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: 11.07.13 11:12 */ package net.semanticmetadata.lire.impl.searcher; import java.awt.image.BufferedImage; import java.io.IOException; import java.util.HashMap; import java.util.Iterator; import java.util.LinkedList; import java.util.List; import java.util.TreeSet; import java.util.logging.Level; import java.util.logging.Logger; 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.impl.SimpleImageDuplicates; import net.semanticmetadata.lire.impl.SimpleImageSearchHits; import net.semanticmetadata.lire.impl.SimpleResult; import net.semanticmetadata.lire.impl.docbuilder.GenericDocumentBuilder; import net.semanticmetadata.lire.indexing.parallel.ImageInfo; 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; /** * 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 GenericFastImageSearcher extends AbstractImageSearcher { protected Logger logger = Logger.getLogger(getClass().getName()); Class<?> descriptorClass; String fieldName; private LireFeature cachedInstance = null; private boolean isCaching = false; private LinkedList<byte[]> featureCache; private IndexReader reader; private int maxHits = 10; protected TreeSet<SimpleResult> docs; private float maxDistance; /** * Creates a new ImageSearcher for the given feature. * * @param maxHits the maximum number of hits * @param descriptorClass the feature class. It has to implement {@link LireFeature} * @param fieldName a custom field name for the index. * @see LireFeature */ public GenericFastImageSearcher(int maxHits, Class<?> descriptorClass, String fieldName) { this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.descriptorClass = descriptorClass; this.fieldName = fieldName; try { this.cachedInstance = (LireFeature) this.descriptorClass.newInstance(); } 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()); } init(); } /** * Creates a new ImageSearcher for the given feature. * * @param maxHits the maximum number of hits * @param descriptorClass the feature class. It has to implement {@link LireFeature} * @see LireFeature */ public GenericFastImageSearcher(int maxHits, Class<?> descriptorClass) { this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.descriptorClass = descriptorClass; try { this.cachedInstance = (LireFeature) this.descriptorClass.newInstance(); } 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()); } this.fieldName = cachedInstance.getFieldName(); init(); } private void init() { // put all respective features into an in-memory cache ... if (isCaching && reader != null) { int docs = reader.numDocs(); featureCache = new LinkedList<byte[]>(); 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); featureCache.add(cachedInstance.getByteArrayRepresentation()); } } catch (IOException e) { e.printStackTrace(); } } } /** * Creates a n ImageSearcher for the given feature. If isCaching is set to true, the features will be hold in memory, * which speeds up search significantly. However, this takes sometimes a lot of memory, so use it carefully. * @param maxHits the maximum number of hits * @param descriptorClass the feature class. It has to implement {@link LireFeature} * @param fieldName a custom field name for the index. * @param isCaching set to true if you want to search in-memory. * @param reader the IndexReader used for accessing the index. */ public GenericFastImageSearcher(int maxHits, Class<?> descriptorClass, String fieldName, boolean isCaching, IndexReader reader) { this.isCaching = isCaching; this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.descriptorClass = descriptorClass; this.fieldName = fieldName; try { this.cachedInstance = (LireFeature) this.descriptorClass.newInstance(); } 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()); } this.reader = reader; init(); } /** * Creates a n ImageSearcher for the given feature. If isCaching is set to true, the features will be hold in memory, * which speeds up search significantly. However, this takes sometimes a lot of memory, so use it carefully. * @param maxHits the maximum number of hits * @param descriptorClass the feature class. It has to implement {@link LireFeature} * @param isCaching * @param reader reader the IndexReader used for accessing the index. */ public GenericFastImageSearcher(int maxHits, Class<?> descriptorClass, boolean isCaching, IndexReader reader) { this.isCaching = isCaching; this.maxHits = maxHits; docs = new TreeSet<SimpleResult>(); this.descriptorClass = descriptorClass; try { this.cachedInstance = (LireFeature) this.descriptorClass.newInstance(); } 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()); } this.reader = reader; this.fieldName = cachedInstance.getFieldName(); init(); } public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException { return this.search(image, null, reader); } public ImageSearchHits search(BufferedImage image, ImageInfo imageInfo, IndexReader reader) throws IOException { if (image == null) return null; logger.finer("Starting extraction."); LireFeature lireFeature = null; SimpleImageSearchHits searchHits = null; try { lireFeature = (LireFeature) descriptorClass.newInstance(); // 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"); float maxDistance = findSimilar(reader, lireFeature); 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 reader * @param lireFeature * @return the maximum distance found for normalizing. * @throws java.io.IOException */ protected float findSimilar(IndexReader reader, LireFeature lireFeature) throws IOException { maxDistance = -1f; // overallMaxDistance = -1f; // clear result set ... docs.clear(); // Needed for check whether the document is deleted. Bits liveDocs = MultiFields.getLiveDocs(reader); Document d; float tmpDistance; int docs = reader.numDocs(); if (!isCaching) { // we read each and every document from the index and then we compare it to the query. 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, lireFeature); assert (tmpDistance >= 0); // 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(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(tmpDistance, d, i)); // and set our new distance border ... maxDistance = this.docs.last().getDistance(); } } } else { // we use the in-memory cache to find the matching docs from the index. int count = 0; for (Iterator<byte[]> iterator = featureCache.iterator(); iterator.hasNext();) { cachedInstance.setByteArrayRepresentation(iterator.next()); if (reader.hasDeletions() && !liveDocs.get(count)) { count++; continue; // if it is deleted, just ignore it. } else { tmpDistance = lireFeature.getDistance(cachedInstance); assert (tmpDistance >= 0); // 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(tmpDistance, reader.document(count), count)); 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(tmpDistance, reader.document(count), count)); // and set our new distance border ... maxDistance = this.docs.last().getDistance(); } count++; } } } return maxDistance; } /** * 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); 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 ImageDuplicates findDuplicates(IndexReader reader) throws IOException { // get the first document: SimpleImageDuplicates simpleImageDuplicates = null; try { // if (!IndexReader.indexExists(reader.directory())) // throw new FileNotFoundException("No index found at this specific location."); Document doc = reader.document(0); 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); HashMap<Float, List<String>> duplicates = new HashMap<Float, List<String>>(); // Needed for check whether the document is deleted. Bits liveDocs = MultiFields.getLiveDocs(reader); int docs = reader.numDocs(); int numDuplicates = 0; 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); if (!duplicates.containsKey(distance)) { duplicates.put(distance, new LinkedList<String>()); } else { numDuplicates++; } duplicates.get(distance).add(d.getField(DocumentBuilder.FIELD_NAME_IDENTIFIER).stringValue()); } if (numDuplicates == 0) return null; LinkedList<List<String>> results = new LinkedList<List<String>>(); for (float f : duplicates.keySet()) { if (duplicates.get(f).size() > 1) { results.add(duplicates.get(f)); } } simpleImageDuplicates = new SimpleImageDuplicates(results); } 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 simpleImageDuplicates; } public String toString() { return "GenericSearcher using " + descriptorClass.getName(); } }