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: 13.02.15 19:17 */ package net.semanticmetadata.lire.impl.custom; 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.CEDD; import net.semanticmetadata.lire.imageanalysis.LireFeature; import net.semanticmetadata.lire.impl.SimpleImageSearchHits; import net.semanticmetadata.lire.impl.SimpleResult; import net.semanticmetadata.lire.utils.ImageUtils; import net.semanticmetadata.lire.utils.MetricsUtils; 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.*; import java.util.logging.Level; import java.util.logging.Logger; /** * A ImageSearcher that retrieves just the first result, caches the whole index and optimizes search time by * bundling searches. Please note that as soon as the instance is created, changes in the index are not * reflected. * * @author Mathias Lux, mathias@juggle.at */ public class SingleNddCeddImageSearcher extends AbstractImageSearcher { protected Logger logger = Logger.getLogger(getClass().getName()); Class<?> descriptorClass = CEDD.class; String fieldName = null; protected LireFeature cachedInstance = null; protected boolean isCaching = true; protected ArrayList<double[]> featureCache; protected IndexReader reader; protected TreeSet<SimpleResult> docs; HashMap<double[], LinkedList<Integer>> hashMap; protected double maxDistance; protected boolean useSimilarityScore = false; private boolean halfDimensions = false; /** * Creates a new ImageSearcher for searching just one single image based on CEDD from a RAM cached data set. * * @param reader the index reader pointing to the index. It will be cached first, so changes will not be reflected in this instance. */ public SingleNddCeddImageSearcher(IndexReader reader) { init(reader); } /** * Creates a new ImageSearcher for searching just one single image based on CEDD from a RAM cached data set. * Set approximate to true if you want to speed up search and loose accuracy. * * @param reader the index reader pointing to the index. It will be cached first, so changes will not be reflected in this instance. * @param approximate set to true if you want to trade accuracy to speed, setting to true is faster (~ double speed), but less accurate */ public SingleNddCeddImageSearcher(IndexReader reader, boolean approximate) { this.halfDimensions = approximate; init(reader); } /** * Eventually to be used with other LireFeature classes. * @param reader * @param approximate * @param descriptorClass */ public SingleNddCeddImageSearcher(IndexReader reader, boolean approximate, Class descriptorClass, String fieldName) { this.halfDimensions = approximate; this.descriptorClass = descriptorClass; this.fieldName = fieldName; init(reader); } protected void init(IndexReader reader) { this.reader = reader; if (reader.hasDeletions()) { throw new UnsupportedOperationException( "The index has to be optimized first to be cached! Use IndexWriter.forceMerge(0) to do this."); } docs = new TreeSet<SimpleResult>(); try { this.cachedInstance = (LireFeature) this.descriptorClass.newInstance(); if (fieldName == null) fieldName = this.cachedInstance.getFieldName(); } catch (InstantiationException e) { logger.log(Level.SEVERE, "Error instantiating class for generic image searcher (" + descriptorClass.getName() + "): " + e.getMessage()); } catch (IllegalAccessException e) { logger.log(Level.SEVERE, "Error instantiating class for generic image searcher (" + descriptorClass.getName() + "): " + e.getMessage()); } // put all respective features into an in-memory cache ... if (isCaching && reader != null) { int docs = reader.numDocs(); featureCache = new ArrayList<double[]>(docs); try { Document d; for (int i = 0; i < docs; i++) { d = reader.document(i); cachedInstance.setByteArrayRepresentation(d.getField(fieldName).binaryValue().bytes, d.getField(fieldName).binaryValue().offset, d.getField(fieldName).binaryValue().length); // normalize features,o we can use L1 if (!halfDimensions) { featureCache.add(normalize(cachedInstance.getDoubleHistogram())); } else { featureCache.add(crunch(cachedInstance.getDoubleHistogram())); } } } catch (IOException e) { e.printStackTrace(); } } } private double[] normalize(double[] doubleHistogram) { double[] result = new double[doubleHistogram.length]; for (int i = 0; i < doubleHistogram.length; i++) { result[i] = doubleHistogram[i] / 8d; } return result; } /** * Reduces dimensions of CEDD to half while normalizing the vector. * @param doubleHistogram * @return */ private double[] crunch(double[] doubleHistogram) { double[] result = new double[doubleHistogram.length / 2]; for (int i = 0; i < doubleHistogram.length; i += 2) { result[i / 2] = doubleHistogram[i] + doubleHistogram[i + 1] / 16d; } return result; } public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException { throw new UnsupportedOperationException("not implemented!"); } /** * @param reader * @param lireFeature * @return the maximum distance found for normalizing. * @throws java.io.IOException */ protected float findSimilar(IndexReader reader, LireFeature lireFeature) throws IOException { maxDistance = Double.MAX_VALUE; // clear result set ... docs.clear(); double tmpDistance; // we use the in-memory cache to find the matching docs from the index. int count = 0; double[] doubleHistogram; if (!halfDimensions) { doubleHistogram = normalize(lireFeature.getDoubleHistogram()); } else { doubleHistogram = crunch(lireFeature.getDoubleHistogram()); } double[] tmp; int index = -1; for (Iterator<double[]> iterator = featureCache.iterator(); iterator.hasNext();) { tmp = iterator.next(); tmpDistance = MetricsUtils.distL1(doubleHistogram, tmp); assert (tmpDistance >= 0); if (tmpDistance < maxDistance) { maxDistance = tmpDistance; index = count; } count++; } this.docs.add(new SimpleResult((float) maxDistance, reader.document(index), index)); return (float) maxDistance; } public SimpleResult findMostSimilar(LireFeature lireFeature) throws IOException { findSimilar(reader, lireFeature); return docs.first(); } public SimpleResult[] findMostSimilar(LireFeature[] lireFeature) throws IOException { return findMostSimilar(lireFeature, 0, lireFeature.length); } public SimpleResult[] findMostSimilar(LireFeature[] lireFeature, int offset, int length) throws IOException { double[] maxDistanceArray = new double[length - offset]; Arrays.fill(maxDistanceArray, Double.MAX_VALUE); double tmpDistance; int count = 0; double[][] dhs = new double[0][]; try { dhs = new double[length][featureCache.get(0).length]; } catch (Exception e) { e.printStackTrace(); } for (int i = 0; i < dhs.length; i++) { if (!halfDimensions) { dhs[i] = normalize(lireFeature[offset + i].getDoubleHistogram()); } else { dhs[i] = crunch(lireFeature[offset + i].getDoubleHistogram()); } } double[] tmp; int[] indexes = new int[length]; Arrays.fill(indexes, -1); for (Iterator<double[]> iterator = featureCache.iterator(); iterator.hasNext();) { tmp = iterator.next(); for (int i = 0; i < dhs.length; i++) { tmpDistance = MetricsUtils.distL1(dhs[i], tmp); assert (tmpDistance >= 0); if (tmpDistance < maxDistanceArray[i]) { maxDistanceArray[i] = tmpDistance; indexes[i] = count; } } count++; } SimpleResult[] results = new SimpleResult[length]; for (int i = 0; i < results.length; i++) { if (indexes[i] >= 0 && indexes[i] < reader.maxDoc()) results[i] = new SimpleResult((float) maxDistanceArray[i], reader.document(indexes[i]), indexes[i]); else results[i] = null; } return results; } /** * Main similarity method called for each and every document in the index. * * @param document * @param lireFeature * @return the distance between the given feature and the feature stored in the document. */ protected float getDistance(Document document, LireFeature lireFeature) { if (document.getField(fieldName).binaryValue() != null && document.getField(fieldName).binaryValue().length > 0) { cachedInstance.setByteArrayRepresentation(document.getField(fieldName).binaryValue().bytes, document.getField(fieldName).binaryValue().offset, document.getField(fieldName).binaryValue().length); return lireFeature.getDistance(cachedInstance); } else { logger.warning("No feature stored in this document! (" + descriptorClass.getName() + ")"); } return 0f; } public ImageSearchHits search(Document doc, IndexReader reader) throws IOException { SimpleImageSearchHits searchHits = null; try { LireFeature lireFeature = (LireFeature) descriptorClass.newInstance(); if (doc.getField(fieldName).binaryValue() != null && doc.getField(fieldName).binaryValue().length > 0) lireFeature.setByteArrayRepresentation(doc.getField(fieldName).binaryValue().bytes, doc.getField(fieldName).binaryValue().offset, doc.getField(fieldName).binaryValue().length); float maxDistance = findSimilar(reader, lireFeature); if (!useSimilarityScore) { searchHits = new SimpleImageSearchHits(this.docs, maxDistance); } else { searchHits = new SimpleImageSearchHits(this.docs, maxDistance, useSimilarityScore); } } 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 ImageDuplicates findDuplicates(IndexReader reader) throws IOException { throw new UnsupportedOperationException("not implemented!"); } public String toString() { return "GenericSearcher using " + descriptorClass.getName(); } }