private static final void incompatibleLoop(
final Cursor<FloatType> psiCursor,
final ArrayList<RandomAccess<FloatType>> randomAccessWeights,
final ArrayList<RandomAccess<FloatType>> randomAccessImgs,
final RealSum realSum,
final int m) {
final FloatType p = psiCursor.next();
double sum = 0;
double sumW = 0;
for (int j = 0; j < m; ++j) {
final RandomAccess<FloatType> randomAccessWeight = randomAccessWeights.get(j);
final RandomAccess<FloatType> randomAccessImg = randomAccessImgs.get(j);
randomAccessWeight.setPosition(psiCursor);
randomAccessImg.setPosition(psiCursor);
final double w = randomAccessWeight.get().get();
final double i = randomAccessImg.get().get();
sum += i * w;
sumW += w;
}
if (sumW > 0) {
final double i = sum / sumW;
realSum.add(i);
p.set((float) i);
}
}
public static void main(final String[] args) {
final int numRuns = 20;
final boolean printIndividualTimes = false;
final long[] dimensions = new long[] {200, 200, 200};
final Img<FloatType> img = ArrayImgs.floats(dimensions);
final Random random = new Random(123914924);
for (final FloatType t : img) t.set(random.nextFloat());
System.out.println("findLocalMaximaNeighborhood");
System.out.println("(using old LocalNeighborhoodCursor)");
BenchmarkHelper.benchmarkAndPrint(
numRuns,
printIndividualTimes,
new Runnable() {
@Override
public void run() {
findLocalMaximaNeighborhood(img);
}
});
System.out.println("findLocalMaximaNeighborhood2");
System.out.println("(using LocalNeighborhoodCursor2 by Bene and Tobias)");
BenchmarkHelper.benchmarkAndPrint(
numRuns,
printIndividualTimes,
new Runnable() {
@Override
public void run() {
findLocalMaximaNeighborhood2(img);
}
});
System.out.println("findLocalMaximaNeighborhood6");
System.out.println("(using RectangleShape)");
BenchmarkHelper.benchmarkAndPrint(
numRuns,
printIndividualTimes,
new Runnable() {
@Override
public void run() {
findLocalMaximaNeighborhood6(img);
}
});
final int n = findLocalMaximaNeighborhood(img);
System.out.println(n);
final int n2 = findLocalMaximaNeighborhood2(img);
System.out.println(n2);
final int n6 = findLocalMaximaNeighborhood6(img);
System.out.println(n6);
}
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()");
}
}
