public void run(ImageProcessor ip) {
if (enlarge && gd.wasOKed())
synchronized (this) {
if (!isEnlarged) {
enlargeCanvas();
isEnlarged = true;
}
}
if (isEnlarged) { // enlarging may have made the ImageProcessor invalid, also for the parallel
// threads
int slice = pfr.getSliceNumber();
if (imp.getStackSize() == 1) ip = imp.getProcessor();
else ip = imp.getStack().getProcessor(slice);
}
ip.setInterpolationMethod(interpolationMethod);
if (fillWithBackground) {
Color bgc = Toolbar.getBackgroundColor();
if (bitDepth == 8) ip.setBackgroundValue(ip.getBestIndex(bgc));
else if (bitDepth == 24) ip.setBackgroundValue(bgc.getRGB());
} else ip.setBackgroundValue(0);
ip.rotate(angle);
if (!gd.wasOKed()) drawGridLines(gridLines);
if (isEnlarged && imp.getStackSize() == 1) {
imp.changes = true;
imp.updateAndDraw();
Undo.setup(Undo.COMPOUND_FILTER_DONE, imp);
}
}
void createMask(ImagePlus imp) {
Roi roi = imp.getRoi();
boolean useInvertingLut = Prefs.useInvertingLut;
Prefs.useInvertingLut = false;
if (roi == null || !(roi.isArea() || roi.getType() == Roi.POINT)) {
createMaskFromThreshold(imp);
Prefs.useInvertingLut = useInvertingLut;
return;
}
ImagePlus maskImp = null;
Frame frame = WindowManager.getFrame("Mask");
if (frame != null && (frame instanceof ImageWindow))
maskImp = ((ImageWindow) frame).getImagePlus();
if (maskImp == null) {
ImageProcessor ip = new ByteProcessor(imp.getWidth(), imp.getHeight());
if (!Prefs.blackBackground) ip.invertLut();
maskImp = new ImagePlus("Mask", ip);
maskImp.show();
}
ImageProcessor ip = maskImp.getProcessor();
ip.setRoi(roi);
ip.setValue(255);
ip.fill(ip.getMask());
maskImp.updateAndDraw();
Prefs.useInvertingLut = useInvertingLut;
}
void scale(ImageProcessor ip) {
if (newWindow) {
Rectangle r = ip.getRoi();
ImagePlus imp2 = imp.createImagePlus();
imp2.setProcessor(title, ip.resize(newWidth, newHeight));
Calibration cal = imp2.getCalibration();
if (cal.scaled()) {
cal.pixelWidth *= 1.0 / xscale;
cal.pixelHeight *= 1.0 / yscale;
}
imp2.show();
imp.trimProcessor();
imp2.trimProcessor();
imp2.changes = true;
} else {
if (processStack && imp.getStackSize() > 1) {
Undo.reset();
StackProcessor sp = new StackProcessor(imp.getStack(), ip);
sp.scale(xscale, yscale, bgValue);
} else {
ip.snapshot();
Undo.setup(Undo.FILTER, imp);
ip.setSnapshotCopyMode(true);
ip.scale(xscale, yscale);
ip.setSnapshotCopyMode(false);
}
imp.killRoi();
imp.updateAndDraw();
imp.changes = true;
}
}
public void refreshForeground() {
// Boundary for Foreground Selection
setColor(0x444444);
drawRect(8, 266, (w * 2) + 4, (h * 2) + 4);
setColor(0x999999);
drawRect(9, 267, (w * 2) + 2, (h * 2) + 2);
setRoi(10, 268, w * 2, h * 2); // Paints the Foreground Color
setColor(Toolbar.getForegroundColor());
fill();
imp.updateAndDraw();
}
public void refreshBackground() {
// Boundary for Background Selection
setColor(0x444444);
drawRect((w * 2) - 12, 276, (w * 2) + 4, (h * 2) + 4);
setColor(0x999999);
drawRect((w * 2) - 11, 277, (w * 2) + 2, (h * 2) + 2);
setRoi((w * 2) - 10, 278, w * 2, h * 2); // Paints the Background Color
setColor(Toolbar.getBackgroundColor());
fill();
imp.updateAndDraw();
}
public void run(String arg) {
int[] wList = WindowManager.getIDList();
if (wList == null) {
IJ.error("No images are open.");
return;
}
double thalf = 0.5;
boolean keep;
GenericDialog gd = new GenericDialog("Bleach correction");
gd.addNumericField("t½:", thalf, 1);
gd.addCheckbox("Keep source stack:", true);
gd.showDialog();
if (gd.wasCanceled()) return;
long start = System.currentTimeMillis();
thalf = gd.getNextNumber();
keep = gd.getNextBoolean();
if (keep) IJ.run("Duplicate...", "title='Bleach corrected' duplicate");
ImagePlus imp1 = WindowManager.getCurrentImage();
int d1 = imp1.getStackSize();
double v1, v2;
int width = imp1.getWidth();
int height = imp1.getHeight();
ImageProcessor ip1, ip2, ip3;
int slices = imp1.getStackSize();
ImageStack stack1 = imp1.getStack();
ImageStack stack2 = imp1.getStack();
int currentSlice = imp1.getCurrentSlice();
for (int n = 1; n <= slices; n++) {
ip1 = stack1.getProcessor(n);
ip3 = stack1.getProcessor(1);
ip2 = stack2.getProcessor(n);
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
v1 = ip1.getPixelValue(x, y);
v2 = ip3.getPixelValue(x, y);
// =B8/(EXP(-C$7*A8))
v1 = (v1 / Math.exp(-n * thalf));
ip2.putPixelValue(x, y, v1);
}
}
IJ.showProgress((double) n / slices);
IJ.showStatus(n + "/" + slices);
}
// stack2.show();
imp1.updateAndDraw();
}
public void run() {
while (!done) {
synchronized (this) {
try {
wait();
} catch (InterruptedException e) {
}
reset(imp, ip); // GL
apply(imp, ip); // GL
imp.updateAndDraw(); // GL
}
}
}
public void run(ImageProcessor ip) {
if (canceled) return;
slice++;
if (imp.getStackSize() > 1 && processStack) imp.setSlice(slice);
if (imp.getType() == ImagePlus.COLOR_RGB) {
ip = (ImageProcessor) imp.getProperty("Mask");
ip.setThreshold(255, 255, ImageProcessor.NO_LUT_UPDATE);
}
if (!analyze(imp, ip)) canceled = true;
if (slice == imp.getStackSize()) {
imp.updateAndDraw();
if (saveRoi != null) imp.setRoi(saveRoi);
}
}
public void actionPerformed(ActionEvent e) {
Button b = (Button) e.getSource();
if (b == null) return;
boolean imageThere = checkImage();
if (imageThere) {
if (b == originalB) {
reset(imp, ip);
filteredB.setEnabled(true);
} else if (b == filteredB) {
apply(imp, ip);
} else if (b == sampleB) {
reset(imp, ip);
sample();
apply(imp, ip);
} else if (b == stackB) {
applyStack();
} else if (b == helpB) {
IJ.showMessage(
"Help",
"Threshold Colour v1.0\n \n"
+ "Modification of Bob Dougherty's BandPass2 plugin by G.Landini to\n"
+ "threshold 24 bit RGB images based on Hue, Saturation and Brightness\n"
+ "or Red, Green and Blue components.\n \n"
+ "Pass: Band-pass filter (anything within range is displayed).\n \n"
+ "Stop: Band-reject filter (anything within range is NOT displayed).\n \n"
+ "Original: Shows the original image and updates the buffer when\n"
+ " switching to another image.\n \n"
+ "Filtered: Shows the filtered image.\n \n"
+ "Stack: Processes the rest of the slices in the stack (if any)\n"
+ " using the current settings.\n \n"
+ "Threshold: Shows the object/background in the foreground and\n"
+ " background colours selected in the ImageJ toolbar.\n \n"
+ "Invert: Swaps the fore/background colours.\n \n"
+ "Sample: (experimental) Sets the ranges of the filters based on the\n"
+ " pixel value componentd in a rectangular, user-defined, ROI.\n \n"
+ "HSB RGB: Selects HSB or RGB space and resets all the filters.\n \n"
+ "Note that the \'thresholded\' image is RGB, not 8 bit grey.");
}
updatePlot();
updateLabels();
imp.updateAndDraw();
} else {
IJ.beep();
IJ.showStatus("No Image");
}
notify();
}
void setStackDisplayRange(ImagePlus imp) {
ImageStack stack = imp.getStack();
double min = Double.MAX_VALUE;
double max = -Double.MAX_VALUE;
int n = stack.getSize();
for (int i = 1; i <= n; i++) {
if (!silentMode) IJ.showStatus("Calculating stack min and max: " + i + "/" + n);
ImageProcessor ip = stack.getProcessor(i);
ip.resetMinAndMax();
if (ip.getMin() < min) min = ip.getMin();
if (ip.getMax() > max) max = ip.getMax();
}
imp.getProcessor().setMinAndMax(min, max);
imp.updateAndDraw();
}
/**
* Execute the plugin functionality: duplicate and scale the given image.
*
* @return an Object[] array with the name and the scaled ImagePlus. Does NOT show the new, image;
* just returns it.
*/
public Object[] exec(
ImagePlus imp, String myMethod, int radius, double par1, double par2, boolean doIwhite) {
// 0 - Check validity of parameters
if (null == imp) return null;
ImageProcessor ip = imp.getProcessor();
int xe = ip.getWidth();
int ye = ip.getHeight();
// int [] data = (ip.getHistogram());
IJ.showStatus("Thresholding...");
long startTime = System.currentTimeMillis();
// 1 Do it
if (imp.getStackSize() == 1) {
ip.snapshot();
Undo.setup(Undo.FILTER, imp);
}
// Apply the selected algorithm
if (myMethod.equals("Bernsen")) {
Bernsen(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Contrast")) {
Contrast(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Mean")) {
Mean(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Median")) {
Median(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("MidGrey")) {
MidGrey(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Niblack")) {
Niblack(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Otsu")) {
Otsu(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Phansalkar")) {
Phansalkar(imp, radius, par1, par2, doIwhite);
} else if (myMethod.equals("Sauvola")) {
Sauvola(imp, radius, par1, par2, doIwhite);
}
// IJ.showProgress((double)(255-i)/255);
imp.updateAndDraw();
imp.getProcessor().setThreshold(255, 255, ImageProcessor.NO_LUT_UPDATE);
// 2 - Return the threshold and the image
IJ.showStatus("\nDone " + (System.currentTimeMillis() - startTime) / 1000.0);
return new Object[] {imp};
}
public void run(ImageProcessor ip) {
String[] imageNames = getOpenImageNames();
if (imageNames[0] == "None") {
IJ.error("need at least 2 binary open images");
return;
}
double previousMinOverlap = Prefs.get("BVTB.BinaryFeatureExtractor.minOverlap", 0);
boolean previousCombine = Prefs.get("BVTB.BinaryFeatureExtractor.combine", false);
GenericDialog gd = new GenericDialog("Binary Feature Extractor");
gd.addChoice("Objects image", imageNames, imageNames[0]);
gd.addChoice("Selector image", imageNames, imageNames[1]);
gd.addNumericField("Object_overlap in % (0=off)", previousMinOverlap, 0, 9, "");
gd.addCheckbox("Combine objects and selectors", previousCombine);
gd.addCheckbox("Count output", true);
gd.addCheckbox("Analysis tables", false);
gd.showDialog();
if (gd.wasCanceled()) {
return;
}
String objectsImgTitle = gd.getNextChoice();
String selectorsImgTitle = gd.getNextChoice();
double minOverlap = gd.getNextNumber();
boolean combineImages = gd.getNextBoolean();
boolean showCountOutput = gd.getNextBoolean();
boolean showAnalysis = gd.getNextBoolean();
if (gd.invalidNumber() || minOverlap < 0 || minOverlap > 100) {
IJ.error("invalid number");
return;
}
Prefs.set("BVTB.BinaryFeatureExtractor.minOverlap", minOverlap);
Prefs.set("BVTB.BinaryFeatureExtractor.combine", combineImages);
if (objectsImgTitle.equals(selectorsImgTitle)) {
IJ.error("images need to be different");
return;
}
ImagePlus objectsImp = WindowManager.getImage(objectsImgTitle);
ImageProcessor objectsIP = objectsImp.getProcessor();
ImagePlus selectorsImp = WindowManager.getImage(selectorsImgTitle);
ImageProcessor selectorsIP = selectorsImp.getProcessor();
if (!objectsIP.isBinary() || !selectorsIP.isBinary()) {
IJ.error("works with 8-bit binary images only");
return;
}
if ((objectsImp.getWidth() != selectorsImp.getWidth())
|| objectsImp.getHeight() != selectorsImp.getHeight()) {
IJ.error("images need to be of the same size");
return;
}
// close any existing RoiManager before instantiating a new one for this analysis
RoiManager oldRM = RoiManager.getInstance2();
if (oldRM != null) {
oldRM.close();
}
RoiManager objectsRM = new RoiManager(true);
ResultsTable objectsRT = new ResultsTable();
ParticleAnalyzer analyzeObjects =
new ParticleAnalyzer(analyzerOptions, measurementFlags, objectsRT, 0.0, 999999999.9);
analyzeObjects.setRoiManager(objectsRM);
analyzeObjects.analyze(objectsImp);
objectsRM.runCommand("Show None");
int objectNumber = objectsRT.getCounter();
Roi[] objectRoi = objectsRM.getRoisAsArray();
ResultsTable measureSelectorsRT = new ResultsTable();
Analyzer overlapAnalyzer = new Analyzer(selectorsImp, measurementFlags, measureSelectorsRT);
ImagePlus outputImp =
IJ.createImage("output", "8-bit black", objectsImp.getWidth(), objectsImp.getHeight(), 1);
ImageProcessor outputIP = outputImp.getProcessor();
double[] measuredOverlap = new double[objectNumber];
outputIP.setValue(255.0);
for (int o = 0; o < objectNumber; o++) {
selectorsImp.killRoi();
selectorsImp.setRoi(objectRoi[o]);
overlapAnalyzer.measure();
measuredOverlap[o] = measureSelectorsRT.getValue("%Area", o);
if (minOverlap != 0.0 && measuredOverlap[o] >= minOverlap) {
outputIP.fill(objectRoi[o]);
finalCount++;
} else if (minOverlap == 0.0 && measuredOverlap[o] > 0.0) {
outputIP.fill(objectRoi[o]);
finalCount++;
}
}
// measureSelectorsRT.show("Objects");
selectorsImp.killRoi();
RoiManager selectorRM = new RoiManager(true);
ResultsTable selectorRT = new ResultsTable();
ParticleAnalyzer.setRoiManager(selectorRM);
ParticleAnalyzer analyzeSelectors =
new ParticleAnalyzer(analyzerOptions, measurementFlags, selectorRT, 0.0, 999999999.9);
analyzeSelectors.analyze(selectorsImp);
selectorRM.runCommand("Show None");
int selectorNumber = selectorRT.getCounter();
if (combineImages) {
outputImp.updateAndDraw();
Roi[] selectorRoi = selectorRM.getRoisAsArray();
ResultsTable measureObjectsRT = new ResultsTable();
Analyzer selectorAnalyzer = new Analyzer(outputImp, measurementFlags, measureObjectsRT);
double[] selectorOverlap = new double[selectorNumber];
outputIP.setValue(255.0);
for (int s = 0; s < selectorNumber; s++) {
outputImp.killRoi();
outputImp.setRoi(selectorRoi[s]);
selectorAnalyzer.measure();
selectorOverlap[s] = measureObjectsRT.getValue("%Area", s);
if (selectorOverlap[s] > 0.0d) {
outputIP.fill(selectorRoi[s]);
}
}
selectorRoi = null;
selectorAnalyzer = null;
measureObjectsRT = null;
}
// selectorRT.show("Selectors");
outputImp.killRoi();
String outputImageTitle = WindowManager.getUniqueName("Extracted_" + objectsImgTitle);
outputImp.setTitle(outputImageTitle);
outputImp.show();
outputImp.changes = true;
if (showCountOutput) {
String[] openTextWindows = WindowManager.getNonImageTitles();
boolean makeNewTable = true;
for (int w = 0; w < openTextWindows.length; w++) {
if (openTextWindows[w].equals("BFE_Results")) {
makeNewTable = false;
}
}
TextWindow existingCountTable = ResultsTable.getResultsWindow();
if (makeNewTable) {
countTable = new ResultsTable();
countTable.setPrecision(0);
countTable.setValue("Image", 0, outputImageTitle);
countTable.setValue("Objects", 0, objectNumber);
countTable.setValue("Selectors", 0, selectorNumber);
countTable.setValue("Extracted", 0, finalCount);
countTable.show("BFE_Results");
} else {
IJ.renameResults("BFE_Results", "Results");
countTable = ResultsTable.getResultsTable();
countTable.setPrecision(0);
countTable.incrementCounter();
countTable.addValue("Image", outputImageTitle);
countTable.addValue("Objects", objectNumber);
countTable.addValue("Selectors", selectorNumber);
countTable.addValue("Extracted", finalCount);
IJ.renameResults("Results", "BFE_Results");
countTable.show("BFE_Results");
}
}
if (showAnalysis) {
ResultsTable extractedRT = new ResultsTable();
ParticleAnalyzer analyzeExtracted =
new ParticleAnalyzer(
ParticleAnalyzer.CLEAR_WORKSHEET | ParticleAnalyzer.RECORD_STARTS,
measurementFlags,
extractedRT,
0.0,
999999999.9);
analyzeExtracted.analyze(outputImp);
objectsRT.show("Objects");
selectorRT.show("Selectors");
extractedRT.show("Extracted");
} else {
objectsRT = null;
selectorRT = null;
}
objectsRM = null;
measureSelectorsRT = null;
analyzeObjects = null;
overlapAnalyzer = null;
objectRoi = null;
selectorRM = null;
objectsImp.killRoi();
objectsImp.changes = false;
selectorsImp.changes = false;
}
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"); }
/** Ask for parameters and then execute. */
public void run(String arg) {
// 1 - Obtain the currently active image:
ImagePlus imp = IJ.getImage();
if (null == imp) {
IJ.showMessage("There must be at least one image open");
return;
}
if (imp.getBitDepth() != 8) {
IJ.showMessage("Error", "Only 8-bit images are supported");
return;
}
// 2 - Ask for parameters:
GenericDialog gd = new GenericDialog("Auto Local Threshold");
String[] methods = {
"Try all",
"Bernsen",
"Contrast",
"Mean",
"Median",
"MidGrey",
"Niblack",
"Otsu",
"Phansalkar",
"Sauvola"
};
gd.addMessage("Auto Local Threshold v1.5");
gd.addChoice("Method", methods, methods[0]);
gd.addNumericField("Radius", 15, 0);
gd.addMessage("Special paramters (if different from default)");
gd.addNumericField("Parameter_1", 0, 0);
gd.addNumericField("Parameter_2", 0, 0);
gd.addCheckbox("White objects on black background", true);
if (imp.getStackSize() > 1) {
gd.addCheckbox("Stack", false);
}
gd.addMessage("Thresholded result is always shown in white [255].");
gd.showDialog();
if (gd.wasCanceled()) return;
// 3 - Retrieve parameters from the dialog
String myMethod = gd.getNextChoice();
int radius = (int) gd.getNextNumber();
double par1 = (double) gd.getNextNumber();
double par2 = (double) gd.getNextNumber();
boolean doIwhite = gd.getNextBoolean();
boolean doIstack = false;
int stackSize = imp.getStackSize();
if (stackSize > 1) doIstack = gd.getNextBoolean();
// 4 - Execute!
// long start = System.currentTimeMillis();
if (myMethod.equals("Try all")) {
ImageProcessor ip = imp.getProcessor();
int xe = ip.getWidth();
int ye = ip.getHeight();
int ml = methods.length;
ImagePlus imp2, imp3;
ImageStack tstack = null, stackNew;
if (stackSize > 1 && doIstack) {
boolean doItAnyway = true;
if (stackSize > 25) {
YesNoCancelDialog d =
new YesNoCancelDialog(
IJ.getInstance(),
"Auto Local Threshold",
"You might run out of memory.\n \nDisplay "
+ stackSize
+ " slices?\n \n \'No\' will process without display and\noutput results to the log window.");
if (!d.yesPressed()) {
// doIlog=true; //will show in the log window
doItAnyway = false;
}
if (d.cancelPressed()) return;
}
for (int j = 1; j <= stackSize; j++) {
imp.setSlice(j);
ip = imp.getProcessor();
tstack = new ImageStack(xe, ye);
for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
imp2 = new ImagePlus("Auto Threshold", tstack);
imp2.updateAndDraw();
for (int k = 1; k < ml; k++) {
imp2.setSlice(k);
Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
}
// if (doItAnyway){
CanvasResizer cr = new CanvasResizer();
stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
imp3 = new ImagePlus("Auto Threshold", stackNew);
imp3.updateAndDraw();
MontageMaker mm = new MontageMaker();
mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true); // 3 columns and 3 rows
}
imp.setSlice(1);
// if (doItAnyway)
IJ.run("Images to Stack", "method=[Copy (center)] title=Montage");
return;
} else { // single image try all
tstack = new ImageStack(xe, ye);
for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
imp2 = new ImagePlus("Auto Threshold", tstack);
imp2.updateAndDraw();
for (int k = 1; k < ml; k++) {
imp2.setSlice(k);
// IJ.log("analyzing slice with "+methods[k]);
Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
}
// imp2.setSlice(1);
CanvasResizer cr = new CanvasResizer();
stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
imp3 = new ImagePlus("Auto Threshold", stackNew);
imp3.updateAndDraw();
MontageMaker mm = new MontageMaker();
mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true);
return;
}
} else { // selected a method
if (stackSize > 1 && doIstack) { // whole stack
// if (doIstackHistogram) {// one global histogram
// Object[] result = exec(imp, myMethod, noWhite, noBlack, doIwhite, doIset, doIlog,
// doIstackHistogram );
// }
// else{ // slice by slice
for (int k = 1; k <= stackSize; k++) {
imp.setSlice(k);
Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
}
// }
imp.setSlice(1);
} else { // just one slice
Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
}
// 5 - If all went well, show the image:
// not needed here as the source image is binarised
}
}
ImageProcessor setup(ImagePlus imp) {
ImageProcessor ip;
int type = imp.getType();
if (type != ImagePlus.COLOR_RGB) return null;
ip = imp.getProcessor();
int id = imp.getID();
int slice = imp.getCurrentSlice();
if ((id != previousImageID) | (slice != previousSlice) | (flag)) {
flag = false; // if true, flags a change from HSB to RGB or viceversa
numSlices = imp.getStackSize();
stack = imp.getStack();
width = stack.getWidth();
height = stack.getHeight();
numPixels = width * height;
hSource = new byte[numPixels];
sSource = new byte[numPixels];
bSource = new byte[numPixels];
// restore = (int[])ip.getPixelsCopy(); //This runs into trouble sometimes, so do it the
// long way:
int[] temp = (int[]) ip.getPixels();
restore = new int[numPixels];
for (int i = 0; i < numPixels; i++) restore[i] = temp[i];
fillMask = new int[numPixels];
// Get hsb or rgb from image.
ColorProcessor cp = (ColorProcessor) ip;
IJ.showStatus("Gathering data");
if (isRGB) cp.getRGB(hSource, sSource, bSource);
else cp.getHSB(hSource, sSource, bSource);
IJ.showStatus("done");
// Create a spectrum ColorModel for the Hue histogram plot.
Color c;
byte[] reds = new byte[256];
byte[] greens = new byte[256];
byte[] blues = new byte[256];
for (int i = 0; i < 256; i++) {
c = Color.getHSBColor(i / 255f, 1f, 1f);
reds[i] = (byte) c.getRed();
greens[i] = (byte) c.getGreen();
blues[i] = (byte) c.getBlue();
}
ColorModel cm = new IndexColorModel(8, 256, reds, greens, blues);
// Make an image with just the hue from the RGB image and the spectrum LUT.
// This is just for a hue histogram for the plot. Do not show it.
// ByteProcessor bpHue = new ByteProcessor(width,height,h,cm);
ByteProcessor bpHue = new ByteProcessor(width, height, hSource, cm);
ImagePlus impHue = new ImagePlus("Hue", bpHue);
// impHue.show();
ByteProcessor bpSat = new ByteProcessor(width, height, sSource, cm);
ImagePlus impSat = new ImagePlus("Sat", bpSat);
// impSat.show();
ByteProcessor bpBri = new ByteProcessor(width, height, bSource, cm);
ImagePlus impBri = new ImagePlus("Bri", bpBri);
// impBri.show();
plot.setHistogram(impHue, 0);
splot.setHistogram(impSat, 1);
bplot.setHistogram(impBri, 2);
updateLabels();
updatePlot();
updateScrollBars();
imp.updateAndDraw();
}
previousImageID = id;
previousSlice = slice;
return ip;
}
/*------------------------------------------------------------------*/
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 */