/**
 * 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
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.image.model;

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

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.FeatureExtractor;
import org.openimaj.image.FImage;
import org.openimaj.image.feature.DoubleFV2FImage;
import org.openimaj.image.feature.FImage2DoubleFV;
import org.openimaj.io.IOUtils;
import org.openimaj.io.ReadWriteableBinary;
import org.openimaj.math.matrix.algorithm.pca.ThinSvdPrincipalComponentAnalysis;
import org.openimaj.ml.pca.FeatureVectorPCA;
import org.openimaj.ml.training.BatchTrainer;
import org.openimaj.util.array.ArrayUtils;

/**
 * Implementation of EigenImages. Can be used for things like face recognition
 * ala the classic EigenFaces algorithm.
 * <p>
 * Fundamentally, the EigenImages technique is a way to perform dimensionality
 * reduction on an image using PCA. This implementation can be trained through
 * the {@link BatchTrainer} interface (which will internally normalise the data
 * and perform PCA). Once trained, instances can be used as
 * {@link FeatureExtractor}s to extract low(er) dimensional features from
 * images.
 * <p>
 * Methods are also provided to reconstruct an image from its feature vector
 * (see {@link #reconstruct(DoubleFV) and #reconstruct(double[])}, and to
 * visualise a specific principal component as an image (see
 * {@link #visualisePC(int)}.
 * 
 * @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 EigenImages implements BatchTrainer<FImage>, FeatureExtractor<DoubleFV, FImage>, ReadWriteableBinary {
        private FeatureVectorPCA pca;
        private int width;
        private int height;
        private int numComponents;

        /**
         * For serialisation
         */
        protected EigenImages() {
        }

        /**
         * Construct with the given number of principal components.
         * 
         * @param numComponents
         *            the number of PCs
         */
        public EigenImages(int numComponents) {
                this.numComponents = numComponents;
                pca = new FeatureVectorPCA(new ThinSvdPrincipalComponentAnalysis(numComponents));
        }

        @Override
        public DoubleFV extractFeature(FImage img) {
                final DoubleFV feature = FImage2DoubleFV.INSTANCE.extractFeature(img);

                return pca.project(feature);
        }

        @Override
        public void train(List<? extends FImage> data) {
                final double[][] features = new double[data.size()][];

                width = data.get(0).width;
                height = data.get(0).height;

                for (int i = 0; i < features.length; i++)
                        features[i] = FImage2DoubleFV.INSTANCE.extractFeature(data.get(i)).values;

                pca.learnBasis(features);
        }

        /**
         * Reconstruct an image from a weight vector
         * 
         * @param weights
         *            the weight vector
         * @return the reconstructed image
         */
        public FImage reconstruct(DoubleFV weights) {
                return DoubleFV2FImage.extractFeature(pca.generate(weights), width, height);
        }

        /**
         * Reconstruct an image from a weight vector
         * 
         * @param weights
         *            the weight vector
         * @return the reconstructed image
         */
        public FImage reconstruct(double[] weights) {
                return new FImage(ArrayUtils.reshapeFloat(pca.generate(weights), width, height));
        }

        /**
         * Draw a principal component as an image. The image will be normalised so
         * it can be displayed correctly.
         * 
         * @param pc
         *            the index of the PC to draw.
         * @return an image showing the PC.
         */
        public FImage visualisePC(int pc) {
                return new FImage(ArrayUtils.reshapeFloat(pca.getPrincipalComponent(pc), width, height)).normalise();
        }

        @Override
        public void readBinary(DataInput in) throws IOException {
                width = in.readInt();
                height = in.readInt();
                numComponents = in.readInt();
                pca = IOUtils.read(in);
        }

        @Override
        public byte[] binaryHeader() {
                return "EigI".getBytes();
        }

        @Override
        public void writeBinary(DataOutput out) throws IOException {
                out.writeInt(width);
                out.writeInt(height);
                out.writeInt(numComponents);
                IOUtils.write(pca, out);
        }

        @Override
        public String toString() {
                return String.format("EigenImages[width=%d; height=%d; pca=%s]", width, height, pca);
        }

        /**
         * Get the number of PCA components selected by this {@link EigenImages}
         * object.
         * 
         * @return the number of PCA components.
         */
        public int getNumComponents() {
                return numComponents;
        }
}