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package ij.process;
import ij.measure.Calibration;
/** 16-bit image statistics, including histogram. */
public class ShortStatistics extends ImageStatistics {
/** Construct an ImageStatistics object from a ShortProcessor
using the standard measurement options (area, mean,
mode, min and max). */
public ShortStatistics(ImageProcessor ip) {
this(ip, AREA+MEAN+MODE+MIN_MAX, null);
}
/** Constructs a ShortStatistics object from a ShortProcessor using
the specified measurement options. The 'cal' argument, which
can be null, is currently ignored. */
public ShortStatistics(ImageProcessor ip, int mOptions, Calibration cal) {
this.width = ip.getWidth();
this.height = ip.getHeight();
setup(ip, cal);
nBins = 256;
double minT = ip.getMinThreshold();
int minThreshold,maxThreshold;
if ((mOptions&LIMIT)==0 || minT==ImageProcessor.NO_THRESHOLD)
{minThreshold=0; maxThreshold=65535;}
else
{minThreshold=(int)minT; maxThreshold=(int)ip.getMaxThreshold();}
int[] hist = ip.getHistogram(); // 65536 bin histogram
histogram16 =hist;
float[] cTable = cal!=null?cal.getCTable():null;
getRawMinAndMax(hist, minThreshold, maxThreshold);
histMin = min;
histMax = max;
getStatistics(ip, hist, (int)min, (int)max, cTable);
if ((mOptions&MODE)!=0)
getMode();
if ((mOptions&ELLIPSE)!=0 || (mOptions&SHAPE_DESCRIPTORS)!=0)
fitEllipse(ip, mOptions);
else if ((mOptions&CENTROID)!=0)
getCentroid(ip, minThreshold, maxThreshold);
if ((mOptions&(CENTER_OF_MASS|SKEWNESS|KURTOSIS))!=0)
calculateMoments(ip, minThreshold, maxThreshold, cTable);
if ((mOptions&MIN_MAX)!=0 && cTable!=null)
getCalibratedMinAndMax(hist, (int)min, (int)max, cTable);
if ((mOptions&MEDIAN)!=0)
calculateMedian(hist, minThreshold, maxThreshold, cal);
if ((mOptions&AREA_FRACTION)!=0)
calculateAreaFraction(ip, hist);
}
void getRawMinAndMax(int[] hist, int minThreshold, int maxThreshold) {
int min = minThreshold;
while ((hist[min]==0) && (min<65535))
min++;
this.min = min;
int max = maxThreshold;
while ((hist[max]==0) && (max>0))
max--;
this.max = max;
}
void getStatistics(ImageProcessor ip, int[] hist, int min, int max, float[] cTable) {
int count;
double value;
double sum = 0.0;
double sum2 = 0.0;
nBins = ip.getHistogramSize();
histMin = ip.getHistogramMin();
histMax = ip.getHistogramMax();
if (histMin==0.0 && histMax==0.0) {
histMin = min;
histMax = max;
} else {
if (min<histMin) min = (int)histMin;
if (max>histMax) max = (int)histMax;
}
binSize = (histMax-histMin)/nBins;
double scale = 1.0/binSize;
int hMin = (int)histMin;
histogram = new int[nBins]; // 256 bin histogram
int index;
int maxCount = 0;
for (int i=min; i<=max; i++) {
count = hist[i];
if (count>maxCount) {
maxCount = count;
dmode = i;
}
pixelCount += count;
value = cTable==null?i:cTable[i];
sum += value*count;
sum2 += (value*value)*count;
index = (int)(scale*(i-hMin));
if (index>=nBins)
index = nBins-1;
histogram[index] += count;
}
area = pixelCount*pw*ph;
mean = sum/pixelCount;
umean = mean;
calculateStdDev(pixelCount, sum, sum2);
if (cTable!=null)
dmode = cTable[(int)dmode];
}
void getMode() {
int count;
maxCount = 0;
for (int i=0; i<nBins; i++) {
count = histogram[i];
if (count > maxCount) {
maxCount = count;
mode = i;
}
}
//ij.IJ.write("mode2: "+mode+" "+dmode+" "+maxCount);
}
void getCentroid(ImageProcessor ip, int minThreshold, int maxThreshold) {
short[] pixels = (short[])ip.getPixels();
byte[] mask = ip.getMaskArray();
boolean limit = minThreshold>0 || maxThreshold<65535;
int count=0, i, mi, v;
double xsum=0.0, ysum=0.0;
for (int y=ry,my=0; y<(ry+rh); y++,my++) {
i = y*width + rx;
mi = my*rw;
for (int x=rx; x<(rx+rw); x++) {
if (mask==null||mask[mi++]!=0) {
if (limit) {
v = pixels[i]&0xffff;
if (v>=minThreshold&&v<=maxThreshold) {
count++;
xsum+=x;
ysum+=y;
}
} else {
count++;
xsum+=x;
ysum+=y;
}
}
i++;
}
}
xCentroid = xsum/count+0.5;
yCentroid = ysum/count+0.5;
if (cal!=null) {
xCentroid = cal.getX(xCentroid);
yCentroid = cal.getY(yCentroid, height);
}
}
void calculateMoments(ImageProcessor ip, int minThreshold, int maxThreshold, float[] cTable) {
short[] pixels = (short[])ip.getPixels();
byte[] mask = ip.getMaskArray();
int i, mi, iv;
double v, v2, sum1=0.0, sum2=0.0, sum3=0.0, sum4=0.0, xsum=0.0, ysum=0.0;
for (int y=ry,my=0; y<(ry+rh); y++,my++) {
i = y*width + rx;
mi = my*rw;
for (int x=rx; x<(rx+rw); x++) {
if (mask==null || mask[mi++]!=0) {
iv = pixels[i]&0xffff;
if (iv>=minThreshold&&iv<=maxThreshold) {
v = cTable!=null?cTable[iv]:iv;
v2 = v*v;
sum1 += v;
sum2 += v2;
sum3 += v*v2;
sum4 += v2*v2;
xsum += x*v;
ysum += y*v;
}
}
i++;
}
}
double mean2 = mean*mean;
double variance = sum2/pixelCount - mean2;
double sDeviation = Math.sqrt(variance);
skewness = ((sum3 - 3.0*mean*sum2)/pixelCount + 2.0*mean*mean2)/(variance*sDeviation);
kurtosis = (((sum4 - 4.0*mean*sum3 + 6.0*mean2*sum2)/pixelCount - 3.0*mean2*mean2)/(variance*variance)-3.0);
xCenterOfMass = xsum/sum1+0.5;
yCenterOfMass = ysum/sum1+0.5;
if (cal!=null) {
xCenterOfMass = cal.getX(xCenterOfMass);
yCenterOfMass = cal.getY(yCenterOfMass, height);
}
}
void getCalibratedMinAndMax(int[] hist, int minValue, int maxValue, float[] cTable) {
min = Double.MAX_VALUE;
max = -Double.MAX_VALUE;
double v = 0.0;
for (int i=minValue; i<=maxValue; i++) {
if (hist[i]>0) {
v = cTable[i];
if (v<min) min = v;
if (v>max) max = v;
}
}
}
}
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