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opencv/modules/imgproc/src/distransform.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// 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"
#define ICV_DIST_SHIFT 16
#define ICV_INIT_DIST0 (INT_MAX >> 2)
static CvStatus
icvInitTopBottom( int* temp, int tempstep, CvSize size, int border )
{
int i, j;
for( i = 0; i < border; i++ )
{
int* ttop = (int*)(temp + i*tempstep);
int* tbottom = (int*)(temp + (size.height + border*2 - i - 1)*tempstep);
for( j = 0; j < size.width + border*2; j++ )
{
ttop[j] = ICV_INIT_DIST0;
tbottom[j] = ICV_INIT_DIST0;
}
}
return CV_OK;
}
static CvStatus CV_STDCALL
icvDistanceTransform_3x3_C1R( const uchar* src, int srcstep, int* temp,
int step, float* dist, int dststep, CvSize size, const float* metrics )
{
const int BORDER = 1;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], ICV_DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], ICV_DIST_SHIFT );
const float scale = 1.f/(1 << ICV_DIST_SHIFT);
srcstep /= sizeof(src[0]);
step /= sizeof(temp[0]);
dststep /= sizeof(dist[0]);
icvInitTopBottom( temp, step, size, BORDER );
// forward pass
for( i = 0; i < size.height; i++ )
{
const uchar* s = src + i*srcstep;
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
for( j = 0; j < BORDER; j++ )
tmp[-j-1] = tmp[size.width + j] = ICV_INIT_DIST0;
for( j = 0; j < size.width; j++ )
{
if( !s[j] )
tmp[j] = 0;
else
{
int t0 = tmp[j-step-1] + DIAG_DIST;
int t = tmp[j-step] + HV_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step+1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-1] + HV_DIST;
if( t0 > t ) t0 = t;
tmp[j] = t0;
}
}
}
// backward pass
for( i = size.height - 1; i >= 0; i-- )
{
float* d = (float*)(dist + i*dststep);
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
for( j = size.width - 1; j >= 0; j-- )
{
int t0 = tmp[j];
if( t0 > HV_DIST )
{
int t = tmp[j+step+1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step] + HV_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step-1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+1] + HV_DIST;
if( t0 > t ) t0 = t;
tmp[j] = t0;
}
d[j] = (float)(t0 * scale);
}
}
return CV_OK;
}
static CvStatus CV_STDCALL
icvDistanceTransform_5x5_C1R( const uchar* src, int srcstep, int* temp,
int step, float* dist, int dststep, CvSize size, const float* metrics )
{
const int BORDER = 2;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], ICV_DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], ICV_DIST_SHIFT );
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], ICV_DIST_SHIFT );
const float scale = 1.f/(1 << ICV_DIST_SHIFT);
srcstep /= sizeof(src[0]);
step /= sizeof(temp[0]);
dststep /= sizeof(dist[0]);
icvInitTopBottom( temp, step, size, BORDER );
// forward pass
for( i = 0; i < size.height; i++ )
{
const uchar* s = src + i*srcstep;
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
for( j = 0; j < BORDER; j++ )
tmp[-j-1] = tmp[size.width + j] = ICV_INIT_DIST0;
for( j = 0; j < size.width; j++ )
{
if( !s[j] )
tmp[j] = 0;
else
{
int t0 = tmp[j-step*2-1] + LONG_DIST;
int t = tmp[j-step*2+1] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step-2] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step-1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step] + HV_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step+1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-step+2] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j-1] + HV_DIST;
if( t0 > t ) t0 = t;
tmp[j] = t0;
}
}
}
// backward pass
for( i = size.height - 1; i >= 0; i-- )
{
float* d = (float*)(dist + i*dststep);
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
for( j = size.width - 1; j >= 0; j-- )
{
int t0 = tmp[j];
if( t0 > HV_DIST )
{
int t = tmp[j+step*2+1] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step*2-1] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step+2] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step+1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step] + HV_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step-1] + DIAG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+step-2] + LONG_DIST;
if( t0 > t ) t0 = t;
t = tmp[j+1] + HV_DIST;
if( t0 > t ) t0 = t;
tmp[j] = t0;
}
d[j] = (float)(t0 * scale);
}
}
return CV_OK;
}
static CvStatus CV_STDCALL
icvDistanceTransformEx_5x5_C1R( const uchar* src, int srcstep, int* temp,
int step, float* dist, int dststep, int* labels, int lstep,
CvSize size, const float* metrics )
{
const int BORDER = 2;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], ICV_DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], ICV_DIST_SHIFT );
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], ICV_DIST_SHIFT );
const float scale = 1.f/(1 << ICV_DIST_SHIFT);
srcstep /= sizeof(src[0]);
step /= sizeof(temp[0]);
dststep /= sizeof(dist[0]);
lstep /= sizeof(labels[0]);
icvInitTopBottom( temp, step, size, BORDER );
// forward pass
for( i = 0; i < size.height; i++ )
{
const uchar* s = src + i*srcstep;
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
int* lls = (int*)(labels + i*lstep);
for( j = 0; j < BORDER; j++ )
tmp[-j-1] = tmp[size.width + j] = ICV_INIT_DIST0;
for( j = 0; j < size.width; j++ )
{
if( !s[j] )
{
tmp[j] = 0;
//assert( lls[j] != 0 );
}
else
{
int t0 = ICV_INIT_DIST0, t;
int l0 = 0;
t = tmp[j-step*2-1] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep*2-1];
}
t = tmp[j-step*2+1] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep*2+1];
}
t = tmp[j-step-2] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep-2];
}
t = tmp[j-step-1] + DIAG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep-1];
}
t = tmp[j-step] + HV_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep];
}
t = tmp[j-step+1] + DIAG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep+1];
}
t = tmp[j-step+2] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-lstep+2];
}
t = tmp[j-1] + HV_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j-1];
}
tmp[j] = t0;
lls[j] = l0;
}
}
}
// backward pass
for( i = size.height - 1; i >= 0; i-- )
{
float* d = (float*)(dist + i*dststep);
int* tmp = (int*)(temp + (i+BORDER)*step) + BORDER;
int* lls = (int*)(labels + i*lstep);
for( j = size.width - 1; j >= 0; j-- )
{
int t0 = tmp[j];
int l0 = lls[j];
if( t0 > HV_DIST )
{
int t = tmp[j+step*2+1] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep*2+1];
}
t = tmp[j+step*2-1] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep*2-1];
}
t = tmp[j+step+2] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep+2];
}
t = tmp[j+step+1] + DIAG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep+1];
}
t = tmp[j+step] + HV_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep];
}
t = tmp[j+step-1] + DIAG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep-1];
}
t = tmp[j+step-2] + LONG_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+lstep-2];
}
t = tmp[j+1] + HV_DIST;
if( t0 > t )
{
t0 = t;
l0 = lls[j+1];
}
tmp[j] = t0;
lls[j] = l0;
}
d[j] = (float)(t0 * scale);
}
}
return CV_OK;
}
static CvStatus
icvGetDistanceTransformMask( int maskType, float *metrics )
{
if( !metrics )
return CV_NULLPTR_ERR;
switch (maskType)
{
case 30:
metrics[0] = 1.0f;
metrics[1] = 1.0f;
break;
case 31:
metrics[0] = 1.0f;
metrics[1] = 2.0f;
break;
case 32:
metrics[0] = 0.955f;
metrics[1] = 1.3693f;
break;
case 50:
metrics[0] = 1.0f;
metrics[1] = 1.0f;
metrics[2] = 2.0f;
break;
case 51:
metrics[0] = 1.0f;
metrics[1] = 2.0f;
metrics[2] = 3.0f;
break;
case 52:
metrics[0] = 1.0f;
metrics[1] = 1.4f;
metrics[2] = 2.1969f;
break;
default:
return CV_BADRANGE_ERR;
}
return CV_OK;
}
namespace cv
{
struct DTColumnInvoker
{
DTColumnInvoker( const CvMat* _src, CvMat* _dst, const int* _sat_tab, const float* _sqr_tab)
{
src = _src;
dst = _dst;
sat_tab = _sat_tab + src->rows*2 + 1;
sqr_tab = _sqr_tab;
}
void operator()( const BlockedRange& range ) const
{
int i, i1 = range.begin(), i2 = range.end();
int m = src->rows;
size_t sstep = src->step, dstep = dst->step/sizeof(float);
AutoBuffer<int> _d(m);
int* d = _d;
for( i = i1; i < i2; i++ )
{
const uchar* sptr = src->data.ptr + i + (m-1)*sstep;
float* dptr = dst->data.fl + i;
int j, dist = m-1;
for( j = m-1; j >= 0; j--, sptr -= sstep )
{
dist = (dist + 1) & (sptr[0] == 0 ? 0 : -1);
d[j] = dist;
}
dist = m-1;
for( j = 0; j < m; j++, dptr += dstep )
{
dist = dist + 1 - sat_tab[dist - d[j]];
d[j] = dist;
dptr[0] = sqr_tab[dist];
}
}
}
const CvMat* src;
CvMat* dst;
const int* sat_tab;
const float* sqr_tab;
};
struct DTRowInvoker
{
DTRowInvoker( CvMat* _dst, const float* _sqr_tab, const float* _inv_tab )
{
dst = _dst;
sqr_tab = _sqr_tab;
inv_tab = _inv_tab;
}
void operator()( const BlockedRange& range ) const
{
const float inf = 1e15f;
int i, i1 = range.begin(), i2 = range.end();
int n = dst->cols;
AutoBuffer<uchar> _buf((n+2)*2*sizeof(float) + (n+2)*sizeof(int));
float* f = (float*)(uchar*)_buf;
float* z = f + n;
int* v = alignPtr((int*)(z + n + 1), sizeof(int));
for( i = i1; i < i2; i++ )
{
float* d = (float*)(dst->data.ptr + i*dst->step);
int p, q, k;
v[0] = 0;
z[0] = -inf;
z[1] = inf;
f[0] = d[0];
for( q = 1, k = 0; q < n; q++ )
{
float fq = d[q];
f[q] = fq;
for(;;k--)
{
p = v[k];
float s = (fq + sqr_tab[q] - d[p] - sqr_tab[p])*inv_tab[q - p];
if( s > z[k] )
{
k++;
v[k] = q;
z[k] = s;
z[k+1] = inf;
break;
}
}
}
for( q = 0, k = 0; q < n; q++ )
{
while( z[k+1] < q )
k++;
p = v[k];
d[q] = std::sqrt(sqr_tab[std::abs(q - p)] + f[p]);
}
}
}
CvMat* dst;
const float* sqr_tab;
const float* inv_tab;
};
}
static void
icvTrueDistTrans( const CvMat* src, CvMat* dst )
{
const float inf = 1e15f;
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_Error( CV_StsUnmatchedSizes, "" );
if( CV_MAT_TYPE(src->type) != CV_8UC1 ||
CV_MAT_TYPE(dst->type) != CV_32FC1 )
CV_Error( CV_StsUnsupportedFormat,
"The input image must have 8uC1 type and the output one must have 32fC1 type" );
int i, m = src->rows, n = src->cols;
cv::AutoBuffer<uchar> _buf(std::max(m*2*sizeof(float) + (m*3+1)*sizeof(int), n*2*sizeof(float)));
// stage 1: compute 1d distance transform of each column
float* sqr_tab = (float*)(uchar*)_buf;
int* sat_tab = cv::alignPtr((int*)(sqr_tab + m*2), sizeof(int));
int shift = m*2;
for( i = 0; i < m; i++ )
sqr_tab[i] = (float)(i*i);
for( i = m; i < m*2; i++ )
sqr_tab[i] = inf;
for( i = 0; i < shift; i++ )
sat_tab[i] = 0;
for( ; i <= m*3; i++ )
sat_tab[i] = i - shift;
cv::parallel_for(cv::BlockedRange(0, n), cv::DTColumnInvoker(src, dst, sat_tab, sqr_tab));
// stage 2: compute modified distance transform for each row
float* inv_tab = sqr_tab + n;
inv_tab[0] = sqr_tab[0] = 0.f;
for( i = 1; i < n; i++ )
{
inv_tab[i] = (float)(0.5/i);
sqr_tab[i] = (float)(i*i);
}
cv::parallel_for(cv::BlockedRange(0, m), cv::DTRowInvoker(dst, sqr_tab, inv_tab));
}
/*********************************** IPP functions *********************************/
typedef CvStatus (CV_STDCALL * CvIPPDistTransFunc)( const uchar* src, int srcstep,
void* dst, int dststep,
CvSize size, const void* metrics );
typedef CvStatus (CV_STDCALL * CvIPPDistTransFunc2)( uchar* src, int srcstep,
CvSize size, const int* metrics );
/***********************************************************************************/
typedef CvStatus (CV_STDCALL * CvDistTransFunc)( const uchar* src, int srcstep,
int* temp, int tempstep,
float* dst, int dststep,
CvSize size, const float* metrics );
/****************************************************************************************\
Non-inplace and Inplace 8u->8u Distance Transform for CityBlock (a.k.a. L1) metric
(C) 2006 by Jay Stavinzky.
\****************************************************************************************/
//BEGIN ATS ADDITION
/* 8-bit grayscale distance transform function */
static void
icvDistanceATS_L1_8u( const CvMat* src, CvMat* dst )
{
int width = src->cols, height = src->rows;
int a;
uchar lut[256];
int x, y;
const uchar *sbase = src->data.ptr;
uchar *dbase = dst->data.ptr;
int srcstep = src->step;
int dststep = dst->step;
CV_Assert( CV_IS_MASK_ARR( src ) && CV_MAT_TYPE( dst->type ) == CV_8UC1 );
CV_Assert( CV_ARE_SIZES_EQ( src, dst ));
////////////////////// forward scan ////////////////////////
for( x = 0; x < 256; x++ )
lut[x] = CV_CAST_8U(x+1);
//init first pixel to max (we're going to be skipping it)
dbase[0] = (uchar)(sbase[0] == 0 ? 0 : 255);
//first row (scan west only, skip first pixel)
for( x = 1; x < width; x++ )
dbase[x] = (uchar)(sbase[x] == 0 ? 0 : lut[dbase[x-1]]);
for( y = 1; y < height; y++ )
{
sbase += srcstep;
dbase += dststep;
//for left edge, scan north only
a = sbase[0] == 0 ? 0 : lut[dbase[-dststep]];
dbase[0] = (uchar)a;
for( x = 1; x < width; x++ )
{
a = sbase[x] == 0 ? 0 : lut[MIN(a, dbase[x - dststep])];
dbase[x] = (uchar)a;
}
}
////////////////////// backward scan ///////////////////////
a = dbase[width-1];
// do last row east pixel scan here (skip bottom right pixel)
for( x = width - 2; x >= 0; x-- )
{
a = lut[a];
dbase[x] = (uchar)(CV_CALC_MIN_8U(a, dbase[x]));
}
// right edge is the only error case
for( y = height - 2; y >= 0; y-- )
{
dbase -= dststep;
// do right edge
a = lut[dbase[width-1+dststep]];
dbase[width-1] = (uchar)(MIN(a, dbase[width-1]));
for( x = width - 2; x >= 0; x-- )
{
int b = dbase[x+dststep];
a = lut[MIN(a, b)];
dbase[x] = (uchar)(MIN(a, dbase[x]));
}
}
}
//END ATS ADDITION
/* Wrapper function for distance transform group */
CV_IMPL void
cvDistTransform( const void* srcarr, void* dstarr,
int distType, int maskSize,
const float *mask,
void* labelsarr, int labelType )
{
float _mask[5] = {0};
CvMat srcstub, *src = (CvMat*)srcarr;
CvMat dststub, *dst = (CvMat*)dstarr;
CvMat lstub, *labels = (CvMat*)labelsarr;
src = cvGetMat( src, &srcstub );
dst = cvGetMat( dst, &dststub );
if( !CV_IS_MASK_ARR( src ) || (CV_MAT_TYPE( dst->type ) != CV_32FC1 &&
(CV_MAT_TYPE(dst->type) != CV_8UC1 || distType != CV_DIST_L1 || labels)) )
CV_Error( CV_StsUnsupportedFormat,
"source image must be 8uC1 and the distance map must be 32fC1 "
"(or 8uC1 in case of simple L1 distance transform)" );
if( !CV_ARE_SIZES_EQ( src, dst ))
CV_Error( CV_StsUnmatchedSizes, "the source and the destination images must be of the same size" );
if( maskSize != CV_DIST_MASK_3 && maskSize != CV_DIST_MASK_5 && maskSize != CV_DIST_MASK_PRECISE )
CV_Error( CV_StsBadSize, "Mask size should be 3 or 5 or 0 (presize)" );
if( distType == CV_DIST_C || distType == CV_DIST_L1 )
maskSize = !labels ? CV_DIST_MASK_3 : CV_DIST_MASK_5;
else if( distType == CV_DIST_L2 && labels )
maskSize = CV_DIST_MASK_5;
if( maskSize == CV_DIST_MASK_PRECISE )
{
icvTrueDistTrans( src, dst );
return;
}
if( labels )
{
labels = cvGetMat( labels, &lstub );
if( CV_MAT_TYPE( labels->type ) != CV_32SC1 )
CV_Error( CV_StsUnsupportedFormat, "the output array of labels must be 32sC1" );
if( !CV_ARE_SIZES_EQ( labels, dst ))
CV_Error( CV_StsUnmatchedSizes, "the array of labels has a different size" );
if( maskSize == CV_DIST_MASK_3 )
CV_Error( CV_StsNotImplemented,
"3x3 mask can not be used for \"labeled\" distance transform. Use 5x5 mask" );
}
if( distType == CV_DIST_C || distType == CV_DIST_L1 || distType == CV_DIST_L2 )
{
icvGetDistanceTransformMask( (distType == CV_DIST_C ? 0 :
distType == CV_DIST_L1 ? 1 : 2) + maskSize*10, _mask );
}
else if( distType == CV_DIST_USER )
{
if( !mask )
CV_Error( CV_StsNullPtr, "" );
memcpy( _mask, mask, (maskSize/2 + 1)*sizeof(float));
}
CvSize size = cvGetMatSize(src);
if( CV_MAT_TYPE(dst->type) == CV_8UC1 )
{
icvDistanceATS_L1_8u( src, dst );
}
else
{
int border = maskSize == CV_DIST_MASK_3 ? 1 : 2;
cv::Ptr<CvMat> temp = cvCreateMat( size.height + border*2, size.width + border*2, CV_32SC1 );
if( !labels )
{
CvDistTransFunc func = maskSize == CV_DIST_MASK_3 ?
icvDistanceTransform_3x3_C1R :
icvDistanceTransform_5x5_C1R;
func( src->data.ptr, src->step, temp->data.i, temp->step,
dst->data.fl, dst->step, size, _mask );
}
else
{
cvZero( labels );
if( labelType == CV_DIST_LABEL_CCOMP )
{
CvSeq *contours = 0;
cv::Ptr<CvMemStorage> st = cvCreateMemStorage();
cv::Ptr<CvMat> src_copy = cvCreateMat( size.height+border*2, size.width+border*2, src->type );
cvCopyMakeBorder(src, src_copy, cvPoint(border, border), IPL_BORDER_CONSTANT, cvScalarAll(255));
cvCmpS( src_copy, 0, src_copy, CV_CMP_EQ );
cvFindContours( src_copy, st, &contours, sizeof(CvContour),
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, cvPoint(-border, -border));
for( int label = 1; contours != 0; contours = contours->h_next, label++ )
{
CvScalar area_color = cvScalarAll(label);
cvDrawContours( labels, contours, area_color, area_color, -255, -1, 8 );
}
}
else
{
int k = 1;
for( int i = 0; i < src->rows; i++ )
{
const uchar* srcptr = src->data.ptr + src->step*i;
int* labelptr = (int*)(labels->data.ptr + labels->step*i);
for( int j = 0; j < src->cols; j++ )
if( srcptr[j] == 0 )
labelptr[j] = k++;
}
}
icvDistanceTransformEx_5x5_C1R( src->data.ptr, src->step, temp->data.i, temp->step,
dst->data.fl, dst->step, labels->data.i, labels->step, size, _mask );
}
}
}
void cv::distanceTransform( InputArray _src, OutputArray _dst, OutputArray _labels,
int distanceType, int maskSize, int labelType )
{
Mat src = _src.getMat();
_dst.create(src.size(), CV_32F);
_labels.create(src.size(), CV_32S);
CvMat c_src = src, c_dst = _dst.getMat(), c_labels = _labels.getMat();
cvDistTransform(&c_src, &c_dst, distanceType, maskSize, 0, &c_labels, labelType);
}
void cv::distanceTransform( InputArray _src, OutputArray _dst,
int distanceType, int maskSize )
{
Mat src = _src.getMat();
_dst.create(src.size(), CV_32F);
Mat dst = _dst.getMat();
CvMat c_src = src, c_dst = _dst.getMat();
cvDistTransform(&c_src, &c_dst, distanceType, maskSize, 0, 0, -1);
}
/* End of file. */