/**
 * 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
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.image.processing.face.feature;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.AbstractList;
import java.util.List;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.data.dataset.Dataset;
import org.openimaj.experiment.dataset.util.DatasetAdaptors;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.FeatureVectorProvider;
import org.openimaj.image.FImage;
import org.openimaj.image.model.EigenImages;
import org.openimaj.image.processing.face.alignment.FaceAligner;
import org.openimaj.image.processing.face.detection.DetectedFace;
import org.openimaj.io.IOUtils;
import org.openimaj.ml.training.BatchTrainer;

/**
 * A {@link FacialFeature} for EigenFaces.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 */
@Reference(
                type = ReferenceType.Inproceedings,
                author = { "Turk, M.A.", "Pentland, A.P." },
                title = "Face recognition using eigenfaces",
                year = "1991",
                booktitle = "Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91., IEEE Computer Society Conference on",
                pages = { "586 ", "591" },
                month = "jun",
                number = "",
                volume = "",
                customData = {
                                "keywords", "eigenfaces;eigenvectors;face images;face recognition system;face space;feature space;human faces;two-dimensional recognition;unsupervised learning;computerised pattern recognition;eigenvalues and eigenfunctions;",
                                "doi", "10.1109/CVPR.1991.139758"
                })
public class EigenFaceFeature implements FacialFeature, FeatureVectorProvider<DoubleFV> {
        /**
         * A {@link FacialFeatureExtractor} for producing EigenFaces. Unlike most
         * {@link FacialFeatureExtractor}s, this one either needs to be trained or
         * provided with a pre-trained {@link EigenImages} object.
         * <p>
         * A {@link FaceAligner} can be used to produce aligned faces for training
         * and feature extraction.
         * 
         * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
         * 
         * @param <T>
         * 
         */
        public static class Extractor<T extends DetectedFace>
                        implements
                        FacialFeatureExtractor<EigenFaceFeature, T>,
                        BatchTrainer<T>
        {
                EigenImages eigen = null;
                FaceAligner<T> aligner = null;

                /**
                 * Construct with the requested number of components (the number of PCs
                 * to keep) and a face aligner. The principal components must be learned
                 * by calling {@link #train(List)}.
                 * 
                 * @param numComponents
                 *            the number of principal components to keep.
                 * @param aligner
                 *            the face aligner
                 */
                public Extractor(int numComponents, FaceAligner<T> aligner) {
                        this(new EigenImages(numComponents), aligner);
                }

                /**
                 * Construct with given pre-trained {@link EigenImages} basis and a face
                 * aligner.
                 * 
                 * @param basis
                 *            the pre-trained basis
                 * @param aligner
                 *            the face aligner
                 */
                public Extractor(EigenImages basis, FaceAligner<T> aligner) {
                        this.eigen = basis;
                        this.aligner = aligner;
                }

                @Override
                public EigenFaceFeature extractFeature(T face) {
                        final FImage patch = aligner.align(face);

                        final DoubleFV fv = eigen.extractFeature(patch);

                        return new EigenFaceFeature(fv);
                }

                @Override
                public void readBinary(DataInput in) throws IOException {
                        eigen.readBinary(in);

                        final String alignerClass = in.readUTF();
                        aligner = IOUtils.newInstance(alignerClass);
                        aligner.readBinary(in);
                }

                @Override
                public byte[] binaryHeader() {
                        return this.getClass().getName().getBytes();
                }

                @Override
                public void writeBinary(DataOutput out) throws IOException {
                        eigen.writeBinary(out);

                        out.writeUTF(aligner.getClass().getName());
                        aligner.writeBinary(out);
                }

                @Override
                public void train(final List<? extends T> data) {
                        final List<FImage> patches = new AbstractList<FImage>() {

                                @Override
                                public FImage get(int index) {
                                        return aligner.align(data.get(index));
                                }

                                @Override
                                public int size() {
                                        return data.size();
                                }

                        };

                        eigen.train(patches);
                }

                /**
                 * Train from a dataset
                 * 
                 * @param data
                 *            the dataset
                 */
                public void train(final Dataset<? extends T> data) {
                        train(DatasetAdaptors.asList(data));
                }

                @Override
                public String toString() {
                        return String.format("EigenFaceFeature.Extractor[aligner=%s]", this.aligner);
                }
        }

        private DoubleFV fv;

        protected EigenFaceFeature() {
                this(null);
        }

        /**
         * Construct the EigenFaceFeature with the given feature vector.
         * 
         * @param fv
         *            the feature vector
         */
        public EigenFaceFeature(DoubleFV fv) {
                this.fv = fv;
        }

        @Override
        public void readBinary(DataInput in) throws IOException {
                fv = new DoubleFV();
                fv.readBinary(in);
        }

        @Override
        public byte[] binaryHeader() {
                return getClass().getName().getBytes();
        }

        @Override
        public void writeBinary(DataOutput out) throws IOException {
                fv.writeBinary(out);
        }

        @Override
        public DoubleFV getFeatureVector() {
                return fv;
        }
}