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ImageStatistics.java
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329 lines (302 loc) · 8.62 KB
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package ij.process;
import ij.measure.*;
import java.awt.*;
/** Statistics, including the histogram, of an image or selection. */
public class ImageStatistics implements Measurements {
/** Use the hIstogram() method to get the histogram as a double array. */
public int[] histogram;
/** Int pixel count (limited to 2^31-1) */
public int pixelCount;
/** Long pixel count */
public long longPixelCount;
/** Mode */
public double dmode;
/** Mode of 256 bin histogram (counts limited to 2^31-1) */
public int mode;
public double area;
public double min;
public double max;
public double mean;
public double median;
public double stdDev;
public double skewness;
public double kurtosis;
public double xCentroid;
public double yCentroid;
public double xCenterOfMass;
public double yCenterOfMass;
public double roiX, roiY, roiWidth, roiHeight;
/** Uncalibrated mean */
public double umean;
/** Length of major axis of fitted ellipse */
public double major;
/** Length of minor axis of fitted ellipse */
public double minor;
/** Angle in degrees of fitted ellipse */
public double angle;
/** Bin width 1 histogram of 16-bit images */
public int[] histogram16;
/** Long histogram; use getHIstogram() to retrieve. */
protected long[] longHistogram;
public double areaFraction;
/** Used internally by AnalyzeParticles */
public int xstart, ystart;
/** Used by HistogramWindow */
public boolean stackStatistics;
/** Minimum threshold when "Limit to threshold" enabled */
public double lowerThreshold = Double.NaN;
/** Maximum threshold when "Limit to threshold" enabled */
public double upperThreshold = Double.NaN;
public double histMin;
public double histMax;
public int histYMax;
public int maxCount;
public int nBins = 256;
public double binSize = 1.0;
protected int width, height;
protected int rx, ry, rw, rh;
protected double pw, ph;
protected Calibration cal;
EllipseFitter ef;
/** Calculates and returns uncalibrated (raw) statistics for the
* specified image, including histogram, area, mean, min and
* max, standard deviation and mode. Use ImageProcessor.setRoi(x,y,width,height)
* to limit statistics to a rectangular area and ImageProcessor.setRoi(Roi)
* to limit to a non-rectangular area.
* @see ij.process.ImageProcessor#setRoi(int,int,int,int)
* @see ij.process.ImageProcessor#setRoi(Roi)
* @see ij.process.ImageProcessor#getStats
*/
public static ImageStatistics getStatistics(ImageProcessor ip) {
return getStatistics(ip, AREA+MEAN+STD_DEV+MODE+MIN_MAX+RECT, null);
}
/** Calculates and returns statistics for the specified
* image using the specified measurent options
* and calibration. Use ImageProcessor.setRoi(x,y,width,height)
* to limit statistics to a rectangular area and ImageProcessor.setRoi(Roi)
* to limit to a non-rectangular area.
* @see ij.process.ImageProcessor#setRoi(int,int,int,int)
* @see ij.process.ImageProcessor#setRoi(Roi)
* @see ij.measure.Measurements
*/
public static ImageStatistics getStatistics(ImageProcessor ip, int mOptions, Calibration cal) {
Object pixels = ip.getPixels();
if (pixels instanceof byte[])
return new ByteStatistics(ip, mOptions, cal);
else if (pixels instanceof short[])
return new ShortStatistics(ip, mOptions, cal);
else if (pixels instanceof int[])
return new ColorStatistics(ip, mOptions, cal);
else if (pixels instanceof float[])
return new FloatStatistics(ip, mOptions, cal);
else
throw new IllegalArgumentException("Pixels are not byte, short, int or float");
}
void getRawMinAndMax(int minThreshold, int maxThreshold) {
int min = minThreshold;
while ((histogram[min] == 0) && (min < 255))
min++;
this.min = min;
int max = maxThreshold;
while ((histogram[max] == 0) && (max > 0))
max--;
this.max = max;
}
void getRawStatistics(int minThreshold, int maxThreshold) {
int count;
double value;
double sum = 0.0;
double sum2 = 0.0;
for (int i=minThreshold; i<=maxThreshold; i++) {
count = histogram[i];
longPixelCount += count;
sum += (double)i*count;
value = i;
sum2 += (value*value)*count;
if (count>maxCount) {
maxCount = count;
mode = i;
}
}
pixelCount = (int)longPixelCount;
area = longPixelCount*pw*ph;
mean = sum/longPixelCount;
umean = mean;
dmode = mode;
calculateStdDev(longPixelCount, sum, sum2);
histMin = 0.0;
histMax = 255.0;
}
void calculateStdDev(double n, double sum, double sum2) {
if (n>0.0) {
stdDev = (n*sum2-sum*sum)/n;
if (stdDev>0.0)
stdDev = Math.sqrt(stdDev/(n-1.0));
else
stdDev = 0.0;
} else
stdDev = 0.0;
}
void setup(ImageProcessor ip, Calibration cal) {
width = ip.getWidth();
height = ip.getHeight();
this.cal = cal;
Rectangle roi = ip.getRoi();
if (roi != null) {
rx = roi.x;
ry = roi.y;
rw = roi.width;
rh = roi.height;
}
else {
rx = 0;
ry = 0;
rw = width;
rh = height;
}
if (cal!=null) {
pw = cal.pixelWidth;
ph = cal.pixelHeight;
} else {
pw = 1.0;
ph = 1.0;
}
roiX = cal!=null?cal.getX(rx):rx;
roiY = cal!=null?cal.getY(ry, height):ry;
roiWidth = rw*pw;
roiHeight = rh*ph;
}
void getCentroid(ImageProcessor ip) {
byte[] mask = ip.getMaskArray();
int count=0, mi;
double xsum=0.0, ysum=0.0;
for (int y=ry,my=0; y<(ry+rh); y++,my++) {
mi = my*rw;
for (int x=rx; x<(rx+rw); x++) {
if (mask==null||mask[mi++]!=0) {
count++;
xsum += x;
ysum += y;
}
}
}
xCentroid = xsum/count+0.5;
yCentroid = ysum/count+0.5;
if (cal!=null) {
xCentroid = cal.getX(xCentroid);
yCentroid = cal.getY(yCentroid, height);
}
}
void fitEllipse(ImageProcessor ip, int mOptions) {
ImageProcessor originalMask = null;
boolean limitToThreshold = (mOptions&LIMIT)!=0 && ip.isThreshold();
if (limitToThreshold) {
ImageProcessor mask = ip.getMask();
Rectangle r = ip.getRoi();
if (mask==null) {
mask = new ByteProcessor(r.width, r.height);
mask.invert();
} else {
originalMask = mask;
mask = mask.duplicate();
}
int n = r.width*r.height;
double t1 = ip.getMinThreshold();
double t2 = ip.getMaxThreshold();
double value;
for (int y=0; y<r.height; y++) {
for (int x=0; x<r.width; x++) {
value = ip.getf(r.x+x, r.y+y);
if (value<t1 || value>t2)
mask.setf(x, y, 0f);
}
}
ip.setMask(mask);
}
if (ef==null)
ef = new EllipseFitter();
ef.fit(ip, this);
if (limitToThreshold) {
if (originalMask==null)
ip.setMask(null);
else
ip.setMask(originalMask);
}
double psize = (Math.abs(pw-ph)/pw)<.01?pw:0.0;
major = ef.major*psize;
minor = ef.minor*psize;
angle = ef.angle;
xCentroid = ef.xCenter;
yCentroid = ef.yCenter;
if (cal!=null) {
xCentroid = cal.getX(xCentroid);
yCentroid = cal.getY(yCentroid, height);
}
}
public void drawEllipse(ImageProcessor ip) {
if (ef!=null)
ef.drawEllipse(ip);
}
void calculateMedian(int[] hist, int first, int last, Calibration cal) {
//ij.IJ.log("calculateMedian: "+first+" "+last+" "+hist.length+" "+pixelCount);
if (pixelCount==0 || first<0 || last>hist.length) {
median = Double.NaN;
return;
}
double sum = 0;
int i = first-1;
double halfCount = pixelCount/2.0;
do {
sum += hist[++i];
} while (sum<=halfCount && i<last);
median = cal!=null?cal.getCValue(i):i;
}
void calculateAreaFraction(ImageProcessor ip, int[] hist) {
int sum = 0;
int total = 0;
int t1 = (int)Math.round(ip.getMinThreshold());
int t2 = (int)Math.round(ip.getMaxThreshold());
if (t1==ImageProcessor.NO_THRESHOLD) {
for (int i=0; i<hist.length; i++)
total += hist[i];
sum = total - hist[0];
} else {
for (int i=0; i<hist.length; i++) {
if (i>=t1 && i<=t2)
sum += hist[i];
total += hist[i];
}
}
areaFraction = sum*100.0/total;
}
/** Returns the histogram as an array of doubles. */
public double[] histogram() {
double[] hist = new double[histogram.length];
for (int i=0; i<hist.length; i++) {
if (longHistogram!=null)
hist[i] = longHistogram[i];
else
hist[i] = histogram[i];
}
return hist;
}
/** Returns the histogram as an array of longs. */
public long[] getHistogram() {
double[] hist = histogram();
long[] hist2 = new long[hist.length];
for (int i=0; i<hist.length; i++)
hist2[i] = (long)hist[i];
return hist2;
}
public String toString() {
return "stats[count="+pixelCount+", mean="+mean+", min="+min+", max="+max+"]";
}
protected void saveThreshold(double minThreshold, double maxThreshold, Calibration cal) {
if (cal!=null) {
minThreshold = cal.getCValue(minThreshold);
maxThreshold = cal.getCValue(maxThreshold);
}
lowerThreshold = minThreshold;
upperThreshold = maxThreshold;
}
}