protected void computeAdvanced(final long startPos, final long loopSize) {
final LocalizableByDimCursor<S> cursorIn = image.createLocalizableByDimCursor();
final LocalizableCursor<T> cursorOut = output.createLocalizableCursor();
// move to the starting position of the current thread
cursorOut.fwd(startPos);
// do as many pixels as wanted by this thread
for (long j = 0; j < loopSize; ++j) {
cursorOut.fwd();
cursorIn.setPosition(cursorOut);
converter.convert(cursorIn.getType(), cursorOut.getType());
}
cursorIn.close();
cursorOut.close();
}
@Override
public boolean process() {
floatImage = null;
if (output == null) {
output = new Labeling<L>(labelingFactory, input.getDimensions(), null);
} else {
/*
* Initialize the output to all background
*/
LocalizableCursor<LabelingType<L>> c = output.createLocalizableCursor();
List<L> background = c.getType().intern(new ArrayList<L>());
for (LabelingType<L> t : c) {
t.setLabeling(background);
}
c.close();
}
/*
* Get the smoothed image.
*/
Image<FloatType> kernel =
FourierConvolution.createGaussianKernel(input.getContainerFactory(), scale);
FourierConvolution<FloatType, FloatType> convolution =
new FourierConvolution<FloatType, FloatType>(getFloatImage(), kernel);
if (!convolution.process()) return false;
Image<FloatType> smoothed = convolution.getResult();
/*
* Find the local maxima and label them individually.
*/
PickImagePeaks<FloatType> peakPicker = new PickImagePeaks<FloatType>(smoothed);
peakPicker.setSuppression(scale);
peakPicker.process();
Labeling<L> seeds = output.createNewLabeling();
LocalizableByDimCursor<LabelingType<L>> lc = seeds.createLocalizableByDimCursor();
LocalizableByDimCursor<FloatType> imageCursor = smoothed.createLocalizableByDimCursor();
int[] dimensions = input.getDimensions();
for (int[] peak : peakPicker.getPeakList()) {
if (!filterPeak(imageCursor, peak, dimensions, false)) continue;
lc.setPosition(peak);
lc.getType().setLabel(names.next());
}
imageCursor.close();
/*
* Find the local minima and label them all the same.
*/
List<L> background = lc.getType().intern(names.next());
Converter<FloatType, FloatType> invert =
new Converter<FloatType, FloatType>() {
@Override
public void convert(FloatType input, FloatType output) {
output.setReal(-input.getRealFloat());
}
};
ImageConverter<FloatType, FloatType> invSmoothed =
new ImageConverter<FloatType, FloatType>(smoothed, smoothed, invert);
invSmoothed.process();
peakPicker = new PickImagePeaks<FloatType>(smoothed);
peakPicker.setSuppression(scale);
peakPicker.process();
imageCursor = smoothed.createLocalizableByDimCursor();
for (int[] peak : peakPicker.getPeakList()) {
if (!filterPeak(imageCursor, peak, dimensions, true)) continue;
lc.setPosition(peak);
lc.getType().setLabeling(background);
}
lc.close();
imageCursor.close();
smoothed = null;
invSmoothed = null;
Image<FloatType> gradientImage = getGradientImage();
if (gradientImage == null) return false;
/*
* Run the seeded watershed on the image.
*/
Watershed.seededWatershed(gradientImage, seeds, structuringElement, output);
return true;
}
