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.demos.acmmm11.presentation.slides.tutorial;
031
032import org.openimaj.image.MBFImage;
033import org.openimaj.image.colour.ColourSpace;
034import org.openimaj.ml.clustering.FloatCentroidsResult;
035import org.openimaj.ml.clustering.assignment.hard.ExactFloatAssigner;
036import org.openimaj.ml.clustering.kmeans.FloatKMeans;
037import org.openimaj.video.Video;
038
039/**
040 * Slide showing segmentation using k-means clustering
041 * 
042 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
043 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
044 * 
045 */
046
047public class SegmentationTutorial extends TutorialPanel {
048        private static final long serialVersionUID = 1L;
049
050        private FloatCentroidsResult cluster;
051
052        /**
053         * Default constructor
054         * 
055         * @param capture
056         * @param width
057         * @param height
058         */
059        public SegmentationTutorial(Video<MBFImage> capture, int width, int height) {
060                super("Posterisation with K-Means", capture, width, height);
061        }
062
063        @Override
064        public void doTutorial(MBFImage toDraw) {
065                final MBFImage space = ColourSpace.convert(toDraw, ColourSpace.CIE_Lab);
066
067                if (cluster == null)
068                        cluster = clusterPixels(space);
069
070                if (cluster == null)
071                        return;
072
073                final float[][] centroids = cluster.getCentroids();
074
075                final ExactFloatAssigner assigner = new ExactFloatAssigner(cluster);
076
077                for (int y = 0; y < space.getHeight(); y++) {
078                        for (int x = 0; x < space.getWidth(); x++) {
079                                final float[] pixel = space.getPixelNative(x, y);
080                                final int centroid = assigner.assign(pixel);
081                                space.setPixelNative(x, y, centroids[centroid]);
082                        }
083                }
084
085                toDraw.internalAssign(ColourSpace.convert(space, ColourSpace.RGB));
086        }
087
088        private FloatCentroidsResult clusterPixels(MBFImage toDraw) {
089                final float[][] testP = toDraw.getBand(0).pixels;
090                float sum = 0;
091
092                for (int i = 0; i < testP.length; i++)
093                        for (int j = 0; j < testP[i].length; j++)
094                                sum += testP[i][j];
095
096                if (sum == 0)
097                        return null;
098
099                final FloatKMeans k = FloatKMeans.createExact(3, 2);
100                final float[][] imageData = toDraw.getPixelVectorNative(new float[toDraw.getWidth() * toDraw.getHeight() * 3][3]);
101
102                return k.cluster(imageData);
103        }
104}