/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.tika.parser.recognition;
import org.apache.tika.config.Field;
import org.apache.tika.config.Initializable;
import org.apache.tika.config.Param;
import org.apache.tika.exception.TikaConfigException;
import org.apache.tika.exception.TikaException;
import org.apache.tika.metadata.Metadata;
import org.apache.tika.mime.MediaType;
import org.apache.tika.parser.AbstractParser;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.parser.recognition.tf.TensorflowRESTRecogniser;
import org.apache.tika.sax.XHTMLContentHandler;
import org.apache.tika.utils.AnnotationUtils;
import org.apache.tika.utils.ServiceLoaderUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.xml.sax.ContentHandler;
import org.xml.sax.SAXException;
import java.io.IOException;
import java.io.InputStream;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import java.util.ArrayList;
import java.util.Locale;
import java.util.Map;
import java.util.Set;
/**
* This parser recognises objects from Images.
* The Object Recognition implementation can be switched using 'class' argument.
* <p>
* <b>Example Usage : </b>
* <pre>
* <properties>
* <parsers>
* <parser class="org.apache.tika.parser.recognition.ObjectRecognitionParser">
* <mime>image/jpeg</mime>
* <params>
* <param name="topN" type="int">2</param>
* <param name="minConfidence" type="double">0.015</param>
* <param name="class" type="string">org.apache.tika.parser.recognition.tf.TensorflowRESTRecogniser</param>
* </params>
* </parser>
* </parsers>
* </properties>
* </pre>
*
* @since Apache Tika 1.14
*/
public class ObjectRecognitionParser extends AbstractParser implements Initializable {
private static final Logger LOG = LoggerFactory.getLogger(ObjectRecognitionParser.class);
public static final String MD_KEY = "OBJECT";
public static final String MD_REC_IMPL_KEY =
ObjectRecognitionParser.class.getPackage().getName() + ".object.rec.impl";
private static final Comparator<RecognisedObject> DESC_CONFIDENCE_SORTER =
new Comparator<RecognisedObject>() {
@Override
public int compare(RecognisedObject o1, RecognisedObject o2) {
return Double.compare(o2.getConfidence(), o1.getConfidence());
}
};
@Field
private double minConfidence = 0.05;
@Field
private int topN = 2;
private ObjectRecogniser recogniser = new TensorflowRESTRecogniser();
@Field(name = "class")
public void setRecogniser(String recogniserClass) {
this.recogniser = ServiceLoaderUtils.newInstance(recogniserClass);
}
@Override
public void initialize(Map<String, Param> params) throws TikaConfigException {
AnnotationUtils.assignFieldParams(recogniser, params);
recogniser.initialize(params);
LOG.info("minConfidence = {}, topN={}", minConfidence, topN);
LOG.info("Recogniser = {}", recogniser.getClass().getName());
LOG.info("Recogniser Available = {}", recogniser.isAvailable());
}
@Override
public Set<MediaType> getSupportedTypes(ParseContext context) {
return recogniser.isAvailable() ? recogniser.getSupportedMimes() : Collections.<MediaType>emptySet();
}
@Override
public synchronized void parse(InputStream stream, ContentHandler handler, Metadata metadata, ParseContext context)
throws IOException, SAXException, TikaException {
if (!recogniser.isAvailable()) {
LOG.warn("{} is not available for service", recogniser.getClass());
return;
}
metadata.set(MD_REC_IMPL_KEY, recogniser.getClass().getName());
long start = System.currentTimeMillis();
List<RecognisedObject> objects = recogniser.recognise(stream, handler, metadata, context);
LOG.debug("Found {} objects", objects != null ? objects.size() : 0);
LOG.debug("Time taken {}ms", System.currentTimeMillis() - start);
if (objects != null && !objects.isEmpty()) {
Collections.sort(objects, DESC_CONFIDENCE_SORTER);
int count = 0;
List<RecognisedObject> acceptedObjects = new ArrayList<RecognisedObject>(topN);
// first process all the MD objects
for (RecognisedObject object: objects){
if (object.getConfidence() >= minConfidence) {
if (object.getConfidence() >= minConfidence) {
count++;
LOG.debug("Add {}", object);
String mdValue = String.format(Locale.ENGLISH, "%s (%.5f)",
object.getLabel(), object.getConfidence());
metadata.add(MD_KEY, mdValue);
acceptedObjects.add(object);
if (count >= topN) {
break;
}
}
else{
LOG.warn("Object {} confidence {} less than min {}", object, object.getConfidence(), minConfidence);
}
}
}
// now the handler
XHTMLContentHandler xhtml = new XHTMLContentHandler(handler, metadata);
xhtml.startDocument();
xhtml.startElement("ol", "id", "objects");
count = 0;
for (RecognisedObject object : acceptedObjects) {
//writing to handler
xhtml.startElement("li", "id", object.getId());
String text = String.format(Locale.ENGLISH, " %s [%s](confidence = %f )",
object.getLabel(), object.getLabelLang(), object.getConfidence());
xhtml.characters(text);
xhtml.endElement("li");
}
xhtml.endElement("ol");
xhtml.endDocument();
} else {
LOG.warn("NO objects");
metadata.add("no.objects", Boolean.TRUE.toString());
}
}
}