void constructRings() {
m_dvRs = new ArrayList();
m_cvRings = new ArrayList();
m_cvRings.add(new CircleImage(0));
m_dvRs.add(0.);
double r = 1., dr = 0.001;
double ri = r - dr, ro = r + dr;
m_cvRings.add(new Ring(ri, ro));
m_dvRs.add(r);
r = Math.sqrt(2);
ri = r - dr;
ro = r + dr;
m_cvRings.add(new Ring(ri, ro));
m_dvRs.add(r);
int nr;
for (nr = 2; nr <= m_nRMax; nr++) {
m_cvRings.add(new Ring(nr - 1, nr));
m_dvRs.add((double) nr);
}
m_cRefRing = new Ring(m_nRRefI, m_nRRefO);
m_cvRings.add(m_cRefRing);
m_dvRs.add((double) m_nRMax + 1);
ImageShapeHandler.setFrameRanges(m_cvRings, new intRange(0, w - 1), new intRange(0, h - 1));
}
public void calAutoCorrelation(
int
nRefType) { // computing the cross correlation coefficient between pixel values of points
// separated by the radius of rings
m_nRefType = nRefType;
int numRings = m_cvRings.size();
calRefPixels();
m_cPixelMeanSems = CommonStatisticsMethods.buildMeanSem(m_pnPixelsLR);
int len;
double mean = m_cPixelMeanSems.mean, sem2 = m_cPixelMeanSems.sem2;
double medianMean[] = new double[numRings - 1];
double medianSem2[] = new double[numRings - 1];
double crossProduct[] = new double[numRings - 1];
double numCrossPairs[] = new double[numRings - 1];
double crossProduct_median[] = new double[numRings - 1];
double numCrossPairs_median[] = new double[numRings - 1];
ArrayList<Double> medians[] = new ArrayList[numRings - 1];
int i, j, pixel0;
for (i = 0; i < numRings - 1; i++) {
crossProduct[i] = 0;
numCrossPairs[i] = 0;
crossProduct_median[i] = 0;
numCrossPairs_median[i] = 0;
medianMean[i] = 0;
medianSem2[i] = 0;
medians[i] = new ArrayList();
}
int[][] pixels_LMC = new int[h][w];
ImageShape shape;
ArrayList<Point> points = new ArrayList();
ArrayList<Point> scanningTrace = new ArrayList();
double dp, median;
Point p0, p;
int index = 0, y0 = 0, y;
Histogram hist;
int nRef, mean1;
while (true) {
p0 = new Point(m_cIMSC.getPosition());
y = p0.y;
scanningTrace.add(p0);
pixel0 = m_pnPixelsLR[p0.y][p0.x];
nRef = getRefPixel(p0);
dp = pixel0 - mean;
CommonMethods.fillHistograms(m_pnPixels, nRef, m_cvRings, p0, m_cvHists);
for (i = 0; i < numRings - 1; i++) {
shape = m_cvRings.get(i);
shape.setCenter(p0);
shape.setFrameRanges(
new intRange(p0.x, w - 1),
new intRange(
p0.y,
h - 1)); // this is to avoid double counting the pair of points with given distance
shape.getInnerPoints(points);
shape.setFrameRanges(new intRange(0, w - 1), new intRange(0, h - 1));
len = points.size();
for (j = 0; j < len; j++) {
p = points.get(j);
crossProduct[i] += dp * (m_pnPixels[p.y][p.x] - nRef - mean);
}
numCrossPairs[i] += len;
numCrossPairs_median[i] += 1;
hist = m_cvHists.get(i);
median = hist.getPercentileValue();
medianMean[i] += median;
medianSem2[i] += median * median;
medians[i].add(median);
if (y > y0) {
IJ.showStatus("computing autocorrelation: " + PrintAssist.ToString(y) + "-th line");
IJ.showProgress(y / h);
y0 = y;
}
}
if (m_cIMSC.done()) break;
m_cIMSC.move();
index++;
}
double dAutoCorr_mean, meanMedian, cp;
MeanSem0 ms = new MeanSem0();
int num;
for (i = 0; i < numRings - 1; i++) {
m_pdAutoCorr_mean[i] = crossProduct[i] / (Math.sqrt(sem2 * sem2) * (numCrossPairs[i]));
num = medians[i].size();
medianMean[i] /= num;
medianSem2[i] /= num;
ms.updateMeanSquareSum(num, medianMean[i], medianSem2[i]);
meanMedian = ms.mean;
cp = 0;
for (j = 0; j < num; j++) {
p = scanningTrace.get(j);
cp += (m_pnPixelsLR[p.y][p.x] - mean) * (medians[i].get(j) - meanMedian);
}
m_pdAutoCorr_median[i] = cp / (Math.sqrt(sem2 * ms.sem2) * (num));
}
}
public double dist(IntensityPeakObject_GaussianNodeGroup ipo) {
double dx = (IPOGNode.xcr - ipo.IPOGNode.xcr);
double dy = (IPOGNode.ycr - ipo.IPOGNode.ycr);
double dist = Math.sqrt(dx * dx + dy * dy);
return dist;
}
