/**
* Collects the points to build the observation array, by iterating in a hypercube around the
* given location. Points found out of the image are not included.
*
* @param image the source image to sample.
* @param point the location around which to collect the samples
* @param span the span size of the hypercube to sample, such that in dimension <code>d</code>,
* the cube sampled if a of size <code>2 x span[d] + 1</code>.
* @return an {@link Observation} object containing the sampled data.
*/
public static final <T extends RealType<T>> Observation gatherObservationData(
final RandomAccessibleInterval<T> image, final Localizable point, final long[] span) {
final int ndims = image.numDimensions();
RectangleNeighborhoodGPL<T> neighborhood = new RectangleNeighborhoodGPL<T>(image);
neighborhood.setSpan(span);
neighborhood.setPosition(point);
int n_pixels = (int) neighborhood.size();
double[] tmp_I = new double[n_pixels];
double[][] tmp_X = new double[n_pixels][ndims];
RectangleCursor<T> cursor = neighborhood.localizingCursor();
long[] pos = new long[image.numDimensions()];
int index = 0;
while (cursor.hasNext()) {
cursor.fwd();
cursor.localize(pos); // This is the absolute roi position
if (cursor.isOutOfBounds()) {
continue;
}
for (int i = 0; i < ndims; i++) {
tmp_X[index][i] = pos[i];
}
tmp_I[index] = cursor.get().getRealDouble();
index++;
}
// Now we possibly resize the arrays, in case we have been too close to
// the
// image border.
double[][] X = null;
double[] I = null;
if (index == n_pixels) {
// Ok, we have gone through the whole square
X = tmp_X;
I = tmp_I;
} else {
// Re-dimension the arrays
X = new double[index][ndims];
I = new double[index];
System.arraycopy(tmp_X, 0, X, 0, index);
System.arraycopy(tmp_I, 0, I, 0, index);
}
Observation obs = new Observation();
obs.I = I;
obs.X = X;
return obs;
}
@SuppressWarnings({"rawtypes", "unchecked"})
public static <T extends Type<T>> ImageRenderer<T>[] createSuitableRenderer(
final RandomAccessibleInterval<T> img) {
final List<ImageRenderer> res = new ArrayList<ImageRenderer>();
if (Util.getTypeFromInterval(img) instanceof LabelingType) {
res.add(new ColorLabelingRenderer());
res.add(new BoundingBoxLabelRenderer());
res.add(new BoundingBoxRandomColorLabelRenderer());
} else {
final T type = img.randomAccess().get();
if (type instanceof RealType) {
res.add(new Real2GreyRenderer(((RealType) Util.getTypeFromInterval(img)).getMinValue()));
for (int d = 0; d < img.numDimensions(); d++) {
if ((img.dimension(d) > 1) && (img.dimension(d) < 4)) {
res.add(new Real2ColorRenderer(d));
}
}
}
}
return res.toArray(new ImageRenderer[res.size()]);
}
@Override
public RandomAccessibleInterval<V> compute(
RandomAccessibleInterval<T> input, RandomAccessibleInterval<V> output) {
setUpNeighbours(input.numDimensions());
// OutOfBounds for marker
V zeroV = output.randomAccess().get().createVariable();
zeroV.set(getVMinValue(zeroV));
OutOfBounds<V> marker =
new OutOfBoundsConstantValueFactory<V, RandomAccessibleInterval<V>>(zeroV).create(output);
Cursor<V> cur = new Cursor<V>(output);
// OutOfBounds for mask
T zeroT = input.randomAccess().get().createVariable();
zeroT.set(getTMinValue(zeroT));
OutOfBounds<T> mask =
new OutOfBoundsConstantValueFactory<T, RandomAccessibleInterval<T>>(zeroT).create(input);
scanInRasterOrder(cur, marker, mask);
scanInAntiRasterOrder(cur, marker, mask);
propagate(marker, mask, m_neighbours);
return output;
}
/** {@inheritDoc} */
@Override
public Labeling<L> compute(final RandomAccessibleInterval<T> op, final Labeling<L> r) {
initRegionGrowing(op);
final LinkedList<ValuePair<int[], L>> q = new LinkedList<ValuePair<int[], L>>();
// image and random access to keep track of the already visited
// pixel
// positions
if (m_allowOverlap) {
NativeImgLabeling<L, IntType> tmp =
new NativeImgLabeling<L, IntType>(
new ArrayImgFactory<IntType>().create(resultDims(op), new IntType()));
m_visitedLabRA = tmp.randomAccess();
} else {
BitType bt = new BitType();
Img<BitType> tmp = null;
try {
tmp = new ArrayImgFactory<BitType>().imgFactory(bt).create(op, bt);
} catch (IncompatibleTypeException e) {
//
}
m_visitedRA = tmp.randomAccess();
}
// access to the resulting labeling
RandomAccess<LabelingType<L>> resRA = r.randomAccess();
L label;
int[] pos = new int[op.numDimensions()];
do {
while ((label = nextSeedPosition(pos)) != null) {
// already visited?
setVisitedPosition(pos);
if (isMarkedAsVisited(label)) {
continue;
}
markAsVisited(label);
q.addLast(new ValuePair<int[], L>(pos.clone(), label));
// set new labeling
resRA.setPosition(pos);
setLabel(resRA, label);
if (m_mode == GrowingMode.ASYNCHRONOUS) {
growProcess(q, resRA, op);
}
}
if (m_mode == GrowingMode.SYNCHRONOUS) {
growProcess(q, resRA, op);
}
} while (hasMoreSeedingPoints());
return r;
}
public Cursor(final RandomAccessibleInterval<U> ra) {
m_ra = ra.randomAccess();
m_numPixel = numPixels(ra);
m_lastPos = new long[ra.numDimensions()];
for (int i = 0; i < m_lastPos.length; i++) {
m_lastPos[i] = ra.dimension(i) - 1;
}
m_breaks = new long[ra.numDimensions()];
ra.dimensions(m_breaks);
for (int i = 1; i < m_breaks.length; i++) {
m_breaks[i] *= m_breaks[i - 1];
}
setToOrigin();
}
/**
* @param permuted
* @return
*/
private static IntervalIterator createIntervalIterator(
final RandomAccessibleInterval<?> permuted) {
final long[] dims = new long[permuted.numDimensions()];
permuted.dimensions(dims);
dims[0] = 1;
dims[1] = 1;
final IntervalIterator ii = new IntervalIterator(dims);
return ii;
}
protected void generateHistogramData(DataContainer<T> container) {
double ch1BinWidth = getXBinWidth(container);
double ch2BinWidth = getYBinWidth(container);
// get the 2 images for the calculation of Pearson's
final RandomAccessibleInterval<T> img1 = getImageCh1(container);
final RandomAccessibleInterval<T> img2 = getImageCh2(container);
final RandomAccessibleInterval<BitType> mask = container.getMask();
// get the cursors for iterating through pixels in images
TwinCursor<T> cursor =
new TwinCursor<T>(
img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor());
// create new image to put the scatter-plot in
final ImgFactory<LongType> scatterFactory = new ArrayImgFactory<LongType>();
plotImage = scatterFactory.create(new int[] {xBins, yBins}, new LongType());
// create access cursors
final RandomAccess<LongType> histogram2DCursor = plotImage.randomAccess();
// iterate over images
long[] pos = new long[plotImage.numDimensions()];
while (cursor.hasNext()) {
cursor.fwd();
double ch1 = cursor.getFirst().getRealDouble();
double ch2 = cursor.getSecond().getRealDouble();
/* Scale values for both channels to fit in the range.
* Moreover mirror the y value on the x axis.
*/
pos[0] = getXValue(ch1, ch1BinWidth, ch2, ch2BinWidth);
pos[1] = getYValue(ch1, ch1BinWidth, ch2, ch2BinWidth);
// set position of input/output cursor
histogram2DCursor.setPosition(pos);
// get current value at position and increment it
long count = histogram2DCursor.get().getIntegerLong();
count++;
histogram2DCursor.get().set(count);
}
xBinWidth = ch1BinWidth;
yBinWidth = ch2BinWidth;
xLabel = getLabelCh1();
yLabel = getLabelCh2();
xMin = getXMin(container);
xMax = getXMax(container);
yMin = getYMin(container);
yMax = getYMax(container);
}
@Override
public void compute(final RandomAccessibleInterval<T> input, final IterableInterval<V> output) {
final Cursor<V> cursor = output.localizingCursor();
final RandomAccess<T> access = input.randomAccess();
while (cursor.hasNext()) {
cursor.fwd();
for (int d = 0; d < input.numDimensions(); d++) {
if (d != dim) {
access.setPosition(cursor.getIntPosition(d - d > dim ? -1 : 0), d);
}
}
method.compute(new DimensionIterable(input.dimension(dim), access), cursor.get());
}
}
public static final <T extends RealType<T>> void addGaussianSpotToImage(
RandomAccessibleInterval<T> img, double[] params) {
IterableInterval<T> iterImg = Views.iterable(img);
Cursor<T> lc = iterImg.localizingCursor();
int nDims = img.numDimensions();
double[] position = new double[nDims];
double val;
T var = iterImg.firstElement().createVariable();
while (lc.hasNext()) {
lc.fwd();
lc.localize(position);
val = gaussian.val(position, params);
var.setReal(val);
lc.get().add(var);
}
}
/**
* Compute the steepest descent images of the template at the identity warp. Each steepest descent
* image comprises the partial derivatives of template intensities with respect to one parameter
* of the warp function.
*
* <p>The result is stored in the <em>n+1</em> dimensional {@link #target} image. Dimension
* <em>n</em> is used to index the partial derivative. For example, the partial derivative by the
* second parameter of the warp function is stored in slice <em>n=1</em>.
*
* @param gradients n+1 dimensional image of partial derivatives of the template. Dimension n is
* used to index the partial derivative. For example, the partial derivative by Y is stored in
* slice n=1.
* @param warpFunction The warp function to be applied to the template. The partial derivatives of
* template intensities with respect to the parameters of this warp function are computed.
* @param target Image of <em>n+1</em> dimensions to store the steepest descent Dimension
* <em>n</em> is used to index the parameters of the warp function. For example, the partial
* derivative of the template image intensity by parameter 2 of the warp function at pixel
* <em>(x,y)</em> is stored at position <em>(x,y,1)</em>.
*/
public static <T extends NumericType<T>> void computeSteepestDescents(
final RandomAccessibleInterval<T> gradients,
final WarpFunction warpFunction,
final RandomAccessibleInterval<T> target) {
final int n = gradients.numDimensions() - 1;
final int numParameters = warpFunction.numParameters();
final T tmp = Util.getTypeFromInterval(gradients).createVariable();
for (int p = 0; p < numParameters; ++p) {
for (int d = 0; d < n; ++d) {
final Cursor<T> gd =
Views.flatIterable(Views.hyperSlice(gradients, n, d)).localizingCursor();
for (final T t : Views.flatIterable(Views.hyperSlice(target, n, p))) {
tmp.set(gd.next());
tmp.mul(warpFunction.partial(gd, d, p));
t.add(tmp);
}
}
}
}
private static void adjustForOSEM(
final HashMap<ViewId, RandomAccessibleInterval<FloatType>> weights,
final WeightType weightType,
final double osemspeedup) {
if (osemspeedup == 1.0) return;
if (weightType == WeightType.PRECOMPUTED_WEIGHTS
|| weightType == WeightType.WEIGHTS_ONLY
|| weightType == WeightType.LOAD_WEIGHTS) {
for (final RandomAccessibleInterval<FloatType> w : weights.values()) {
for (final FloatType f : Views.iterable(w))
f.set(
Math.min(
1, f.get() * (float) osemspeedup)); // individual contribution never higher than 1
}
} else if (weightType == WeightType.NO_WEIGHTS) {
for (final RandomAccessibleInterval<FloatType> w : weights.values()) {
final RandomAccess<FloatType> r = w.randomAccess();
final long[] min = new long[w.numDimensions()];
w.min(min);
r.setPosition(min);
r.get()
.set(
Math.min(
1,
r.get().get()
* (float) osemspeedup)); // individual contribution never higher than 1
}
} else if (weightType == WeightType.VIRTUAL_WEIGHTS) {
for (final RandomAccessibleInterval<FloatType> w : weights.values())
((NormalizingRandomAccessibleInterval<FloatType>) w).setOSEMspeedup(osemspeedup);
} else {
throw new RuntimeException(
"Weight Type: "
+ weightType.name()
+ " not supported in ProcessForDeconvolution.adjustForOSEM()");
}
}
public Align(final RandomAccessibleInterval<T> template, final ImgFactory<T> factory) {
this.template = template;
final T type = Util.getTypeFromInterval(template);
n = template.numDimensions();
warpFunction = new AffineWarp(n);
numParameters = warpFunction.numParameters();
currentTransform = new AffineTransform(n);
final long[] dim = new long[n + 1];
for (int d = 0; d < n; ++d) dim[d] = template.dimension(d);
dim[n] = n;
final Img<T> gradients = factory.create(dim, type);
gradients(Views.extendBorder(template), gradients);
dim[n] = numParameters;
descent = factory.create(dim, type);
computeSteepestDescents(gradients, warpFunction, descent);
Hinv = computeInverseHessian(descent);
error = factory.create(template, type);
}
/** Compute the inverse Hessian matrix from the the steepest descent images. */
public static <T extends RealType<T>> double[][] computeInverseHessian(
final RandomAccessibleInterval<T> descent) {
final int n = descent.numDimensions() - 1;
final int numParameters = (int) descent.dimension(n);
final long[] dim = new long[n + 1];
descent.dimensions(dim);
dim[n] = 1;
final LocalizingIntervalIterator pos = new LocalizingIntervalIterator(dim);
final RandomAccess<T> r = descent.randomAccess();
final double[] deriv = new double[numParameters];
final double[][] H = new double[numParameters][numParameters];
while (pos.hasNext()) {
pos.fwd();
r.setPosition(pos);
for (int p = 0; p < numParameters; ++p) {
deriv[p] = r.get().getRealDouble();
r.fwd(n);
}
for (int i = 0; i < numParameters; ++i)
for (int j = 0; j < numParameters; ++j) H[i][j] += deriv[i] * deriv[j];
}
return new Matrix(H).inverse().getArray();
}
/**
* TODO
*
* @param r The segmentation image.
* @param op0 Source intensity image.
* @param op1 Start position.
*/
public final void compute(
RandomAccessibleInterval<T> op0, final long[] op1, final RandomAccessibleInterval<T> r) {
final RandomAccess<T> rc = r.randomAccess();
final RandomAccess<T> op0c = op0.randomAccess();
op0c.setPosition(op1);
final T floodVal = op0c.get().copy();
final LinkedList<long[]> q = new LinkedList<long[]>();
q.addFirst(op1.clone());
long[] pos, nextPos;
long[] perm = new long[r.numDimensions()];
while (!q.isEmpty()) {
pos = q.removeLast();
rc.setPosition(pos);
if (rc.get().compareTo(floodVal) == 0) {
continue;
}
op0c.setPosition(pos);
if (op0c.get().compareTo(floodVal) == 0) {
// set new label
rc.get().set(floodVal);
switch (m_type) {
case EIGHT_CONNECTED:
Arrays.fill(perm, -1);
int i = r.numDimensions() - 1;
boolean add;
while (i > -1) {
nextPos = pos.clone();
add = true;
// Modify position
for (int j = 0; j < r.numDimensions(); j++) {
nextPos[j] += perm[j];
// Check boundaries
if (nextPos[j] < 0 || nextPos[j] >= r.dimension(j)) {
add = false;
break;
}
}
if (add) {
q.addFirst(nextPos);
}
// Calculate next permutation
for (i = perm.length - 1; i > -1; i--) {
if (perm[i] < 1) {
perm[i]++;
for (int j = i + 1; j < perm.length; j++) {
perm[j] = -1;
}
break;
}
}
}
break;
case FOUR_CONNECTED:
default:
for (int j = 0; j < r.numDimensions(); j++) {
if (pos[j] + 1 < r.dimension(j)) {
nextPos = pos.clone();
nextPos[j]++;
q.addFirst(nextPos);
}
if (pos[j] - 1 >= 0) {
nextPos = pos.clone();
nextPos[j]--;
q.addFirst(nextPos);
}
}
break;
}
}
}
}
/**
* 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;
}
/**
* @param img
* @param processorFactory
* @param converter
* @return wrapped {@link ImagePlus}
*/
@SuppressWarnings({"unchecked", "rawtypes"})
public static final <T extends RealType<T>> ImagePlus wrap(
final ImgPlus<T> img,
final ImageProcessorFactory processorFactory,
final Converter<T, FloatType> converter) {
// we always want to have 5 dimensions
final RandomAccessibleInterval permuted = extendAndPermute(img);
final int width = (int) permuted.dimension(0);
final int height = (int) permuted.dimension(1);
final ImagePlus r = new ImagePlus();
final ImageStack is = new ImageStack(width, height);
final RandomAccessibleInterval<T> access =
img.iterationOrder().equals(((IterableRealInterval<?>) permuted).iterationOrder())
? img
: permuted;
final IntervalIterator ii = createIntervalIterator(access);
final long[] min = new long[access.numDimensions()];
final long[] max = new long[access.numDimensions()];
max[0] = permuted.max(0);
max[1] = permuted.max(1);
// number of planes = num tasks
int numSlices = 1;
for (int d = 2; d < access.numDimensions(); d++) {
numSlices *= access.dimension(d);
}
// parallelization
final ImageProcessor[] slices = new ImageProcessor[numSlices];
final ExecutorService service =
new ThreadPoolExecutorService(
KNIMEConstants.GLOBAL_THREAD_POOL.createSubPool(KNIPConstants.THREADS_PER_NODE));
final ArrayList<Future<Void>> futures = new ArrayList<Future<Void>>();
final T inType = img.firstElement();
int i = 0;
while (ii.hasNext()) {
ii.fwd();
for (int d = 2; d < ii.numDimensions(); d++) {
min[d] = ii.getIntPosition(d);
max[d] = min[d];
}
final int proxy = i++;
futures.add(
service.submit(
new Callable<Void>() {
final FinalInterval tmp = new FinalInterval(min, max);
@Override
public Void call() throws Exception {
final Cursor<T> cursor = Views.iterable(Views.interval(access, tmp)).cursor();
final ImageProcessor ip = processorFactory.createProcessor(width, height, inType);
final FloatType outProxy = new FloatType();
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
converter.convert(cursor.next(), outProxy);
ip.setf(x, y, outProxy.get());
}
}
slices[proxy] = ip;
return null;
}
}));
}
for (final Future<Void> f : futures) {
try {
f.get();
} catch (final InterruptedException e) {
e.printStackTrace();
} catch (final ExecutionException e) {
e.printStackTrace();
}
}
// add slices to stack
for (ImageProcessor slice : slices) {
is.addSlice("", slice);
}
// set calibration
final double[] newCalibration = getNewCalibration(img);
Calibration cal = new Calibration();
cal.pixelWidth = newCalibration[0];
cal.pixelHeight = newCalibration[1];
cal.pixelDepth = newCalibration[3];
r.setCalibration(cal);
r.setStack(
is, (int) permuted.dimension(2), (int) permuted.dimension(3), (int) permuted.dimension(4));
r.setTitle(img.getName());
return r;
}