001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.image.model.pixel;
031
032import org.openimaj.image.MBFImage;
033import org.openimaj.image.pixel.statistics.HistogramModel;
034
035/**
036 * An {@link MBFPixelClassificationModel} that classifies an individual pixel by
037 * comparing it to a joint (colour) histogram. The histogram is learnt from the
038 * positive pixel samples given in training. The probability returned by the
039 * classification is determined from the value of the histogram bin in which the
040 * pixel being classified falls.
041 * 
042 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
043 */
044public class HistogramPixelModel extends MBFPixelClassificationModel {
045        private static final long serialVersionUID = 1L;
046
047        /**
048         * The model histogram; public for speed.
049         */
050        public HistogramModel model;
051
052        /**
053         * Construct with the given number of histogram bins per dimension.
054         * 
055         * @param nbins
056         *            number of bins per dimension.
057         */
058        public HistogramPixelModel(int... nbins) {
059                super(nbins.length);
060                model = new HistogramModel(nbins);
061        }
062
063        @Override
064        protected float classifyPixel(Float[] pix) {
065                int bin = 0;
066
067                for (int i = 0; i < ndims; i++) {
068                        int b = (int) (pix[i] * (model.histogram.nbins[i]));
069                        if (b >= model.histogram.nbins[i])
070                                b = model.histogram.nbins[i] - 1;
071
072                        int f = 1;
073                        for (int j = 0; j < i; j++)
074                                f *= model.histogram.nbins[j];
075
076                        bin += f * b;
077                }
078
079                return (float) model.histogram.values[bin];
080        }
081
082        @Override
083        public String toString() {
084                return model.toString();
085        }
086
087        @Override
088        public HistogramPixelModel clone() {
089                final HistogramPixelModel newmodel = new HistogramPixelModel();
090                newmodel.model = model.clone();
091                newmodel.ndims = ndims;
092                return newmodel;
093        }
094
095        @Override
096        public void learnModel(MBFImage... images) {
097                model.estimateModel(images);
098        }
099}