@Override
@SuppressWarnings("unchecked")
public O calculate(final I kernel, final Dimensions paddedDimensions) {
Dimensions paddedFFTInputDimensions;
// if an fftsize op has been set recompute padded size
if (fftSizeOp != null) {
long[][] sizes = fftSizeOp.calculate(paddedDimensions);
paddedFFTInputDimensions = new FinalDimensions(sizes[0]);
} else {
paddedFFTInputDimensions = paddedDimensions;
}
// compute where to place the final Interval for the kernel so that the
// coordinate in the center
// of the kernel is shifted to position (0,0).
final Interval kernelConvolutionInterval =
paddingIntervalCentered.calculate(kernel, paddedFFTInputDimensions);
final Interval kernelConvolutionIntervalOrigin =
paddingIntervalOrigin.calculate(kernel, kernelConvolutionInterval);
return (O)
Views.interval(
Views.extendPeriodic(
Views.interval(
Views.extendValue(kernel, Util.getTypeFromInterval(kernel).createVariable()),
kernelConvolutionInterval)),
kernelConvolutionIntervalOrigin);
}
/**
* Walk through an Interval on an Img using a Cursor
*
* @param img
* @param interval
*/
protected static void walkThrough(final Img<IntType> img, final Interval interval) {
final Cursor<IntType> c = Views.interval(img, interval).cursor();
int i = 0;
while (c.hasNext()) {
c.fwd();
i += c.get().get();
}
j = i;
}
@Test
public void testRandomAccessibleView() {
@SuppressWarnings("unchecked")
final RandomAccessible<ByteType> res =
(RandomAccessible<ByteType>)
ops.run(MapViewRandomAccessToRandomAccess.class, in, op, new ByteType());
final Cursor<ByteType> iterable = Views.iterable(Views.interval(res, in)).cursor();
while (iterable.hasNext()) {
assertEquals((byte) 10, iterable.next().get());
}
}
/**
* Walk through an Interval on an Img using a LocalizingCursor, localizing on every step.
*
* @param img
* @param interval
*/
protected static void localizingWalkThrough(final Img<IntType> img, final Interval interval) {
final Cursor<IntType> c = Views.interval(img, interval).localizingCursor();
final long[] pos = new long[interval.numDimensions()];
int i = 0;
while (c.hasNext()) {
c.fwd();
i += c.get().get();
c.localize(pos);
}
j = (int) pos[0] + i;
}
public static <T extends Type<T> & Comparable<T>> int countLocalMaxima(
final RandomAccessibleInterval<T> img, final Shape shape) {
int nMaxima = 0;
final RandomAccessibleInterval<T> source = Views.interval(img, Intervals.expand(img, -1));
final Cursor<T> center = Views.iterable(source).cursor();
A:
for (final Neighborhood<T> neighborhood : shape.neighborhoods(source)) {
final T c = center.next();
for (final T t : neighborhood) if (t.compareTo(c) > 0) continue A;
++nMaxima;
}
return nMaxima;
}
public static <T extends Type<T> & Comparable<T>> int findLocalMaximaNeighborhood2(
final RandomAccessibleInterval<T> img) {
// final ArrayList< Point > maxima = new ArrayList< Point >();
int nMaxima = 0;
final Cursor<T> center =
Views.iterable(Views.interval(img, Intervals.expand(img, -1))).localizingCursor();
final LocalNeighborhood2<T> neighborhood = new LocalNeighborhood2<T>(img, center);
final LocalNeighborhoodCursor2<T> nc = neighborhood.cursor();
A:
while (center.hasNext()) {
final T t = center.next();
neighborhood.updateCenter(center);
nc.reset();
while (nc.hasNext()) {
final T n = nc.next();
if (n.compareTo(t) > 0) continue A;
}
// maxima.add( new Point( center ) );
++nMaxima;
}
return nMaxima;
}
/**
* Fuses one stack, i.e. all angles/illuminations for one timepoint and channel
*
* @param timepoint
* @param channel
* @return
*/
public boolean fuseStacksAndGetPSFs(
final TimePoint timepoint,
final Channel channel,
final ImgFactory<FloatType> imgFactory,
final int osemIndex,
double osemspeedup,
WeightType weightType,
final HashMap<Channel, ChannelPSF> extractPSFLabels,
final long[] psfSize,
final HashMap<Channel, ArrayList<Pair<Pair<Angle, Illumination>, String>>> psfFiles,
final boolean transformLoadedPSFs) {
// TODO: get rid of this hack
if (files != null) {
weightType = WeightType.LOAD_WEIGHTS;
IOFunctions.println("WARNING: LOADING WEIGHTS FROM IMAGES, files.length()=" + files.length);
}
// get all views that are fused for this timepoint & channel
this.viewDescriptions =
FusionHelper.assembleInputData(spimData, timepoint, channel, viewIdsToProcess);
if (this.viewDescriptions.size() == 0) return false;
this.imgs = new HashMap<ViewId, RandomAccessibleInterval<FloatType>>();
this.weights = new HashMap<ViewId, RandomAccessibleInterval<FloatType>>();
final Img<FloatType> overlapImg;
if (weightType == WeightType.WEIGHTS_ONLY)
overlapImg = imgFactory.create(bb.getDimensions(), new FloatType());
else overlapImg = null;
final boolean extractPSFs =
(extractPSFLabels != null) && (extractPSFLabels.get(channel).getLabel() != null);
final boolean loadPSFs = (psfFiles != null);
if (extractPSFs) ePSF = new ExtractPSF<FloatType>();
else if (loadPSFs) ePSF = loadPSFs(channel, viewDescriptions, psfFiles, transformLoadedPSFs);
else {
ePSF = assignOtherChannel(channel, extractPSFLabels);
}
if (ePSF == null) return false;
// remember the extracted or loaded PSFs
extractPSFLabels.get(channel).setExtractPSFInstance(ePSF);
// we will need to run some batches until all is fused
for (int i = 0; i < viewDescriptions.size(); ++i) {
final ViewDescription vd = viewDescriptions.get(i);
IOFunctions.println(
"Transforming view "
+ i
+ " of "
+ (viewDescriptions.size() - 1)
+ " (viewsetup="
+ vd.getViewSetupId()
+ ", tp="
+ vd.getTimePointId()
+ ")");
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Reserving memory for transformed & weight image.");
// creating the output
RandomAccessibleInterval<FloatType> transformedImg; // might be null if WEIGHTS_ONLY
final RandomAccessibleInterval<FloatType>
weightImg; // never null (except LOAD_WEIGHTS which is not implemented yet)
if (weightType == WeightType.WEIGHTS_ONLY) transformedImg = overlapImg;
else transformedImg = imgFactory.create(bb.getDimensions(), new FloatType());
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Transformed image factory: "
+ imgFactory.getClass().getSimpleName());
// loading the input if necessary
final RandomAccessibleInterval<FloatType> img;
if (weightType == WeightType.WEIGHTS_ONLY && !extractPSFs) {
img = null;
} else {
IOFunctions.println("(" + new Date(System.currentTimeMillis()) + "): Loading image.");
img = ProcessFusion.getImage(new FloatType(), spimData, vd, true);
if (Img.class.isInstance(img))
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Input image factory: "
+ ((Img<FloatType>) img).factory().getClass().getSimpleName());
}
// initializing weights
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Initializing transformation & weights: "
+ weightType.name());
spimData.getViewRegistrations().getViewRegistration(vd).updateModel();
final AffineTransform3D transform =
spimData.getViewRegistrations().getViewRegistration(vd).getModel();
final long[] offset = new long[] {bb.min(0), bb.min(1), bb.min(2)};
if (weightType == WeightType.PRECOMPUTED_WEIGHTS || weightType == WeightType.WEIGHTS_ONLY)
weightImg = imgFactory.create(bb.getDimensions(), new FloatType());
else if (weightType == WeightType.NO_WEIGHTS)
weightImg =
Views.interval(
new ConstantRandomAccessible<FloatType>(
new FloatType(1), transformedImg.numDimensions()),
transformedImg);
else if (weightType == WeightType.VIRTUAL_WEIGHTS) {
final Blending blending = getBlending(img, blendingBorder, blendingRange, vd);
weightImg =
new TransformedRealRandomAccessibleInterval<FloatType>(
blending, new FloatType(), transformedImg, transform, offset);
} else // if ( processType == ProcessType.LOAD_WEIGHTS )
{
IOFunctions.println("WARNING: LOADING WEIGHTS FROM: '" + new File(files[i]) + "'");
ImagePlus imp = StackImgLoaderIJ.open(new File(files[i]));
weightImg = imgFactory.create(bb.getDimensions(), new FloatType());
StackImgLoaderIJ.imagePlus2ImgLib2Img(imp, (Img<FloatType>) weightImg, false);
imp.close();
if (debugImport) {
imp = ImageJFunctions.show(weightImg);
imp.setTitle("ViewSetup " + vd.getViewSetupId() + " Timepoint " + vd.getTimePointId());
}
}
// split up into many parts for multithreading
final Vector<ImagePortion> portions =
FusionHelper.divideIntoPortions(
Views.iterable(transformedImg).size(), Threads.numThreads() * 4);
// set up executor service
final ExecutorService taskExecutor = Executors.newFixedThreadPool(Threads.numThreads());
final ArrayList<Callable<String>> tasks = new ArrayList<Callable<String>>();
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Transforming image & computing weights.");
for (final ImagePortion portion : portions) {
if (weightType == WeightType.WEIGHTS_ONLY) {
final Interval imgInterval =
new FinalInterval(
ViewSetupUtils.getSizeOrLoad(
vd.getViewSetup(),
vd.getTimePoint(),
spimData.getSequenceDescription().getImgLoader()));
final Blending blending = getBlending(imgInterval, blendingBorder, blendingRange, vd);
tasks.add(
new TransformWeights(
portion, imgInterval, blending, transform, overlapImg, weightImg, offset));
} else if (weightType == WeightType.PRECOMPUTED_WEIGHTS) {
final Blending blending = getBlending(img, blendingBorder, blendingRange, vd);
tasks.add(
new TransformInputAndWeights(
portion, img, blending, transform, transformedImg, weightImg, offset));
} else if (weightType == WeightType.NO_WEIGHTS
|| weightType == WeightType.VIRTUAL_WEIGHTS
|| weightType == WeightType.LOAD_WEIGHTS) {
tasks.add(new TransformInput(portion, img, transform, transformedImg, offset));
} else {
throw new RuntimeException(weightType.name() + " not implemented yet.");
}
}
try {
// invokeAll() returns when all tasks are complete
taskExecutor.invokeAll(tasks);
} catch (final InterruptedException e) {
IOFunctions.println("Failed to compute fusion: " + e);
e.printStackTrace();
return false;
}
taskExecutor.shutdown();
// extract PSFs if wanted
if (extractPSFs) {
final ArrayList<double[]> llist =
getLocationsOfCorrespondingBeads(
timepoint, vd, extractPSFLabels.get(channel).getLabel());
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Extracting PSF for viewsetup "
+ vd.getViewSetupId()
+ " using label '"
+ extractPSFLabels.get(channel).getLabel()
+ "'"
+ " ("
+ llist.size()
+ " corresponding detections available)");
ePSF.extractNextImg(img, vd, transform, llist, psfSize);
}
if (weightType != WeightType.WEIGHTS_ONLY) imgs.put(vd, transformedImg);
weights.put(vd, weightImg);
}
// normalize the weights
final ArrayList<RandomAccessibleInterval<FloatType>> weightsSorted =
new ArrayList<RandomAccessibleInterval<FloatType>>();
for (final ViewDescription vd : viewDescriptions) weightsSorted.add(weights.get(vd));
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Computing weight normalization for deconvolution.");
final WeightNormalizer wn;
if (weightType == WeightType.WEIGHTS_ONLY
|| weightType == WeightType.PRECOMPUTED_WEIGHTS
|| weightType == WeightType.LOAD_WEIGHTS) wn = new WeightNormalizer(weightsSorted);
else if (weightType == WeightType.VIRTUAL_WEIGHTS)
wn = new WeightNormalizer(weightsSorted, imgFactory);
else // if ( processType == ProcessType.NO_WEIGHTS )
wn = null;
if (wn != null && !wn.process()) return false;
// put the potentially modified weights back
for (int i = 0; i < viewDescriptions.size(); ++i)
weights.put(viewDescriptions.get(i), weightsSorted.get(i));
this.minOverlappingViews = wn.getMinOverlappingViews();
this.avgOverlappingViews = wn.getAvgOverlappingViews();
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Minimal number of overlapping views: "
+ getMinOverlappingViews()
+ ", using "
+ (this.minOverlappingViews = Math.max(1, this.minOverlappingViews)));
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Average number of overlapping views: "
+ getAvgOverlappingViews()
+ ", using "
+ (this.avgOverlappingViews = Math.max(1, this.avgOverlappingViews)));
if (osemIndex == 1) osemspeedup = getMinOverlappingViews();
else if (osemIndex == 2) osemspeedup = getAvgOverlappingViews();
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Adjusting for OSEM speedup = "
+ osemspeedup);
if (weightType == WeightType.WEIGHTS_ONLY)
displayWeights(osemspeedup, weightsSorted, overlapImg, imgFactory);
else adjustForOSEM(weights, weightType, osemspeedup);
IOFunctions.println(
"("
+ new Date(System.currentTimeMillis())
+ "): Finished precomputations for deconvolution.");
return true;
}
@Override
public Cursor<T> cursor() {
return new RandomAccessibleIntervalCursor<T>(Views.interval(rai, new long[dims.length], dims));
}
