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.docs.tutorial.adv.faces.eigen;
031
032import java.io.IOException;
033import java.util.ArrayList;
034import java.util.HashMap;
035import java.util.List;
036import java.util.Map;
037
038import org.openimaj.data.dataset.GroupedDataset;
039import org.openimaj.data.dataset.ListDataset;
040import org.openimaj.data.dataset.VFSGroupDataset;
041import org.openimaj.experiment.dataset.split.GroupedRandomSplitter;
042import org.openimaj.experiment.dataset.util.DatasetAdaptors;
043import org.openimaj.feature.DoubleFV;
044import org.openimaj.feature.DoubleFVComparison;
045import org.openimaj.image.DisplayUtilities;
046import org.openimaj.image.FImage;
047import org.openimaj.image.ImageUtilities;
048import org.openimaj.image.model.EigenImages;
049
050/**
051 * OpenIMAJ Hello world!
052 * 
053 */
054public class App {
055        /**
056         * Main method
057         * 
058         * @param args
059         * @throws IOException
060         */
061        public static void main(String[] args) throws IOException {
062                /*
063                 * Load the data, and create some training and test data
064                 */
065                final VFSGroupDataset<FImage> dataset =
066                                new VFSGroupDataset<FImage>("zip:http://datasets.openimaj.org/att_faces.zip",
067                                                ImageUtilities.FIMAGE_READER);
068
069                final GroupedRandomSplitter<String, FImage> splits = new GroupedRandomSplitter<String, FImage>(dataset, 5, 0, 5);
070                final GroupedDataset<String, ListDataset<FImage>, FImage> training = splits.getTrainingDataset();
071                final GroupedDataset<String, ListDataset<FImage>, FImage> testing = splits.getTestDataset();
072
073                /*
074                 * Now learn the PCA basis
075                 */
076                final List<FImage> basisImages = DatasetAdaptors.asList(training);
077                final int nEigenvectors = 100;
078                final EigenImages eigen = new EigenImages(nEigenvectors);
079                eigen.train(basisImages);
080
081                /*
082                 * Display the top 12 eigenimages
083                 */
084                final List<FImage> eigenFaces = new ArrayList<FImage>();
085                for (int i = 0; i < 12; i++) {
086                        eigenFaces.add(eigen.visualisePC(i));
087                }
088                DisplayUtilities.display("EigenFaces", eigenFaces);
089
090                /*
091                 * Build a map of person->[features] for all the training data
092                 */
093                final Map<String, DoubleFV[]> features = new HashMap<String, DoubleFV[]>();
094                for (final String person : training.getGroups()) {
095                        final DoubleFV[] fvs = new DoubleFV[5];
096
097                        for (int i = 0; i < 5; i++) {
098                                final FImage face = training.get(person).get(i);
099                                fvs[i] = eigen.extractFeature(face);
100                        }
101                        features.put(person, fvs);
102                }
103
104                /*
105                 * Now we can test our performance on the test set
106                 */
107                double correct = 0, incorrect = 0;
108                for (final String truePerson : testing.getGroups()) {
109                        for (final FImage face : testing.get(truePerson)) {
110                                final DoubleFV testFeature = eigen.extractFeature(face);
111
112                                String bestPerson = null;
113                                double minDistance = Double.MAX_VALUE;
114                                for (final String person : features.keySet()) {
115                                        for (final DoubleFV fv : features.get(person)) {
116                                                final double distance = fv.compare(testFeature, DoubleFVComparison.EUCLIDEAN);
117
118                                                if (distance < minDistance) {
119                                                        minDistance = distance;
120                                                        bestPerson = person;
121                                                }
122                                        }
123                                }
124
125                                System.out.println("Actual: " + truePerson + "\tguess: " + bestPerson);
126
127                                if (truePerson.equals(bestPerson))
128                                        correct++;
129                                else
130                                        incorrect++;
131                        }
132                }
133
134                System.out.println("Accuracy: " + (correct / (correct + incorrect)));
135        }
136}