/**
 * 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
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package org.openimaj.image.processing.convolution;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.image.FImage;
import org.openimaj.image.processor.SinglebandImageProcessor;

/**
 * Fast approximate Gaussian smoothing using repeated fast box filtering.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 */
@Reference(
                type = ReferenceType.Inproceedings,
                author = { "Kovesi, P." },
                title = "Fast Almost-Gaussian Filtering",
                year = "2010",
                booktitle = "Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on",
                pages = { "121", "125" },
                month = "Dec",
                customData = {
                                "keywords", "Gaussian processes;approximation theory;band-pass filters;image processing;Gaussian bandpass filters;fast almost-Gaussian filtering;image averaging;integral images;log-Gabor filters;separable moving average filters;summed area tables;symmetric transfer function;Approximation methods;Bandwidth;Computer vision;Frequency domain analysis;Laplace equations;Pixel;Transfer functions;Difference of Gaussian filtering;Gaussian smoothing",
                                "doi", "10.1109/DICTA.2010.30"
                })
public class FFastGaussianConvolve implements SinglebandImageProcessor<Float, FImage> {
        private final int n;
        private final int m;
        private SinglebandImageProcessor<Float, FImage> wlBox;
        private SinglebandImageProcessor<Float, FImage> wuBox;

        /**
         * Construct an {@link FFastGaussianConvolve} to approximate blurring with a
         * Gaussian of standard deviation sigma.
         * 
         * @param sigma
         *            Standard deviation of the approximated Gaussian
         * @param n
         *            Number of filtering iterations to perform (usually between 3
         *            and 6)
         */
        public FFastGaussianConvolve(float sigma, int n) {
                if (sigma < 1.8) {
                        // std.devs of less than 1.8 are not well approximated.
                        this.m = 1;
                        this.n = 1;
                        this.wlBox = new FGaussianConvolve(sigma);
                } else {
                        final float ss = sigma * sigma;
                        final double wIdeal = Math.sqrt((12.0 * ss / n) + 1.0);
                        final int wl = (((int) wIdeal) % 2 == 0) ? (int) wIdeal - 1 : (int) wIdeal;
                        final int wu = wl + 2;

                        this.n = n;
                        this.m = Math.round((12 * ss - n * wl * wl - 4 * n * wl - 3 * n) / (-4 * wl - 4));

                        this.wlBox = new AverageBoxFilter(wl);
                        this.wuBox = new AverageBoxFilter(wu);
                }
        }

        @Override
        public void processImage(FImage image) {
                for (int i = 0; i < m; i++)
                        wlBox.processImage(image);
                for (int i = 0; i < n - m; i++)
                        wuBox.processImage(image);
        }
}