/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  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.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * 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 OWNER 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.
 */
package org.openimaj.image.processing.threshold;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.image.FImage;
import org.openimaj.image.processor.ImageProcessor;
import org.openimaj.util.array.ArrayUtils;

/**
 * Otsu's adaptive thresholding algorithm.
 * 
 * @see <a
 *      href="http://en.wikipedia.org/wiki/Otsu's_method">http://en.wikipedia.org/wiki/Otsu&apos;s_method</a>
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 */
@Reference(
                type = ReferenceType.Article,
                author = { "Nobuyuki Otsu" },
                title = "A Threshold Selection Method from Gray-Level Histograms",
                year = "1979",
                journal = "Systems, Man and Cybernetics, IEEE Transactions on",
                pages = { "62", "66" },
                number = "1",
                volume = "9",
                customData = {
                                "keywords",
                                "Displays;Gaussian distribution;Histograms;Least squares approximation;Marine vehicles;Q measurement;Radar tracking;Sea measurements;Surveillance;Target tracking",
                                "doi", "10.1109/TSMC.1979.4310076",
                                "ISSN", "0018-9472"
                })
public class OtsuThreshold implements ImageProcessor<FImage> {
        private static final int DEFAULT_NUM_BINS = 256;
        int numBins = DEFAULT_NUM_BINS;

        /**
         * Default constructor
         */
        public OtsuThreshold() {

        }

        /**
         * Construct with the given number of histogram bins
         * 
         * @param numBins
         *            the number of histogram bins
         */
        public OtsuThreshold(int numBins) {
                this.numBins = numBins;
        }

        protected static int[] makeHistogram(FImage fimg, int numBins) {
                final int[] histData = new int[numBins];

                // Calculate histogram
                for (int r = 0; r < fimg.height; r++) {
                        for (int c = 0; c < fimg.width; c++) {
                                final int h = (int) (fimg.pixels[r][c] * (numBins - 1));
                                histData[h]++;
                        }
                }

                return histData;
        }

        protected static int[] makeHistogram(float[] data, int numBins, float min, float max) {
                final int[] histData = new int[numBins];

                // Calculate histogram
                for (int c = 0; c < data.length; c++) {
                        final float d = (data[c] - min) / (max - min);
                        final int h = (int) (d * (numBins - 1));
                        histData[h]++;
                }

                return histData;
        }

        /**
         * Estimate the threshold for the given image.
         * 
         * @param img
         *            the image
         * @param numBins
         *            the number of histogram bins
         * @return the estimated threshold
         */
        public static float calculateThreshold(FImage img, int numBins) {
                final int[] histData = makeHistogram(img, numBins);

                // Total number of pixels
                final int total = img.getWidth() * img.getHeight();

                return computeThresholdFromHistogram(histData, total);
        }

        /**
         * Estimate the threshold for the given data.
         * <p>
         * Internally, the data will be min-max normalised before the histogram is
         * built, and the specified number of bins will cover the entire
         * <code>max-min</code> range. The returned threshold will have
         * <code>min</code> added to it to return it to the original range.
         * 
         * @param data
         *            the data
         * @param numBins
         *            the number of histogram bins
         * @return the estimated threshold
         */
        public static float calculateThreshold(float[] data, int numBins) {
                final float min = ArrayUtils.minValue(data);
                final float max = ArrayUtils.maxValue(data);
                final int[] histData = makeHistogram(data, numBins, min, max);

                return computeThresholdFromHistogram(histData, data.length) + min;
        }

        /**
         * Estimate the threshold for the given histogram.
         * 
         * @param histData
         *            the histogram
         * @param total
         *            the total number of items in the histogram
         * @return the estimated threshold
         */
        public static float computeThresholdFromHistogram(int[] histData, int total) {
                final int numBins = histData.length;
                float sum = 0;
                for (int t = 0; t < numBins; t++)
                        sum += t * histData[t];

                float sumB = 0;
                int wB = 0;
                int wF = 0;

                float varMax = 0;
                float threshold = 0;

                for (int t = 0; t < numBins; t++) {
                        wB += histData[t]; // Weight Background
                        if (wB == 0)
                                continue;

                        wF = total - wB; // Weight Foreground
                        if (wF == 0)
                                break;

                        sumB += (t * histData[t]);

                        final float mB = sumB / wB; // Mean Background
                        final float mF = (sum - sumB) / wF; // Mean Foreground

                        // Calculate Between Class Variance
                        final float varBetween = (float) wB * (float) wF * (mB - mF) * (mB - mF);

                        // Check if new maximum found
                        if (varBetween > varMax) {
                                varMax = varBetween;
                                threshold = t;
                        }
                }

                return threshold / (numBins - 1);
        }

        @Override
        public void processImage(FImage image) {
                final float threshold = calculateThreshold(image, numBins);

                image.threshold(threshold);
        }
}