(_space_ofs),
temp(&_temp), dest(&_dest), scale_index(_scale_index), space_weight(_space_weight), expLUT(_expLUT)
{
}
virtual void operator() (const Range& range) const
{
int i, j, k;
Size size = dest->size();
#if CV_SIMD128
int CV_DECL_ALIGNED(16) idxBuf[4];
bool haveSIMD128 = hasSIMD128();
#endif
for( i = range.start; i < range.end; i++ )
{
const float* sptr = temp->ptr<float>(i+radius) + radius*cn;
float* dptr = dest->ptr<float>(i);
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
float val0 = sptr[j];
k = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 vecwsum = v_setzero_f32();
v_float32x4 vecvsum = v_setzero_f32();
const v_float32x4 _val0 = v_setall_f32(sptr[j]);
const v_float32x4 _scale_index = v_setall_f32(scale_index);
for (; k <= maxk - 4; k += 4)
{
v_float32x4 _sw = v_load(space_weight + k);
v_float32x4 _val = v_float32x4(sptr[j + space_ofs[k]],
sptr[j + space_ofs[k + 1]],
sptr[j + space_ofs[k + 2]],
sptr[j + space_ofs[k + 3]]);
v_float32x4 _alpha = v_abs(_val - _val0) * _scale_index;
v_int32x4 _idx = v_round(_alpha);
v_store(idxBuf, _idx);
_alpha -= v_cvt_f32(_idx);
v_float32x4 _explut = v_float32x4(expLUT[idxBuf[0]],
expLUT[idxBuf[1]],
expLUT[idxBuf[2]],
expLUT[idxBuf[3]]);
v_float32x4 _explut1 = v_float32x4(expLUT[idxBuf[0] + 1],
expLUT[idxBuf[1] + 1],
expLUT[idxBuf[2] + 1],
expLUT[idxBuf[3] + 1]);
v_float32x4 _w = _sw * (_explut + (_alpha * (_explut1 - _explut)));
_val *= _w;
vecwsum += _w;
vecvsum += _val;
}
float *bufFloat = (float*)idxBuf;
v_float32x4 sum4 = v_reduce_sum4(vecwsum, vecvsum, vecwsum, vecvsum);
v_store(bufFloat, sum4);
sum += bufFloat[1];
wsum += bufFloat[0];
}
#endif
for( ; 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
{
CV_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];
k = 0;
#if CV_SIMD128
if( haveSIMD128 )
{
v_float32x4 sumw = v_setzero_f32();
v_float32x4 sumb = v_setzero_f32();
v_float32x4 sumg = v_setzero_f32();
v_float32x4 sumr = v_setzero_f32();
const v_float32x4 _b0 = v_setall_f32(b0);
const v_float32x4 _g0 = v_setall_f32(g0);
const v_float32x4 _r0 = v_setall_f32(r0);
const v_float32x4 _scale_index = v_setall_f32(scale_index);
for( ; k <= maxk-4; k += 4 )
{
v_float32x4 _sw = v_load(space_weight + k);
const float* const sptr_k0 = sptr + j + space_ofs[k];
const float* const sptr_k1 = sptr + j + space_ofs[k+1];
const float* const sptr_k2 = sptr + j + space_ofs[k+2];
const float* const sptr_k3 = sptr + j + space_ofs[k+3];
v_float32x4 _v0 = v_load(sptr_k0);
v_float32x4 _v1 = v_load(sptr_k1);
v_float32x4 _v2 = v_load(sptr_k2);
v_float32x4 _v3 = v_load(sptr_k3);
v_float32x4 _b, _g, _r, _dummy;
v_transpose4x4(_v0, _v1, _v2, _v3, _b, _g, _r, _dummy);
v_float32x4 _bt = v_abs(_b - _b0);
v_float32x4 _gt = v_abs(_g - _g0);
v_float32x4 _rt = v_abs(_r - _r0);
v_float32x4 _alpha = _scale_index * (_bt + _gt + _rt);
v_int32x4 _idx = v_round(_alpha);
v_store((int*)idxBuf, _idx);
_alpha -= v_cvt_f32(_idx);
v_float32x4 _explut = v_float32x4(expLUT[idxBuf[0]],
expLUT[idxBuf[1]],
expLUT[idxBuf[2]],
expLUT[idxBuf[3]]);
v_float32x4 _explut1 = v_float32x4(expLUT[idxBuf[0] + 1],
expLUT[idxBuf[1] + 1],
expLUT[idxBuf[2] + 1],
expLUT[idxBuf[3] + 1]);
v_float32x4 _w = _sw * (_explut + (_alpha * (_explut1 - _explut)));
_b *= _w;
_g *= _w;
_r *= _w;
sumw += _w;
sumb += _b;
sumg += _g;
sumr += _r;
}
v_float32x4 sum4 = v_reduce_sum4(sumw, sumb, sumg, sumr);
float *bufFloat = (float*)idxBuf;
v_store(bufFloat, sum4);
wsum += bufFloat[0];
sum_b += bufFloat[1];
sum_g += bufFloat[2];
sum_r += bufFloat[3];
}
#endif
for(; 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;
}
}
}
}
private:
int cn, radius, maxk, *space_ofs;
const Mat* temp;
Mat *dest;
float scale_index, *space_weight, *expLUT;
};
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, 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.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 );
if(std::abs(minValSrc - maxValSrc) < FLT_EPSILON)
{
src.copyTo(dst);
return;
}
// temporary copy of the image with borders for easy processing
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
const double insteadNaNValue = -5. * sigma_color;
patchNaNs( temp, insteadNaNValue ); // this replacement of NaNs makes the assumption that depth values are nonnegative
// TODO: make insteadNaNValue avalible in the outside function interface to control the cases breaking the assumption
// allocate lookup tables
std::vector<float> _space_weight(d*d);
std::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;
std::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);
}
// parallel_for usage
BilateralFilter_32f_Invoker body(cn, radius, maxk, space_ofs, temp, dst, scale_index, space_weight, expLUT);
parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
}
#ifdef HAVE_IPP
#define IPP_BILATERAL_PARALLEL 1
#ifdef HAVE_IPP_IW
class ipp_bilateralFilterParallel: public ParallelLoopBody
{
public:
ipp_bilateralFilterParallel(::ipp::IwiImage &_src, ::ipp::IwiImage &_dst, int _radius, Ipp32f _valSquareSigma, Ipp32f _posSquareSigma, ::ipp::IwiBorderType _borderType, bool *_ok):
src(_src), dst(_dst)
{
pOk = _ok;
radius = _radius;
valSquareSigma = _valSquareSigma;
posSquareSigma = _posSquareSigma;
borderType = _borderType;
*pOk = true;
}
~ipp_bilateralFilterParallel() {}
virtual void operator() (const Range& range) const
{
if(*pOk == false)
return;
try
{
::ipp::IwiTile tile = ::ipp::IwiRoi(0, range.start, dst.m_size.width, range.end - range.start);
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterBilateral, src, dst, radius, valSquareSigma, posSquareSigma, ::ipp::IwDefault(), borderType, tile);
}
catch(::ipp::IwException)
{
*pOk = false;
return;
}
}
private:
::ipp::IwiImage &src;
::ipp::IwiImage &dst;
int radius;
Ipp32f valSquareSigma;
Ipp32f posSquareSigma;
::ipp::IwiBorderType borderType;
bool *pOk;
const ipp_bilateralFilterParallel& operator= (const ipp_bilateralFilterParallel&);
};
#endif
static bool ipp_bilateralFilter(Mat &src, Mat &dst, int d, double sigmaColor, double sigmaSpace, int borderType)
{
#ifdef HAVE_IPP_IW
CV_INSTRUMENT_REGION_IPP()
int radius = IPP_MAX(((d <= 0)?cvRound(sigmaSpace*1.5):d/2), 1);
Ipp32f valSquareSigma = (Ipp32f)((sigmaColor <= 0)?1:sigmaColor*sigmaColor);
Ipp32f posSquareSigma = (Ipp32f)((sigmaSpace <= 0)?1:sigmaSpace*sigmaSpace);
// Acquire data and begin processing
try
{
::ipp::IwiImage iwSrc = ippiGetImage(src);
::ipp::IwiImage iwDst = ippiGetImage(dst);
::ipp::IwiBorderSize borderSize(radius);
::ipp::IwiBorderType ippBorder(ippiGetBorder(iwSrc, borderType, borderSize));
if(!ippBorder)
return false;
const int threads = ippiSuggestThreadsNum(iwDst, 2);
if(IPP_BILATERAL_PARALLEL && threads > 1) {
bool ok = true;
Range range(0, (int)iwDst.m_size.height);
ipp_bilateralFilterParallel invoker(iwSrc, iwDst, radius, valSquareSigma, posSquareSigma, ippBorder, &ok);
if(!ok)
return false;
parallel_for_(range, invoker, threads*4);
if(!ok)
return false;
} else {
CV_INSTRUMENT_FUN_IPP(::ipp::iwiFilterBilateral, iwSrc, iwDst, radius, valSquareSigma, posSquareSigma, ::ipp::IwDefault(), ippBorder);
}
}
catch (::ipp::IwException)
{
return false;
}
return true;
#else
CV_UNUSED(src); CV_UNUSED(dst); CV_UNUSED(d); CV_UNUSED(sigmaColor); CV_UNUSED(sigmaSpace); CV_UNUSED(borderType);
return false;
#endif
}
#endif
}
void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
double sigmaColor, double sigmaSpace,
int borderType )
{
CV_INSTRUMENT_REGION()
_dst.create( _src.size(), _src.type() );
CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
ocl_bilateralFilter_8u(_src, _dst, d, sigmaColor, sigmaSpace, borderType))
Mat src = _src.getMat(), dst = _dst.getMat();
CV_IPP_RUN_FAST(ipp_bilateralFilter(src, dst, d, sigmaColor, sigmaSpace, borderType));
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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