/* * #%L * ImageJ software for multidimensional image processing and analysis. * %% * Copyright (C) 2014 - 2020 ImageJ developers. * %% * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * #L% */ package net.imagej.ops.threshold.localSauvola; import net.imagej.ops.Ops; import net.imagej.ops.map.neighborhood.CenterAwareIntegralComputerOp; import net.imagej.ops.special.computer.AbstractBinaryComputerOp; import net.imagej.ops.stats.IntegralMean; import net.imagej.ops.stats.IntegralVariance; import net.imagej.ops.threshold.apply.LocalThresholdIntegral; import net.imglib2.RandomAccessibleInterval; import net.imglib2.algorithm.neighborhood.RectangleNeighborhood; import net.imglib2.converter.Converter; import net.imglib2.converter.RealDoubleConverter; import net.imglib2.type.logic.BitType; import net.imglib2.type.numeric.RealType; import net.imglib2.type.numeric.real.DoubleType; import net.imglib2.view.composite.Composite; import org.scijava.Priority; import org.scijava.plugin.Parameter; import org.scijava.plugin.Plugin; /** * <p> * Local thresholding algorithm as proposed by Sauvola et al. * </p> * <p> * This implementation improves execution speed by using integral images for the * computations of mean and standard deviation in the local windows. A * significant improvement can be observed for increased window sizes ( * {@code span > 10}). It operates on {@link RandomAccessibleInterval}s of * {@link RealType}, i.e. explicit conversion to an integral image is <b>not</b> * required. * </p> * * @see LocalSauvolaThreshold * @see LocalThresholdIntegral * @author Stefan Helfrich (University of Konstanz) */ @Plugin(type = Ops.Threshold.LocalSauvolaThreshold.class, priority = Priority.LOW - 1) public class LocalSauvolaThresholdIntegral<T extends RealType<T>> extends LocalThresholdIntegral<T> implements Ops.Threshold.LocalSauvolaThreshold { @Parameter(required = false) private double k = 0.5d; @Parameter(required = false) private double r = 0.5d; @SuppressWarnings("unchecked") @Override protected CenterAwareIntegralComputerOp<T, BitType> unaryComputer() { final CenterAwareIntegralComputerOp<T, BitType> op = new LocalSauvolaThresholdComputer<>(ops().op(IntegralMean.class, DoubleType.class, RectangleNeighborhood.class), ops() .op(IntegralVariance.class, DoubleType.class, RectangleNeighborhood.class)); op.setEnvironment(ops()); return op; } private class LocalSauvolaThresholdComputer<I extends RealType<I>> extends AbstractBinaryComputerOp<I, RectangleNeighborhood<Composite<DoubleType>>, BitType> implements CenterAwareIntegralComputerOp<I, BitType> { private final IntegralMean<DoubleType> integralMean; private final IntegralVariance<DoubleType> integralVariance; public LocalSauvolaThresholdComputer( final IntegralMean<DoubleType> integralMean, final IntegralVariance<DoubleType> integralVariance) { super(); this.integralMean = integralMean; this.integralVariance = integralVariance; } @Override public void compute(final I center, final RectangleNeighborhood<Composite<DoubleType>> neighborhood, final BitType output) { final DoubleType mean = new DoubleType(); integralMean.compute(neighborhood, mean); final DoubleType variance = new DoubleType(); integralVariance.compute(neighborhood, variance); final DoubleType stdDev = new DoubleType(Math.sqrt(variance.get())); final DoubleType threshold = new DoubleType(mean.getRealDouble() * (1.0d + k * ((Math.sqrt(stdDev.getRealDouble()) / r) - 1.0))); // Set value final Converter<I, DoubleType> conv = new RealDoubleConverter<>(); final DoubleType centerPixelAsDoubleType = variance; // NB: Reuse // DoubleType conv.convert(center, centerPixelAsDoubleType); output.set(centerPixelAsDoubleType.compareTo(threshold) > 0); } } @Override protected int[] requiredIntegralImages() { return new int[] { 1, 2 }; } }