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 java.util.List;
033
034import org.openimaj.image.FImage;
035import org.openimaj.image.Image;
036import org.openimaj.image.model.ImageClassificationModel;
037import org.openimaj.util.pair.IndependentPair;
038
039/**
040 * Simple model for classifying pixels. When learning assumes ALL provided
041 * sample pixels are positive exemplars, and that anything not given is
042 * negative.
043 * 
044 * @author Jonathon Hare
045 * @param <Q>
046 *            Type of pixel
047 * @param <T>
048 *            Type of image
049 * 
050 */
051public abstract class PixelClassificationModel<Q, T extends Image<Q, T>> implements ImageClassificationModel<T> {
052        private static final long serialVersionUID = 1L;
053
054        protected abstract float classifyPixel(Q pix);
055
056        @Override
057        public FImage classifyImage(T im) {
058                final FImage out = new FImage(im.getWidth(), im.getHeight());
059
060                for (int y = 0; y < im.getHeight(); y++) {
061                        for (int x = 0; x < im.getWidth(); x++) {
062                                out.pixels[y][x] = classifyPixel(im.getPixel(x, y));
063                        }
064                }
065
066                return out;
067        }
068
069        protected abstract T[] getArray(int length);
070
071        @Override
072        public boolean estimate(List<? extends IndependentPair<T, FImage>> data) {
073                final T[] samples = getArray(data.size());
074                for (int i = 0; i < data.size(); i++) {
075                        samples[i] = data.get(i).firstObject();
076                }
077                learnModel(samples);
078                return true;
079        }
080
081        @Override
082        public int numItemsToEstimate() {
083                return 1; // need a minimum of 1 sample
084        }
085
086        @Override
087        public FImage predict(T data) {
088                return classifyImage(data);
089        }
090
091        @Override
092        public abstract PixelClassificationModel<Q, T> clone();
093}