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segm.cc
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//Color Image Segmentation
//This is the implementation of the algorithm described in
//D. Comaniciu, P. Meer,
//Robust Analysis of Feature Spaces: Color Image Segmentation,
//http://www.caip.rutgers.edu/~meer/RIUL/PAPERS/feature.ps.gz
//appeared in Proceedings of CVPR'97, San Juan, Puerto Rico.
// ===========================================================================
// ===== Module: segm.cc
// ===== -------------------------------------------------------------- ======
// ===== Version 01 Date: 04/22/97
// ===== -------------------------------------------------------------- ======
// ===========================================================================
// ===== Written by Dorin Comaniciu
// ===== e-mail: comanici@caip.rutgers.edu
// ===========================================================================
// Permission to use, copy, or modify this software and its documentation
// for educational and research purposes only is hereby granted without
// fee, provided that this copyright notice appear on all copies and
// related documentation. For any other uses of this software, in original
// or modified form, including but not limited to distribution in whole
// or in part, specific prior permission must be obtained from
// the author(s).
//
// THE SOFTWARE IS PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND,
// EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY
// WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
//
// IN NO EVENT SHALL RUTGERS UNIVERSITY BE LIABLE FOR ANY SPECIAL,
// INCIDENTAL, INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND, OR ANY
// DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
// WHETHER OR NOT ADVISED OF THE POSSIBILITY OF DAMAGE, AND ON ANY
// THEORY OF LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE USE
// OR PERFORMANCE OF THIS SOFTWARE.
// ===========================================================================
//
#include <math.h>
#include <stdlib.h>
#include <limits.h>
#include <iostream>
//#include <fstream>
//#include <fstream.h>
//#include <strstream.h>
#include <memory.h>
#include <time.h>
#include "segm.hh"
using namespace std;
int option = 2;
static const int rect_gen_contor=3;
// Radius of the searching window
static float gen_RADIUS[3]={2, 3, 4};
static float rect_RADIUS[rect_gen_contor]={8, 6, 4};
static float fix_RADIUS[rect_gen_contor];
static float final_RADIUS;
static float RADIUS2;
static float RADIUS;
static float max_dist;
static int my_threshold[3]={50, 100, 400};
static int my_rap[3]={4, 2, 1};
static int act_threshold;
#define my_abs(a) ((a) > 0 ? (a): (-a))
#define SQRT2 1.4142
#define SQRT3 1.7321
static const float BIG_NUM = 1.0e+20;
// Coefficient matrix for xyz and rgb spaces
static const int XYZ[3][3] = { { 4125, 3576, 1804 },
{ 2125, 7154, 721 },
{ 193, 1192, 9502 } };
static const float RGB[3][3] = { { 3.2405, -1.5371, -0.4985 },
{-0.9693, 1.8760, 0.0416 },
{ 0.0556, -0.2040, 1.0573 } };
// Constants for LUV transformation
static const float Xn = 0.9505;
static const float Yn = 1.0;
static const float Zn = 1.0888;
static const float Un_prime = 0.1978;
static const float Vn_prime = 0.4683;
static const float Lt = 0.008856;
// # of samples
static const int Max_J = 25;
// Limit of the number of failed trials
static const int Max_trials = 50;
// Defaults values for the parameters.
static const int sam_max = 60;
// Few more trials at the end
static const int MAX_TRIAL=10;
// Used in 3-D histogram computation
static const int FIRST_SIZE=262144; // 2^18
static const int SEC_SIZE=64; // 2^6
// Make coordinate computation faster
// my_neigh is for auto_segm, my_neigh_r works with region
static int my_neigh[8];
static int my_neigh_r[8];
// Results
static int ORIG_COLORS;
static int SEGM_COLORS;
Boolean my_write_PGM_file( char*, Octet*,int,int);
void covariance_w(const int N, int M, const int p, int ** data,
int *w, float T[], float C[p_max][p_max]);
void mean_s(const int N, const int p, int J[], int **data, float T[]);
void my_convert(int, float *, int *);
void reverse_map(Octet *, Octet *, int *, Octet *, float T[][p_max]);
// Class constructor
SegmenterMS::SegmenterMS( )
{
_p = 0;
_p_ptr = 0;
_rrows = 0;
_rcolms = 0;
_data_all = nil;
_data = nil;
_NJ = Max_J;
}
// Class destructor
SegmenterMS::~SegmenterMS( )
{
if ( _data_all ) {
for ( register int i = 0; i < _p; i++ )
if ( _data_all[i] ) delete [] _data_all[i];
delete [] _data_all;
}
if ( _data ) {
for ( register int i = 0; i < _p; i++ )
if ( _data[i] ) delete [] _data[i];
delete [] _data;
}
}
// LUV (final_T[]) to RGB (TI[]) conversion
void my_convert(int selects, float *final_T, int *TI)
{
// this condition is always true
if ( selects & Lightness && selects & Ustar && selects & Vstar )
{
if(final_T[0]<0.1)
{
TI[0]=0;TI[1]=0;TI[2]=0;
}
else
{
float my_x, my_y, my_z;
if(final_T[0]< 8.0)
my_y = Yn * final_T[0] / 903.3;
else
my_y = Yn * pow((final_T[0] + 16.0) / 116.0, 3);
float u_prime = final_T[1] / (13 * final_T[0]) + Un_prime;
float v_prime = final_T[2] / (13 * final_T[0]) + Vn_prime;
my_x = 9 * u_prime * my_y / (4 * v_prime);
my_z = (12 - 3 * u_prime - 20 * v_prime) * my_y / (4 * v_prime);
TI[0] =int((RGB[0][0]*my_x + RGB[0][1]*my_y + RGB[0][2]*my_z)*255.0);
TI[1] =int((RGB[1][0]*my_x + RGB[1][1]*my_y + RGB[1][2]*my_z)*255.0);
TI[2] =int((RGB[2][0]*my_x + RGB[2][1]*my_y + RGB[2][2]*my_z)*255.0);
if(TI[0]>255) TI[0]=255;
else if(TI[0]<0) TI[0]=0;
if(TI[1]>255) TI[1]=255;
else if(TI[1]<0) TI[1]=0;
if(TI[2]>255) TI[2]=255;
else if(TI[2]<0) TI[2]=0;
}
}
else
{
TI[0]=(int)final_T[0];
TI[1]=(int)final_T[1];
TI[2]=(int)final_T[2];
}
}
// RGB to LUV conversion
// To gain speed the conversion works on a table of colors (_col_RGB[])
// rather than on the whole image
void SegmenterMS::convert_RGB_LUV( RasterIpChannels* signal, long selects )
{
int x, y, z, my_temp;
float l_star, u_star, v_star;
float u_prime, v_prime;
register int temp_col, temp_index, temp_ind;
register int j,k;
int a00=XYZ[0][0], a01=XYZ[0][1], a02=XYZ[0][2];
int a10=XYZ[1][0], a11=XYZ[1][1], a12=XYZ[1][2];
int a20=XYZ[2][0], a21=XYZ[2][1], a22=XYZ[2][2];
int *A00 = new int[MAXV]; int *A01 = new int[MAXV]; int *A02 = new int[MAXV];
int *A10 = new int[MAXV]; int *A11 = new int[MAXV]; int *A12 = new int[MAXV];
int *A20 = new int[MAXV]; int *A21 = new int[MAXV]; int *A22 = new int[MAXV];
for(j=0; j<MAXV;j++)
{
A00[j]=a00*j; A01[j]=a01*j; A02[j]=a02*j;
A10[j]=a10*j; A11[j]=a11*j; A12[j]=a12*j;
A20[j]=a20*j; A21[j]=a21*j; A22[j]=a22*j;
}
float *my_pow = new float[MAXV];
for(j=0; j<MAXV;j++)
my_pow[j]= 116.0 * pow(j/255.0, 0.3333333) - 16;
Octet* temp_ch0 = signal->chdata(0);
Octet* temp_ch1 = signal->chdata(1);
Octet* temp_ch2 = signal->chdata(2);
int pp;
int *temp0, *temp1, *temp2;
pp = _p_ptr;
if ( selects & Lightness ) temp0 = _data_all[pp++];
if ( selects & Ustar ) temp1 = _data_all[pp++];
if ( selects & Vstar ) temp2 = _data_all[pp++];
_p_ptr=pp;
for ( j = 0; j < _n_colors; j++)
{
temp_col=_col_RGB[j];
int R=temp_col>>16; int G=(temp_col>>8) & 255; int B=temp_col & 255;
x = A00[R] + A01[G] + A02[B];
y = A10[R] + A11[G] + A12[B];
z = A20[R] + A21[G] + A22[B];
float tval = y / 2550000.0; //Yn==1
if ( tval > Lt) l_star = my_pow[(int)(tval*255+0.5)];
else l_star = 903.3 * tval;
my_temp = x + 15 * y + 3 * z;
if(my_temp)
{
u_prime = (float)(x << 2) / (float)(my_temp);
v_prime = (float)(9 * y) / (float)(my_temp);
}
else
{
u_prime=4.0;
v_prime=9.0/15.0;
}
tval=13*l_star;
u_star = tval* (u_prime - Un_prime); // Un_prime = 0.1978
v_star = tval* (v_prime - Vn_prime); // Vn_prime = 0.4683
_col0[j] = (int)(l_star+0.5);
if(u_star>0) _col1[j] = (int)(u_star+0.5);
else _col1[j] = (int)(u_star-0.5);
if(v_star>0) _col2[j] = (int)(v_star+0.5);
else _col2[j] = (int)(v_star-0.5);
}
for(j=0;j<_ro_col;j++)
{
temp_col=(((((int)temp_ch0[j])<<8)+(int)temp_ch1[j])<<8)+(int)temp_ch2[j];
temp_ind=_col_misc[temp_col>>6];
for(k=temp_ind;k<temp_ind+SEC_SIZE;k++)
if(_col_RGB[k]==temp_col)
{
temp_index=_col_index[j]=k;
break;
}
temp0[j]=_col0[temp_index];
temp1[j]=_col1[temp_index];
temp2[j]=_col2[temp_index];
}
delete [] my_pow;
delete [] A22; delete [] A21; delete [] A20;
delete [] A12; delete [] A11; delete [] A10;
delete [] A02; delete [] A01; delete [] A00;
delete [] _col_misc;
delete [] _col_RGB;
cerr<<":";
}
// 3-D Histogram computation
// Implement a trade-off between speed and required memory
void SegmenterMS::my_histogram(RasterIpChannels* signal, long selects)
{
int *first_tab= new int[FIRST_SIZE];
_col_misc= new int[FIRST_SIZE];
int **third_tab;
int *fourth_tab;
int *fifth_tab=new int[SEC_SIZE];
_n_colors=0;
register int k,j,p,r;
int temp_ind, sec_ind, third_ind;
int first_contor=0, third_contor=0;
memset(first_tab,0,sizeof(int)*FIRST_SIZE);
memset(_col_misc,0,sizeof(int)*FIRST_SIZE);
register Octet* ch0 = signal->chdata(0);
register Octet* ch1 = signal->chdata(1);
register Octet* ch2 = signal->chdata(2);
//first_tab -> how many
for(k=0;k<_ro_col;k++)
{
temp_ind=(((ch0[k]<<8)+ch1[k])<<2)+(ch2[k]>>6);
first_tab[temp_ind]++;
}
//_col_misc -> memo position
for(k=0;k<FIRST_SIZE;k++)
if(first_tab[k])
{
_col_misc[k]=first_contor;
first_contor++;
}
//contors
fourth_tab=new int[first_contor];
memset(fourth_tab,0,sizeof(int)*first_contor);
//tab of pointers to reduced colors
third_tab=new int *[first_contor];
first_contor=0;
for(k=0;k<FIRST_SIZE;k++)
if(first_tab[k])
{
third_tab[first_contor]=new int[first_tab[k]];
first_contor++;
}
for(k=0;k<_ro_col;k++)
{
temp_ind=(((ch0[k]<<8)+ch1[k])<<2)+(ch2[k]>>6);
sec_ind=ch2[k] & 63;
third_ind=_col_misc[temp_ind];
third_tab[third_ind][fourth_tab[third_ind]]=sec_ind;
fourth_tab[third_ind]++;
}
for(k=0;k<first_contor;k++)
{
memset(fifth_tab,0,sizeof(int)*SEC_SIZE);
for(j=0;j<fourth_tab[k];j++)
fifth_tab[third_tab[k][j]]++;
for(j=0;j<SEC_SIZE;j++)
if(fifth_tab[j])
_n_colors++;
}
_col_RGB=new int[_n_colors];
_m_colors=new int[_n_colors];
k=0;p=0;
for(r=0;r<FIRST_SIZE;r++)
if(first_tab[r])
{
memset(fifth_tab,0,sizeof(int)*SEC_SIZE);
for(j=0;j<fourth_tab[k];j++)
fifth_tab[third_tab[k][j]]++;
_col_misc[r]=p;
for(j=0;j<SEC_SIZE;j++)
if(fifth_tab[j])
{
_col_RGB[p]=(r<<6)+j;
_m_colors[p]=fifth_tab[j];
p++;
}
delete [] third_tab[k];
k++;
}
delete [] third_tab;
delete [] fourth_tab;
delete [] fifth_tab;
delete [] first_tab;
_col_all = new int*[3];
_col0=_col_all[0] = new int[_n_colors];
_col1=_col_all[1] = new int[_n_colors];
_col2=_col_all[2] = new int[_n_colors];
_col_index = new int[_ro_col];
cerr<<":";
}
// Update _col_remain[], _m_col_remain, and _n_col_remain
void SegmenterMS::my_actual(Octet *my_class)
{
register int i;
int temp_contor=n_remain;
register int *temp_rem= new int[_ro_col];
memcpy(temp_rem,gen_remain,sizeof(int)*temp_contor);
n_remain=0;
for(i=0;i<temp_contor;i++)
if(!my_class[temp_rem[i]])
gen_remain[n_remain++]=temp_rem[i];
delete [] temp_rem;
memset(_col_remain,0,sizeof(int)*_n_col_remain);
memset(_m_col_remain,0,sizeof(int)*_n_colors);
_n_col_remain=0;
for(i=0;i<n_remain;i++)
_m_col_remain[_col_index[gen_remain[i]]]++;
for(i=0;i<_n_colors;i++)
if(_m_col_remain[i])
{
_col_remain[_n_col_remain]=i;
_n_col_remain++;
}
}
// if more than "how_many" neighbors, consider the point
void SegmenterMS::test_neigh(Octet* my_class, int *selected, int* my_contor, int how_many)
{
register int i,j,p,k;
register Octet* local_class=my_class;
register int temp_contor=*my_contor;
register int my_index;
if(auto_segm) my_index=n_remain;
else my_index=_ro_col;
for ( p = 0, i; p < my_index; p++ )
{
if(auto_segm) i=gen_remain[p];
else i=p;
if(!local_class[i])
{
int neigh_contor=0, no_neigh=1;
for(j=0;j<8;j++)
{
k=i+my_neigh[j];
if(k>=0 && k<_ro_col && local_class[k])
{
if(auto_segm && gen_class[k]!=255) continue;
neigh_contor++;
if(neigh_contor>how_many)
{
no_neigh=0;
break;
}
}
}
if(!no_neigh)
{
if(auto_segm) selected[*my_contor]=i;
*my_contor=*my_contor+1;
}
}
}
for(i=temp_contor;i<*my_contor;i++)
local_class[selected[i]]=1;
}
// Find the feature vectors inside the given window
// Use Improved Absolute Error Inequality Criterion
// when computing Euclidean distance
// See J.S.Pan el al, Fast Clustering Alg. for VQ, Pattern Recognition,
// Vol. 29, No. 3, pp. 511-518, 1996
void SegmenterMS::new_auto_loop(float *final_T, Octet *sel_col)
{
float L,U,V,RAD2,R;
register int TT0=0, TT1=0, TT2=0;
register int local_contor=0;
float final_T0=final_T[0], final_T1=final_T[1], final_T2=final_T[2];
float RADIUS_S2=SQRT2*RADIUS, RADIUS_S3=SQRT3*RADIUS;
for ( register int p = 0, k; p < _n_col_remain; p++ )
{
k=_col_remain[p];
L=_col0[k]-final_T0; if((L=my_abs(L))>=RADIUS) continue;
U=_col1[k]-final_T1; if((R=my_abs(U)+L)>=RADIUS_S2) continue;
V=_col2[k]-final_T2; if(R+my_abs(V)>=RADIUS_S3) continue;
RAD2=L*L+U*U+V*V;
if(RAD2<RADIUS2)
{
register int r=_m_col_remain[k];
TT0+=_col0[k]*r; TT1+=_col1[k]*r; TT2+=_col2[k]*r;
local_contor+=r;
sel_col[k]=1;
}
}
final_T[0]=(float)TT0/(float)local_contor;
final_T[1]=(float)TT1/(float)local_contor;
final_T[2]=(float)TT2/(float)local_contor;
}
// The same as above, but for non auto_segmentation
void SegmenterMS::nauto_loop(float *final_T, int *selected,
Octet *my_class, int *my_contor)
{
float L,U,V,RAD2,R;
register int local_contor=*my_contor;
float final_T0=final_T[0], final_T1=final_T[1], final_T2=final_T[2];
float RADIUS_S2=SQRT2*RADIUS, RADIUS_S3=SQRT3*RADIUS;
for ( register int k = 0; k < _n_points; k++ )
{
L=_data[0][k]-final_T0; if((L=my_abs(L))>=RADIUS) continue;
U=_data[1][k]-final_T1; if((R=my_abs(U)+L)>=RADIUS_S2) continue;
V=_data[2][k]-final_T2; if(R+my_abs(V)>=RADIUS_S3) continue;
RAD2=L*L+U*U+V*V;
if(RAD2<RADIUS2)
{
selected[local_contor++]=k;
my_class[k]=1;
}
}
*my_contor=local_contor;
}
// Set the Radius of the window
void set_RADIUS(int gen_gen_contor, int final)
{
if(final==2) RADIUS=final_RADIUS*1.26;
else if(final==1) RADIUS=final_RADIUS;
else RADIUS=fix_RADIUS[gen_gen_contor];
RADIUS2=RADIUS*RADIUS;
}
// Test if the clusters have the same mean
int test_same_cluster(int rect, float T[][p_max])
{
float L,U,V,RAD2;
for(register int k=0; k<rect;k++)
{
L=T[k][0]-T[rect][0]; U=T[k][1]-T[rect][1]; V=T[k][2]-T[rect][2];
RAD2=L*L+U*U+V*V;
if(RAD2<1)
return 1;
}
return 0;
}
// First take only pixels inside the search windows at their final locations
// Then inflate windows to double volume and retain only pixels which are
// neighbors with the previous
void SegmenterMS::get_codeblock1(float T[][p_max], int n_rects)
{
float L,U,V,RAD2, R, min_RAD2;
int min_ind;
register int i,k,u;
register int pres_class, my_flag;
register float *ptr;
if(auto_segm) set_RADIUS(0,0);
else set_RADIUS(2,0);
for(k=0;k<_ro_col;k++)
{
min_RAD2=BIG_NUM; min_ind=0;
for(i=0;i<n_rects;i++)
{
ptr=T[i];
L=_data0[k]-ptr[0]; if(my_abs(L)>=RADIUS) continue;
U=_data1[k]-ptr[1]; if(my_abs(U)>=RADIUS) continue;
V=_data2[k]-ptr[2]; if(my_abs(V)>=RADIUS) continue;
RAD2=L*L+U*U+V*V;
if(RAD2<min_RAD2)
{
min_RAD2=RAD2;
min_ind=i;
}
}
if(min_RAD2<RADIUS2) gen_class[k]=min_ind;
else gen_class[k]=n_rects;
}
if(auto_segm) set_RADIUS(0,1);
else set_RADIUS(0,0);
for(k=0;k<_ro_col;k++)
if(gen_class[k]==n_rects)
for(i=0;i<8;i++)
{
u=k+my_neigh[i];
if(u>=0 && u<_ro_col)
if((pres_class=gen_class[u])!=n_rects)
{
ptr=T[pres_class];
L=_data0[k]-ptr[0]; if(my_abs(L)>=RADIUS) continue;
U=_data1[k]-ptr[1]; if(my_abs(U)>=RADIUS) continue;
V=_data2[k]-ptr[2]; if(my_abs(V)>=RADIUS) continue;
RAD2=L*L+U*U+V*V;
if(RAD2<RADIUS2) gen_class[k]=pres_class;
}
}
}
// Final allocation
void SegmenterMS::get_codeblock(float T[][p_max], int n_rects)
{
float L,U,V,RAD2, min_RAD2;
register int min_ind;
register int i,k;
register float *ptr;
for(k=0;k<_ro_col;k++)
{
min_RAD2=BIG_NUM;
min_ind=0;
for(i=0;i<n_rects;i++)
{
ptr=T[i];
L=_data0[k]-ptr[0]; U=_data1[k]-ptr[1]; V=_data2[k]-ptr[2];
RAD2=L*L+U*U+V*V;
if(RAD2<min_RAD2)
{
min_RAD2=RAD2;
min_ind=i;
}
}
gen_class[k]=min_ind;
}
}
// Compute the mean of feature vectors mapped into the same color
void SegmenterMS::new_codebook(float T[][p_max], int n_rects)
{
register int i,k;
register int *tab_contor = new int[n_rects];
register int prez_class;
register float *ptr;
memset(tab_contor,0,sizeof(int)*n_rects);
for(i=0;i<n_rects;i++)
{
T[i][0]=0.0; T[i][1]=0.0; T[i][2]=0.0;
}
for(k=0;k<_ro_col;k++)
if((prez_class=gen_class[k])!=n_rects)
{
ptr=T[prez_class];
ptr[0]+=_data0[k]; ptr[1]+=_data1[k]; ptr[2]+=_data2[k];
tab_contor[prez_class]++;
}
for(i=0;i<n_rects;i++)
{
T[i][0]/=tab_contor[i]; T[i][1]/=tab_contor[i]; T[i][2]/=tab_contor[i];
}
delete [] tab_contor;
}
// Determine the final feature palette
void SegmenterMS::optimize(float T[][p_max], int n_rects)
{
get_codeblock1(T,n_rects);
new_codebook(T,n_rects);
if(auto_segm)
get_codeblock(T,n_rects);
cerr<<":";
}
// Inverse of the mapping array used in color elimination
void reverse_map(Octet *inv_map, Octet *my_map, int *n_rects, Octet *valid_class, float T[][p_max])
{
float sec_T[Max_rects][p_max];
register int u=0, k, j;
for(j=0;j<*n_rects;j++)
{
if(valid_class[j])
{
for(k=0;k<3;k++)
sec_T[u][k]=T[j][k];
my_map[j]=u;
inv_map[u]=j;
u++;
}
}
my_map[*n_rects]=u;
inv_map[u]=*n_rects;
*n_rects=u;
for(j=0;j<*n_rects;j++)
for(k=0;k<3;k++)
T[j][k]=sec_T[j][k];
}
// Eliminate colors that have less than "my_lim" connected pixels
void SegmenterMS::eliminate_class(Octet *my_class,int *my_max_region, int *n_rects,int my_lim, Octet* inv_map, float T[][p_max], REGION *first_region)
{
register int j, k;
register Octet *valid_class;
register REGION *current_region=first_region;
valid_class=new Octet[*n_rects];
for(j=0;j<*n_rects;j++)
{
if(my_max_region[j]<my_lim) valid_class[j]=0;
else valid_class[j]=1;
}
while(1)
{
if((current_region->my_class<*n_rects &&
!valid_class[current_region->my_class]))
for(k=0;k<current_region->my_contor;k++)
gen_class[current_region->my_region[k]]=*n_rects;
if(current_region->next_region_str)
current_region=current_region->next_region_str;
else break;
}
Octet my_map[Max_rects];
reverse_map(inv_map,my_map,n_rects,valid_class,T);
for(k=0;k<_ro_col;k++)
my_class[k]=my_map[gen_class[k]];
delete [] valid_class;
memcpy(gen_class,my_class,_ro_col);
}
// Eliminate regions with less than "my_lim" pixels
void SegmenterMS::eliminate_region(int *n_rects,int my_lim, float T[][p_max], REGION* first_region)
{
register int j,u,k,p, pres_class, min_ind;
register REGION *current_region=first_region;
register int* region;
float *ptr;
float L,U,V,RAD2,minRAD2;
int increm;
while(1)
{
if(current_region->my_contor<my_lim)
{
set_RADIUS(0,0); increm=4;
region=current_region->my_region;
for(k=0;k<current_region->my_contor;k++)
gen_class[region[k]]=*n_rects;
while(1)
{
Boolean my_flag=0;
RADIUS+=increm; RADIUS2=RADIUS*RADIUS; increm+=4;
for(k=0;k<current_region->my_contor;k++)
if(gen_class[p=region[k]]==(*n_rects))
{
minRAD2=RADIUS2;
for(j=1;j<8;j+=2)
{
u=p+my_neigh[j];
if(u>=0 && u<_ro_col)
if((pres_class=gen_class[u])!=(*n_rects))
{
ptr=T[pres_class];
L=_data0[p]-ptr[0]; U=_data1[p]-ptr[1];
V=_data2[p]-ptr[2]; RAD2=L*L+U*U+V*V;
if(RAD2<minRAD2)
{
minRAD2=RAD2; min_ind=pres_class;
}
}
}
if(minRAD2<RADIUS2) gen_class[p]=min_ind;
my_flag=1;
}
if(!my_flag) break;
}
}
if(current_region->next_region_str)
current_region=current_region->next_region_str;
else break;
}
}
// Destroy the region list
void SegmenterMS::destroy_region_list(REGION *first_region)
{
register REGION *current_region=first_region;
while(1)
{
delete [] current_region->my_region;
first_region=current_region;
if(current_region->next_region_str)
{
current_region=current_region->next_region_str;
delete first_region;
}
else
{
delete first_region;
break;
}
}
}
// Connected component main routine
void SegmenterMS::find_other_neigh(int k, int *my_ptr_tab, REGION *current_region)
{
register int *ptr_tab=my_ptr_tab;
register int i,u, j=k, sec_signal;
register int contor=0;
register int region_contor=current_region->my_contor;
register int region_class=current_region->my_class;
ptr_tab[contor]=j;
while(1)
{
sec_signal=0;
for(i=1;i<9;i+=2)
{
u=j+my_neigh[i];
if(u>=0 && u<_ro_col)
if(gen_class[u]==region_class && !taken[u])
{
sec_signal=1;
conn_selected[region_contor++]=u;
taken[u]=1;
ptr_tab[++contor]=u;
}
}
if(sec_signal) j=ptr_tab[contor];
else
{
if(contor>1) j=ptr_tab[--contor];
else break;
}
}
current_region->my_contor=region_contor;
}
// Create the region list
REGION *SegmenterMS::create_region_list(int *my_max_region, int change_type)
{
register int k, local_label=0;
register REGION *first_region, *prev_region, *current_region;
taken = new Octet[_ro_col];
memset(taken,0,_ro_col);
conn_selected = new int[_ro_col];
int *ptr_tab=new int[_ro_col];
for(k=0;k<_ro_col;k++)
if(!taken[k])
{
current_region=new REGION;
current_region->my_contor=0;
current_region->my_class=gen_class[k];
current_region->my_label=local_label;
if(k!=0) prev_region->next_region_str=current_region;
if(k==0){ first_region=current_region;}
local_label++;
conn_selected[current_region->my_contor++]=k;
taken[k]=1;
find_other_neigh(k,ptr_tab,current_region);
if(change_type==0)
if(my_max_region[current_region->my_class]<current_region->my_contor)
my_max_region[current_region->my_class]=current_region->my_contor;
current_region->my_region=new int[current_region->my_contor];
memcpy(current_region->my_region,conn_selected,sizeof(int)*current_region->my_contor);
prev_region=current_region;
}
current_region->next_region_str=0;
delete [] ptr_tab; delete [] taken; delete [] conn_selected;
return first_region;
}
// Find connected components and remove small regions of classes
// with small regions
void SegmenterMS::conn_comp(Octet *my_class, int *n_rects, Octet *inv_map, float T[][p_max],int my_lim, int change_type)
{
REGION *first_region;
int *my_max_region;
if(change_type==0)
{
my_max_region = new int[(*n_rects)+1];
memset(my_max_region,0,sizeof(int)*((*n_rects)+1));
}
first_region=create_region_list(my_max_region, change_type);
if(change_type==0) //elliminate classes with small regions
eliminate_class(my_class,my_max_region,n_rects,my_lim,inv_map,T,first_region);
else if(change_type==1) //elliminate small regions
eliminate_region(n_rects,my_lim,T,first_region);
destroy_region_list(first_region);
if(change_type==0) delete [] my_max_region;
cerr<<":";
}
// Cut a rectangle from the entire input data
// Deletes the previous rectangle, if any
void SegmenterMS::cut_rectangle( sRectangle* rect )
{
if ( _data ) {
for ( register int i = 0; i < _p; i++ )
if ( _data[i] ) delete [] _data[i];
delete [] _data;
}
// Set the dimensions of the currently processed region.
_rrows = rect->height;
_rcolms = rect->width;
_data = new int*[_p];
register int my_x = rect->x;
register int my_y = rect->y;
register int i, j, d;
for ( i = 0; i < _p; i++ )
_data[i] = new int[_rcolms*_rrows];
if(auto_segm)
for ( d = 0; d < _p; d++ )
memcpy(_data[d], _data_all[d],sizeof(int)*_ro_col);
else
{
int idx1 = my_y * _colms + my_x;
int idx2 = 0;
for ( j = my_y, d;
j < my_y + _rrows; j++, idx1 += _colms - _rcolms )
for ( i = my_x; i < my_x + _rcolms; i++, idx1++, idx2++ )
{
for ( d = 0; d < _p; d++ )
_data[d][idx2] = _data_all[d][idx1];
}
}
cerr<<":";
}
// Compute the mean of N points given by J[]
void mean_s(const int N, const int p, int J[], int **data, float T[])
{
int TT[p_max];
register int k, i, j;
for ( i = 0; i < p; i++ )
TT[i] = 0;
for ( i = 0; i < N; i++ )
{
k = J[i];
for ( j = 0; j < p; j++ )
TT[j] += data[j][k];
}
for ( i = 0; i < p; i++ )
T[i] = (float)TT[i] / (float)N;
}
// Build a subsample set of 9 points
int SegmenterMS::subsample(float *Xmean )
{
int J[9];
register int my_contor=0, uj, i0;
//printf("index = %d, n_remain: %d\n", abs((int(float(n_remain)*float(rand())/float(SHRT_MAX))) % n_remain), n_remain);
if(auto_segm) {
// festr: this can reference memory out of the bounds gen_remain[int(float(n_remain)*float(rand())/float(SHRT_MAX))];
i0 = J[my_contor] = gen_remain[ abs(int(float(n_remain)*float(rand())/float(SHRT_MAX)) % n_remain) ];
} else {
i0=J[my_contor]=int(float(_n_points)*float(rand())/float(SHRT_MAX));
}
my_contor++;
for(register int i=0;i<8;i++){
uj=i0 + my_neigh_r[i];
if(uj>=0 && uj<_n_points)
{
if((auto_segm && gen_class[uj]!=255)) break;
else
{
J[my_contor] = uj;
my_contor++;
}
}
}
mean_s(my_contor, _p, J, _data, Xmean);
return 1;
}
// Sampling routine with all needed tests
float SegmenterMS::my_sampling( int rect, float T[Max_rects][p_max])
{
register int k, c;
register float L,U,V,Res;
register float my_dist=max_dist, my_sqrt_dist=fix_RADIUS[0];
float TJ[Max_J][p_max];
int l = 0; //contor of number of subsample sets
int ll = 0; //contor of trials
float Xmean[p_max];
float Obj_fct[Max_J];
//Max_trials = max number of failed trials
//_NJ = max number of subsample sets
while ( (ll < Max_trials) && (l < _NJ ) )
{
if ( subsample(Xmean) ) // the subsample procedure succeeded
{
ll = 0; c=0;
// Save the mean
for ( k = 0; k < _p; k++ ) TJ[l][k] = Xmean[k];
// Compute the square residuals (Euclid dist.)
if(auto_segm)
for ( register int p = 0; p < _n_col_remain; p++ )
{