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: 04.05.13 12:47 */ 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.indexing.hashing.LocalitySensitiveHashing; import org.apache.lucene.document.Document; import org.apache.lucene.index.FieldInvertState; import org.apache.lucene.index.IndexReader; import org.apache.lucene.index.Term; import org.apache.lucene.search.*; import org.apache.lucene.search.similarities.DefaultSimilarity; import java.awt.image.BufferedImage; import java.io.IOException; import java.io.InputStream; import java.util.TreeSet; /** * This class allows for searching based on {@link net.semanticmetadata.lire.indexing.hashing.BitSampling} * HashingMode. First a number of candidates is retrieved from the index, then the candidates are re-ranked. * The number of candidates can be tuned with the numHashedResults parameter in the constructor. The higher * this parameter, the better the results, but the slower the search. * @author Mathias Lux, mathias@juggle.at, 2013-04-12 */ public class LshImageSearcher extends AbstractImageSearcher { private int maxResultsHashBased = 1000; private int maximumHits = 100; private String featureFieldName = DocumentBuilder.FIELD_NAME_OPPONENT_HISTOGRAM; private LireFeature feature; private String hashesFieldName = null; /** * Creates a new searcher for BitSampling based hashes. * @param maximumHits how many hits the searcher shall return. * @param featureFieldName the field hashFunctionsFileName of the feature. * @param hashesFieldName the field hashFunctionsFileName of the hashes. * @param feature an instance of the feature. */ public LshImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, LireFeature feature) { this.maximumHits = maximumHits; this.featureFieldName = featureFieldName; this.hashesFieldName = hashesFieldName; this.feature = feature; try { LocalitySensitiveHashing.readHashFunctions(); } catch (IOException e) { System.err.println("Error reading hash functions from default location."); e.printStackTrace(); } } public LshImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, LireFeature feature, int numHashedResults) { this.maximumHits = maximumHits; this.featureFieldName = featureFieldName; this.hashesFieldName = hashesFieldName; this.feature = feature; maxResultsHashBased = numHashedResults; try { LocalitySensitiveHashing.readHashFunctions(); } catch (IOException e) { System.err.println("Error reading hash functions from default location."); e.printStackTrace(); } } public LshImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, LireFeature feature, InputStream hashes) { this.maximumHits = maximumHits; this.featureFieldName = featureFieldName; this.hashesFieldName = hashesFieldName; this.feature = feature; try { LocalitySensitiveHashing.readHashFunctions(); hashes.close(); } catch (IOException e) { System.err.println("Error reading has functions from given input stream."); e.printStackTrace(); } } public LshImageSearcher(int maximumHits, String featureFieldName, String hashesFieldName, LireFeature feature, InputStream hashes, int numHashedResults) { this.maximumHits = maximumHits; this.featureFieldName = featureFieldName; this.hashesFieldName = hashesFieldName; this.feature = feature; maxResultsHashBased = numHashedResults; try { LocalitySensitiveHashing.readHashFunctions(); hashes.close(); } catch (IOException e) { System.err.println("Error reading has functions from given input stream."); e.printStackTrace(); } } public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException { try { LireFeature queryFeature = feature.getClass().newInstance(); queryFeature.extract(image); int[] ints = LocalitySensitiveHashing.generateHashes(queryFeature.getDoubleHistogram()); String[] hashes = new String[ints.length]; for (int i = 0; i < ints.length; i++) { hashes[i] = Integer.toString(ints[i]); } return search(hashes, queryFeature, reader); } catch (Exception e) { e.printStackTrace(); } return null; } public ImageSearchHits search(Document doc, IndexReader reader) throws IOException { try { LireFeature queryFeature = feature.getClass().newInstance(); queryFeature.setByteArrayRepresentation(doc.getBinaryValue(featureFieldName).bytes, doc.getBinaryValue(featureFieldName).offset, doc.getBinaryValue(featureFieldName).length); return search(doc.getValues(hashesFieldName)[0].split(" "), queryFeature, reader); } catch (Exception e) { e.printStackTrace(); } return null; } private ImageSearchHits search(String[] hashes, LireFeature queryFeature, IndexReader reader) throws IOException { // first search by text: IndexSearcher searcher = new IndexSearcher(reader); searcher.setSimilarity(new DefaultSimilarity() { @Override public float tf(float freq) { return 1; } @Override public float idf(long docFreq, long numDocs) { return 1; } @Override public float coord(int overlap, int maxOverlap) { return 1; } @Override public float queryNorm(float sumOfSquaredWeights) { return 1; } @Override public float sloppyFreq(int distance) { return 1; } @Override public float lengthNorm(FieldInvertState state) { return 1; } }); BooleanQuery query = new BooleanQuery(); for (int i = 0; i < hashes.length; i++) { // be aware that the hashFunctionsFileName of the field must match the one you put the hashes in before. query.add(new BooleanClause(new TermQuery(new Term(hashesFieldName, hashes[i] + "")), BooleanClause.Occur.SHOULD)); } TopDocs docs = searcher.search(query, maxResultsHashBased); // then re-rank TreeSet<SimpleResult> resultScoreDocs = new TreeSet<SimpleResult>(); float maxDistance = 0f; float tmpScore = 0f; for (int i = 0; i < docs.scoreDocs.length; i++) { feature.setByteArrayRepresentation( reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).bytes, reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).offset, reader.document(docs.scoreDocs[i].doc).getBinaryValue(featureFieldName).length); tmpScore = queryFeature.getDistance(feature); if (resultScoreDocs.size() < maximumHits) { resultScoreDocs.add( new SimpleResult(tmpScore, reader.document(docs.scoreDocs[i].doc), docs.scoreDocs[i].doc)); maxDistance = Math.max(maxDistance, tmpScore); } else if (tmpScore < maxDistance) { resultScoreDocs.add( new SimpleResult(tmpScore, reader.document(docs.scoreDocs[i].doc), docs.scoreDocs[i].doc)); } while (resultScoreDocs.size() > maximumHits) { resultScoreDocs.remove(resultScoreDocs.last()); maxDistance = resultScoreDocs.last().getDistance(); } // resultScoreDocs.add(new SimpleResult(tmpScore, reader.document(docs.scoreDocs[i].doc), docs.scoreDocs[i].doc)); } return new SimpleImageSearchHits(resultScoreDocs, maxDistance); } public ImageDuplicates findDuplicates(IndexReader reader) throws IOException { throw new UnsupportedOperationException("not implemented."); } }