/**
* TODO
*
* @param cumulativeMinCutoff
* @param cumulativeMaxCutoff
* @param state
* @param setupAssignments
*/
public static void initBrightness(
final double cumulativeMinCutoff,
final double cumulativeMaxCutoff,
final ViewerState state,
final SetupAssignments setupAssignments) {
final Source<?> source = state.getSources().get(state.getCurrentSource()).getSpimSource();
final int timepoint = state.getCurrentTimepoint();
if (!source.isPresent(timepoint)) return;
if (!UnsignedShortType.class.isInstance(source.getType())) return;
@SuppressWarnings("unchecked")
final RandomAccessibleInterval<UnsignedShortType> img =
(RandomAccessibleInterval<UnsignedShortType>)
source.getSource(timepoint, source.getNumMipmapLevels() - 1);
final long z = (img.min(2) + img.max(2) + 1) / 2;
final int numBins = 6535;
final Histogram1d<UnsignedShortType> histogram =
new Histogram1d<UnsignedShortType>(
Views.iterable(Views.hyperSlice(img, 2, z)),
new Real1dBinMapper<UnsignedShortType>(0, 65535, numBins, false));
final DiscreteFrequencyDistribution dfd = histogram.dfd();
final long[] bin = new long[] {0};
double cumulative = 0;
int i = 0;
for (; i < numBins && cumulative < cumulativeMinCutoff; ++i) {
bin[0] = i;
cumulative += dfd.relativeFrequency(bin);
}
final int min = i * 65535 / numBins;
for (; i < numBins && cumulative < cumulativeMaxCutoff; ++i) {
bin[0] = i;
cumulative += dfd.relativeFrequency(bin);
}
final int max = i * 65535 / numBins;
final MinMaxGroup minmax = setupAssignments.getMinMaxGroups().get(0);
minmax.getMinBoundedValue().setCurrentValue(min);
minmax.getMaxBoundedValue().setCurrentValue(max);
}
@Override
public long computeBin(final Histogram1d<T> hist) {
long[] histogram = hist.toLongArray();
// Otsu's threshold algorithm
// C++ code by Jordan Bevik <*****@*****.**>
// ported to ImageJ plugin by G.Landini
int k, kStar; // k = the current threshold; kStar = optimal threshold
int L = histogram.length; // The total intensity of the image
long N1, N; // N1 = # points with intensity <=k; N = total number of
// points
long Sk; // The total intensity for all histogram points <=k
long S;
double BCV, BCVmax; // The current Between Class Variance and maximum
// BCV
double num, denom; // temporary bookkeeping
// Initialize values:
S = 0;
N = 0;
for (k = 0; k < L; k++) {
S += k * histogram[k]; // Total histogram intensity
N += histogram[k]; // Total number of data points
}
Sk = 0;
N1 = histogram[0]; // The entry for zero intensity
BCV = 0;
BCVmax = 0;
kStar = 0;
// Look at each possible threshold value,
// calculate the between-class variance, and decide if it's a max
for (k = 1; k < L - 1; k++) { // No need to check endpoints k = 0 or k =
// L-1
Sk += k * histogram[k];
N1 += histogram[k];
// The float casting here is to avoid compiler warning about loss of
// precision and
// will prevent overflow in the case of large saturated images
denom = (double) (N1) * (N - N1); // Maximum value of denom is
// (N^2)/4 =
// approx. 3E10
if (denom != 0) {
// Float here is to avoid loss of precision when dividing
num = ((double) N1 / N) * S - Sk; // Maximum value of num =
// 255*N =
// approx 8E7
BCV = (num * num) / denom;
} else BCV = 0;
if (BCV >= BCVmax) { // Assign the best threshold found so far
BCVmax = BCV;
kStar = k;
}
}
// kStar += 1; // Use QTI convention that intensity -> 1 if intensity >=
// k
// (the algorithm was developed for I-> 1 if I <= k.)
return kStar;
}
