Java tutorial
/* * This file is part of the LIRE project: http://lire-project.net * 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.searchers.custom; import net.semanticmetadata.lire.builders.DocumentBuilder; import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature; import net.semanticmetadata.lire.searchers.ImageSearchHits; import net.semanticmetadata.lire.searchers.SimpleImageSearchHits; import net.semanticmetadata.lire.searchers.SimpleResult; 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.search.TopDocs; 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 * * TODO: revisit for performance, feature caching, etc. * * @author Mathias Lux, mathias@juggle.at */ public class TopDocsImageSearcher { protected Logger logger = Logger.getLogger(getClass().getName()); Class<?> descriptorClass; String fieldName; private int maxHits = 10; protected TreeSet<SimpleResult> docs; public TopDocsImageSearcher(int maxHits, Class<?> descriptorClass, String fieldName) { this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.descriptorClass = descriptorClass; this.fieldName = fieldName; } public ImageSearchHits search(BufferedImage image, IndexReader reader, TopDocs results) throws IOException { logger.finer("Starting extraction."); GlobalFeature globalFeature = null; SimpleImageSearchHits searchHits = null; try { globalFeature = (GlobalFeature) descriptorClass.newInstance(); // Scaling image is especially with the correlogram features very important! BufferedImage bimg = image; if (Math.max(image.getHeight(), image.getWidth()) > DocumentBuilder.MAX_IMAGE_DIMENSION) { bimg = ImageUtils.scaleImage(image, DocumentBuilder.MAX_IMAGE_DIMENSION); } globalFeature.extract(bimg); logger.fine("Extraction from image finished"); double maxDistance = findSimilar(results, reader, globalFeature); searchHits = new SimpleImageSearchHits(this.docs, maxDistance); } 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 searchHits; } /** * @param results * @param reader * @param globalFeature * @return the maximum distance found for normalizing. * @throws java.io.IOException */ protected double findSimilar(TopDocs results, IndexReader reader, GlobalFeature globalFeature) throws IOException { double maxDistance = -1d, overallMaxDistance = -1d; boolean hasDeletions = reader.hasDeletions(); // clear result set ... docs.clear(); // Needed for check whether the document is deleted. Bits liveDocs = MultiFields.getLiveDocs(reader); int docs = results.totalHits; 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(results.scoreDocs[i].doc); double distance = getDistance(d, globalFeature); assert (distance >= 0); // calculate the overall max distance to normalize score afterwards if (overallMaxDistance < distance) { overallMaxDistance = distance; } // if it is the first document: if (maxDistance < 0) { maxDistance = distance; } // if the array is not full yet: if (this.docs.size() < maxHits) { this.docs.add(new SimpleResult(distance, results.scoreDocs[i].doc)); if (distance > maxDistance) maxDistance = distance; } else if (distance < 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(distance, results.scoreDocs[i].doc)); // and set our new distance border ... maxDistance = this.docs.last().getDistance(); } } return maxDistance; } protected double getDistance(Document d, GlobalFeature globalFeature) { double distance = 0d; GlobalFeature lf; try { lf = (GlobalFeature) descriptorClass.newInstance(); lf.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length); distance = globalFeature.getDistance(lf); } 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(TopDocs results, Document d, IndexReader reader) throws IOException { SimpleImageSearchHits searchHits = null; try { GlobalFeature lf;// = (GlobalFeature) descriptorClass.newInstance(); lf = (GlobalFeature) descriptorClass.newInstance(); lf.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length); double maxDistance = findSimilar(results, reader, lf); searchHits = new SimpleImageSearchHits(this.docs, maxDistance); } 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 searchHits; } public String toString() { return "TopDocsImageSearcher using " + descriptorClass.getName(); } }