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

import org.openimaj.data.dataset.GroupedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.experiment.dataset.util.DatasetAdaptors;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.DoubleFVComparator;
import org.openimaj.feature.FVProviderExtractor;
import org.openimaj.image.processing.face.alignment.FaceAligner;
import org.openimaj.image.processing.face.detection.DetectedFace;
import org.openimaj.image.processing.face.feature.EigenFaceFeature.Extractor;
import org.openimaj.ml.annotation.IncrementalAnnotator;
import org.openimaj.ml.annotation.basic.KNNAnnotator;

/**
 * Implementation of a {@link FaceRecogniser} based on Eigenfaces. Any kind of
 * machine learning implementation can be used for the actual classification.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 * @param <FACE>
 *            Type of {@link DetectedFace}
 * @param <PERSON>
 *            Type of object representing a person
 */
public class EigenFaceRecogniser<FACE extends DetectedFace, PERSON>
                extends
                LazyFaceRecogniser<FACE, PERSON, Extractor<FACE>>
{
        protected EigenFaceRecogniser() {
        }

        /**
         * Construct with the given feature extractor and underlying
         * {@link FaceRecogniser}.
         * 
         * @param extractor
         *            the feature extractor
         * @param internalRecogniser
         *            the face recogniser
         */
        public EigenFaceRecogniser(Extractor<FACE> extractor,
                        FaceRecogniser<FACE, PERSON> internalRecogniser)
        {
                super(extractor, internalRecogniser);
        }

        /**
         * Construct with the given feature extractor and underlying
         * {@link IncrementalAnnotator}.
         * 
         * @param extractor
         *            the feature extractor
         * @param annotator
         *            the annotator
         */
        public EigenFaceRecogniser(Extractor<FACE> extractor,
                        IncrementalAnnotator<FACE, PERSON> annotator)
        {
                this(extractor, AnnotatorFaceRecogniser.create(annotator));
        }

        /**
         * Convenience method to create an {@link EigenFaceRecogniser} with a
         * standard KNN classifier, incorporating a threshold on the maximum
         * distance (or minimum similarity) to allow a match.
         * 
         * @param <FACE>
         *            The type of {@link DetectedFace}
         * @param <PERSON>
         *            the type representing a person
         * @param numComponents
         *            the number of principal components to keep
         * @param aligner
         *            the face aligner
         * @param k
         *            the number of nearest neighbours
         * @param compar
         *            the distance comparison function
         * @param threshold
         *            a distance threshold to limit matches.
         * @return a new {@link EigenFaceRecogniser}
         */
        public static <FACE extends DetectedFace, PERSON>
                        EigenFaceRecogniser<FACE, PERSON> create(int numComponents, FaceAligner<FACE> aligner, int k,
                                        DoubleFVComparator compar, float threshold)
        {
                final Extractor<FACE> extractor = new Extractor<FACE>(numComponents, aligner);
                final FVProviderExtractor<DoubleFV, FACE> extractor2 = FVProviderExtractor.create(extractor);

                final KNNAnnotator<FACE, PERSON, DoubleFV> knn =
                                KNNAnnotator.create(extractor2, compar, k, threshold);

                return new EigenFaceRecogniser<FACE, PERSON>(extractor, knn);
        }

        /**
         * Convenience method to create an {@link EigenFaceRecogniser} with a
         * standard KNN classifier.
         * 
         * @param <FACE>
         *            The type of {@link DetectedFace}
         * @param <PERSON>
         *            the type representing a person
         * @param numComponents
         *            the number of principal components to keep
         * @param aligner
         *            the face aligner
         * @param k
         *            the number of nearest neighbours
         * @param compar
         *            the distance comparison function
         * @return a new {@link EigenFaceRecogniser}
         */
        public static <FACE extends DetectedFace, PERSON>
                        EigenFaceRecogniser<FACE, PERSON> create(int numComponents, FaceAligner<FACE> aligner, int k,
                                        DoubleFVComparator compar)
        {
                final Extractor<FACE> extractor = new Extractor<FACE>(numComponents, aligner);
                final FVProviderExtractor<DoubleFV, FACE> extractor2 = FVProviderExtractor.create(extractor);

                final KNNAnnotator<FACE, PERSON, DoubleFV> knn =
                                KNNAnnotator.create(extractor2, compar, k);

                return new EigenFaceRecogniser<FACE, PERSON>(extractor, knn);
        }

        @Override
        protected void beforeBatchTrain(GroupedDataset<PERSON, ListDataset<FACE>, FACE> dataset) {
                extractor.train(DatasetAdaptors.asList(dataset));
        }

        @Override
        public String toString() {
                return String.format("EigenFaceRecogniser[extractor=%s; recogniser=%s]",
                                this.extractor, this.internalRecogniser);
        }
}