001/*******************************************************************************
002 * This software is provided as a supplement to the authors' textbooks on digital
003 * image processing published by Springer-Verlag in various languages and editions.
004 * Permission to use and distribute this software is granted under the BSD 2-Clause
005 * "Simplified" License (see http://opensource.org/licenses/BSD-2-Clause).
006 * Copyright (c) 2006-2023 Wilhelm Burger, Mark J. Burge. All rights reserved.
007 * Visit https://imagingbook.com for additional details.
008 ******************************************************************************/
009
010package imagingbook.common.threshold.global;
011
012import imagingbook.common.histogram.HistogramUtils;
013
014/**
015 * <p>
016 * Slow version of the {@link IsodataThresholder} using explicit recalculation of background and foreground means in
017 * every iteration. See [1], Alg. 9.2.
018 * </p>
019 * <p>
020 * [1] W. Burger, M.J. Burge, <em>Digital Image Processing &ndash; An Algorithmic Introduction</em>, 3rd ed, Springer
021 * (2022).
022 * </p>
023 *
024 * @author WB
025 * @version 2022/08/01
026 */
027public class IsodataThresholderSlow implements GlobalThresholder {
028        
029        
030        private static int MAX_ITERATIONS = 100;
031        
032        public IsodataThresholderSlow() {
033                super();
034        }
035
036        @Override
037        public float getThreshold(int[] h) {
038                int K = h.length;
039                int q = (int) HistogramUtils.mean(h, 0, K-1);   // start with the total mean
040                int qq;
041                
042                int i = 0;      // iteration counter
043                do {
044                        i++;
045                        int nB = HistogramUtils.count(h, 0, q);
046                        int nF = HistogramUtils.count(h, q+1, K-1);
047                        if (nB == 0 || nF == 0)
048                                return NoThreshold;
049                        double meanB = HistogramUtils.mean(h, 0, q);
050                        double meanF = HistogramUtils.mean(h, q+1, K-1);
051                        qq = q;
052                        q = (int)((meanB + meanF)/2);
053                } while(q != qq && i < MAX_ITERATIONS);
054                
055                return q;
056        }
057
058}