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#include "_cv.h"
/*
* This file includes the code, contributed by Simon Perreault
* (the function icvMedianBlur_8u_CnR_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
*/
// uncomment the line below to force SSE2 mode
//#define CV_SSE2 1
/****************************************************************************************\
Box Filter
\****************************************************************************************/
static void icvSumRow_8u32s( const uchar* src0, int* dst, void* params );
static void icvSumRow_32f64f( const float* src0, double* dst, void* params );
static void icvSumCol_32s8u( const int** src, uchar* dst, int dst_step,
int count, void* params );
static void icvSumCol_32s16s( const int** src, short* dst, int dst_step,
int count, void* params );
static void icvSumCol_32s32s( const int** src, int* dst, int dst_step,
int count, void* params );
static void icvSumCol_64f32f( const double** src, float* dst, int dst_step,
int count, void* params );
CvBoxFilter::CvBoxFilter()
{
min_depth = CV_32S;
sum = 0;
sum_count = 0;
normalized = false;
}
CvBoxFilter::CvBoxFilter( int _max_width, int _src_type, int _dst_type,
bool _normalized, CvSize _ksize,
CvPoint _anchor, int _border_mode,
CvScalar _border_value )
{
min_depth = CV_32S;
sum = 0;
sum_count = 0;
normalized = false;
init( _max_width, _src_type, _dst_type, _normalized,
_ksize, _anchor, _border_mode, _border_value );
}
CvBoxFilter::~CvBoxFilter()
{
clear();
}
void CvBoxFilter::init( int _max_width, int _src_type, int _dst_type,
bool _normalized, CvSize _ksize,
CvPoint _anchor, int _border_mode,
CvScalar _border_value )
{
CV_FUNCNAME( "CvBoxFilter::init" );
__BEGIN__;
sum = 0;
normalized = _normalized;
if( (normalized && CV_MAT_TYPE(_src_type) != CV_MAT_TYPE(_dst_type)) ||
(!normalized && CV_MAT_CN(_src_type) != CV_MAT_CN(_dst_type)))
CV_ERROR( CV_StsUnmatchedFormats,
"In case of normalized box filter input and output must have the same type.\n"
"In case of unnormalized box filter the number of input and output channels must be the same" );
min_depth = CV_MAT_DEPTH(_src_type) == CV_8U ? CV_32S : CV_64F;
CvBaseImageFilter::init( _max_width, _src_type, _dst_type, 1, _ksize,
_anchor, _border_mode, _border_value );
scale = normalized ? 1./(ksize.width*ksize.height) : 1;
if( CV_MAT_DEPTH(src_type) == CV_8U )
x_func = (CvRowFilterFunc)icvSumRow_8u32s;
else if( CV_MAT_DEPTH(src_type) == CV_32F )
x_func = (CvRowFilterFunc)icvSumRow_32f64f;
else
CV_ERROR( CV_StsUnsupportedFormat, "Unknown/unsupported input image format" );
if( CV_MAT_DEPTH(dst_type) == CV_8U )
{
if( !normalized )
CV_ERROR( CV_StsBadArg, "Only normalized box filter can be used for 8u->8u transformation" );
y_func = (CvColumnFilterFunc)icvSumCol_32s8u;
}
else if( CV_MAT_DEPTH(dst_type) == CV_16S )
{
if( normalized || CV_MAT_DEPTH(src_type) != CV_8U )
CV_ERROR( CV_StsBadArg, "Only 8u->16s unnormalized box filter is supported in case of 16s output" );
y_func = (CvColumnFilterFunc)icvSumCol_32s16s;
}
else if( CV_MAT_DEPTH(dst_type) == CV_32S )
{
if( normalized || CV_MAT_DEPTH(src_type) != CV_8U )
CV_ERROR( CV_StsBadArg, "Only 8u->32s unnormalized box filter is supported in case of 32s output");
y_func = (CvColumnFilterFunc)icvSumCol_32s32s;
}
else if( CV_MAT_DEPTH(dst_type) == CV_32F )
{
if( CV_MAT_DEPTH(src_type) != CV_32F )
CV_ERROR( CV_StsBadArg, "Only 32f->32f box filter (normalized or not) is supported in case of 32f output" );
y_func = (CvColumnFilterFunc)icvSumCol_64f32f;
}
else{
CV_ERROR( CV_StsBadArg, "Unknown/unsupported destination image format" );
}
__END__;
}
void CvBoxFilter::start_process( CvSlice x_range, int width )
{
CvBaseImageFilter::start_process( x_range, width );
int i, psz = CV_ELEM_SIZE(work_type);
uchar* s;
buf_end -= buf_step;
buf_max_count--;
assert( buf_max_count >= max_ky*2 + 1 );
s = sum = buf_end + cvAlign((width + ksize.width - 1)*CV_ELEM_SIZE(src_type), ALIGN);
sum_count = 0;
width *= psz;
for( i = 0; i < width; i++ )
s[i] = (uchar)0;
}
static void
icvSumRow_8u32s( const uchar* src, int* dst, void* params )
{
const CvBoxFilter* state = (const CvBoxFilter*)params;
int ksize = state->get_kernel_size().width;
int width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
int i, k;
width = (width - 1)*cn; ksize *= cn;
for( k = 0; k < cn; k++, src++, dst++ )
{
int s = 0;
for( i = 0; i < ksize; i += cn )
s += src[i];
dst[0] = s;
for( i = 0; i < width; i += cn )
{
s += src[i+ksize] - src[i];
dst[i+cn] = s;
}
}
}
static void
icvSumRow_32f64f( const float* src, double* dst, void* params )
{
const CvBoxFilter* state = (const CvBoxFilter*)params;
int ksize = state->get_kernel_size().width;
int width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
int i, k;
width = (width - 1)*cn; ksize *= cn;
for( k = 0; k < cn; k++, src++, dst++ )
{
double s = 0;
for( i = 0; i < ksize; i += cn )
s += src[i];
dst[0] = s;
for( i = 0; i < width; i += cn )
{
s += (double)src[i+ksize] - src[i];
dst[i+cn] = s;
}
}
}
static void
icvSumCol_32s8u( const int** src, uchar* dst,
int dst_step, int count, void* params )
{
#define BLUR_SHIFT 24
CvBoxFilter* state = (CvBoxFilter*)params;
int ksize = state->get_kernel_size().height;
int i, width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
double scale = state->get_scale();
int iscale = cvFloor(scale*(1 << BLUR_SHIFT));
int* sum = (int*)state->get_sum_buf();
int* _sum_count = state->get_sum_count_ptr();
int sum_count = *_sum_count;
width *= cn;
src += sum_count;
count += ksize - 1 - sum_count;
for( ; count--; src++ )
{
const int* sp = src[0];
if( sum_count+1 < ksize )
{
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
sum[i] += sp[i];
sum_count++;
}
else
{
const int* sm = src[-ksize+1];
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
int t0 = CV_DESCALE(s0*iscale, BLUR_SHIFT), t1 = CV_DESCALE(s1*iscale, BLUR_SHIFT);
s0 -= sm[i]; s1 -= sm[i+1];
sum[i] = s0; sum[i+1] = s1;
dst[i] = (uchar)t0; dst[i+1] = (uchar)t1;
}
for( ; i < width; i++ )
{
int s0 = sum[i] + sp[i], t0 = CV_DESCALE(s0*iscale, BLUR_SHIFT);
sum[i] = s0 - sm[i]; dst[i] = (uchar)t0;
}
dst += dst_step;
}
}
*_sum_count = sum_count;
#undef BLUR_SHIFT
}
static void
icvSumCol_32s16s( const int** src, short* dst,
int dst_step, int count, void* params )
{
CvBoxFilter* state = (CvBoxFilter*)params;
int ksize = state->get_kernel_size().height;
int ktotal = ksize*state->get_kernel_size().width;
int i, width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
int* sum = (int*)state->get_sum_buf();
int* _sum_count = state->get_sum_count_ptr();
int sum_count = *_sum_count;
dst_step /= sizeof(dst[0]);
width *= cn;
src += sum_count;
count += ksize - 1 - sum_count;
for( ; count--; src++ )
{
const int* sp = src[0];
if( sum_count+1 < ksize )
{
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
sum[i] += sp[i];
sum_count++;
}
else if( ktotal < 128 )
{
const int* sm = src[-ksize+1];
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
dst[i] = (short)s0; dst[i+1] = (short)s1;
s0 -= sm[i]; s1 -= sm[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
{
int s0 = sum[i] + sp[i];
dst[i] = (short)s0;
sum[i] = s0 - sm[i];
}
dst += dst_step;
}
else
{
const int* sm = src[-ksize+1];
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
dst[i] = CV_CAST_16S(s0); dst[i+1] = CV_CAST_16S(s1);
s0 -= sm[i]; s1 -= sm[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
{
int s0 = sum[i] + sp[i];
dst[i] = CV_CAST_16S(s0);
sum[i] = s0 - sm[i];
}
dst += dst_step;
}
}
*_sum_count = sum_count;
}
static void
icvSumCol_32s32s( const int** src, int * dst,
int dst_step, int count, void* params )
{
CvBoxFilter* state = (CvBoxFilter*)params;
int ksize = state->get_kernel_size().height;
int i, width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
int* sum = (int*)state->get_sum_buf();
int* _sum_count = state->get_sum_count_ptr();
int sum_count = *_sum_count;
dst_step /= sizeof(dst[0]);
width *= cn;
src += sum_count;
count += ksize - 1 - sum_count;
for( ; count--; src++ )
{
const int* sp = src[0];
if( sum_count+1 < ksize )
{
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
sum[i] += sp[i];
sum_count++;
}
else
{
const int* sm = src[-ksize+1];
for( i = 0; i <= width - 2; i += 2 )
{
int s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
dst[i] = s0; dst[i+1] = s1;
s0 -= sm[i]; s1 -= sm[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
{
int s0 = sum[i] + sp[i];
dst[i] = s0;
sum[i] = s0 - sm[i];
}
dst += dst_step;
}
}
*_sum_count = sum_count;
}
static void
icvSumCol_64f32f( const double** src, float* dst,
int dst_step, int count, void* params )
{
CvBoxFilter* state = (CvBoxFilter*)params;
int ksize = state->get_kernel_size().height;
int i, width = state->get_width();
int cn = CV_MAT_CN(state->get_src_type());
double scale = state->get_scale();
bool normalized = state->is_normalized();
double* sum = (double*)state->get_sum_buf();
int* _sum_count = state->get_sum_count_ptr();
int sum_count = *_sum_count;
dst_step /= sizeof(dst[0]);
width *= cn;
src += sum_count;
count += ksize - 1 - sum_count;
for( ; count--; src++ )
{
const double* sp = src[0];
if( sum_count+1 < ksize )
{
for( i = 0; i <= width - 2; i += 2 )
{
double s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
sum[i] += sp[i];
sum_count++;
}
else
{
const double* sm = src[-ksize+1];
if( normalized )
for( i = 0; i <= width - 2; i += 2 )
{
double s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
double t0 = s0*scale, t1 = s1*scale;
s0 -= sm[i]; s1 -= sm[i+1];
dst[i] = (float)t0; dst[i+1] = (float)t1;
sum[i] = s0; sum[i+1] = s1;
}
else
for( i = 0; i <= width - 2; i += 2 )
{
double s0 = sum[i] + sp[i], s1 = sum[i+1] + sp[i+1];
dst[i] = (float)s0; dst[i+1] = (float)s1;
s0 -= sm[i]; s1 -= sm[i+1];
sum[i] = s0; sum[i+1] = s1;
}
for( ; i < width; i++ )
{
double s0 = sum[i] + sp[i], t0 = s0*scale;
sum[i] = s0 - sm[i]; dst[i] = (float)t0;
}
dst += dst_step;
}
}
*_sum_count = sum_count;
}
/****************************************************************************************\
Median Filter
\****************************************************************************************/
#define CV_MINMAX_8U(a,b) \
(t = CV_FAST_CAST_8U((a) - (b)), (b) += t, a -= t)
#if CV_SSE2 && !defined __SSE2__
#define __SSE2__ 1
#include "emmintrin.h"
#endif
#if defined(__VEC__) || defined(__ALTIVEC__)
#include <altivec.h>
#undef bool
#endif
#if defined(__GNUC__)
#define align(x) __attribute__ ((aligned (x)))
#elif CV_SSE2 && (defined(__ICL) || (_MSC_VER >= 1300))
#define align(x) __declspec(align(x))
#else
#define align(x)
#endif
#if _MSC_VER >= 1200
#pragma warning( disable: 4244 )
#endif
/**
* 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 align(16)
{
ushort coarse[16];
ushort fine[16][16];
} Histogram;
/**
* 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; \
*((ushort*) 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;
#if defined __SSE2__ || defined __MMX__ || defined __ALTIVEC__
#define MEDIAN_HAVE_SIMD 1
#else
#define MEDIAN_HAVE_SIMD 0
#endif
/**
* Adds histograms \a x and \a y and stores the result in \a y. Makes use of
* SSE2, MMX or Altivec, if available.
*/
#if defined(__SSE2__)
static inline void histogram_add( const ushort x[16], ushort y[16] )
{
_mm_store_si128( (__m128i*) &y[0], _mm_add_epi16(
_mm_load_si128((__m128i*) &y[0]), _mm_load_si128((__m128i*) &x[0] )));
_mm_store_si128( (__m128i*) &y[8], _mm_add_epi16(
_mm_load_si128((__m128i*) &y[8]), _mm_load_si128((__m128i*) &x[8] )));
}
#elif defined(__MMX__)
static inline void histogram_add( const ushort x[16], ushort y[16] )
{
*(__m64*) &y[0] = _mm_add_pi16( *(__m64*) &y[0], *(__m64*) &x[0] );
*(__m64*) &y[4] = _mm_add_pi16( *(__m64*) &y[4], *(__m64*) &x[4] );
*(__m64*) &y[8] = _mm_add_pi16( *(__m64*) &y[8], *(__m64*) &x[8] );
*(__m64*) &y[12] = _mm_add_pi16( *(__m64*) &y[12], *(__m64*) &x[12] );
}
#elif defined(__ALTIVEC__)
static inline void histogram_add( const ushort x[16], ushort y[16] )
{
*(vector ushort*) &y[0] = vec_add( *(vector ushort*) &y[0], *(vector ushort*) &x[0] );
*(vector ushort*) &y[8] = vec_add( *(vector ushort*) &y[8], *(vector ushort*) &x[8] );
}
#else
static inline void histogram_add( const ushort x[16], ushort y[16] )
{
int i;
for( i = 0; i < 16; ++i )
y[i] = (ushort)(y[i] + x[i]);
}
#endif
/**
* Subtracts histogram \a x from \a y and stores the result in \a y. Makes use
* of SSE2, MMX or Altivec, if available.
*/
#if defined(__SSE2__)
static inline void histogram_sub( const ushort x[16], ushort y[16] )
{
_mm_store_si128( (__m128i*) &y[0], _mm_sub_epi16(
_mm_load_si128((__m128i*) &y[0]), _mm_load_si128((__m128i*) &x[0] )));
_mm_store_si128( (__m128i*) &y[8], _mm_sub_epi16(
_mm_load_si128((__m128i*) &y[8]), _mm_load_si128((__m128i*) &x[8] )));
}
#elif defined(__MMX__)
static inline void histogram_sub( const ushort x[16], ushort y[16] )
{
*(__m64*) &y[0] = _mm_sub_pi16( *(__m64*) &y[0], *(__m64*) &x[0] );
*(__m64*) &y[4] = _mm_sub_pi16( *(__m64*) &y[4], *(__m64*) &x[4] );
*(__m64*) &y[8] = _mm_sub_pi16( *(__m64*) &y[8], *(__m64*) &x[8] );
*(__m64*) &y[12] = _mm_sub_pi16( *(__m64*) &y[12], *(__m64*) &x[12] );
}
#elif defined(__ALTIVEC__)
static inline void histogram_sub( const ushort x[16], ushort y[16] )
{
*(vector ushort*) &y[0] = vec_sub( *(vector ushort*) &y[0], *(vector ushort*) &x[0] );
*(vector ushort*) &y[8] = vec_sub( *(vector ushort*) &y[8], *(vector ushort*) &x[8] );
}
#else
static inline void histogram_sub( const ushort x[16], ushort y[16] )
{
int i;
for( i = 0; i < 16; ++i )
y[i] = (ushort)(y[i] - x[i]);
}
#endif
static inline void histogram_muladd( int a, const ushort x[16],
ushort y[16] )
{
int i;
for ( i = 0; i < 16; ++i )
y[i] = (ushort)(y[i] + a * x[i]);
}
static CvStatus CV_STDCALL
icvMedianBlur_8u_CnR_O1( uchar* src, int src_step, uchar* dst, int dst_step,
CvSize size, int kernel_size, int cn, int pad_left, int pad_right )
{
int r = (kernel_size-1)/2;
const int m = size.height, n = size.width;
int i, j, k, c;
const unsigned char *p, *q;
Histogram H[4];
ushort *h_coarse, *h_fine, luc[4][16];
if( size.height < r || size.width < r )
return CV_BADSIZE_ERR;
assert( src );
assert( dst );
assert( r >= 0 );
assert( size.width >= 2*r+1 );
assert( size.height >= 2*r+1 );
assert( src_step != 0 );
assert( dst_step != 0 );
h_coarse = (ushort*) cvAlloc( 1 * 16 * n * cn * sizeof(ushort) );
h_fine = (ushort*) cvAlloc( 16 * 16 * n * cn * sizeof(ushort) );
memset( h_coarse, 0, 1 * 16 * n * cn * sizeof(ushort) );
memset( h_fine, 0, 16 * 16 * n * cn * sizeof(ushort) );
/* First row initialization */
for ( j = 0; j < n; ++j ) {
for ( c = 0; c < cn; ++c ) {
COP( c, j, src[cn*j+c], += r+1 );
}
}
for ( i = 0; i < r; ++i ) {
for ( j = 0; j < n; ++j ) {
for ( c = 0; c < cn; ++c ) {
COP( c, j, src[src_step*i+cn*j+c], ++ );
}
}
}
for ( i = 0; i < m; ++i ) {
/* Update column histograms for entire row. */
p = src + src_step * MAX( 0, i-r-1 );
q = p + cn * n;
for ( j = 0; p != q; ++j ) {
for ( c = 0; c < cn; ++c, ++p ) {
COP( c, j, *p, -- );
}
}
p = src + src_step * MIN( m-1, i+r );
q = p + cn * n;
for ( j = 0; p != q; ++j ) {
for ( c = 0; c < cn; ++c, ++p ) {
COP( c, j, *p, ++ );
}
}
/* First column initialization */
memset( H, 0, cn*sizeof(H[0]) );
memset( luc, 0, cn*sizeof(luc[0]) );
if ( pad_left ) {
for ( c = 0; c < cn; ++c ) {
histogram_muladd( r, &h_coarse[16*n*c], H[c].coarse );
}
}
for ( j = 0; j < (pad_left ? r : 2*r); ++j ) {
for ( c = 0; c < cn; ++c ) {
histogram_add( &h_coarse[16*(n*c+j)], H[c].coarse );
}
}
for ( c = 0; c < cn; ++c ) {
for ( k = 0; k < 16; ++k ) {
histogram_muladd( 2*r+1, &h_fine[16*n*(16*c+k)], &H[c].fine[k][0] );
}
}
for ( j = pad_left ? 0 : r; j < (pad_right ? n : n-r); ++j ) {
for ( c = 0; c < cn; ++c ) {
int t = 2*r*r + 2*r, b, sum = 0;
ushort* segment;
histogram_add( &h_coarse[16*(n*c + MIN(j+r,n-1))], H[c].coarse );
/* Find median at coarse level */
for ( k = 0; k < 16 ; ++k ) {
sum += H[c].coarse[k];
if ( sum > t ) {
sum -= H[c].coarse[k];
break;
}
}
assert( k < 16 );
/* Update corresponding histogram segment */
if ( luc[c][k] <= j-r ) {
memset( &H[c].fine[k], 0, 16 * sizeof(ushort) );
for ( luc[c][k] = j-r; luc[c][k] < MIN(j+r+1,n); ++luc[c][k] ) {
histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
}
if ( luc[c][k] < j+r+1 ) {
histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
luc[c][k] = (ushort)(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[dst_step*i+cn*j+c] = (uchar)(16*k + b);
break;
}
}
assert( b < 16 );
}
}
}
#if defined(__MMX__)
_mm_empty();
#endif
cvFree(&h_coarse);
cvFree(&h_fine);
#undef HOP
#undef COP
return CV_OK;
}
#if _MSC_VER >= 1200
#pragma warning( default: 4244 )
#endif
static CvStatus CV_STDCALL
icvMedianBlur_8u_CnR_Om( uchar* src, int src_step, uchar* dst, int dst_step,
CvSize size, int m, int cn )
{
#define N 16
int zone0[4][N];
int zone1[4][N*N];
int x, y;
int n2 = m*m/2;
int nx = (m + 1)/2 - 1;
uchar* src_max = src + size.height*src_step;
uchar* src_right = src + size.width*cn;
#define UPDATE_ACC01( pix, cn, op ) \
{ \
int p = (pix); \
zone1[cn][p] op; \
zone0[cn][p >> 4] op; \
}
if( size.height < nx || size.width < nx )
return CV_BADSIZE_ERR;
if( m == 3 )
{
size.width *= cn;
for( y = 0; y < size.height; y++, dst += dst_step )
{
const uchar* src0 = src + src_step*(y-1);
const uchar* src1 = src0 + src_step;
const uchar* src2 = src1 + src_step;
if( y == 0 )
src0 = src1;
else if( y == size.height - 1 )
src2 = src1;
for( x = 0; x < 2*cn; x++ )
{
int x0 = x < cn ? x : size.width - 3*cn + x;
int x2 = x < cn ? x + cn : size.width - 2*cn + x;
int x1 = x < cn ? x0 : x2, t;
int p0 = src0[x0], p1 = src0[x1], p2 = src0[x2];
int p3 = src1[x0], p4 = src1[x1], p5 = src1[x2];
int p6 = src2[x0], p7 = src2[x1], p8 = src2[x2];
CV_MINMAX_8U(p1, p2); CV_MINMAX_8U(p4, p5);
CV_MINMAX_8U(p7, p8); CV_MINMAX_8U(p0, p1);
CV_MINMAX_8U(p3, p4); CV_MINMAX_8U(p6, p7);
CV_MINMAX_8U(p1, p2); CV_MINMAX_8U(p4, p5);
CV_MINMAX_8U(p7, p8); CV_MINMAX_8U(p0, p3);
CV_MINMAX_8U(p5, p8); CV_MINMAX_8U(p4, p7);
CV_MINMAX_8U(p3, p6); CV_MINMAX_8U(p1, p4);
CV_MINMAX_8U(p2, p5); CV_MINMAX_8U(p4, p7);
CV_MINMAX_8U(p4, p2); CV_MINMAX_8U(p6, p4);
CV_MINMAX_8U(p4, p2);
dst[x1] = (uchar)p4;
}
for( x = cn; x < size.width - cn; x++ )
{
int p0 = src0[x-cn], p1 = src0[x], p2 = src0[x+cn];
int p3 = src1[x-cn], p4 = src1[x], p5 = src1[x+cn];
int p6 = src2[x-cn], p7 = src2[x], p8 = src2[x+cn];
int t;
CV_MINMAX_8U(p1, p2); CV_MINMAX_8U(p4, p5);
CV_MINMAX_8U(p7, p8); CV_MINMAX_8U(p0, p1);
CV_MINMAX_8U(p3, p4); CV_MINMAX_8U(p6, p7);
CV_MINMAX_8U(p1, p2); CV_MINMAX_8U(p4, p5);
CV_MINMAX_8U(p7, p8); CV_MINMAX_8U(p0, p3);
CV_MINMAX_8U(p5, p8); CV_MINMAX_8U(p4, p7);
CV_MINMAX_8U(p3, p6); CV_MINMAX_8U(p1, p4);
CV_MINMAX_8U(p2, p5); CV_MINMAX_8U(p4, p7);
CV_MINMAX_8U(p4, p2); CV_MINMAX_8U(p6, p4);
CV_MINMAX_8U(p4, p2);
dst[x] = (uchar)p4;
}
}
return CV_OK;
}
for( x = 0; x < size.width; x++, dst += cn )
{
uchar* dst_cur = dst;
uchar* src_top = src;
uchar* src_bottom = src;
int k, c;
int x0 = -1;
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;
}
if( x <= m/2 )
nx++;
if( nx < m )
x0 = x < m/2 ? 0 : (nx-1)*cn;
// 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 )
{
if( x0 >= 0 )
UPDATE_ACC01( src_bottom[x0+c], c, += (m - nx) );
for( k = 0; k < nx*cn; k += cn )
UPDATE_ACC01( src_bottom[k+c], c, ++ );
}
else
{
if( x0 >= 0 )
UPDATE_ACC01( src_bottom[x0+c], c, += (m - nx)*(m/2+1) );
for( k = 0; k < nx*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 < nx; 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 < nx*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 < nx*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( x0 >= 0 )
{
for( c = 0; c < cn; c++ )
{
UPDATE_ACC01( src_top[x0+c], c, -= (m - nx) );
UPDATE_ACC01( src_bottom[x0+c], c, += (m - nx) );
}
}
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;
}
if( x >= m/2 )
src += cn;
if( src + nx*cn > src_right ) nx--;
}
#undef N
#undef UPDATE_ACC
return CV_OK;
}
/****************************************************************************************\
Bilateral Filtering
\****************************************************************************************/
static void
icvBilateralFiltering_8u( const CvMat* src, CvMat* dst, int d,
double sigma_color, double sigma_space )
{
CvMat* temp = 0;
float* color_weight = 0;
float* space_weight = 0;
int* space_ofs = 0;
CV_FUNCNAME( "icvBilateralFiltering_8u" );
__BEGIN__;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
int cn = CV_MAT_CN(src->type);
int i, j, k, maxk, radius;
CvSize size = cvGetMatSize(src);
if( (CV_MAT_TYPE(src->type) != CV_8UC1 &&
CV_MAT_TYPE(src->type) != CV_8UC3) ||
!CV_ARE_TYPES_EQ(src, dst) )
CV_ERROR( CV_StsUnsupportedFormat,
"Both source and destination must be 8-bit, single-channel or 3-channel images" );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
if( d == 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
CV_CALL( temp = cvCreateMat( src->rows + radius*2,
src->cols + radius*2, src->type ));
CV_CALL( cvCopyMakeBorder( src, temp, cvPoint(radius,radius), IPL_BORDER_REPLICATE ));
CV_CALL( color_weight = (float*)cvAlloc(cn*256*sizeof(color_weight[0])));
CV_CALL( space_weight = (float*)cvAlloc(d*d*sizeof(space_weight[0])));
CV_CALL( space_ofs = (int*)cvAlloc(d*d*sizeof(space_ofs[0])));
// initialize color-related bilateral filter coefficients
for( i = 0; i < 256*cn; i++ )
color_weight[i] = (float)exp(i*i*gauss_color_coeff);
// initialize space-related bilateral filter coefficients
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = i*temp->step + j*cn;
}
for( i = 0; i < size.height; i++ )
{
const uchar* sptr = temp->data.ptr + (i+radius)*temp->step + radius*cn;
uchar* dptr = dst->data.ptr + i*dst->step;
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
int val0 = sptr[j];
for( k = 0; k < maxk; k++ )
{
int val = sptr[j + space_ofs[k]];
float w = space_weight[k]*color_weight[abs(val - val0)];
sum += val*w;
wsum += w;
}
// overflow is not possible here => there is no need to use CV_CAST_8U
dptr[j] = (uchar)cvRound(sum/wsum);
}
}
else
{
assert( cn == 3 );
for( j = 0; j < size.width*3; j += 3 )
{
float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
int b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
for( k = 0; k < maxk; k++ )
{
const uchar* sptr_k = sptr + j + space_ofs[k];
int b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float w = space_weight[k]*color_weight[abs(b - b0) +
abs(g - g0) + 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;
}
}
}
__END__;
cvReleaseMat( &temp );
cvFree( &color_weight );
cvFree( &space_weight );
cvFree( &space_ofs );
}
static void icvBilateralFiltering_32f( const CvMat* src, CvMat* dst, int d,
double sigma_color, double sigma_space )
{
CvMat* temp = 0;
float* space_weight = 0;
int* space_ofs = 0;
float *expLUT = 0;
CV_FUNCNAME( "icvBilateralFiltering_32f" );
__BEGIN__;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
int cn = CV_MAT_CN(src->type);
int i, j, k, maxk, radius;
double minValSrc=-1, maxValSrc=1;
const int kExpNumBinsPerChannel = 1 << 12;
int kExpNumBins = 0;
float lastExpVal = 1.f;
int temp_step;
float len, scale_index;
CvMat src_reshaped;
CvSize size = cvGetMatSize(src);
if( (CV_MAT_TYPE(src->type) != CV_32FC1 &&
CV_MAT_TYPE(src->type) != CV_32FC3) ||
!CV_ARE_TYPES_EQ(src, dst) )
CV_ERROR( CV_StsUnsupportedFormat,
"Both source and destination must be 32-bit float, single-channel or 3-channel images" );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
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)
CV_CALL( cvReshape( src, &src_reshaped, 1 ) );
CV_CALL( cvMinMaxLoc(&src_reshaped, &minValSrc, &maxValSrc) );
// temporary copy of the image with borders for easy processing
CV_CALL( temp = cvCreateMat( src->rows + radius*2,
src->cols + radius*2, src->type ));
temp_step = temp->step/sizeof(float);
CV_CALL( cvCopyMakeBorder( src, temp, cvPoint(radius,radius), IPL_BORDER_REPLICATE ));
// allocate lookup tables
CV_CALL( space_weight = (float*)cvAlloc(d*d*sizeof(space_weight[0])));
CV_CALL( space_ofs = (int*)cvAlloc(d*d*sizeof(space_ofs[0])));
// assign a length which is slightly more than needed
len = (float)(maxValSrc - minValSrc) * cn;
kExpNumBins = kExpNumBinsPerChannel * cn;
CV_CALL( expLUT = (float*)cvAlloc((kExpNumBins+2) * sizeof(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)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 = sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = i*temp_step + j*cn;
}
for( i = 0; i < size.height; i++ )
{
const float* sptr = temp->data.fl + (i+radius)*temp_step + radius*cn;
float* dptr = (float*)(dst->data.ptr + i*dst->step);
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
float val0 = sptr[j];
for( k = 0; k < maxk; k++ )
{
float val = sptr[j + space_ofs[k]];
float alpha = (float)(fabs(val - val0)*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum += val*w;
wsum += w;
}
dptr[j] = (float)(sum/wsum);
}
}
else
{
assert( cn == 3 );
for( j = 0; j < size.width*3; j += 3 )
{
float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
for( k = 0; k < maxk; k++ )
{
const float* sptr_k = sptr + j + space_ofs[k];
float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float alpha = (float)((fabs(b - b0) + fabs(g - g0) + fabs(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;
}
}
}
__END__;
cvReleaseMat( &temp );
cvFree( &space_weight );
cvFree( &space_ofs );
cvFree( &expLUT );
}
//////////////////////////////// IPP smoothing functions /////////////////////////////////
icvFilterMedian_8u_C1R_t icvFilterMedian_8u_C1R_p = 0;
icvFilterMedian_8u_C3R_t icvFilterMedian_8u_C3R_p = 0;
icvFilterMedian_8u_C4R_t icvFilterMedian_8u_C4R_p = 0;
icvFilterBox_8u_C1R_t icvFilterBox_8u_C1R_p = 0;
icvFilterBox_8u_C3R_t icvFilterBox_8u_C3R_p = 0;
icvFilterBox_8u_C4R_t icvFilterBox_8u_C4R_p = 0;
icvFilterBox_32f_C1R_t icvFilterBox_32f_C1R_p = 0;
icvFilterBox_32f_C3R_t icvFilterBox_32f_C3R_p = 0;
icvFilterBox_32f_C4R_t icvFilterBox_32f_C4R_p = 0;
typedef CvStatus (CV_STDCALL * CvSmoothFixedIPPFunc)
( const void* src, int srcstep, void* dst, int dststep,
CvSize size, CvSize ksize, CvPoint anchor );
//////////////////////////////////////////////////////////////////////////////////////////
CV_IMPL void
cvSmooth( const void* srcarr, void* dstarr, int smooth_type,
int param1, int param2, double param3, double param4 )
{
CvBoxFilter box_filter;
CvSepFilter gaussian_filter;
CvMat* temp = 0;
CV_FUNCNAME( "cvSmooth" );
__BEGIN__;
int coi1 = 0, coi2 = 0;
CvMat srcstub, *src = (CvMat*)srcarr;
CvMat dststub, *dst = (CvMat*)dstarr;
CvSize size;
int src_type, dst_type, depth, cn;
double sigma1 = 0, sigma2 = 0;
bool have_ipp = icvFilterMedian_8u_C1R_p != 0;
CV_CALL( src = cvGetMat( src, &srcstub, &coi1 ));
CV_CALL( dst = cvGetMat( dst, &dststub, &coi2 ));
if( coi1 != 0 || coi2 != 0 )
CV_ERROR( CV_BadCOI, "" );
src_type = CV_MAT_TYPE( src->type );
dst_type = CV_MAT_TYPE( dst->type );
depth = CV_MAT_DEPTH(src_type);
cn = CV_MAT_CN(src_type);
size = cvGetMatSize(src);
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedSizes, "" );
if( smooth_type != CV_BLUR_NO_SCALE && !CV_ARE_TYPES_EQ( src, dst ))
CV_ERROR( CV_StsUnmatchedFormats,
"The specified smoothing algorithm requires input and ouput arrays be of the same type" );
if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE ||
smooth_type == CV_GAUSSIAN || smooth_type == CV_MEDIAN )
{
// automatic detection of kernel size from sigma
if( smooth_type == CV_GAUSSIAN )
{
sigma1 = param3;
sigma2 = param4 ? param4 : param3;
if( param1 == 0 && sigma1 > 0 )
param1 = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
if( param2 == 0 && sigma2 > 0 )
param2 = cvRound(sigma2*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
}
if( param2 == 0 )
param2 = size.height == 1 ? 1 : param1;
if( param1 < 1 || (param1 & 1) == 0 || param2 < 1 || (param2 & 1) == 0 )
CV_ERROR( CV_StsOutOfRange,
"Both mask width and height must be >=1 and odd" );
if( param1 == 1 && param2 == 1 )
{
cvConvert( src, dst );
EXIT;
}
}
if( have_ipp && (smooth_type == CV_BLUR || (smooth_type == CV_MEDIAN && param1 <= 15)) &&
size.width >= param1 && size.height >= param2 && param1 > 1 && param2 > 1 )
{
CvSmoothFixedIPPFunc ipp_median_box_func = 0;
if( smooth_type == CV_BLUR )
{
ipp_median_box_func =
src_type == CV_8UC1 ? icvFilterBox_8u_C1R_p :
src_type == CV_8UC3 ? icvFilterBox_8u_C3R_p :
src_type == CV_8UC4 ? icvFilterBox_8u_C4R_p :
src_type == CV_32FC1 ? icvFilterBox_32f_C1R_p :
src_type == CV_32FC3 ? icvFilterBox_32f_C3R_p :
src_type == CV_32FC4 ? icvFilterBox_32f_C4R_p : 0;
}
else if( smooth_type == CV_MEDIAN )
{
ipp_median_box_func =
src_type == CV_8UC1 ? icvFilterMedian_8u_C1R_p :
src_type == CV_8UC3 ? icvFilterMedian_8u_C3R_p :
src_type == CV_8UC4 ? icvFilterMedian_8u_C4R_p : 0;
}
if( ipp_median_box_func )
{
CvSize el_size = { param1, param2 };
CvPoint el_anchor = { param1/2, param2/2 };
int stripe_size = 1 << 14; // the optimal value may depend on CPU cache,
// overhead of the current IPP code etc.
const uchar* shifted_ptr;
int y, dy = 0;
int temp_step, dst_step = dst->step;
CV_CALL( temp = icvIPPFilterInit( src, stripe_size, el_size ));
shifted_ptr = temp->data.ptr +
el_anchor.y*temp->step + el_anchor.x*CV_ELEM_SIZE(src_type);
temp_step = temp->step ? temp->step : CV_STUB_STEP;
for( y = 0; y < src->rows; y += dy )
{
dy = icvIPPFilterNextStripe( src, temp, y, el_size, el_anchor );
IPPI_CALL( ipp_median_box_func( shifted_ptr, temp_step,
dst->data.ptr + y*dst_step, dst_step, cvSize(src->cols, dy),
el_size, el_anchor ));
}
EXIT;
}
}
if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE )
{
CV_CALL( box_filter.init( src->cols, src_type, dst_type,
smooth_type == CV_BLUR, cvSize(param1, param2) ));
CV_CALL( box_filter.process( src, dst ));
}
else if( smooth_type == CV_MEDIAN )
{
int img_size_mp = size.width*size.height;
img_size_mp = (img_size_mp + (1<<19)) >> 20;
if( depth != CV_8U || (cn != 1 && cn != 3 && cn != 4) )
CV_ERROR( CV_StsUnsupportedFormat,
"Median filter only supports 8uC1, 8uC3 and 8uC4 images" );
if( size.width < param1*2 || size.height < param1*2 ||
param1 <= 3 + (img_size_mp < 1 ? 12 : img_size_mp < 4 ? 6 : 2)*(MEDIAN_HAVE_SIMD ? 1 : 3))
{
// Special case optimized for 3x3
IPPI_CALL( icvMedianBlur_8u_CnR_Om( src->data.ptr, src->step,
dst->data.ptr, dst->step, size, param1, cn ));
}
else
{
const int r = (param1 - 1) / 2;
const int CACHE_SIZE = (int) ( 0.95 * 256 * 1024 / cn ); // assume a 256 kB cache size
const int STRIPES = (int) cvCeil( (double) (size.width - 2*r) /
(CACHE_SIZE / sizeof(Histogram) - 2*r) );
const int STRIPE_SIZE = (int) cvCeil(
(double) ( size.width + STRIPES*2*r - 2*r ) / STRIPES );
for( int i = 0; i < size.width; i += STRIPE_SIZE - 2*r )
{
int stripe = STRIPE_SIZE;
// Make sure that the filter kernel fits into one stripe.
if( i + STRIPE_SIZE - 2*r >= size.width ||
size.width - (i + STRIPE_SIZE - 2*r) < 2*r+1 )
stripe = size.width - i;
IPPI_CALL( icvMedianBlur_8u_CnR_O1( src->data.ptr + cn*i, src->step,
dst->data.ptr + cn*i, dst->step, cvSize(stripe, size.height),
param1, cn, i == 0, stripe == size.width - i ));
if( stripe == size.width - i )
break;
}
}
}
else if( smooth_type == CV_GAUSSIAN )
{
CvSize ksize = { param1, param2 };
float* kx = (float*)cvStackAlloc( ksize.width*sizeof(kx[0]) );
float* ky = (float*)cvStackAlloc( ksize.height*sizeof(ky[0]) );
CvMat KX = cvMat( 1, ksize.width, CV_32F, kx );
CvMat KY = cvMat( 1, ksize.height, CV_32F, ky );
CvSepFilter::init_gaussian_kernel( &KX, sigma1 );
if( ksize.width != ksize.height || fabs(sigma1 - sigma2) > FLT_EPSILON )
CvSepFilter::init_gaussian_kernel( &KY, sigma2 );
else
KY.data.fl = kx;
if( have_ipp && size.width >= param1*3 &&
size.height >= param2 && param1 > 1 && param2 > 1 )
{
int done;
CV_CALL( done = icvIPPSepFilter( src, dst, &KX, &KY,
cvPoint(ksize.width/2,ksize.height/2)));
if( done )
EXIT;
}
CV_CALL( gaussian_filter.init( src->cols, src_type, dst_type, &KX, &KY ));
CV_CALL( gaussian_filter.process( src, dst ));
}
else if( smooth_type == CV_BILATERAL )
{
if( param2 != 0 && (param2 != param1 || param1 % 2 == 0) )
CV_ERROR( CV_StsBadSize, "Bilateral filter only supports square windows of odd size" );
switch( src_type )
{
case CV_32FC1:
case CV_32FC3:
CV_CALL( icvBilateralFiltering_32f( src, dst, param1, param3, param4 ));
break;
case CV_8UC1:
case CV_8UC3:
CV_CALL( icvBilateralFiltering_8u( src, dst, param1, param3, param4 ));
break;
default:
CV_ERROR( CV_StsUnsupportedFormat,
"Unknown/unsupported format: bilateral filter only supports 8uC1, 8uC3, 32fC1 and 32fC3 formats" );
}
}
__END__;
cvReleaseMat( &temp );
}
/* End of file. */