Java tutorial
/* * 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 * * Updated: 23.06.13 18:16 */ package net.semanticmetadata.lire.impl; import net.semanticmetadata.lire.AbstractImageSearcher; import net.semanticmetadata.lire.DocumentBuilder; import net.semanticmetadata.lire.ImageDuplicates; import net.semanticmetadata.lire.ImageSearchHits; import net.semanticmetadata.lire.imageanalysis.LireFeature; import net.semanticmetadata.lire.imageanalysis.OpponentHistogram; 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.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 FastOpponentImageSearcher extends AbstractImageSearcher { protected Logger logger = Logger.getLogger(getClass().getName()); private OpponentHistogram cachedInstance = null; private int maxHits = 10; protected TreeSet<SimpleResult> docs; private byte[] tempBinaryValue; private double maxDistance; private float overallMaxDistance; public FastOpponentImageSearcher(int maxHits) { this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.cachedInstance = new OpponentHistogram(); } public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException { logger.finer("Starting extraction."); OpponentHistogram lireFeature = null; SimpleImageSearchHits searchHits = null; lireFeature = new OpponentHistogram(); // Scaling image is especially with the correlogram features very important! BufferedImage bimg = image; if (Math.max(image.getHeight(), image.getWidth()) > GenericDocumentBuilder.MAX_IMAGE_DIMENSION) { bimg = ImageUtils.scaleImage(image, GenericDocumentBuilder.MAX_IMAGE_DIMENSION); } lireFeature.extract(bimg); logger.fine("Extraction from image finished"); double maxDistance = findSimilar(reader, lireFeature); searchHits = new SimpleImageSearchHits(this.docs, (float) maxDistance); return searchHits; } /** * @param reader * @param lireFeature * @return the maximum distance found for normalizing. * @throws java.io.IOException */ protected double findSimilar(IndexReader reader, LireFeature lireFeature) throws IOException { maxDistance = -1f; // clear result set ... docs.clear(); // Needed for check whether the document is deleted. Bits liveDocs = MultiFields.getLiveDocs(reader); Document d; double tmpDistance; int docs = reader.numDocs(); byte[] histogram = lireFeature.getByteArrayRepresentation(); for (int i = 0; i < docs; i++) { if (reader.hasDeletions() && !liveDocs.get(i)) continue; // if it is deleted, just ignore it. d = reader.document(i); tmpDistance = getDistance(d, histogram); assert (tmpDistance >= 0); // calculate the overall max distance to normalize score afterwards // if (overallMaxDistance < tmpDistance) { // overallMaxDistance = tmpDistance; // } // if it is the first document: if (maxDistance < 0) { maxDistance = tmpDistance; } // if the array is not full yet: if (this.docs.size() < maxHits) { this.docs.add(new SimpleResult((float) tmpDistance, d, i)); if (tmpDistance > maxDistance) maxDistance = tmpDistance; } else if (tmpDistance < maxDistance) { // if it is nearer to the sample than at least on of the current set: // remove the last one ... this.docs.remove(this.docs.last()); // add the new one ... this.docs.add(new SimpleResult((float) tmpDistance, d, i)); // and set our new distance border ... maxDistance = this.docs.last().getDistance(); } } return maxDistance; } /** * Main similarity method called for each and every document in the index. * * @param document * @param histogram * @return the distance between the given feature and the feature stored in the document. */ protected double getDistance(Document document, byte[] histogram) { if (document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue() != null && document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length > 0) { return cachedInstance.getDistance(histogram, 0, histogram.length, document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().bytes, document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().offset, document.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length); } else { logger.warning("No feature stored in this document!"); } return 0f; } public ImageSearchHits search(Document doc, IndexReader reader) throws IOException { SimpleImageSearchHits searchHits = null; OpponentHistogram lireFeature = new OpponentHistogram(); if (doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue() != null && doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length > 0) lireFeature.setByteArrayRepresentation( doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().bytes, doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().offset, doc.getField(DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM).binaryValue().length); double maxDistance = findSimilar(reader, lireFeature); searchHits = new SimpleImageSearchHits(this.docs, (float) maxDistance); return searchHits; } public ImageDuplicates findDuplicates(IndexReader reader) throws IOException { throw new UnsupportedOperationException("not implemented"); } public String toString() { return getClass().getName(); } }