Extended SDM Metrics

Krig, Scott

doi:10.1007/978-1-4302-5930-5_12

Scott Krig¹

29k Accesses

Abstract

Figure D-1 provides a visualization of image texture using SDM’s.

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Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Figure D-1 provides a visualization of image texture using SDM’s.

Listing D-1 illustrates the extended SDM metrics from Chapter 3. The code is available online at

http://www.apress.com/source-code/ComputerVisionMetrics

Listing D-1. Extended SDM Metrics from Chapter 3

/*

** CREATED 1991 (C) KRIG RESEARCH, SCOTT KRIG - UNPUBLISHED SOFTWARE

** PORTED TO MAC 2014

**

** THIS SOFTWARE MAY BE USED FREELY FOR ACADEMIC AND RESEARCH PURPOSES.

** REFERENCE THIS BOOK AND PROVIDE THIS NOTICE WHEN USING THE SOFTWARE.

*/

using namespace std;

#include <math.h>

#include <stdio.h>

#include <opencv2/opencv.hpp>

#include "/usr/local/include/opencv/cv.h"

#include "/usr/local/include/opencv2/core/core.hpp"

#include "/usr/local/include/opencv2/highgui/highgui.hpp"

#include <iostream>

using namespace cv;

#define TINY 0.0000000001

#define F6U "%6f.3"

#define F6F "%.6f"

#define F3F "%.3f"

#define FXF "%.0f"

#define FALSE 0

#define TRUE 1

typedef struct area {

int x;

int y;

int dx;

int dy;

} area_t;

typedef struct {

double t0;

double t90;

double t135;

double t45;

double tave;

} ctab;

typedef struct {

double median;

double ave;

double adev;

double sdev;

double svar;

double skew;

double curt;

int min;

int max;

ctab xcentroid;

ctab ycentroid;

ctab _asm;

ctab low_frequency_coverage;

ctab total_coverage;

ctab corrected_coverage;

ctab total_power;

ctab relative_power;

ctab locus_length;

ctab locus_mean_density;

ctab bin_mean_density;

ctab containment;

ctab linearity;

ctab linearity_strength;

ctab autocorrelation;

ctab covariance;

ctab inertia; /* haralick contrast */

ctab absolute_value;

ctab inverse_difference; /* haralick */

ctab entropy; /* haralick */

ctab correlation; /* haralick */

} glob_t;

glob_t gt;

/* FUNCTIONS */

int i_sort (

int *x,

int n,

int parm)

{

int k,i,ii;

int y,found;

int xi;

int n2, n2p;

x--;

for (k=1; k<n+1; k++) {

y = x[k];

for (i=k-1, found = FALSE; i>=0 && !found; ) {

xi = x[i];

ii = i+1;

if (y < xi) {

x[ii] = xi;

i--;

} else {

found = TRUE;

}

x[ii] = y;

}

if (parm == 0) return 0;

n2p = (n2=(n>>1))+1;

return ( n % 2 ? x[n2p] : (x[n2] + x[n2p]) >>1 );

}

int lmoment (

int *data,

int n,

double *median,

double *ave,

double *adev,

double *sdev,

double *svar,

double *skew,

double *curt)

{

int j;

double s,p,ep=0.0;

if (n <= 1) return 0;

s=0.0;

for (j=1; j<=n;j++) s += ( double )data[j];

*ave=s/n;

*adev=(*svar)=(*skew)=(*curt)=0.0;

for (j=1;j<=n;j++) {

*adev += abs(s=( double )data[j]-(*ave));

*svar += (p=s*s);

*skew += (p *= s);

*curt += (p *= s);

}

*adev /=n;

*svar = (*svar - ep*ep / n) / (n-1);

*sdev= sqrt (*svar);

if (*svar) {

s = (n*(*svar)*(*sdev));

if (s != 0) *skew /=s;

else *skew = 0;

s = (n*(*svar)*(*svar))-3.0;

if (s != 0) *curt = (*curt) / s;

else *curt = 0;

} else {

*skew = *curt = 0.0;

}

*median = 0;

if (n > 20000) return 0;

*median = ( double )i_sort(data, n, 1);

return 0;

}

int mean_sdev (

int xp,

int yp,

int *xdata,

double *xmean,

double *xsdev,

double *ymean,

double *ysdev)

{

double u_x1, a_x1;

int mx, my,v,t,x,y,z, offset;

int dif[256];

/* first calculate mean */

offset = 256 * yp;

x = y = 0;

for (z=0; z < 256; x += xdata[offset+z], z++);

for (z=0; z < 256; y += xdata[xp + (z*256)], z++);

mx = x / 256.;

*xmean = ( double )mx;

my = y / 256.;

*ymean = ( double )my;

/* now calculate standard deviation */

x = y = 0;

z=0;

while (z < 256) {

v = mx - xdata[offset+z];

x += v*v;

v = my - xdata[xp + (z*256)];

y += v*v;

z++;

}

*xsdev = x / 256;

*ysdev = y / 256;

return 0;

}

int lohi (

int n,

int *cv,

int *lo,

int *hi)

{

int x;

int lv, hv;

lv = 0x1fffff;

hv =0;

x=0;

while (x < n) {

if (cv[x] < lv) lv = cv[x];

if (cv[x] > hv) hv = cv[x];

x++;

}

*lo = lv;

*hi = hv;

return 0;

}

int savegt (

ctab *ctp,

double dv1,

double dv2,

double dv3,

double dv4)

{

ctp->t0 = dv1;

ctp->t90 = dv2;

ctp->t135 = dv3;

ctp->t45 = dv4;

ctp->tave = (dv1 + dv2 + dv3 + dv4) / 4;

return 0;

}

int gtput (

char *prompt,

char *fs,

ctab *ctp,

FILE *fstream)

{

char str[256];

char form[256];

fputs (prompt, fstream);

sprintf (form, "%s %s %s %s %s \n", fs, fs, fs, fs, fs);

sprintf (str, form, ctp->t0, ctp->t90, ctp->t135, ctp->t45, ctp->tave);

fputs (str, fstream);

return 0;

}

int put_txfile (

FILE *fstream)

{

char str[256];

sprintf (str, "gray value moments: min :%u max:%u mean:%u\n",gt.min,gt.max, ( int )gt.ave);

fputs (str, fstream);

sprintf (str, "moments: adev :%.4f sdev :%.4f svar :%.4f skew:%.6f curt:%.6f \n",

gt.adev, gt.sdev, gt.svar, gt.skew, gt.curt);

fputs (str, fstream);

fputs ("\n", fstream);

fputs (" --------------------------------------\n", fstream);

fputs (" 0deg 90deg 135deg 45deg ave \n", fstream);

fputs (" --------------------------------------\n", fstream);

gtput(" xcentroid ", FXF, &gt.xcentroid, fstream);

gtput(" ycentroid ", FXF, &gt.ycentroid, fstream);

gtput("low_frequency_coverage ", F3F, &gt.low_frequency_coverage, fstream);

gtput("total_coverage ", F3F, &gt.total_coverage, fstream);

gtput("corrected_coverage ", F3F, &gt.corrected_coverage, fstream);

gtput("total_power ", F3F, &gt.total_power, fstream);

gtput("relative_power ", F3F, &gt.relative_power, fstream);

gtput("locus_length ", FXF, &gt.locus_length, fstream);

gtput("locus_mean_density ", FXF, &gt.locus_mean_density, fstream);

gtput("bin_mean_density ", FXF, &gt.bin_mean_density, fstream);

gtput("containment ", F3F, &gt.containment, fstream);

gtput("linearity ", F3F, &gt.linearity, fstream);

gtput("linearity_strength ", F3F, &gt.linearity_strength, fstream);

return 0;

}

int texture (

char *filename)

{

char str[256];

int pmx[256], pmy[256];

int x,y,z,dx,dy,dz,sz,bpp;

int accum, tmin, tmax;

int tmin2, tmax2, yc;

int *data;

int mval0, mval90, mval135, mval45;

double median, ave, adev, sdev, svar, skew, curt;

double median2, ave2, adev2, sdev2, svar2, skew2, curt2;

int *dm0, *dm90, *dm135, *dm45;

FILE *fstream;

int i0, i90, i135, i45, iave, n;

int c0, c90, c135, c45, cave;

int p0, p90, p135, p45, pave;

double d0, d90, d135, d45, dave;

double f0, f90, f135, f45;

/**************************************************************/

/* READ THE INPUT IMAGE, EXPECT IT TO BE 8-bit UNSIGNED INT */

/* Mat type conversion is simple in openCV, try it later */

Mat imageIn = cv::imread(filename);

dx = imageIn.rows;

dy = imageIn.cols;

unsigned char *pixels = imageIn.data;

cout << " dx " << dx << " dy " << dy << " elemSize() " << imageIn.elemSize() << endl ;

data = ( int *) malloc (dx * dy * 4);

if (data == 0)

{

cout << " malloc error in texture()" << endl ;

}

for (y=0; y < dy; y++) {

for (x=0; x < dx; x++) {

int pixel = ( int )*(imageIn.ptr(x,y));

if (pixel > 255) { pixel = 255; }

data[(y * dx) + x] = pixel;

}

/**********************************************************/

/* PART 1 - get normal types of statistics from pixel data */

lmoment(data, sz, &median, &ave, &adev, &sdev, &svar, &skew, &curt);

lohi(sz, data, &tmin, &tmax);

gt.median = median;

gt.ave = ave;

gt.adev = adev;

gt.sdev = sdev;

gt.svar = svar;

gt.skew = skew;

gt.curt = curt;

gt.min = tmin;

gt.max = tmax;

fstream = fopen ("SDMExtended.txt", "w");

if (fstream <= 0) {

cout << "#cannot create file" << endl ;

return 0;

}

sprintf (str, "texture for object: %s\n", filename);

fputs (str, fstream);

sprintf (str, "area: %u, %u \n", dx, dy);

fputs (str, fstream);

/**********************************************************/

/* PART 2 - calculate the 4 spatial dependency matricies */

dm0 = ( int *) malloc ( 256*256*4 );

dm90 = ( int *) malloc ( 256*256*4 );

dm135 = ( int *) malloc ( 256*256*4 );

dm45 = ( int *) malloc ( 256*256*4 );

if ((dm0==0) || (dm90==0) || (dm135==0) || (dm45==0)) {

cout << " malloc error in texture2" << endl ;

return 0;

}

x=0;

while (x < 256*256) {

dm0[x] = dm90[x] = dm135[x] = dm45[x] = 0;

x++;

}

y=0;

while (y < dy-1) {

yc = dx * y;

x=0;

while (x < dx-1) {

dm0[ (data[yc + x]&0xff) + ((( data[yc + x + 1] )<< 8)&0xff00) ]++;

dm0[ (data[yc + x + 1]&0xff) + ((( data[yc + x] )<< 8)&0xff00) ]++;

dm90[ (data[yc + x]&0xff) + ((( data[yc + x + dx] )<< 8)&0xff00) ]++;

dm90[ (data[yc + x + dx]&0xff) + ((( data[yc + x] )<< 8)&0xff00) ]++;

dm135[ (data[yc + x]&0xff) + ((( data[yc + x + dx + 1] )<< 8)&0xff00) ]++;

dm135[ (data[yc + x + dx + 1]&0xff) + ((( data[yc + x] )<< 8)&0xff00) ]++;

dm45[ (data[yc + x + 1]&0xff) + ((( data[yc + x + dx] )<< 8)&0xff00) ]++;

dm45[ (data[yc + x + dx]&0xff) + ((( data[yc + x + 1] )<< 8)&0xff00) ]++;

x++;

}

y++;

}

/***************** CALCULATE TEXTURE METRICS ******************/

/* centroid */

pmx[0] = pmx[1] = pmx[2] = pmx[3] = 0;

pmy[0] = pmy[1] = pmy[2] = pmy[3] = 0;

i0 = i90 = i135 = i45 = 0;

y=0;

while (y < 256) {

x=0;

while (x < 256) {

z = x + (256 * y);

pmx[0] += (x * dm0[z]);

pmy[0] += (y * dm0[z]); i0 += dm0[z];

pmx[1] += (x * dm90[z]);

pmy[1] += (y * dm90[z]); i90 += dm90[z];

pmx[2] += (x * dm135[z]);

pmy[2] += (y * dm135[z]); i135 += dm135[z];

pmx[3] += (x * dm45[z]);

pmy[3] += (y * dm45[z]); i45 += dm45[z];

x++;

}

y++;

}

pmx[0] = pmx[0] / i0;

pmy[0] = pmy[0] / i0;

pmx[1] = pmx[1] / i90;

pmy[1] = pmy[1] / i90;

pmx[2] = pmx[2] / i135;

pmy[2] = pmy[2] / i135;

pmx[3] = pmx[3] / i45;

pmy[3] = pmy[3] / i45;

x = (pmx[0] + pmx[1] + pmx[2] + pmx[3]) / 4;

y = (pmy[0] + pmy[1] + pmy[2] + pmy[3]) / 4;

gt.xcentroid.t0 = pmx[0];

gt.ycentroid.t0 = pmy[0];

gt.xcentroid.t90 = pmx[1];

gt.ycentroid.t90 = pmy[1];

gt.xcentroid.t135 = pmx[2];

gt.ycentroid.t135 = pmy[2];

gt.xcentroid.t45 = pmx[3];

gt.ycentroid.t45 = pmy[3];

gt.xcentroid.tave = x;

gt.ycentroid.tave = y;

/* low frequency coverage */

i0 = i90 = i135 = i45 = 0;

c0 = c90 = c135 = c45 = 0;

x=0;

while (x < 256*256) {

if ((dm0[x] != 0) && (dm0[x] < 3)) i0++;

if ((dm90[x] != 0) && (dm90[x] < 3)) i90++;

if ((dm135[x] != 0) && (dm135[x] < 3)) i135++;

if ((dm45[x] != 0) && (dm45[x] < 3)) i45++;

if (!dm0[x]) c0++;

if (!dm90[x]) c90++;

if (!dm135[x]) c135++;

if (!dm45[x]) c45++;

x++;

}

d0 = ( double )i0 / 0x10000;

d90 = ( double )i90 / 0x10000;

d135 = ( double )i135 / 0x10000;

d45 = ( double )i45 / 0x10000;

savegt(&gt.low_frequency_coverage, d0, d90, d135, d45);

d0 = ( double )c0 / 0x10000;

d90 = ( double )c90 / 0x10000;

d135 = ( double )c135 / 0x10000;

d45 = ( double )c45 / 0x10000;

savegt(&gt.total_coverage, d0, d90, d135, d45);

d0 = (c0-i0) / ( double )0x10000;

d90 = (c90-i90) / ( double )0x10000;

d135 = (c135-i135) / ( double )0x10000;

d45 = (c45-i45) / ( double )0x10000;

savegt(&gt.corrected_coverage, d0, d90, d135, d45);

/* power */

i0 = i90 = i135 = i45 = 0;

c0 = c90 = c135 = c45 = 0;

p0 = p90 = p135 = p45 = 0;

y=0;

while (y < 256) {

z = y * 256;

x=0;

while (x < 256) {

n = x-y;

if (n < 0) n = -n;

if (dm0[x+z] != 0) { i0 += n; c0++; }

if (dm90[x+z] != 0) { i90 += n; c90++; }

if (dm135[x+z] != 0) { i135 += n; c135++; }

if (dm45[x+z] != 0) { i45 += n; c45++; }

x++;

}

y++;

}

d0 = (i0 / 0x10000);

d90 = (i90 / 0x10000);

d135 = (i135 / 0x10000);

d45 = (i45 / 0x10000);

savegt(&gt.total_power, d0, d90, d135, d45);

d0 = (i0 / c0);

d90 = (i90 / c90);

d135 = (i135 / c135);

d45 = (i45 / c45);

savegt(&gt.relative_power, d0, d90, d135, d45);

/* locus density */

d0 = d90 = d135 = d45 = 0.0;

c0 = c90 = c135 = c45 = 0;

p0 = p90 = p135 = p45 = 0;

y=0;

while (y < 256) {

z = y * 256;

i0 = i90 = i135 = i45 = 0;

x=0;

while (x < 256) {

n = x-y;

if (n < 0) n = -n;

if ((dm0[x+z] != 0) && (n < 7)) { c0++; p0 += dm0[x+z]; }

if ((dm90[x+z] != 0) && (n < 7)) { c90++; p90 += dm90[x+z]; }

if ((dm135[x+z] != 0) && (n < 7)) { c135++; p135 += dm135[x+z]; }

if ((dm45[x+z] != 0) && (n < 7)) { c45++; p45 += dm45[x+z]; }

if ((dm0[x+z] == 0) && (n < 7)) { i0++; }

if ((dm90[x+z] == 0) && (n < 7)) { i90++; }

if ((dm135[x+z] == 0) && (n < 7)) { i135++; }

if ((dm45[x+z] == 0) && (n < 7)) { i45++; }

x++;

}

if (!i0) d0 += 1;

if (!i90) d90 += 1;

if (!i135) d135 += 1;

if (!i45) d45 += 1;

y++;

}

savegt(&gt.locus_length, d0, d90, d135, d45);

d0 = (p0/c0);

d90 = (p90/c90);

d135 = (p135/c135);

d45 = (p45/c45);

savegt(&gt.locus_mean_density, d0, d90, d135, d45);

/* density */

c0 = c90 = c135 = c45 = 0;

p0 = p90 = p135 = p45 = 0;

x=0;

while ( x < 256*256) {

if (dm0[x] != 0) { c0 += dm0[x]; p0++; }

if (dm90[x] != 0) { c90 += dm90[x]; p90++; }

if (dm135[x] != 0) { c135 += dm135[x]; p135++; }

if (dm45[x] != 0) { c45 += dm45[x]; p45++; }

x++;

}

d0 = c0 / p0;

d90 = c90 / p90;

d135 = c135 / p135;

d45 = c45 / p45;

savegt(&gt.bin_mean_density, d0, d90, d135, d45);

/* containment */

i0 = i90 = i135 = i45 = 0;

x=0;

while (x < 256) {

if (dm0[x]) i0++; if (dm0[256*256 - x - 1]) i0++;

if (dm90[x]) i90++; if (dm90[256*256 - x - 1]) i90++;

if (dm135[x]) i135++; if (dm135[256*256 - x - 1]) i135++;

if (dm45[x]) i45++; if (dm45[256*256 - x - 1]) i45++;

if (dm0[x*256]) i0++; if (dm0[(x*256)+255]) i0++;

if (dm90[x*256]) i90++; if (dm90[(x*256)+255]) i90++;

if (dm135[x*256]) i135++; if (dm135[(x*256)+255]) i135++;

if (dm45[x*256]) i45++; if (dm45[(x*256)+255]) i45++;

x++;

}

d0 = 1.0 - (( double )i0 / 1024.0);

d90 = 1.0 - (( double )i90 / 1024.0);

d135 = 1.0 - (( double )i135 / 1024.0);

d45 = 1.0 - (( double )i45 / 1024.0);

savegt(&gt.containment, d0, d90, d135, d45);

/* linearity */

i0 = i90 = i135 = i45 = 0;

c0 = c90 = c135 = c45 = 0;

y=0;

while (y < 256) {

z = y * 256;

if (dm0[z + y] > 1) { i0++; c0 += dm0[z+y]; }

if (dm90[z + y] > 1) { i90++; c90 += dm90[z+y]; }

if (dm135[z + y] > 1) { i135++; c135 += dm135[z+y]; }

if (dm45[z + y] > 1) { i45++; c45 += dm45[z+y]; }

y++;

}

d0 = ( double )i0 / 256.;

d90 = ( double )i90 / 256.;

d135 = ( double )i135 / 256.;

d45 = ( double )i45 / 256.;

savegt(&gt.linearity, d0, d90, d135, d45);

/* linearity strength */

d0 = (c0/(i0+.00001)) / 256.;

d90 = (c90/(i90+.00001)) / 256.;

d135 = (c135/(i135+.00001)) / 256.;

d45 = (c45/(i45+.00001)) / 256.;

savegt(&gt.linearity_strength, d0, d90, d135, d45);

/* WRITE ALL STATISTICS IN gt . STRUCTURE TO OUTPUT FILE */

put_txfile(fstream);

/* clip to max value 255 */

mval0 = mval90 = mval135 = mval45 = 0;

x=0;

while (x < 256*256) {

if (dm0[x] > 255) dm0[x] = 255;

if (dm90[x] > 255) dm90[x] = 255;

if (dm135[x] > 255) dm135[x] = 255;

if (dm45[x] > 255) dm45[x] = 255;

x++;

}

/******************************************************/

/* Convert data to unsigned char to write into png */

unsigned char *dm0b = ( unsigned char *) malloc ( 256*256);

unsigned char *dm90b = ( unsigned char *) malloc ( 256*256);

unsigned char *dm135b = ( unsigned char *) malloc ( 256*256);

unsigned char *dm45b = ( unsigned char *) malloc ( 256*256);

if ((dm0b==0) || (dm90b==0) || (dm135b==0) || (dm45b==0)) {

cout << " malloc error in texture3" << endl ;

return 0;

}

x=0;

while (x < 256*256) {

dm0b[x] = ( unsigned char ) (dm0[x] & 0xff);

dm90b[x] = ( unsigned char ) (dm90[x] & 0xff);

dm135b[x] = ( unsigned char ) (dm135[x] & 0xff);

dm45b[x] = ( unsigned char ) (dm45[x] & 0xff);

x++;

}

/*

* write output to 4 quadrants: 0=0, 1=90, 2=135, 3=145

*/

char outfile[256];

sprintf (outfile, "%s_SDMQUadrant_0deg_8UC1. png ", filename);

Mat SDMQuadrant0(256, 256, CV_8UC1, dm0b);

imwrite(outfile, SDMQuadrant0);

sprintf (outfile, "%s_SDMQUadrant_90deg_8UC1. png ", filename);

Mat SDMQuadrant90(256, 256, CV_8UC1, dm90b);

imwrite(outfile, SDMQuadrant90);

sprintf (outfile, "%s_SDMQUadrant_135deg_8UC1. png ", filename);

Mat SDMQuadrant135(256, 256, CV_8UC1, dm135b);

imwrite(outfile, SDMQuadrant135);

sprintf (outfile, "%s_SDMQUadrant_45deg_8UC1. png ", filename);

Mat SDMQuadrant45(256, 256, CV_8UC1, dm45b);

imwrite(outfile, SDMQuadrant45);

free (dm0);

free (dm90);

free (dm135);

free (dm45);

free (data);

free (dm0b);

free (dm90b);

free (dm135b);

free (dm45b);

fclose (fstream);

return 0;

}

int main ( int argc, char **argv)

{

cout << "8-bit unsigned image expected as input" << endl ;

texture (argv[1]);

return 0;

}

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Authors and Affiliations

CA, US
Scott Krig

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Krig, S. (2014). Extended SDM Metrics. In: Computer Vision Metrics. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5930-5_12

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DOI: https://doi.org/10.1007/978-1-4302-5930-5_12
Published: 26 May 2014
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4302-5929-9
Online ISBN: 978-1-4302-5930-5
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