/*
* 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
*
* Updated: 18.01.15 07:31
*/
package net.semanticmetadata.lire.searchers;
import net.semanticmetadata.lire.builders.DocumentBuilder;
import net.semanticmetadata.lire.builders.GlobalDocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.features.GlobalFeature;
import net.semanticmetadata.lire.imageanalysis.features.LireFeature;
import net.semanticmetadata.lire.indexers.parallel.ExtractorItem;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.*;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.TermQuery;
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.Logger;
/**
* A generic image searcher for global features that uses DocValues instead of Lucene text fields. Please make sure you
* created the index using th useDocValues option in
*
* @author Mathias Lux, mathias@juggle.at
*/
public class GenericDocValuesImageSearcher extends AbstractImageSearcher {
protected Logger logger = Logger.getLogger(getClass().getName());
protected String fieldName;
protected LireFeature cachedInstance = null;
protected ExtractorItem extractorItem;
protected IndexReader reader = null;
protected int maxHits = 50;
protected TreeSet<SimpleResult> docs = new TreeSet<SimpleResult>();
protected double maxDistance;
protected boolean useSimilarityScore = false;
private BinaryDocValues docValues = null;
public GenericDocValuesImageSearcher(int maxHits, Class<? extends GlobalFeature> globalFeature, IndexReader reader) {
this.maxHits = maxHits;
this.extractorItem = new ExtractorItem(globalFeature);
this.fieldName = extractorItem.getFieldName();
try {
this.cachedInstance = (GlobalFeature) extractorItem.getExtractorInstance().getClass().newInstance();
} catch (InstantiationException e) {
e.printStackTrace();
} catch (IllegalAccessException e) {
e.printStackTrace();
}
this.reader = reader;
init();
}
protected void init() {
// put all respective features into an in-memory cache ...
if (reader != null) {
try {
docValues = MultiDocValues.getBinaryValues(reader, cachedInstance.getFieldName());
} catch (IOException e) {
e.printStackTrace();
}
}
}
/**
* @param lireFeature
* @return the maximum distance found for normalizing.
* @throws IOException
*/
protected double findSimilar(LireFeature lireFeature) throws IOException {
maxDistance = -1d;
// 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();
// 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.
cachedInstance.setByteArrayRepresentation(docValues.get(i).bytes, docValues.get(i).offset, docValues.get(i).length);
tmpDistance = cachedInstance.getDistance(lireFeature);
assert (tmpDistance >= 0);
// if the array is not full yet:
if (this.docs.size() < maxHits) {
this.docs.add(new SimpleResult(tmpDistance, 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, i));
// and set our new distance border ...
maxDistance = this.docs.last().getDistance();
}
}
return maxDistance;
}
// This is an approach based on DocValues. It's extremely fast, even without caching, but I don't know if it's in
// RAM or not, ie. if I can fill up RAM with all documents at once.
public ImageSearchHits search(int doc) throws IOException {
SimpleImageSearchHits searchHits = null;
LireFeature lireFeature = extractorItem.getFeatureInstance();
// BinaryDocValues binaryValues = MultiDocValues.getBinaryValues(reader, lireFeature.getFieldName());
lireFeature.setByteArrayRepresentation(docValues.get(doc).bytes, docValues.get(doc).offset, docValues.get(doc).length);
double maxDistance = findSimilar(lireFeature);
if (!useSimilarityScore) {
searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
} else {
searchHits = new SimpleImageSearchHits(this.docs, maxDistance, useSimilarityScore);
}
return searchHits;
}
public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
IndexSearcher is = new IndexSearcher(reader);
TermQuery tq = new TermQuery(new Term(DocumentBuilder.FIELD_NAME_IDENTIFIER, doc.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0]));
TopDocs topDocs = is.search(tq, 1);
if (topDocs.totalHits > 0) {
return search(topDocs.scoreDocs[0].doc);
} else return null;
}
public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException {
logger.finer("Starting extraction.");
SimpleImageSearchHits searchHits = null;
GlobalDocumentBuilder globalDocumentBuilder = new GlobalDocumentBuilder();
GlobalFeature globalFeature = globalDocumentBuilder.extractGlobalFeature(image, (GlobalFeature) extractorItem.getExtractorInstance());
double maxDistance = findSimilar(globalFeature);
if (!useSimilarityScore) {
searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
} else {
searchHits = new SimpleImageSearchHits(this.docs, maxDistance, useSimilarityScore);
}
return searchHits;
}
public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
throw new UnsupportedOperationException("not implemented yet.");
}
public String toString() {
return "GenericDocValuesImageSearcher using " + extractorItem.getExtractorClass().getName();
}
}