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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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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"
namespace cv
{
static const int DIST_SHIFT = 16;
static const int INIT_DIST0 = (INT_MAX >> 2);
#define CV_FLT_TO_FIX(x,n) cvRound((x)*(1<<(n)))
static void
initTopBottom( Mat& temp, int border )
{
Size size = temp.size();
for( int i = 0; i < border; i++ )
{
int* ttop = temp.ptr<int>(i);
int* tbottom = temp.ptr<int>(size.height - i - 1);
for( int j = 0; j < size.width; j++ )
{
ttop[j] = INIT_DIST0;
tbottom[j] = INIT_DIST0;
}
}
}
static void
distanceTransform_3x3( const Mat& _src, Mat& _temp, Mat& _dist, const float* metrics )
{
const int BORDER = 1;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int srcstep = (int)(_src.step/sizeof(src[0]));
int step = (int)(_temp.step/sizeof(temp[0]));
int dststep = (int)(_dist.step/sizeof(dist[0]));
Size size = _src.size();
initTopBottom( _temp, 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] = 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);
}
}
}
static void
distanceTransform_5x5( const Mat& _src, Mat& _temp, Mat& _dist, const float* metrics )
{
const int BORDER = 2;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], DIST_SHIFT );
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int srcstep = (int)(_src.step/sizeof(src[0]));
int step = (int)(_temp.step/sizeof(temp[0]));
int dststep = (int)(_dist.step/sizeof(dist[0]));
Size size = _src.size();
initTopBottom( _temp, 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] = 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);
}
}
}
static void
distanceTransformEx_5x5( const Mat& _src, Mat& _temp, Mat& _dist, Mat& _labels, const float* metrics )
{
const int BORDER = 2;
int i, j;
const int HV_DIST = CV_FLT_TO_FIX( metrics[0], DIST_SHIFT );
const int DIAG_DIST = CV_FLT_TO_FIX( metrics[1], DIST_SHIFT );
const int LONG_DIST = CV_FLT_TO_FIX( metrics[2], DIST_SHIFT );
const float scale = 1.f/(1 << DIST_SHIFT);
const uchar* src = _src.ptr();
int* temp = _temp.ptr<int>();
float* dist = _dist.ptr<float>();
int* labels = _labels.ptr<int>();
int srcstep = (int)(_src.step/sizeof(src[0]));
int step = (int)(_temp.step/sizeof(temp[0]));
int dststep = (int)(_dist.step/sizeof(dist[0]));
int lstep = (int)(_labels.step/sizeof(dist[0]));
Size size = _src.size();
initTopBottom( _temp, 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] = INIT_DIST0;
for( j = 0; j < size.width; j++ )
{
if( !s[j] )
{
tmp[j] = 0;
//assert( lls[j] != 0 );
}
else
{
int t0 = 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);
}
}
}
static void getDistanceTransformMask( int maskType, float *metrics )
{
CV_Assert( metrics != 0 );
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:
CV_Error(CV_StsBadArg, "Unknown metric type");
}
}
struct DTColumnInvoker : ParallelLoopBody
{
DTColumnInvoker( const Mat* _src, Mat* _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 Range& range ) const
{
int i, i1 = range.start, 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->ptr(m-1) + i;
float* dptr = dst->ptr<float>() + 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 Mat* src;
Mat* dst;
const int* sat_tab;
const float* sqr_tab;
};
struct DTRowInvoker : ParallelLoopBody
{
DTRowInvoker( Mat* _dst, const float* _sqr_tab, const float* _inv_tab )
{
dst = _dst;
sqr_tab = _sqr_tab;
inv_tab = _inv_tab;
}
void operator()( const Range& range ) const
{
const float inf = 1e15f;
int i, i1 = range.start, 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 = dst->ptr<float>(i);
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]);
}
}
}
Mat* dst;
const float* sqr_tab;
const float* inv_tab;
};
static void
trueDistTrans( const Mat& src, Mat& dst )
{
const float inf = 1e15f;
CV_Assert( src.size() == dst.size() );
CV_Assert( src.type() == CV_8UC1 && dst.type() == CV_32FC1 );
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::Range(0, n), cv::DTColumnInvoker(&src, &dst, sat_tab, sqr_tab), src.total()/(double)(1<<16));
// 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::Range(0, m), cv::DTRowInvoker(&dst, sqr_tab, inv_tab));
}
/****************************************************************************************\
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
distanceATS_L1_8u( const Mat& src, Mat& dst )
{
int width = src.cols, height = src.rows;
int a;
uchar lut[256];
int x, y;
const uchar *sbase = src.ptr();
uchar *dbase = dst.ptr();
int srcstep = (int)src.step;
int dststep = (int)dst.step;
CV_Assert( src.type() == CV_8UC1 && dst.type() == CV_8UC1 );
CV_Assert( src.size() == dst.size() );
////////////////////// forward scan ////////////////////////
for( x = 0; x < 256; x++ )
lut[x] = cv::saturate_cast<uchar>(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]];
a = 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)];
a = MIN(a, dbase[x]);
dbase[x] = (uchar)(a);
}
}
}
//END ATS ADDITION
}
namespace cv
{
static void distanceTransform_L1_8U(InputArray _src, OutputArray _dst)
{
CV_INSTRUMENT_REGION()
Mat src = _src.getMat();
CV_Assert( src.type() == CV_8UC1);
_dst.create( src.size(), CV_8UC1);
Mat dst = _dst.getMat();
#ifdef HAVE_IPP
CV_IPP_CHECK()
{
IppiSize roi = { src.cols, src.rows };
Ipp32s pMetrics[2] = { 1, 2 }; //L1, 3x3 mask
if (CV_INSTRUMENT_FUN_IPP(ippiDistanceTransform_3x3_8u_C1R, src.ptr<uchar>(), (int)src.step, dst.ptr<uchar>(), (int)dst.step, roi, pMetrics) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
}
#endif
distanceATS_L1_8u(src, dst);
}
}
// Wrapper function for distance transform group
void cv::distanceTransform( InputArray _src, OutputArray _dst, OutputArray _labels,
int distType, int maskSize, int labelType )
{
CV_INSTRUMENT_REGION()
Mat src = _src.getMat(), labels;
bool need_labels = _labels.needed();
CV_Assert( src.type() == CV_8UC1);
_dst.create( src.size(), CV_32F);
Mat dst = _dst.getMat();
if( need_labels )
{
CV_Assert( labelType == DIST_LABEL_PIXEL || labelType == DIST_LABEL_CCOMP );
_labels.create(src.size(), CV_32S);
labels = _labels.getMat();
maskSize = CV_DIST_MASK_5;
}
float _mask[5] = {0};
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 (precise)" );
if( distType == CV_DIST_C || distType == CV_DIST_L1 )
maskSize = !need_labels ? CV_DIST_MASK_3 : CV_DIST_MASK_5;
else if( distType == CV_DIST_L2 && need_labels )
maskSize = CV_DIST_MASK_5;
if( maskSize == CV_DIST_MASK_PRECISE )
{
#ifdef HAVE_IPP
CV_IPP_CHECK()
{
#if IPP_DISABLE_PERF_TRUE_DIST_MT
if(cv::getNumThreads()<=1 || (src.total()<(int)(1<<14)))
#endif
{
IppStatus status;
IppiSize roi = { src.cols, src.rows };
Ipp8u *pBuffer;
int bufSize=0;
status = ippiTrueDistanceTransformGetBufferSize_8u32f_C1R(roi, &bufSize);
if (status>=0)
{
pBuffer = (Ipp8u *)CV_IPP_MALLOC( bufSize );
status = CV_INSTRUMENT_FUN_IPP(ippiTrueDistanceTransform_8u32f_C1R, src.ptr<uchar>(), (int)src.step, dst.ptr<float>(), (int)dst.step, roi, pBuffer);
ippFree( pBuffer );
if (status>=0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
}
}
}
#endif
trueDistTrans( src, dst );
return;
}
CV_Assert( distType == CV_DIST_C || distType == CV_DIST_L1 || distType == CV_DIST_L2 );
getDistanceTransformMask( (distType == CV_DIST_C ? 0 :
distType == CV_DIST_L1 ? 1 : 2) + maskSize*10, _mask );
Size size = src.size();
int border = maskSize == CV_DIST_MASK_3 ? 1 : 2;
Mat temp( size.height + border*2, size.width + border*2, CV_32SC1 );
if( !need_labels )
{
if( maskSize == CV_DIST_MASK_3 )
{
#if defined (HAVE_IPP) && (IPP_VERSION_X100 >= 700)
CV_IPP_CHECK()
{
IppiSize roi = { src.cols, src.rows };
if (CV_INSTRUMENT_FUN_IPP(ippiDistanceTransform_3x3_8u32f_C1R, src.ptr<uchar>(), (int)src.step, dst.ptr<float>(), (int)dst.step, roi, _mask) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
}
#endif
distanceTransform_3x3(src, temp, dst, _mask);
}
else
{
#if defined (HAVE_IPP) && (IPP_VERSION_X100 >= 700)
CV_IPP_CHECK()
{
IppiSize roi = { src.cols, src.rows };
if (CV_INSTRUMENT_FUN_IPP(ippiDistanceTransform_5x5_8u32f_C1R, src.ptr<uchar>(), (int)src.step, dst.ptr<float>(), (int)dst.step, roi, _mask) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
}
#endif
distanceTransform_5x5(src, temp, dst, _mask);
}
}
else
{
labels.setTo(Scalar::all(0));
if( labelType == CV_DIST_LABEL_CCOMP )
{
Mat zpix = src == 0;
connectedComponents(zpix, labels, 8, CV_32S, CCL_WU);
}
else
{
int k = 1;
for( int i = 0; i < src.rows; i++ )
{
const uchar* srcptr = src.ptr(i);
int* labelptr = labels.ptr<int>(i);
for( int j = 0; j < src.cols; j++ )
if( srcptr[j] == 0 )
labelptr[j] = k++;
}
}
distanceTransformEx_5x5( src, temp, dst, labels, _mask );
}
}
void cv::distanceTransform( InputArray _src, OutputArray _dst,
int distanceType, int maskSize, int dstType)
{
CV_INSTRUMENT_REGION()
if (distanceType == CV_DIST_L1 && dstType==CV_8U)
distanceTransform_L1_8U(_src, _dst);
else
distanceTransform(_src, _dst, noArray(), distanceType, maskSize, DIST_LABEL_PIXEL);
}
CV_IMPL void
cvDistTransform( const void* srcarr, void* dstarr,
int distType, int maskSize,
const float * /*mask*/,
void* labelsarr, int labelType )
{
cv::Mat src = cv::cvarrToMat(srcarr);
const cv::Mat dst = cv::cvarrToMat(dstarr);
const cv::Mat labels = cv::cvarrToMat(labelsarr);
cv::distanceTransform(src, dst, labelsarr ? cv::_OutputArray(labels) : cv::_OutputArray(),
distType, maskSize, labelType);
}
/* End of file. */