net.semanticmetadata.lire.impl.ParallelImageSearcher.java Source code

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Here is the source code for net.semanticmetadata.lire.impl.ParallelImageSearcher.java

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/*
 * This file is part of the LIRE project: http://www.semanticmetadata.net/lire
 * LIRE is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * LIRE is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with LIRE; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 * We kindly ask you to refer the any or one of the following publications in
 * any publication mentioning or employing Lire:
 *
 * Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval 
 * An Extensible Java CBIR Library. In proceedings of the 16th ACM International
 * Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
 * URL: http://doi.acm.org/10.1145/1459359.1459577
 *
 * Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
 * 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
 * Arizona, USA, 2011
 * URL: http://dl.acm.org/citation.cfm?id=2072432
 *
 * Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
 * Morgan & Claypool, 2013
 * URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
 *
 * Copyright statement:
 * --------------------
 * (c) 2002-2013 by Mathias Lux (mathias@juggle.at)
 *     http://www.semanticmetadata.net/lire, http://www.lire-project.net
 */

package net.semanticmetadata.lire.impl;

import net.semanticmetadata.lire.AbstractImageSearcher;
import net.semanticmetadata.lire.ImageDuplicates;
import net.semanticmetadata.lire.ImageSearchHits;
import net.semanticmetadata.lire.imageanalysis.LireFeature;
import net.semanticmetadata.lire.utils.ImageUtils;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.util.Bits;

import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.TreeSet;
import java.util.logging.Level;
import java.util.logging.Logger;

/**
 * This file is part of the Caliph and Emir project: http://www.SemanticMetadata.net
 * <br>Date: 01.02.2006
 * <br>Time: 00:17:02
 *
 * @author Mathias Lux, mathias@juggle.at
 */
public class ParallelImageSearcher extends AbstractImageSearcher {

    private Logger logger = Logger.getLogger(getClass().getName());
    Class<?> descriptorClass;
    String fieldName;
    private int maxHits = 10;
    private TreeSet<SimpleResult>[] parDocs;

    public ParallelImageSearcher(int maxHits, Class<?> descriptorClass, String fieldName) {
        this.maxHits = maxHits;
        this.descriptorClass = descriptorClass;
        this.fieldName = fieldName;
    }

    public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException {
        throw new UnsupportedOperationException("Not implemented in this searcher");
    }

    public ImageSearchHits[] search(BufferedImage[] image, IndexReader reader) throws IOException {
        logger.finer("Starting extraction.");
        LireFeature[] lireFeature = new LireFeature[image.length];
        SimpleImageSearchHits[] searchHits = new SimpleImageSearchHits[image.length];
        for (int i = 0; i < image.length; i++) {
            BufferedImage img = image[i];
            try {
                lireFeature[i] = (LireFeature) descriptorClass.newInstance();
                // Scaling image is especially with the correlogram features very important!
                BufferedImage bimg = img;
                if (Math.max(img.getHeight(), img.getWidth()) > GenericDocumentBuilder.MAX_IMAGE_DIMENSION) {
                    bimg = ImageUtils.scaleImage(img, GenericDocumentBuilder.MAX_IMAGE_DIMENSION);
                }
                lireFeature[i].extract(bimg);
                logger.fine("Extraction from image finished");

            } catch (InstantiationException e) {
                logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
            } catch (IllegalAccessException e) {
                logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
            }
        }
        float[] distance = findSimilar(reader, lireFeature);
        for (int i = 0; i < distance.length; i++) {
            searchHits[i] = new SimpleImageSearchHits(parDocs[i], distance[i]);
        }
        return searchHits;

    }

    public ImageSearchHits[] search(Document[] doc, IndexReader reader) throws IOException {
        LireFeature[] lireFeature = new LireFeature[doc.length];
        SimpleImageSearchHits[] searchHits = new SimpleImageSearchHits[doc.length];
        for (int i = 0; i < doc.length; i++) {
            Document doc_ = doc[i];
            try {
                lireFeature[i] = (LireFeature) descriptorClass.newInstance();
                String[] cls = doc_.getValues(fieldName);
                if (cls != null && cls.length > 0) {
                    lireFeature[i].setStringRepresentation(cls[0]);
                }
            } catch (InstantiationException e) {
                logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
            } catch (IllegalAccessException e) {
                logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
            }
        }
        float[] distance = findSimilar(reader, lireFeature);
        for (int i = 0; i < distance.length; i++) {
            searchHits[i] = new SimpleImageSearchHits(parDocs[i], distance[i]);
        }
        return searchHits;

    }

    /**
     * @param reader
     * @param lireFeature
     * @return the maximum distance found for normalizing.
     * @throws java.io.IOException
     */
    @SuppressWarnings("unchecked")
    private float[] findSimilar(IndexReader reader, LireFeature[] lireFeature) throws IOException {
        float[] maxDistance = new float[lireFeature.length];
        float[] overallMaxDistance = new float[lireFeature.length];

        for (int i = 0; i < overallMaxDistance.length; i++) {
            overallMaxDistance[i] = -1f;
            maxDistance[i] = -1f;
        }

        parDocs = new TreeSet[lireFeature.length];
        for (int i = 0; i < parDocs.length; i++) {
            parDocs[i] = new TreeSet<SimpleResult>();
        }

        // Needed for check whether the document is deleted.
        Bits liveDocs = MultiFields.getLiveDocs(reader);

        // clear result set ...

        int docs = reader.numDocs();
        for (int i = 0; i < docs; i++) {
            if (reader.hasDeletions() && !liveDocs.get(i))
                continue; // if it is deleted, just ignore it.

            Document d = reader.document(i);
            float[] distance = getDistance(d, lireFeature);
            // calculate the overall max distance to normalize score afterwards
            for (int j = 0; j < distance.length; j++) {
                float f = distance[j];
                if (overallMaxDistance[j] < f) {
                    overallMaxDistance[j] = f;
                }
                // if it is the first document:
                if (maxDistance[j] < 0) {
                    maxDistance[j] = f;
                }
                // if the array is not full yet:
                if (this.parDocs[j].size() < maxHits) {
                    this.parDocs[j].add(new SimpleResult(f, d, i));
                    if (f > maxDistance[j]) {
                        maxDistance[j] = f;
                    }
                } else if (f < maxDistance[j]) {
                    // if it is nearer to the sample than at least on of the current set:
                    // remove the last one ...
                    this.parDocs[j].remove(this.parDocs[j].last());
                    // add the new one ...
                    this.parDocs[j].add(new SimpleResult(f, d, i));
                    // and set our new distance border ...
                    maxDistance[j] = this.parDocs[j].last().getDistance();
                }

            }
        }
        return maxDistance;
    }

    private float[] getDistance(Document d, LireFeature[] lireFeature) {
        float[] distance = new float[lireFeature.length];
        LireFeature lf;
        try {
            lf = (LireFeature) descriptorClass.newInstance();
            String[] cls = d.getValues(fieldName);
            if (cls != null && cls.length > 0) {
                lf.setStringRepresentation(cls[0]);
                for (int i = 0; i < lireFeature.length; i++) {
                    distance[i] = lireFeature[i].getDistance(lf);
                }

            } else {
                logger.warning("No feature stored in this document!");
            }
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }

        return distance;
    }

    public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
        throw new UnsupportedOperationException("Not implemented in this searcher");

    }

    public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
        throw new UnsupportedOperationException("Not implemented in this searcher");

    }

    public String toString() {
        return "GenericSearcher using " + descriptorClass.getName();
    }
}