private void getintbright() {
weights = new float[ncurves][xpts][ypts];
for (int i = 0; i < ncurves; i++) {
nmeas[i] = 0;
for (int j = 0; j < xpts; j++) {
for (int k = 0; k < ypts; k++) {
nmeas[i] += (int) pch[i][j][k];
}
}
double tempavg = 0.0;
double tempavg2 = 0.0;
double temp2avg = 0.0;
double temp2avg2 = 0.0;
double tempccavg = 0.0;
for (int j = 0; j < xpts; j++) {
for (int k = 0; k < ypts; k++) {
double normed = (double) pch[i][j][k] / (double) nmeas[i];
if (pch[i][j][k] > 0.0f) {
weights[i][j][k] = (float) ((double) nmeas[i] / (normed * (1.0f - normed)));
} else {
weights[i][j][k] = 1.0f;
}
tempavg += normed * (double) j;
tempavg2 += normed * (double) j * (double) j;
temp2avg += normed * (double) k;
temp2avg2 += normed * (double) k * (double) k;
tempccavg += normed * (double) k * (double) j;
}
}
tempccavg -= tempavg * temp2avg;
brightcc[i] = tempccavg / Math.sqrt(tempavg * temp2avg);
tempavg2 -= tempavg * tempavg;
tempavg2 /= tempavg;
bright1[i] = (tempavg2 - 1.0);
temp2avg2 -= temp2avg * temp2avg;
temp2avg2 /= temp2avg;
bright2[i] = (temp2avg2 - 1.0);
intensity1[i] = tempavg;
intensity2[i] = temp2avg;
if (psfflag == 0) {
bright1[i] /= 0.3536;
bright2[i] /= 0.3536;
brightcc[i] /= 0.3536;
} else {
if (psfflag == 1) {
bright1[i] /= 0.078;
bright2[i] /= 0.078;
brightcc[i] /= 0.078;
} else {
bright1[i] /= 0.5;
bright2[i] /= 0.5;
brightcc[i] /= 0.5;
}
}
number1[i] = intensity1[i] / bright1[i];
number2[i] = intensity2[i] / bright2[i];
brightmincc[i] = (bright1[i] * beta) * Math.sqrt(intensity1[i] / intensity2[i]);
}
}
PolygonRoi trimPolygon(PolygonRoi roi, double length) {
int[] x = roi.getXCoordinates();
int[] y = roi.getYCoordinates();
int n = roi.getNCoordinates();
x = smooth(x, n);
y = smooth(y, n);
float[] curvature = getCurvature(x, y, n);
Rectangle r = roi.getBounds();
double threshold = rodbard(length);
// IJ.log("trim: "+length+" "+threshold);
double distance = Math.sqrt((x[1] - x[0]) * (x[1] - x[0]) + (y[1] - y[0]) * (y[1] - y[0]));
x[0] += r.x;
y[0] += r.y;
int i2 = 1;
int x1, y1, x2 = 0, y2 = 0;
for (int i = 1; i < n - 1; i++) {
x1 = x[i];
y1 = y[i];
x2 = x[i + 1];
y2 = y[i + 1];
distance += Math.sqrt((x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1)) + 1;
distance += curvature[i] * 2;
if (distance >= threshold) {
x[i2] = x2 + r.x;
y[i2] = y2 + r.y;
i2++;
distance = 0.0;
}
}
int type = roi.getType() == Roi.FREELINE ? Roi.POLYLINE : Roi.POLYGON;
if (type == Roi.POLYLINE && distance > 0.0) {
x[i2] = x2 + r.x;
y[i2] = y2 + r.y;
i2++;
}
PolygonRoi p = new PolygonRoi(x, y, i2, type);
if (roi.getStroke() != null) p.setStrokeWidth(roi.getStrokeWidth());
p.setStrokeColor(roi.getStrokeColor());
p.setName(roi.getName());
imp.setRoi(p);
return p;
}
public void postProcess() {
double stdDev;
double n = num;
for (int i = 0; i < len; i++) {
if (num > 1) {
stdDev = (n * sum2[i] - sum[i] * sum[i]) / n;
if (stdDev > 0.0) result[i] = (float) Math.sqrt(stdDev / (n - 1.0));
else result[i] = 0f;
} else result[i] = 0f;
}
}
PolygonRoi trimFloatPolygon(PolygonRoi roi, double length) {
FloatPolygon poly = roi.getFloatPolygon();
float[] x = poly.xpoints;
float[] y = poly.ypoints;
int n = poly.npoints;
x = smooth(x, n);
y = smooth(y, n);
float[] curvature = getCurvature(x, y, n);
double threshold = rodbard(length);
// IJ.log("trim: "+length+" "+threshold);
double distance = Math.sqrt((x[1] - x[0]) * (x[1] - x[0]) + (y[1] - y[0]) * (y[1] - y[0]));
int i2 = 1;
double x1, y1, x2 = 0, y2 = 0;
for (int i = 1; i < n - 1; i++) {
x1 = x[i];
y1 = y[i];
x2 = x[i + 1];
y2 = y[i + 1];
distance += Math.sqrt((x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1)) + 1;
distance += curvature[i] * 2;
if (distance >= threshold) {
x[i2] = (float) x2;
y[i2] = (float) y2;
i2++;
distance = 0.0;
}
}
int type = roi.getType() == Roi.FREELINE ? Roi.POLYLINE : Roi.POLYGON;
if (type == Roi.POLYLINE && distance > 0.0) {
x[i2] = (float) x2;
y[i2] = (float) y2;
i2++;
}
PolygonRoi p = new PolygonRoi(x, y, i2, type);
if (roi.getStroke() != null) p.setStrokeWidth(roi.getStrokeWidth());
p.setStrokeColor(roi.getStrokeColor());
p.setDrawOffset(roi.getDrawOffset());
p.setName(roi.getName());
imp.setRoi(p);
return p;
}
float[] getCurvature(float[] x, float[] y, int n) {
float[] x2 = new float[n];
float[] y2 = new float[n];
for (int i = 0; i < n; i++) {
x2[i] = x[i];
y2[i] = y[i];
}
ImageProcessor ipx = new FloatProcessor(n, 1, x, null);
ImageProcessor ipy = new FloatProcessor(n, 1, y, null);
ipx.convolve(kernel, kernel.length, 1);
ipy.convolve(kernel, kernel.length, 1);
float[] indexes = new float[n];
float[] curvature = new float[n];
for (int i = 0; i < n; i++) {
indexes[i] = i;
curvature[i] =
(float) Math.sqrt((x2[i] - x[i]) * (x2[i] - x[i]) + (y2[i] - y[i]) * (y2[i] - y[i]));
}
return curvature;
}
/*------------------------------------------------------------------*/
void getSplineInterpolationCoefficients(double[] c, double tolerance) {
double z[] = {Math.sqrt(3.0) - 2.0};
double lambda = 1.0;
if (c.length == 1) {
return;
}
for (int k = 0; (k < z.length); k++) {
lambda = lambda * (1.0 - z[k]) * (1.0 - 1.0 / z[k]);
}
for (int n = 0; (n < c.length); n++) {
c[n] = c[n] * lambda;
}
for (int k = 0; (k < z.length); k++) {
c[0] = getInitialCausalCoefficientMirrorOnBounds(c, z[k], tolerance);
for (int n = 1; (n < c.length); n++) {
c[n] = c[n] + z[k] * c[n - 1];
}
c[c.length - 1] = getInitialAntiCausalCoefficientMirrorOnBounds(c, z[k], tolerance);
for (int n = c.length - 2; (0 <= n); n--) {
c[n] = z[k] * (c[n + 1] - c[n]);
}
}
} /* end getSplineInterpolationCoefficients */
public boolean dialogItemChanged(GenericDialog gd, AWTEvent e) {
int width = imp.getWidth();
int height = imp.getHeight();
type = gd.getNextChoice();
areaPerPoint = gd.getNextNumber();
color = gd.getNextChoice();
randomOffset = gd.getNextBoolean();
double minArea = (width * height) / 50000.0;
if (type.equals(types[1]) && minArea < 144.0) minArea = 144.0;
else if (minArea < 16) minArea = 16.0;
if (areaPerPoint / (pixelWidth * pixelHeight) < minArea) {
String err = "\"Area per Point\" too small";
if (gd.wasOKed()) IJ.error("Grid", err);
else IJ.showStatus(err);
return true;
}
double tileSize = Math.sqrt(areaPerPoint);
tileWidth = tileSize / pixelWidth;
tileHeight = tileSize / pixelHeight;
if (randomOffset) {
xstart = (int) (random.nextDouble() * tileWidth);
ystart = (int) (random.nextDouble() * tileHeight);
} else {
xstart = (int) (tileWidth / 2.0 + 0.5);
ystart = (int) (tileHeight / 2.0 + 0.5);
}
linesV = (int) ((width - xstart) / tileWidth) + 1;
linesH = (int) ((height - ystart) / tileHeight) + 1;
if (gd.invalidNumber()) return true;
if (type.equals(types[0])) drawLines();
else if (type.equals(types[1])) drawCrosses();
else if (type.equals(types[2])) drawPoints();
else showGrid(null);
return true;
}
public void run(String arg) {
int[] wList = WindowManager.getIDList();
if (wList==null) {
IJ.error("No images are open.");
return;
}
double kernel=3;
double kernelsum = 0;
double kernelvarsum =0;
double kernalvar = 0;
double sigmawidth = 2;
int kernelindex, minpixnumber;
String[] kernelsize = { "3�,"5�, "7�, "9�};
GenericDialog gd = new GenericDialog("Sigma Filter");
gd.addChoice("Kernel size", kernelsize, kernelsize[0]);
gd.addNumericField("Sigma width",sigmawidth , 2);
gd.addNumericField("Minimum number of pixels", 1, 0);
gd.addCheckbox("Keep source:",true);
gd.addCheckbox("Do all stack:",true);
gd.addCheckbox("Modified Lee's FIlter:",true);
gd.showDialog();
if (gd.wasCanceled()) return ;
kernelindex = gd.getNextChoiceIndex();
sigmawidth = gd.getNextNumber();
minpixnumber = ((int)gd.getNextNumber());
boolean keep = gd.getNextBoolean();
boolean doallstack = gd.getNextBoolean();
boolean modified = gd.getNextBoolean();
if (kernelindex==0) kernel = 3;
if (kernelindex==1) kernel = 5;
if (kernelindex==2) kernel = 7;
if (kernelindex==3) kernel = 9;
long start = System.currentTimeMillis();
if (minpixnumber> (kernel*kernel)){
IJ.showMessage("Sigma filter", "There must be more pixels in the kernel than+\n" + "the minimum number to be included");
return;
}
double v, midintensity;
int x, y, ix, iy;
double sum = 0;
double backupsum =0;
int count = 0;
int n = 0;
if (keep) {IJ.run("Select All"); IJ.run("Duplicate...", "title='Sigma filtered' duplicate");}
int radius = (int)(kernel-1)/2;
ImagePlus imp = WindowManager.getCurrentImage();
ImageStack stack1 = imp.getStack();
int width = imp.getWidth();
int height = imp.getHeight();
int nslices = stack1.getSize();
int cslice = imp.getCurrentSlice();
double status = width*height*nslices;
ImageProcessor ip = imp.getProcessor();
int sstart = 1;
if (!doallstack) {sstart = cslice; nslices=sstart;status = status/nslices;};
for (int i=sstart; i<=nslices; i++) {
imp.setSlice(i);
for (x=radius;x<width+radius;x++) {
for (y=radius;y<height+radius;y++) {
midintensity = ip.getPixelValue(x,y);
count = 0;
sum = 0;
kernelsum =0;
kernalvar =0;
kernelvarsum =0;
backupsum = 0;
//calculate mean of kernel value
for (ix=0;ix<kernel;ix++) {
for (iy=0;iy<kernel;iy++) {
v = ip.getPixelValue(x+ix-radius,y+iy-radius);
kernelsum = kernelsum+v;
}
}
double sigmacalcmean = (kernelsum/(kernel*kernel));
//calculate variance of kernel
for (ix=0;ix<kernel;ix++) {
for (iy=0;iy<kernel;iy++) {
v = ip.getPixelValue(x+ix-radius,y+iy-radius);
kernalvar = (v-sigmacalcmean)*(v-sigmacalcmean);
kernelvarsum = kernelvarsum + kernalvar;
}
}
//double variance = kernelvarsum/kernel;
double sigmacalcvar = kernelvarsum/((kernel*kernel)-1);
//calcuate sigma range = sqrt(variance/(mean^2)) � sigmawidth
double sigmarange = sigmawidth*(Math.sqrt((sigmacalcvar) /(sigmacalcmean*sigmacalcmean)));
//calulate sigma top value and bottom value
double sigmatop = midintensity*(1+sigmarange);
double sigmabottom = midintensity*(1-sigmarange);
//calculate mean of values that differ are in sigma range.
for (ix=0;ix<kernel;ix++) {
for (iy=0;iy<kernel;iy++) {
v = ip.getPixelValue(x+ix-radius,y+iy-radius);
if ((v>=sigmabottom)&&(v<=sigmatop)){
sum = sum+v;
count = count+1; }
backupsum = v+ backupsum;
}
}
//if there are too few pixels in the kernal that are within sigma range, the
//mean of the entire kernal is taken. My modification of Lee's filter is to exclude the central value
//from the calculation of the mean as I assume it to be spuriously high or low
if (!(count>(minpixnumber)))
{sum = (backupsum-midintensity);
count = (int)((kernel*kernel)-1);
if (!modified)
{sum = (backupsum);
count = (int)(kernel*kernel);}
}
double val = (sum/count);
ip.putPixelValue(x,y, val);
n = n+1;
double percentage = (((double)n/status)*100);
IJ.showStatus(IJ.d2s(percentage,0) +"% done");
}
// IJ.showProgress(i, status);
}}
imp.updateAndDraw();
IJ.showStatus(IJ.d2s((System.currentTimeMillis()-start)/1000.0, 2)+" seconds"); }
public void Calc_5Fr(ImagePlus imp1, ImagePlus imp2) {
if (imp1.getType() != imp2.getType()) {
error();
return;
}
if (imp1.getType() == 0) { // getType returns 0 for 8-bit, 1 for 16-bit
bitDepth = "8-bit";
Prefs.set("ps.bitDepth", bitDepth);
} else {
bitDepth = "16-bit";
Prefs.set("ps.bitDepth", bitDepth);
}
int width = imp1.getWidth();
int height = imp1.getHeight();
if (width != imp2.getWidth() || height != imp2.getHeight()) {
error();
return;
}
ImageStack stack1 = imp1.getStack();
// if (bgStackTitle != "NoBg") ImageStack stack2 = imp2.getStack();
ImageStack stack2 = imp2.getStack();
ImageProcessor ip = imp1.getProcessor();
int dimension = width * height;
byte[] pixB;
short[] pixS;
float[][] pixF = new float[5][dimension];
float[][] pixFBg = new float[5][dimension];
float a;
float b;
float den;
float aSmp;
float bSmp;
float denSmp;
float aBg;
float bBg;
float denBg;
float retF;
float azimF;
byte[] retB = new byte[dimension];
short[] retS = new short[dimension];
byte[] azimB = new byte[dimension];
short[] azimS = new short[dimension];
// Derived Variables:
float swingAngle = 2f * (float) Math.PI * swing;
float tanSwingAngleDiv2 = (float) Math.tan(swingAngle / 2.f);
float tanSwingAngleDiv2DivSqrt2 = (float) (Math.tan(swingAngle / 2.f) / Math.sqrt(2));
float wavelengthDiv2Pi = wavelength / (2f * (float) Math.PI);
// get the pixels of each slice in the stack and convert to float
for (int i = 0; i < 5; i++) {
if (bitDepth == "8-bit") {
pixB = (byte[]) stack1.getPixels(i + 3);
for (int j = 0; j < dimension; j++) pixF[i][j] = 0xff & pixB[j];
if (bgStackTitle != "NoBg") {
pixB = (byte[]) stack2.getPixels(i + 3);
for (int j = 0; j < dimension; j++) pixFBg[i][j] = 0xff & pixB[j];
}
} else {
pixS = (short[]) stack1.getPixels(i + 3);
for (int j = 0; j < dimension; j++) pixF[i][j] = (float) pixS[j];
if (bgStackTitle != "NoBg") {
pixS = (short[]) stack2.getPixels(i + 3);
for (int j = 0; j < dimension; j++) pixFBg[i][j] = (float) pixS[j];
}
}
}
// Algorithm
// terms a and b
for (int j = 0; j < dimension; j++) {
denSmp = (pixF[1][j] + pixF[2][j] + pixF[3][j] + pixF[4][j] - 4 * pixF[0][j]) / 2;
denBg = denSmp;
a = (pixF[4][j] - pixF[1][j]);
aSmp = a;
aBg = a;
b = (pixF[2][j] - pixF[3][j]);
bSmp = b;
bBg = b;
if (bgStackTitle != "NoBg") {
denBg = (pixFBg[1][j] + pixFBg[2][j] + pixFBg[3][j] + pixFBg[4][j] - 4 * pixFBg[0][j]) / 2;
aBg = pixFBg[4][j] - pixFBg[1][j];
bBg = pixFBg[2][j] - pixFBg[3][j];
}
// Special case of sample retardance half wave, denSmp = 0
if (denSmp == 0) {
retF = (float) wavelength / 4;
azimF =
(float) (a == 0 & b == 0 ? 0 : (azimRef + 90 + 90 * Math.atan2(a, b) / Math.PI) % 180);
} else {
// Retardance, the background correction can be improved by separately considering sample
// retardance values larger than a quarter wave
if (bgStackTitle != "NoBg") {
a = aSmp / denSmp - aBg / denBg;
b = bSmp / denSmp - bBg / denBg;
} else {
a = aSmp / denSmp;
b = bSmp / denSmp;
}
retF = (float) Math.atan(tanSwingAngleDiv2 * Math.sqrt(a * a + b * b));
if (denSmp < 0) retF = (float) Math.PI - retF;
retF = retF * wavelengthDiv2Pi; // convert to nm
if (retF > retCeiling) retF = retCeiling;
// Orientation
if ((bgStackTitle == "NoBg") || ((bgStackTitle != "NoBg") && (Math.abs(denSmp) < 1))) {
a = aSmp;
b = bSmp;
}
azimF =
(float) (a == 0 & b == 0 ? 0 : (azimRef + 90 + 90 * Math.atan2(a, b) / Math.PI) % 180);
}
if (bitDepth == "8-bit") retB[j] = (byte) (((int) (255 * retF / retCeiling)) & 0xff);
else retS[j] = (short) (4095 * retF / retCeiling);
if (mirror == "Yes") azimF = 180 - azimF;
if (bitDepth == "8-bit") azimB[j] = (byte) (((int) azimF) & 0xff);
else azimS[j] = (short) (azimF * 10f);
}
// show the resulting images in slice 1 and 2
imp1.setSlice(3);
if (bitDepth == "8-bit") {
stack1.setPixels(retB, 1);
stack1.setPixels(azimB, 2);
} else {
stack1.setPixels(retS, 1);
stack1.setPixels(azimS, 2);
}
imp1.setSlice(1);
IJ.selectWindow(imp1.getTitle());
Prefs.set("ps.sampleStackTitle", sampleStackTitle);
Prefs.set("ps.bgStackTitle", bgStackTitle);
Prefs.set("ps.mirror", mirror);
Prefs.set("ps.wavelength", wavelength);
Prefs.set("ps.swing", swing);
Prefs.set("ps.retCeiling", retCeiling);
Prefs.set("ps.azimRef", azimRef);
Prefs.savePreferences();
}
/*
if selection is closed shape, create a circle with the same area and centroid, otherwise use<br>
the Pratt method to fit a circle to the points that define the line or multi-point selection.<br>
Reference: Pratt V., Direct least-squares fitting of algebraic surfaces", Computer Graphics, Vol. 21, pages 145-152 (1987).<br>
Original code: Nikolai Chernov's MATLAB script for Newton-based Pratt fit.<br>
(http://www.math.uab.edu/~chernov/cl/MATLABcircle.html)<br>
Java version: https://github.com/mdoube/BoneJ/blob/master/src/org/doube/geometry/FitCircle.java<br>
@authors Nikolai Chernov, Michael Doube, Ved Sharma
*/
void fitCircle(ImagePlus imp) {
Roi roi = imp.getRoi();
if (roi == null) {
noRoi("Fit Circle");
return;
}
if (roi.isArea()) { // create circle with the same area and centroid
ImageProcessor ip = imp.getProcessor();
ip.setRoi(roi);
ImageStatistics stats =
ImageStatistics.getStatistics(ip, Measurements.AREA + Measurements.CENTROID, null);
double r = Math.sqrt(stats.pixelCount / Math.PI);
imp.killRoi();
int d = (int) Math.round(2.0 * r);
IJ.makeOval(
(int) Math.round(stats.xCentroid - r), (int) Math.round(stats.yCentroid - r), d, d);
return;
}
Polygon poly = roi.getPolygon();
int n = poly.npoints;
int[] x = poly.xpoints;
int[] y = poly.ypoints;
if (n < 3) {
IJ.error("Fit Circle", "At least 3 points are required to fit a circle.");
return;
}
// calculate point centroid
double sumx = 0, sumy = 0;
for (int i = 0; i < n; i++) {
sumx = sumx + poly.xpoints[i];
sumy = sumy + poly.ypoints[i];
}
double meanx = sumx / n;
double meany = sumy / n;
// calculate moments
double[] X = new double[n], Y = new double[n];
double Mxx = 0, Myy = 0, Mxy = 0, Mxz = 0, Myz = 0, Mzz = 0;
for (int i = 0; i < n; i++) {
X[i] = x[i] - meanx;
Y[i] = y[i] - meany;
double Zi = X[i] * X[i] + Y[i] * Y[i];
Mxy = Mxy + X[i] * Y[i];
Mxx = Mxx + X[i] * X[i];
Myy = Myy + Y[i] * Y[i];
Mxz = Mxz + X[i] * Zi;
Myz = Myz + Y[i] * Zi;
Mzz = Mzz + Zi * Zi;
}
Mxx = Mxx / n;
Myy = Myy / n;
Mxy = Mxy / n;
Mxz = Mxz / n;
Myz = Myz / n;
Mzz = Mzz / n;
// calculate the coefficients of the characteristic polynomial
double Mz = Mxx + Myy;
double Cov_xy = Mxx * Myy - Mxy * Mxy;
double Mxz2 = Mxz * Mxz;
double Myz2 = Myz * Myz;
double A2 = 4 * Cov_xy - 3 * Mz * Mz - Mzz;
double A1 = Mzz * Mz + 4 * Cov_xy * Mz - Mxz2 - Myz2 - Mz * Mz * Mz;
double A0 = Mxz2 * Myy + Myz2 * Mxx - Mzz * Cov_xy - 2 * Mxz * Myz * Mxy + Mz * Mz * Cov_xy;
double A22 = A2 + A2;
double epsilon = 1e-12;
double ynew = 1e+20;
int IterMax = 20;
double xnew = 0;
int iterations = 0;
// Newton's method starting at x=0
for (int iter = 1; iter <= IterMax; iter++) {
iterations = iter;
double yold = ynew;
ynew = A0 + xnew * (A1 + xnew * (A2 + 4. * xnew * xnew));
if (Math.abs(ynew) > Math.abs(yold)) {
if (IJ.debugMode) IJ.log("Fit Circle: wrong direction: |ynew| > |yold|");
xnew = 0;
break;
}
double Dy = A1 + xnew * (A22 + 16 * xnew * xnew);
double xold = xnew;
xnew = xold - ynew / Dy;
if (Math.abs((xnew - xold) / xnew) < epsilon) break;
if (iter >= IterMax) {
if (IJ.debugMode) IJ.log("Fit Circle: will not converge");
xnew = 0;
}
if (xnew < 0) {
if (IJ.debugMode) IJ.log("Fit Circle: negative root: x = " + xnew);
xnew = 0;
}
}
if (IJ.debugMode)
IJ.log("Fit Circle: n=" + n + ", xnew=" + IJ.d2s(xnew, 2) + ", iterations=" + iterations);
// calculate the circle parameters
double DET = xnew * xnew - xnew * Mz + Cov_xy;
double CenterX = (Mxz * (Myy - xnew) - Myz * Mxy) / (2 * DET);
double CenterY = (Myz * (Mxx - xnew) - Mxz * Mxy) / (2 * DET);
double radius = Math.sqrt(CenterX * CenterX + CenterY * CenterY + Mz + 2 * xnew);
if (Double.isNaN(radius)) {
IJ.error("Fit Circle", "Points are collinear.");
return;
}
CenterX = CenterX + meanx;
CenterY = CenterY + meany;
imp.killRoi();
IJ.makeOval(
(int) Math.round(CenterX - radius),
(int) Math.round(CenterY - radius),
(int) Math.round(2 * radius),
(int) Math.round(2 * radius));
}
public void updatebeta() {
for (int i = 0; i <= ncurves; i++) {
brightmincc[i] = (bright1[i] * beta) / Math.sqrt(intensity1[i] / intensity2[i]);
eminccarray[i].setText("" + (float) brightmincc[i]);
}
}
String getInfo(ImagePlus imp, ImageProcessor ip) {
String s = new String("\n");
s += "Title: " + imp.getTitle() + "\n";
Calibration cal = imp.getCalibration();
int stackSize = imp.getStackSize();
int channels = imp.getNChannels();
int slices = imp.getNSlices();
int frames = imp.getNFrames();
int digits = imp.getBitDepth() == 32 ? 4 : 0;
if (cal.scaled()) {
String unit = cal.getUnit();
String units = cal.getUnits();
s +=
"Width: "
+ IJ.d2s(imp.getWidth() * cal.pixelWidth, 2)
+ " "
+ units
+ " ("
+ imp.getWidth()
+ ")\n";
s +=
"Height: "
+ IJ.d2s(imp.getHeight() * cal.pixelHeight, 2)
+ " "
+ units
+ " ("
+ imp.getHeight()
+ ")\n";
if (slices > 1)
s += "Depth: " + IJ.d2s(slices * cal.pixelDepth, 2) + " " + units + " (" + slices + ")\n";
double xResolution = 1.0 / cal.pixelWidth;
double yResolution = 1.0 / cal.pixelHeight;
int places = Tools.getDecimalPlaces(xResolution, yResolution);
if (xResolution == yResolution)
s += "Resolution: " + IJ.d2s(xResolution, places) + " pixels per " + unit + "\n";
else {
s += "X Resolution: " + IJ.d2s(xResolution, places) + " pixels per " + unit + "\n";
s += "Y Resolution: " + IJ.d2s(yResolution, places) + " pixels per " + unit + "\n";
}
} else {
s += "Width: " + imp.getWidth() + " pixels\n";
s += "Height: " + imp.getHeight() + " pixels\n";
if (stackSize > 1) s += "Depth: " + slices + " pixels\n";
}
if (stackSize > 1)
s +=
"Voxel size: "
+ d2s(cal.pixelWidth)
+ "x"
+ d2s(cal.pixelHeight)
+ "x"
+ d2s(cal.pixelDepth)
+ " "
+ cal.getUnit()
+ "\n";
else
s +=
"Pixel size: "
+ d2s(cal.pixelWidth)
+ "x"
+ d2s(cal.pixelHeight)
+ " "
+ cal.getUnit()
+ "\n";
s += "ID: " + imp.getID() + "\n";
String zOrigin = stackSize > 1 || cal.zOrigin != 0.0 ? "," + d2s(cal.zOrigin) : "";
s += "Coordinate origin: " + d2s(cal.xOrigin) + "," + d2s(cal.yOrigin) + zOrigin + "\n";
int type = imp.getType();
switch (type) {
case ImagePlus.GRAY8:
s += "Bits per pixel: 8 ";
String lut = "LUT";
if (imp.getProcessor().isColorLut()) lut = "color " + lut;
else lut = "grayscale " + lut;
if (imp.isInvertedLut()) lut = "inverting " + lut;
s += "(" + lut + ")\n";
if (imp.getNChannels() > 1) s += displayRanges(imp);
else s += "Display range: " + (int) ip.getMin() + "-" + (int) ip.getMax() + "\n";
break;
case ImagePlus.GRAY16:
case ImagePlus.GRAY32:
if (type == ImagePlus.GRAY16) {
String sign = cal.isSigned16Bit() ? "signed" : "unsigned";
s += "Bits per pixel: 16 (" + sign + ")\n";
} else s += "Bits per pixel: 32 (float)\n";
if (imp.getNChannels() > 1) s += displayRanges(imp);
else {
s += "Display range: ";
double min = ip.getMin();
double max = ip.getMax();
if (cal.calibrated()) {
min = cal.getCValue((int) min);
max = cal.getCValue((int) max);
}
s += IJ.d2s(min, digits) + " - " + IJ.d2s(max, digits) + "\n";
}
break;
case ImagePlus.COLOR_256:
s += "Bits per pixel: 8 (color LUT)\n";
break;
case ImagePlus.COLOR_RGB:
s += "Bits per pixel: 32 (RGB)\n";
break;
}
double interval = cal.frameInterval;
double fps = cal.fps;
if (stackSize > 1) {
ImageStack stack = imp.getStack();
int slice = imp.getCurrentSlice();
String number = slice + "/" + stackSize;
String label = stack.getShortSliceLabel(slice);
if (label != null && label.length() > 0) label = " (" + label + ")";
else label = "";
if (interval > 0.0 || fps != 0.0) {
s += "Frame: " + number + label + "\n";
if (fps != 0.0) {
String sRate =
Math.abs(fps - Math.round(fps)) < 0.00001 ? IJ.d2s(fps, 0) : IJ.d2s(fps, 5);
s += "Frame rate: " + sRate + " fps\n";
}
if (interval != 0.0)
s +=
"Frame interval: "
+ ((int) interval == interval ? IJ.d2s(interval, 0) : IJ.d2s(interval, 5))
+ " "
+ cal.getTimeUnit()
+ "\n";
} else s += "Image: " + number + label + "\n";
if (imp.isHyperStack()) {
if (channels > 1) s += " Channel: " + imp.getChannel() + "/" + channels + "\n";
if (slices > 1) s += " Slice: " + imp.getSlice() + "/" + slices + "\n";
if (frames > 1) s += " Frame: " + imp.getFrame() + "/" + frames + "\n";
}
if (imp.isComposite()) {
if (!imp.isHyperStack() && channels > 1) s += " Channels: " + channels + "\n";
String mode = ((CompositeImage) imp).getModeAsString();
s += " Composite mode: \"" + mode + "\"\n";
}
}
if (ip.getMinThreshold() == ImageProcessor.NO_THRESHOLD) s += "No Threshold\n";
else {
double lower = ip.getMinThreshold();
double upper = ip.getMaxThreshold();
int dp = digits;
if (cal.calibrated()) {
lower = cal.getCValue((int) lower);
upper = cal.getCValue((int) upper);
dp = cal.isSigned16Bit() ? 0 : 4;
}
s += "Threshold: " + IJ.d2s(lower, dp) + "-" + IJ.d2s(upper, dp) + "\n";
}
ImageCanvas ic = imp.getCanvas();
double mag = ic != null ? ic.getMagnification() : 1.0;
if (mag != 1.0) s += "Magnification: " + IJ.d2s(mag, 2) + "\n";
if (cal.calibrated()) {
s += " \n";
int curveFit = cal.getFunction();
s += "Calibration Function: ";
if (curveFit == Calibration.UNCALIBRATED_OD) s += "Uncalibrated OD\n";
else if (curveFit == Calibration.CUSTOM) s += "Custom lookup table\n";
else s += CurveFitter.fList[curveFit] + "\n";
double[] c = cal.getCoefficients();
if (c != null) {
s += " a: " + IJ.d2s(c[0], 6) + "\n";
s += " b: " + IJ.d2s(c[1], 6) + "\n";
if (c.length >= 3) s += " c: " + IJ.d2s(c[2], 6) + "\n";
if (c.length >= 4) s += " c: " + IJ.d2s(c[3], 6) + "\n";
if (c.length >= 5) s += " c: " + IJ.d2s(c[4], 6) + "\n";
}
s += " Unit: \"" + cal.getValueUnit() + "\"\n";
} else s += "Uncalibrated\n";
FileInfo fi = imp.getOriginalFileInfo();
if (fi != null) {
if (fi.url != null && !fi.url.equals("")) s += "URL: " + fi.url + "\n";
else if (fi.directory != null && fi.fileName != null)
s += "Path: " + fi.directory + fi.fileName + "\n";
}
ImageWindow win = imp.getWindow();
if (win != null) {
Point loc = win.getLocation();
Dimension screen = IJ.getScreenSize();
s +=
"Screen location: "
+ loc.x
+ ","
+ loc.y
+ " ("
+ screen.width
+ "x"
+ screen.height
+ ")\n";
}
Overlay overlay = imp.getOverlay();
if (overlay != null) {
String hidden = imp.getHideOverlay() ? " (hidden)" : " ";
int n = overlay.size();
String elements = n == 1 ? " element" : " elements";
s += "Overlay: " + n + elements + (imp.getHideOverlay() ? " (hidden)" : "") + "\n";
} else s += "No Overlay\n";
Roi roi = imp.getRoi();
if (roi == null) {
if (cal.calibrated()) s += " \n";
s += "No Selection\n";
} else if (roi instanceof EllipseRoi) {
s += "\nElliptical Selection\n";
double[] p = ((EllipseRoi) roi).getParams();
double dx = p[2] - p[0];
double dy = p[3] - p[1];
double major = Math.sqrt(dx * dx + dy * dy);
s += " Major: " + IJ.d2s(major, 2) + "\n";
s += " Minor: " + IJ.d2s(major * p[4], 2) + "\n";
s += " X1: " + IJ.d2s(p[0], 2) + "\n";
s += " Y1: " + IJ.d2s(p[1], 2) + "\n";
s += " X2: " + IJ.d2s(p[2], 2) + "\n";
s += " Y2: " + IJ.d2s(p[3], 2) + "\n";
s += " Aspect ratio: " + IJ.d2s(p[4], 2) + "\n";
} else {
s += " \n";
s += roi.getTypeAsString() + " Selection";
String points = null;
if (roi instanceof PointRoi) {
int npoints = ((PolygonRoi) roi).getNCoordinates();
String suffix = npoints > 1 ? "s)" : ")";
points = " (" + npoints + " point" + suffix;
}
String name = roi.getName();
if (name != null) {
s += " (\"" + name + "\")";
if (points != null) s += "\n " + points;
} else if (points != null) s += points;
s += "\n";
Rectangle r = roi.getBounds();
if (roi instanceof Line) {
Line line = (Line) roi;
s += " X1: " + IJ.d2s(line.x1d * cal.pixelWidth) + "\n";
s += " Y1: " + IJ.d2s(yy(line.y1d, imp) * cal.pixelHeight) + "\n";
s += " X2: " + IJ.d2s(line.x2d * cal.pixelWidth) + "\n";
s += " Y2: " + IJ.d2s(yy(line.y2d, imp) * cal.pixelHeight) + "\n";
} else if (cal.scaled()) {
s += " X: " + IJ.d2s(cal.getX(r.x)) + " (" + r.x + ")\n";
s += " Y: " + IJ.d2s(cal.getY(r.y, imp.getHeight())) + " (" + r.y + ")\n";
s += " Width: " + IJ.d2s(r.width * cal.pixelWidth) + " (" + r.width + ")\n";
s += " Height: " + IJ.d2s(r.height * cal.pixelHeight) + " (" + r.height + ")\n";
} else {
s += " X: " + r.x + "\n";
s += " Y: " + yy(r.y, imp) + "\n";
s += " Width: " + r.width + "\n";
s += " Height: " + r.height + "\n";
}
}
return s;
}
private void updateavg() {
nmeas[ncurves] = 0;
avg = new float[xpts][ypts];
avgweights = new float[xpts][ypts];
for (int i = 0; i < ncurves; i++) {
if (include[i]) {
for (int j = 0; j < xpts; j++) {
for (int k = 0; k < ypts; k++) {
avg[j][k] += pch[i][j][k];
nmeas[ncurves] += (int) pch[i][j][k];
}
}
}
}
double tempavg = 0.0;
double tempavg2 = 0.0;
double temp2avg = 0.0;
double temp2avg2 = 0.0;
double tempccavg = 0.0;
for (int i = 0; i < xpts; i++) {
for (int j = 0; j < ypts; j++) {
double normed = (double) avg[i][j] / (double) nmeas[ncurves];
avgweights[i][j] = (float) ((double) nmeas[ncurves] / (normed * (1.0f - normed)));
if (avg[i][j] > 0.0f) {
avgweights[i][j] = (float) ((double) nmeas[ncurves] / (normed * (1.0f - normed)));
} else {
avgweights[i][j] = 1.0f;
}
tempavg += (double) i * normed;
tempavg2 += (double) i * (double) i * normed;
temp2avg += (double) j * normed;
temp2avg2 += (double) j * (double) j * normed;
tempccavg += (double) i * (double) j * normed;
}
}
tempccavg -= tempavg * temp2avg;
brightcc[ncurves] = tempccavg / Math.sqrt(tempavg * temp2avg);
tempavg2 -= tempavg * tempavg;
tempavg2 /= tempavg;
bright1[ncurves] = (tempavg2 - 1.0);
temp2avg2 -= temp2avg * temp2avg;
temp2avg2 /= temp2avg;
bright2[ncurves] = (temp2avg2 - 1.0);
intensity1[ncurves] = tempavg;
intensity2[ncurves] = temp2avg;
if (psfflag == 0) {
bright1[ncurves] /= 0.3536;
bright2[ncurves] /= 0.3536;
brightcc[ncurves] /= 0.3536;
} else {
if (psfflag == 1) {
bright1[ncurves] /= 0.078;
bright2[ncurves] /= 0.078;
brightcc[ncurves] /= 0.078;
} else {
bright1[ncurves] /= 0.5;
bright2[ncurves] /= 0.5;
brightcc[ncurves] /= 0.5;
}
}
number1[ncurves] = intensity1[ncurves] / bright1[ncurves];
number2[ncurves] = intensity2[ncurves] / bright2[ncurves];
brightmincc[ncurves] =
(bright1[ncurves] * beta) * Math.sqrt(intensity1[ncurves] / intensity2[ncurves]);
}
void Niblack(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Niblack recommends K_VALUE = -0.2 for images with black foreground
// objects, and K_VALUE = +0.2 for images with white foreground objects.
// Niblack W. (1986) "An introduction to Digital Image Processing" Prentice-Hall.
// Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value;
int c_value = 0;
byte object;
byte backg;
if (doIwhite) {
k_value = 0.2;
object = (byte) 0xff;
backg = (byte) 0;
} else {
k_value = -0.2;
object = (byte) 0;
backg = (byte) 0xff;
}
if (par1 != 0) {
IJ.log("Niblack: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log(
"Niblack: changed c_value from :"
+ c_value
+ " to:"
+ par2); // requested feature, not in original
c_value = (int) par2;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff) > (int) (mean[i] + k_value * Math.sqrt(var[i]) - c_value))
? object
: backg;
// imp.updateAndDraw();
return;
}
private boolean track(
ImagePlus siPlus, ArrayList<Point2D.Double> xyPoints, ArrayList<Double> timePoints) {
GaussianFit gs = new GaussianFit(shape_, fitMode_);
double cPCF = photonConversionFactor_ / gain_;
// for now, take the active ImageJ image
// (this should be an image of a difraction limited spot)
Roi originalRoi = siPlus.getRoi();
if (null == originalRoi) {
if (!silent_) IJ.error("Please draw a Roi around the spot you want to track");
return false;
}
Polygon pol = FindLocalMaxima.FindMax(siPlus, halfSize_, noiseTolerance_, preFilterType_);
if (pol.npoints == 0) {
if (!silent_) ReportingUtils.showError("No local maxima found in ROI");
else ReportingUtils.logError("No local maxima found in ROI");
return false;
}
int xc = pol.xpoints[0];
int yc = pol.ypoints[0];
// not sure if needed, but look for the maximum local maximum
int max = siPlus.getProcessor().getPixel(pol.xpoints[0], pol.ypoints[0]);
if (pol.npoints > 1) {
for (int i = 1; i < pol.npoints; i++) {
if (siPlus.getProcessor().getPixel(pol.xpoints[i], pol.ypoints[i]) > max) {
max = siPlus.getProcessor().getPixel(pol.xpoints[i], pol.ypoints[i]);
xc = pol.xpoints[i];
yc = pol.ypoints[i];
}
}
}
long startTime = System.nanoTime();
// This is confusing. We like to accomodate stacks with multiple slices
// and stacks with multiple frames (which is actually the correct way
int ch = siPlus.getChannel();
Boolean useSlices = siPlus.getNSlices() > siPlus.getNFrames();
int n = siPlus.getSlice();
int nMax = siPlus.getNSlices();
if (!useSlices) {
n = siPlus.getFrame();
nMax = siPlus.getNFrames();
}
boolean stop = false;
int missedFrames = 0;
int size = 2 * halfSize_;
for (int i = n; i <= nMax && !stop; i++) {
SpotData spot;
// Give user feedback
ij.IJ.showStatus("Tracking...");
ij.IJ.showProgress(i, nMax);
// Search in next slice in same Roi for local maximum
Roi searchRoi = new Roi(xc - size, yc - size, 2 * size + 1, 2 * size + 1);
if (useSlices) {
siPlus.setSliceWithoutUpdate(siPlus.getStackIndex(ch, i, 1));
} else {
siPlus.setSliceWithoutUpdate(siPlus.getStackIndex(ch, 1, i));
}
siPlus.setRoi(searchRoi, false);
// Find maximum in Roi, might not be needed....
pol = FindLocalMaxima.FindMax(siPlus, 2 * halfSize_, noiseTolerance_, preFilterType_);
// do not stray more than 2 pixels in x or y.
// This velocity maximum parameter should be tunable by the user
if (pol.npoints >= 1) {
if (Math.abs(xc - pol.xpoints[0]) < 2 && Math.abs(yc - pol.ypoints[0]) < 2) {
xc = pol.xpoints[0];
yc = pol.ypoints[0];
}
}
// Reset ROI to the original
if (i == n) {
firstX_ = xc;
firstY_ = yc;
}
// Set Roi for fitting centered around maximum
Roi spotRoi = new Roi(xc - halfSize_, yc - halfSize_, 2 * halfSize_, 2 * halfSize_);
siPlus.setRoi(spotRoi, false);
ImageProcessor ip;
try {
if (siPlus.getRoi() != spotRoi) {
ReportingUtils.logError(
"There seems to be a thread synchronization issue going on that causes this weirdness");
}
ip = siPlus.getProcessor().crop();
} catch (ArrayIndexOutOfBoundsException aex) {
ReportingUtils.logError(aex, "ImageJ failed to crop the image, not sure why");
siPlus.setRoi(spotRoi, true);
ip = siPlus.getProcessor().crop();
}
spot = new SpotData(ip, ch, 1, i, 1, i, xc, yc);
double[] paramsOut = gs.dogaussianfit(ip, maxIterations_);
double sx;
double sy;
double a = 1.0;
double theta = 0.0;
if (paramsOut.length >= 4) {
// anormalize the intensity from the Gaussian fit
double N =
cPCF
* paramsOut[GaussianFit.INT]
* (2 * Math.PI * paramsOut[GaussianFit.S] * paramsOut[GaussianFit.S]);
double xpc = paramsOut[GaussianFit.XC];
double ypc = paramsOut[GaussianFit.YC];
double x = (xpc - halfSize_ + xc) * pixelSize_;
double y = (ypc - halfSize_ + yc) * pixelSize_;
double s = paramsOut[GaussianFit.S] * pixelSize_;
// express background in photons after base level correction
double bgr = cPCF * (paramsOut[GaussianFit.BGR] - baseLevel_);
// calculate error using formular from Thompson et al (2002)
// (dx)2 = (s*s + (a*a/12)) / N + (8*pi*s*s*s*s * b*b) / (a*a*N*N)
double sigma =
(s * s + (pixelSize_ * pixelSize_) / 12) / N
+ (8 * Math.PI * s * s * s * s * bgr * bgr) / (pixelSize_ * pixelSize_ * N * N);
sigma = Math.sqrt(sigma);
double width = 2 * s;
if (paramsOut.length >= 6) {
sx = paramsOut[GaussianFit.S1] * pixelSize_;
sy = paramsOut[GaussianFit.S2] * pixelSize_;
a = sx / sy;
}
if (paramsOut.length >= 7) {
theta = paramsOut[GaussianFit.S3];
}
if ((!useWidthFilter_ || (width > widthMin_ && width < widthMax_))
&& (!useNrPhotonsFilter_ || (N > nrPhotonsMin_ && N < nrPhotonsMax_))) {
// If we have a good fit, update position of the box
if (xpc > 0 && xpc < (2 * halfSize_) && ypc > 0 && ypc < (2 * halfSize_)) {
xc += (int) xpc - halfSize_;
yc += (int) ypc - halfSize_;
}
spot.setData(N, bgr, x, y, 0.0, 2 * s, a, theta, sigma);
xyPoints.add(new Point2D.Double(x, y));
timePoints.add(i * timeIntervalMs_);
resultList_.add(spot);
missedFrames = 0;
} else {
missedFrames += 1;
}
} else {
missedFrames += 1;
}
if (endTrackAfterBadFrames_) {
if (missedFrames >= this.endTrackAfterNBadFrames_) {
stop = true;
}
}
}
long endTime = System.nanoTime();
double took = (endTime - startTime) / 1E6;
print("Calculation took: " + took + " milli seconds");
ij.IJ.showStatus("");
siPlus.setSlice(n);
siPlus.setRoi(originalRoi);
return true;
}
/*------------------------------------------------------------------*/
void doIt(ImageProcessor ip) {
int width = ip.getWidth();
int height = ip.getHeight();
double hLine[] = new double[width];
double vLine[] = new double[height];
if (!(ip.getPixels() instanceof float[])) {
throw new IllegalArgumentException("Float image required");
}
switch (operation) {
case GRADIENT_MAGNITUDE:
{
ImageProcessor h = ip.duplicate();
ImageProcessor v = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsH = (float[]) h.getPixels();
float[] floatPixelsV = (float[]) v.getPixels();
getHorizontalGradient(h, FLT_EPSILON);
getVerticalGradient(v, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
Math.sqrt(
floatPixelsH[k] * floatPixelsH[k] + floatPixelsV[k] * floatPixelsV[k]);
}
stepProgressBar();
}
}
break;
case GRADIENT_DIRECTION:
{
ImageProcessor h = ip.duplicate();
ImageProcessor v = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsH = (float[]) h.getPixels();
float[] floatPixelsV = (float[]) v.getPixels();
getHorizontalGradient(h, FLT_EPSILON);
getVerticalGradient(v, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] = (float) Math.atan2(floatPixelsH[k], floatPixelsV[k]);
}
stepProgressBar();
}
}
break;
case LAPLACIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] = (float) (floatPixelsHH[k] + floatPixelsVV[k]);
}
stepProgressBar();
}
}
break;
case LARGEST_HESSIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
(0.5
* (floatPixelsHH[k]
+ floatPixelsVV[k]
+ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
stepProgressBar();
}
}
break;
case SMALLEST_HESSIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
(0.5
* (floatPixelsHH[k]
+ floatPixelsVV[k]
- Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
stepProgressBar();
}
}
break;
case HESSIAN_ORIENTATION:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
if (floatPixelsHV[k] < 0.0) {
floatPixels[k] =
(float)
(-0.5
* Math.acos(
(floatPixelsHH[k] - floatPixelsVV[k])
/ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
} else {
floatPixels[k] =
(float)
(0.5
* Math.acos(
(floatPixelsHH[k] - floatPixelsVV[k])
/ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
}
stepProgressBar();
}
}
break;
default:
throw new IllegalArgumentException("Invalid operation");
}
ip.resetMinAndMax();
imp.updateAndDraw();
} /* end doIt */
void Sauvola(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
// Sauvola recommends K_VALUE = 0.5 and R_VALUE = 128.
// This is a modification of Niblack's thresholding method.
// Sauvola J. and Pietaksinen M. (2000) "Adaptive Document Image Binarization"
// Pattern Recognition, 33(2): 225-236
// http://www.ee.oulu.fi/mvg/publications/show_pdf.php?ID=24
// Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
// This version uses a circular local window, instead of a rectagular one
ImagePlus Meanimp, Varimp;
ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
double k_value = 0.5;
double r_value = 128;
byte object;
byte backg;
if (par1 != 0) {
IJ.log("Sauvola: changed k_value from :" + k_value + " to:" + par1);
k_value = par1;
}
if (par2 != 0) {
IJ.log("Sauvola: changed r_value from :" + r_value + " to:" + par2);
r_value = par2;
}
if (doIwhite) {
object = (byte) 0xff;
backg = (byte) 0;
} else {
object = (byte) 0;
backg = (byte) 0xff;
}
Meanimp = duplicateImage(ip);
ImageConverter ic = new ImageConverter(Meanimp);
ic.convertToGray32();
ipMean = Meanimp.getProcessor();
RankFilters rf = new RankFilters();
rf.rank(ipMean, radius, rf.MEAN); // Mean
// Meanimp.show();
Varimp = duplicateImage(ip);
ic = new ImageConverter(Varimp);
ic.convertToGray32();
ipVar = Varimp.getProcessor();
rf.rank(ipVar, radius, rf.VARIANCE); // Variance
// Varimp.show();
byte[] pixels = (byte[]) ip.getPixels();
float[] mean = (float[]) ipMean.getPixels();
float[] var = (float[]) ipVar.getPixels();
for (int i = 0; i < pixels.length; i++)
pixels[i] =
((int) (pixels[i] & 0xff)
> (int) (mean[i] * (1.0 + k_value * ((Math.sqrt(var[i]) / r_value) - 1.0))))
? object
: backg;
// imp.updateAndDraw();
return;
}