
org.apache.tika.parser.recognition.tf.TensorflowRESTRecogniser Maven / Gradle / Ivy
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* 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,
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* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.tika.parser.recognition.tf;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.net.URI;
import java.util.ArrayList;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Set;
import java.util.Collections;
import java.util.HashSet;
import java.util.Arrays;
import org.apache.http.HttpResponse;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.methods.HttpPost;
import org.apache.http.entity.ByteArrayEntity;
import org.apache.http.impl.client.DefaultHttpClient;
import org.apache.tika.config.Field;
import org.apache.tika.config.InitializableProblemHandler;
import org.apache.tika.config.Param;
import org.apache.tika.exception.TikaConfigException;
import org.apache.tika.exception.TikaException;
import org.apache.tika.io.IOUtils;
import org.apache.tika.metadata.Metadata;
import org.apache.tika.mime.MediaType;
import org.apache.tika.parser.ParseContext;
import org.apache.tika.parser.recognition.ObjectRecogniser;
import org.apache.tika.parser.recognition.RecognisedObject;
import com.github.openjson.JSONArray;
import com.github.openjson.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.xml.sax.ContentHandler;
import org.xml.sax.SAXException;
/**
* Tensor Flow image recogniser which has high performance.
* This implementation uses Tensorflow via REST API.
*
* NOTE : https://wiki.apache.org/tika/TikaAndVision
*
* @since Apache Tika 1.14
*/
public class TensorflowRESTRecogniser implements ObjectRecogniser {
/**
* Some variables are protected, because this class is extended by TensorflowRESTVideoRecognizer class
*/
private static final Logger LOG = LoggerFactory.getLogger(TensorflowRESTRecogniser.class);
private static final Set SUPPORTED_MIMES = Collections.unmodifiableSet(
new HashSet<>(Arrays.asList(new MediaType[]{
MediaType.image("jpeg"),
MediaType.image("png"),
MediaType.image("gif")
})));
protected static final String LABEL_LANG = "eng";
@Field
protected URI apiBaseUri = URI.create("http://localhost:8764/inception/v4");
@Field
protected int topN = 2;
@Field
protected double minConfidence = 0.015;
protected URI apiUri;
protected URI healthUri;
protected boolean available;
protected URI getApiUri(Metadata metadata) {
return apiUri;
}
@Override
public Set getSupportedMimes() {
return SUPPORTED_MIMES;
}
@Override
public boolean isAvailable() {
return available;
}
@Override
public void initialize(Map params) throws TikaConfigException {
try {
healthUri = URI.create(apiBaseUri + "/ping");
apiUri = URI.create(apiBaseUri + String.format(Locale.getDefault(), "/classify/image?topn=%1$d&min_confidence=%2$f",
topN, minConfidence));
DefaultHttpClient client = new DefaultHttpClient();
HttpResponse response = client.execute(new HttpGet(healthUri));
available = response.getStatusLine().getStatusCode() == 200;
LOG.info("Available = {}, API Status = {}", available, response.getStatusLine());
LOG.info("topN = {}, minConfidence = {}", topN, minConfidence);
} catch (Exception e) {
available = false;
throw new TikaConfigException(e.getMessage(), e);
}
}
@Override
public void checkInitialization(InitializableProblemHandler handler)
throws TikaConfigException {
//TODO -- what do we want to check?
}
@Override
public List recognise(InputStream stream,
ContentHandler handler, Metadata metadata, ParseContext context)
throws IOException, SAXException, TikaException {
List recObjs = new ArrayList<>();
try {
DefaultHttpClient client = new DefaultHttpClient();
HttpPost request = new HttpPost(getApiUri(metadata));
try (ByteArrayOutputStream byteStream = new ByteArrayOutputStream()) {
//TODO: convert this to stream, this might cause OOM issue
// InputStreamEntity is not working
// request.setEntity(new InputStreamEntity(stream, -1));
IOUtils.copy(stream, byteStream);
request.setEntity(new ByteArrayEntity(byteStream.toByteArray()));
}
HttpResponse response = client.execute(request);
try (InputStream reply = response.getEntity().getContent()) {
String replyMessage = IOUtils.toString(reply);
if (response.getStatusLine().getStatusCode() == 200) {
JSONObject jReply = new JSONObject(replyMessage);
JSONArray jClasses = jReply.getJSONArray("classnames");
JSONArray jConfidence = jReply.getJSONArray("confidence");
if (jClasses.length() != jConfidence.length()) {
LOG.warn("Classes of size {} is not equal to confidence of size {}", jClasses.length(), jConfidence.length());
}
assert jClasses.length() == jConfidence.length();
for (int i = 0; i < jClasses.length(); i++) {
RecognisedObject recObj = new RecognisedObject(jClasses.getString(i),
LABEL_LANG, jClasses.getString(i), jConfidence.getDouble(i));
recObjs.add(recObj);
}
} else {
LOG.warn("Status = {}", response.getStatusLine());
LOG.warn("Response = {}", replyMessage);
}
}
} catch (Exception e) {
LOG.warn(e.getMessage(), e);
}
LOG.debug("Num Objects found {}", recObjs.size());
return recObjs;
}
}