[
https://issues.apache.org/jira/browse/TIKA-2400?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16261065#comment-16261065
]
ASF GitHub Bot commented on TIKA-2400:
--------------------------------------
chrismattmann closed pull request #208: Fix for TIKA-2400 Standardizing current
Object Recognition REST parsers
URL: https://github.com/apache/tika/pull/208
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git
a/tika-parsers/src/main/java/org/apache/tika/parser/captioning/tf/TensorflowRESTCaptioner.java
b/tika-parsers/src/main/java/org/apache/tika/parser/captioning/tf/TensorflowRESTCaptioner.java
index d49ef0fed..5fd9d9a97 100644
---
a/tika-parsers/src/main/java/org/apache/tika/parser/captioning/tf/TensorflowRESTCaptioner.java
+++
b/tika-parsers/src/main/java/org/apache/tika/parser/captioning/tf/TensorflowRESTCaptioner.java
@@ -72,16 +72,16 @@
MediaType.image("gif")
})));
- private static final String LABEL_LANG = "en";
+ private static final String LABEL_LANG = "eng";
@Field
- private URI apiBaseUri;
+ private URI apiBaseUri = URI.create("http://localhost:8764/inception/v3";);
@Field
- private int captions;
+ private int captions = 5;
@Field
- private int maxCaptionLength;
+ private int maxCaptionLength = 15;
private URI apiUri;
@@ -107,7 +107,7 @@ public boolean isAvailable() {
public void initialize(Map<String, Param> params) throws
TikaConfigException {
try {
healthUri = URI.create(apiBaseUri + "/ping");
- apiUri = URI.create(apiBaseUri +
String.format(Locale.getDefault(),
"/captions?beam_size=%1$d&max_caption_length=%2$d",
+ apiUri = URI.create(apiBaseUri +
String.format(Locale.getDefault(),
"/caption/image?beam_size=%1$d&max_caption_length=%2$d",
captions, maxCaptionLength));
DefaultHttpClient client = new DefaultHttpClient();
diff --git
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/ObjectRecognitionParser.java
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/ObjectRecognitionParser.java
index 37caf4538..a5a126ba9 100644
---
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/ObjectRecognitionParser.java
+++
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/ObjectRecognitionParser.java
@@ -55,11 +55,9 @@
* <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>
+ * <param name="class"
type="string">org.apache.tika.parser.captioning.tf.TensorflowRESTCaptioner</param>
* </params>
* </parser>
* </parsers>
@@ -83,12 +81,6 @@ public int compare(RecognisedObject o1, RecognisedObject o2)
{
}
};
- @Field
- private double minConfidence = 0.05;
-
- @Field
- private int topN = 2;
-
private ObjectRecogniser recogniser;
@Field(name = "class")
@@ -102,7 +94,6 @@ public void initialize(Map<String, Param> params) throws
TikaConfigException {
recogniser.initialize(params);
LOG.info("Recogniser = {}", recogniser.getClass().getName());
LOG.info("Recogniser Available = {}", recogniser.isAvailable());
- LOG.info("minConfidence = {}, topN={}", minConfidence, topN);
}
@Override
@@ -140,29 +131,17 @@ public synchronized void parse(InputStream stream,
ContentHandler handler, Metad
for (RecognisedObject object : objects) {
if (object instanceof CaptionObject) {
if (xhtmlStartVal == null) xhtmlStartVal = "captions";
- LOG.debug("Add {}", object);
- String mdValue = String.format(Locale.ENGLISH, "%s (%.5f)",
- object.getLabel(), object.getConfidence());
- metadata.add(MD_KEY_IMG_CAP, mdValue);
- acceptedObjects.add(object);
+ String labelAndConfidence = String.format(Locale.ENGLISH,
"%s (%.5f)", object.getLabel(), object.getConfidence());
+ metadata.add(MD_KEY_IMG_CAP, labelAndConfidence);
xhtmlIds.add(String.valueOf(count++));
} else {
if (xhtmlStartVal == null) xhtmlStartVal = "objects";
- if (object.getConfidence() >= minConfidence) {
- count++;
- LOG.info("Add {}", object);
- String mdValue = String.format(Locale.ENGLISH, "%s
(%.5f)",
- object.getLabel(), object.getConfidence());
- metadata.add(MD_KEY_OBJ_REC, mdValue);
- acceptedObjects.add(object);
- xhtmlIds.add(object.getId());
- if (count >= topN) {
- break;
- }
- } else {
- LOG.warn("Object {} confidence {} less than min {}",
object, object.getConfidence(), minConfidence);
- }
+ String labelAndConfidence = String.format(Locale.ENGLISH,
"%s (%.5f)", object.getLabel(), object.getConfidence());
+ metadata.add(MD_KEY_OBJ_REC, labelAndConfidence);
+ xhtmlIds.add(object.getId());
}
+ LOG.info("Add {}", object);
+ acceptedObjects.add(object);
}
XHTMLContentHandler xhtml = new XHTMLContentHandler(handler,
metadata);
xhtml.startDocument();
@@ -183,4 +162,4 @@ public synchronized void parse(InputStream stream,
ContentHandler handler, Metad
metadata.add("no.objects", Boolean.TRUE.toString());
}
}
-}
+}
\ No newline at end of file
diff --git
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTRecogniser.java
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTRecogniser.java
index f45c2a95c..c83ea1eb0 100644
---
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTRecogniser.java
+++
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTRecogniser.java
@@ -23,6 +23,7 @@
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;
@@ -56,11 +57,16 @@
* Tensor Flow image recogniser which has high performance.
* This implementation uses Tensorflow via REST API.
* <p>
- * NOTE : //TODO: link to wiki page here
+ * 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<MediaType> SUPPORTED_MIMES =
Collections.unmodifiableSet(
@@ -70,22 +76,27 @@
MediaType.image("gif")
})));
- /**
- * Maximum buffer size for image
- */
- private static final String LABEL_LANG = "en";
+ protected static final String LABEL_LANG = "eng";
+
+ @Field
+ protected URI apiBaseUri =
URI.create("http://localhost:8764/inception/v4";);
@Field
- private URI apiUri =
URI.create("http://localhost:8764/inception/v4/classify?topk=10";);
+ protected int topN = 2;
+
@Field
- private URI healthUri =
URI.create("http://localhost:8764/inception/v4/ping";);
+ protected double minConfidence = 0.015;
- private boolean available;
-
- protected URI getApiUri(Metadata metadata){
- return apiUri;
+ protected URI apiUri;
+
+ protected URI healthUri;
+
+ protected boolean available;
+
+ protected URI getApiUri(Metadata metadata) {
+ return apiUri;
}
-
+
@Override
public Set<MediaType> getSupportedMimes() {
return SUPPORTED_MIMES;
@@ -99,10 +110,16 @@ public boolean isAvailable() {
@Override
public void initialize(Map<String, Param> 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);
@@ -140,9 +157,9 @@ public void checkInitialization(InitializableProblemHandler
handler)
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());
- }
+ 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),
@@ -160,4 +177,4 @@ public void checkInitialization(InitializableProblemHandler
handler)
LOG.debug("Num Objects found {}", recObjs.size());
return recObjs;
}
-}
+}
\ No newline at end of file
diff --git
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTVideoRecogniser.java
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTVideoRecogniser.java
index ec16c7895..2e7b1f156 100644
---
a/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTVideoRecogniser.java
+++
b/tika-parsers/src/main/java/org/apache/tika/parser/recognition/tf/TensorflowRESTVideoRecogniser.java
@@ -17,63 +17,91 @@
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.Collections;
+import java.util.Locale;
+import java.util.Map;
import java.util.Set;
+import java.util.Collections;
+import java.util.HashSet;
import javax.ws.rs.core.UriBuilder;
+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.Tika;
import org.apache.tika.config.Field;
+import org.apache.tika.config.Param;
import org.apache.tika.config.TikaConfig;
+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.mime.MimeType;
import org.apache.tika.mime.MimeTypeException;
+import org.apache.tika.parser.ParseContext;
+import org.apache.tika.parser.recognition.RecognisedObject;
+import org.json.JSONArray;
+import org.json.JSONObject;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
+import org.xml.sax.ContentHandler;
+import org.xml.sax.SAXException;
/**
* Tensor Flow video recogniser which has high performance.
* This implementation uses Tensorflow via REST API.
* <p>
- * NOTE : //TODO: link to wiki page here
+ * NOTE : https://wiki.apache.org/tika/TikaAndVisionVideo
*
* @since Apache Tika 1.15
*/
-public class TensorflowRESTVideoRecogniser extends TensorflowRESTRecogniser{
+public class TensorflowRESTVideoRecogniser extends TensorflowRESTRecogniser {
- private static final Logger LOG =
LoggerFactory.getLogger(TensorflowRESTRecogniser.class);
+ private static final Logger LOG =
LoggerFactory.getLogger(TensorflowRESTVideoRecogniser.class);
- private static final Set<MediaType> SUPPORTED_MIMES =
Collections.singleton(MediaType.video("mp4"));;
+ private static final Set<MediaType> SUPPORTED_MIMES =
Collections.singleton(MediaType.video("mp4"));
@Field
- private URI apiUri =
URI.create("http://localhost:8764/inception/v4/classify/video?topk=10";);
+ private String mode = "fixed";
@Override
- public Set<MediaType> getSupportedMimes() {
- return SUPPORTED_MIMES;
+ protected URI getApiUri(Metadata metadata) {
+ TikaConfig config = TikaConfig.getDefaultConfig();
+ String ext = null;
+ //Find extension for video. It's required for OpenCV in InceptionAPI
to decode video
+ try {
+ MimeType mimeType =
config.getMimeRepository().forName(metadata.get("Content-Type"));
+ ext = mimeType.getExtension();
+ return UriBuilder.fromUri(apiUri).queryParam("ext", ext).build();
+ } catch (MimeTypeException e) {
+ LOG.error("Can't find extension from metadata");
+ return apiUri;
+ }
}
-
+
@Override
- protected URI getApiUri(Metadata metadata){
-
- TikaConfig config = TikaConfig.getDefaultConfig();
- String ext = null;
- //Find extension for video. It's required for OpenCv in InceptionAPI to
decode video
- try {
- MimeType mimeType =
config.getMimeRepository().forName(metadata.get("Content-Type"));
- ext = mimeType.getExtension();
-
- return UriBuilder.fromUri(apiUri).queryParam("ext",
ext).build();
-
- } catch (MimeTypeException e) {
- LOG.error("Can't find extension from metadata");
- return apiUri;
- }
+ public void initialize(Map<String, Param> params) throws
TikaConfigException {
+ try {
+ healthUri = URI.create(apiBaseUri + "/ping");
+ apiUri = URI.create(apiBaseUri +
String.format(Locale.getDefault(),
"/classify/video?topn=%1$d&min_confidence=%2$f&mode=%3$s",
+ topN, minConfidence, mode));
+
+ 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);
+ }
}
-
-
}
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/Im2txtRestDockerfile
b/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/Im2txtRestDockerfile
index 93354c46b..1d9016dd6 100644
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/Im2txtRestDockerfile
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/Im2txtRestDockerfile
@@ -15,54 +15,46 @@
# limitations under the License.
#
-FROM inception-rest-tika
+FROM uscdatascience/tf-tika-base
MAINTAINER Apache Tika Team
-# Install python pillow. TODO: Remove this with the fix for TIKA 2398
-RUN pip install pillow
-
# Download the pretrained im2txt checkpoint
WORKDIR /usr/share/apache-tika/models/dl/image/caption/
-RUN \
+RUN echo "We're downloading the checkpoint file for image captioning, the
shell might look unresponsive. Please be patient." && \
# To get rid of early EOF error
git config --global http.postBuffer 1048576000 && \
- echo "We're downloading the checkpoint file for image captioning, the
shell might look unresponsive. Please be patient." && \
- git clone -b models https://github.com/USCDataScience/img2text.git && \
+ git clone https://github.com/USCDataScience/img2text.git && \
# Join the parts
- cat img2text/models/1M_iters_ckpt_parts_a* >1M_iters_ckpt.tar.gz && \
- tar -xzvf 1M_iters_ckpt.tar.gz && \
- # Delete all files except 1M_iters_ckpt
- rm -rf {1M_iters_ckpt.tar.gz,img2text}
+ cat img2text/models/1M_iters_ckpt_parts_* >1M_iters_ckpt.tar.gz && \
+ tar -xzvf 1M_iters_ckpt.tar.gz && rm -rf 1M_iters_ckpt.tar.gz
-RUN \
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/model_info.xml
\
- -O model_info.xml
+RUN curl -O
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/model_info.xml
# Download server related source files
WORKDIR /usr/share/apache-tika/src/dl/image/caption/
-RUN \
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/caption_generator.py
\
- -O caption_generator.py && \
-
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
\
- -O im2txtapi.py && \
-
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/model_wrapper.py
\
- -O model_wrapper.py && \
-
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/vocabulary.py
\
- -O vocabulary.py && \
-
+RUN curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/caption_generator.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/model_wrapper.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/vocabulary.py
&& \
chmod +x im2txtapi.py
WORKDIR /
+# Remove unnecessary packages comes with base
+RUN apt-get -y remove \
+ apt-utils \
+ curl \
+ git \
+ software-properties-common \
+ unzip \
+ wget
+
# Add symbolic link to im2txtapi.py
RUN ln -s /usr/share/apache-tika/src/dl/image/caption/im2txtapi.py
/usr/bin/im2txtapi
# expose API port, this is the default port
EXPOSE 8764
-CMD im2txtapi
\ No newline at end of file
+CMD im2txtapi
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
b/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
index 75f5e8775..97f1f2afd 100644
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/captioning/tf/im2txtapi.py
@@ -34,16 +34,19 @@
from __future__ import division
from __future__ import print_function
+import json
+import logging
import math
import requests
-import xml.etree.ElementTree as ET
-import logging
import sys
-from time import time
-from PIL import Image
+
+from flask import Flask, request, Response, jsonify
from io import BytesIO
-import flask
+from PIL import Image
+from time import time
+
import tensorflow as tf
+import xml.etree.ElementTree as ET
import model_wrapper
import vocabulary
@@ -94,7 +97,7 @@
tf.logging.set_verbosity(tf.logging.INFO)
-class Initializer(flask.Flask):
+class Initializer(Flask):
"""
Class to initialize the REST API, this class loads the model from the
given checkpoint path in model_info.xml
and prepares a caption_generator object
@@ -123,9 +126,6 @@ def current_time():
return int(1000 * time())
-from flask import request, Response, jsonify
-import json
-
app = Initializer(__name__)
@@ -158,17 +158,17 @@ def index():
<li> <code>/inception/v3/ping </code> - <br/>
<b> Description : </b> checks availability of the service.
returns "pong" with status 200 when it is available
</li>
- <li> <code>/inception/v3/captions</code> - <br/>
+ <li> <code>/inception/v3/caption/image</code> - <br/>
<table>
<tr><th align="left"> Description </th><td> This is a service
that can caption images</td></tr>
<tr><th align="left"> How to supply Image Content </th></tr>
<tr><th align="left"> With HTTP GET : </th> <td>
Include a query parameter <code>url </code> which is an
http url of JPEG image <br/>
- Example: <code> curl
"localhost:8764/inception/v3/captions?url=http://xyz.com/example.jpg";</code>
+ Example: <code> curl
"localhost:8764/inception/v3/caption/image?url=http://xyz.com/example.jpg";</code>
</td></tr>
<tr><th align="left"> With HTTP POST :</th><td>
POST JPEG image content as binary data in request body.
<br/>
- Example: <code> curl -X POST
"localhost:8764/inception/v3/captions" --data-binary @example.jpg </code>
+ Example: <code> curl -X POST
"localhost:8764/inception/v3/caption/image" --data-binary @example.jpg </code>
</td></tr>
</table>
</li>
@@ -184,8 +184,8 @@ def ping_pong():
return "pong"
-@app.route("/inception/v3/captions", methods=["GET", "POST"])
-def gen_captions():
+@app.route("/inception/v3/caption/image", methods=["GET", "POST"])
+def caption_image():
"""API to caption images"""
image_format = "not jpeg"
@@ -201,7 +201,7 @@ def gen_captions():
url = request.args.get("url")
c_type, image_data = get_remote_file(url)
if not image_data:
- return flask.Response(status=400, response=jsonify(error="Could
not HTTP GET %s" % url))
+ return Response(status=400, response=jsonify(error="Could not HTTP
GET %s" % url))
if 'image/jpeg' in c_type:
image_format = "jpeg"
@@ -217,7 +217,7 @@ def gen_captions():
rgb_image = image.convert("RGB")
# convert the RGB image to jpeg
image_bytes = BytesIO()
- rgb_image.save(image_bytes, format="jpeg")
+ rgb_image.save(image_bytes, format="jpeg", quality=95)
jpg_image = image_bytes.getvalue()
image_bytes.close()
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/BaseTFDockerfile
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/BaseTFDockerfile
new file mode 100644
index 000000000..b2cc4114e
--- /dev/null
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/BaseTFDockerfile
@@ -0,0 +1,50 @@
+#
+# 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.
+#
+
+FROM ubuntu:16.04
+MAINTAINER Apache Tika Team
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ apt-utils \
+ curl \
+ git \
+ python \
+ python2.7-dev \
+ software-properties-common \
+ unzip \
+ wget \
+ && \
+ apt-get clean && \
+ rm -rf /var/lib/apt/lists/*
+
+# Install pip
+RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
+ python get-pip.py && \
+ rm get-pip.py
+
+# Install basic python requirements
+RUN pip --no-cache-dir install \
+ flask \
+ numpy \
+ pillow \
+ requests
+
+# Install tensorflow:1.3.0 for python 2.7
+ENV TF_BINARY_URL
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp27-none-linux_x86_64.whl
+RUN pip --no-cache-dir install $TF_BINARY_URL
+
+CMD ["python"]
\ No newline at end of file
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionRestDockerfile
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionRestDockerfile
index 1c5ad4d7f..355c262be 100644
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionRestDockerfile
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionRestDockerfile
@@ -14,37 +14,40 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
-FROM ubuntu
-MAINTAINER Thamme Gowda <thammego...@apache.org> and Kranthi Kiran GV
<kkran...@student.nitw.ac.in>
+FROM uscdatascience/tf-tika-base
+MAINTAINER Apache Tika Team
-# Install missing part of ubuntu core + python stuff
-RUN apt-get update && \
- apt-get install -y python-pip python-dev wget libtcmalloc-minimal4 git
unzip
+# Download the pretrained inception v4 checkpoint & other meta files
+WORKDIR /usr/share/apache-tika/models/dl/image-video/recognition/
-# Install tensorflow and other dependencies
-RUN \
- pip install --upgrade
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl
&& \
- pip install flask requests pillow
+RUN curl -O
http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz && \
+ tar -xzvf inception_v4_2016_09_09.tar.gz && rm -rf
inception_v4_2016_09_09.tar.gz && \
+ curl -O
https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/imagenet_lsvrc_2015_synsets.txt
&& \
+ curl -O
https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/imagenet_metadata.txt
-# Get the TF-slim dependencies
-# Downloading from a specific commit for future compatibility
-RUN wget
https://github.com/tensorflow/models/archive/c15fada28113eca32dc98d6e3bec4755d0d5b4c2.zip
&& \
- unzip c15fada28113eca32dc98d6e3bec4755d0d5b4c2.zip
+# Download server related source files
+WORKDIR /usr/share/apache-tika/src/dl/image-video/recognition/
-RUN \
- wget
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
-O /usr/bin/inceptionapi.py && \
- chmod +x /usr/bin/inceptionapi.py
+RUN curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inception_v4.py
&& \
+ chmod +x inceptionapi.py
-# clean up cache, so we can publish smaller image to hub
-RUN apt-get clean
-ENV
PYTHONPATH="$PYTHONPATH:/models-c15fada28113eca32dc98d6e3bec4755d0d5b4c2/slim"
-ENV LD_PRELOAD="/usr/lib/libtcmalloc_minimal.so.4"
+WORKDIR /
-ENV PYTHONPATH="$PYTHONPATH:$PATH"
-RUN python -c "import inceptionapi"
+# Remove unnecessary packages comes with base
+RUN apt-get -y remove \
+ apt-utils \
+ curl \
+ git \
+ software-properties-common \
+ unzip \
+ wget
-# expose API port, this is the default port
+# Add symbolic link to inceptionapi.py
+RUN ln -s
/usr/share/apache-tika/src/dl/image-video/recognition/inceptionapi.py
/usr/bin/inceptionapi
+
+# Expose API port, this is the default port
EXPOSE 8764
-CMD inceptionapi.py
+CMD inceptionapi
\ No newline at end of file
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionVideoRestDockerfile
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionVideoRestDockerfile
index 2d1cac2be..367045141 100644
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionVideoRestDockerfile
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionVideoRestDockerfile
@@ -15,37 +15,29 @@
# limitations under the License.
#
-FROM ubuntu:16.04
-MAINTAINER Thamme Gowda <thammego...@apache.org> and Kranthi Kiran GV
<kkran...@student.nitw.ac.in> and \
- Madhav Sharan <msha...@usc.edu> and Chris Mattmann
<mattm...@apache.org>
-
-# Install missing part of ubuntu core + python + building dependencies
-RUN apt-get update && \
- apt-get install -y wget git unzip curl \
- libtcmalloc-minimal4 software-properties-common apt-utils \
- build-essential cmake pkg-config \
- libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev \
- libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
- libxvidcore-dev libx264-dev \
- libgtk2.0-dev \
- libatlas-base-dev gfortran \
- python2.7-dev \
- python-pip
-
-# Install ffmpeg
-RUN apt-get install -y ffmpeg
-
-RUN \
- pip install --upgrade pip && \
- pip install numpy
+FROM uscdatascience/tf-tika-base
+MAINTAINER Apache Tika Team
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ cmake \
+ libavcodec-dev \
+ libavformat-dev \
+ libswscale-dev \
+ libv4l-dev \
+ libavutil-dev \
+ pkg-config \
+ && \
+ apt-get clean && \
+ rm -rf /var/lib/apt/lists/*
# Download OpenCV + OpenCV_Contrib
WORKDIR /usr/local/src
RUN \
- wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.2.0.zip &&
\
+ wget -O opencv.zip https://github.com/opencv/opencv/archive/3.2.0.zip &&
\
unzip opencv.zip && \
- wget -O opencv_contrib.zip
https://github.com/Itseez/opencv_contrib/archive/3.2.0.zip && \
+ wget -O opencv_contrib.zip
https://github.com/opencv/opencv_contrib/archive/3.2.0.zip && \
unzip opencv_contrib.zip
# Build + Install OpenCV
@@ -53,42 +45,64 @@ RUN mkdir -p opencv-3.2.0/build
WORKDIR /usr/local/src/opencv-3.2.0/build
RUN cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
- -D INSTALL_PYTHON_EXAMPLES=ON \
- -D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-3.2.0/modules \
- -D BUILD_EXAMPLES=ON ..
-RUN make -j4
-RUN make install
+ -D BUILD_PYTHON_SUPPORT=ON \
+ -D BUILD_EXAMPLES=OFF \
+ -D PYTHON_DEFAULT_EXECUTABLE=/usr/bin/python3 \
+ -D BUILD_opencv_python3=OFF \
+ -D BUILD_opencv_python2=ON \
+ -D WITH_IPP=OFF \
+ -D WITH_FFMPEG=ON \
+ -D WITH_V4L=ON ..
-WORKDIR /
+RUN make -j$(nproc) && make install
-# Install tensorflow and other dependencies
-RUN \
- pip install --upgrade
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl
--ignore-installed && \
- pip install flask requests pillow
+# Download the pretrained inception v4 checkpoint & other meta files
+WORKDIR /usr/share/apache-tika/models/dl/image-video/recognition/
-# Get the TF-slim dependencies
-# Downloading from a specific commit for future compatibility
-RUN wget
https://github.com/tensorflow/models/archive/c15fada28113eca32dc98d6e3bec4755d0d5b4c2.zip
+RUN curl -O
http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz && \
+ tar -xzvf inception_v4_2016_09_09.tar.gz && rm -rf
inception_v4_2016_09_09.tar.gz && \
+ curl -O
https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/imagenet_lsvrc_2015_synsets.txt
&& \
+ curl -O
https://raw.githubusercontent.com/tensorflow/models/master/research/inception/inception/data/imagenet_metadata.txt
-RUN unzip c15fada28113eca32dc98d6e3bec4755d0d5b4c2.zip
+# Download server related source files
+WORKDIR /usr/share/apache-tika/src/dl/image-video/recognition/
-RUN \
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
-O /usr/bin/inceptionapi.py && \
- wget
https://raw.githubusercontent.com/apache/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
-O /usr/bin/video_util.py && \
- chmod +x /usr/bin/inceptionapi.py && \
- chmod +x /usr/bin/video_util.py
+RUN curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inception_v4.py
&& \
+ curl -O
https://raw.githubusercontent.com/ThejanW/tika/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
&& \
+ chmod +x inceptionapi.py
+
+WORKDIR /
-# clean up cache + delete OpenCV source files, so we can publish smaller image
to hub
-RUN apt-get clean
+# Remove unnecessary packages comes with base
+RUN apt-get -y remove \
+ apt-utils \
+ curl \
+ git \
+ software-properties-common \
+ unzip \
+ wget
+
+# Remove unnecessary packages
+RUN apt-get -y remove \
+ apt-utils \
+ build-essential \
+ cmake \
+ libavcodec-dev \
+ libavformat-dev \
+ libswscale-dev \
+ libv4l-dev \
+ libavutil-dev \
+ pkg-config
+
+# Delete OpenCV source files
RUN rm -rf /usr/local/src
-ENV
PYTHONPATH="$PYTHONPATH:/models-c15fada28113eca32dc98d6e3bec4755d0d5b4c2/slim"
-ENV LD_PRELOAD="/usr/lib/libtcmalloc_minimal.so.4"
-ENV PYTHONPATH="$PYTHONPATH:$PATH"
-RUN python -c "import inceptionapi"
+# Add symbolic link to inceptionapi.py
+RUN ln -s
/usr/share/apache-tika/src/dl/image-video/recognition/inceptionapi.py
/usr/bin/inceptionapi
-# expose API port, this is the default port
+# Expose API port, this is the default port
EXPOSE 8764
-CMD inceptionapi.py
+CMD inceptionapi
\ No newline at end of file
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
deleted file mode 100644
index 26a4f2353..000000000
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/classify_image.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed 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.
-#
==============================================================================
-
-"""Simple image classification with Inception.
-
-Run image classification with Inception trained on ImageNet 2012 Challenge data
-set.
-
-This program creates a graph from a saved GraphDef protocol buffer,
-and runs inference on an input JPEG image. It outputs human readable
-strings of the top 5 predictions along with their probabilities.
-
-Change the --image_file argument to any jpg image to compute a
-classification of that image.
-
-Please see the tutorial and website for a detailed description of how
-to use this script to perform image recognition.
-
-https://tensorflow.org/tutorials/image_recognition/
-
-Requirements:
- tensorflow
- tensorflow models (for TF-slim)
-
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os.path
-import re
-import sys
-import tarfile
-
-import numpy as np
-from six.moves import urllib
-import tensorflow as tf
-
-from datasets import imagenet, dataset_utils
-from nets import inception
-from preprocessing import inception_preprocessing
-
-slim = tf.contrib.slim
-
-FLAGS = tf.app.flags.FLAGS
-
-# inception_v4.ckpt
-# Inception V4 checkpoint file.
-# imagenet_metadata.txt
-# Map from synset ID to a human readable string.
-# imagenet_lsvrc_2015_synsets.txt
-# Text representation of a protocol buffer mapping a label to synset ID.
-tf.app.flags.DEFINE_string(
- 'model_dir', '/tmp/imagenet',
- """Path to inception_v4.ckpt, """
- """imagenet_lsvrc_2015_synsets.txt, and """
- """imagenet_metadata.txt.""")
-tf.app.flags.DEFINE_string('image_file', '',
- """Absolute path to image file.""")
-tf.app.flags.DEFINE_integer('num_top_predictions', 5,
- """Display this many predictions.""")
-
-# pylint: disable=line-too-long
-DATA_URL =
'http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz'
-# pylint: enable=line-too-long
-
-
-def create_readable_names_for_imagenet_labels():
- """Create a dict mapping label id to human readable string.
-
- Returns:
- labels_to_names: dictionary where keys are integers from to 1000
- and values are human-readable names.
-
- We retrieve a synset file, which contains a list of valid synset labels
used
- by ILSVRC competition. There is one synset one per line, eg.
- # n01440764
- # n01443537
- We also retrieve a synset_to_human_file, which contains a mapping from
synsets
- to human-readable names for every synset in Imagenet. These are stored in a
- tsv format, as follows:
- # n02119247 black fox
- # n02119359 silver fox
- We assign each synset (in alphabetical order) an integer, starting from 1
- (since 0 is reserved for the background class).
-
- Code is based on
-
https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py#L463
- """
-
- # pylint: disable=line-too-long
-
- dest_directory = FLAGS.model_dir
-
- synset_list = [s.strip() for s in open(os.path.join(
- dest_directory, 'imagenet_lsvrc_2015_synsets.txt')).readlines()]
- num_synsets_in_ilsvrc = len(synset_list)
- assert num_synsets_in_ilsvrc == 1000
-
- synset_to_human_list = open(os.path.join(
- dest_directory, 'imagenet_metadata.txt')).readlines()
- num_synsets_in_all_imagenet = len(synset_to_human_list)
- assert num_synsets_in_all_imagenet == 21842
-
- synset_to_human = {}
- for s in synset_to_human_list:
- parts = s.strip().split('\t')
- assert len(parts) == 2
- synset = parts[0]
- human = parts[1]
- synset_to_human[synset] = human
-
- label_index = 1
- labels_to_names = {0: 'background'}
- for synset in synset_list:
- name = synset_to_human[synset]
- labels_to_names[label_index] = name
- label_index += 1
-
- return labels_to_names
-
-
-def run_inference_on_image(image):
- """Runs inference on an image.
-
- Args:
- image: Image file name.
-
- Returns:
- Nothing
- """
- dest_directory = FLAGS.model_dir
-
- image_size = inception.inception_v4.default_image_size
-
- if not tf.gfile.Exists(image):
- tf.logging.fatal('File does not exist %s', image)
- image_string = tf.gfile.FastGFile(image, 'rb').read()
-
- with tf.Graph().as_default():
- image = tf.image.decode_jpeg(image_string, channels=3)
- processed_image = inception_preprocessing.preprocess_image(
- image, image_size, image_size, is_training=False)
- processed_images = tf.expand_dims(processed_image, 0)
-
- # Create the model, use the default arg scope to configure the batch
- # norm parameters.
- with slim.arg_scope(inception.inception_v4_arg_scope()):
- logits, _ = inception.inception_v4(
- processed_images, num_classes=1001, is_training=False)
- probabilities = tf.nn.softmax(logits)
-
- init_fn = slim.assign_from_checkpoint_fn(
- os.path.join(dest_directory, 'inception_v4.ckpt'),
- slim.get_model_variables('InceptionV4'))
-
- with tf.Session() as sess:
- init_fn(sess)
- probabilities = sess.run(probabilities)
- probabilities = probabilities[0, 0:]
- sorted_inds = [i[0] for i in sorted(
- enumerate(-probabilities), key=lambda x:x[1])]
-
- names = create_readable_names_for_imagenet_labels()
- top_k = FLAGS.num_top_predictions
- for i in range(top_k):
- index = sorted_inds[i]
- print('%s (score = %.5f)' % (names[index], probabilities[index]))
-
-
-def util_download(url, dest_directory):
- """Downloads the file.
-
- Args:
- url: URL to download the file from.
- dest_directory: Destination directory
- Returns:
- Nothing
- """
- filename = url.split('/')[-1]
- filepath = os.path.join(dest_directory, filename)
-
- def _progress(count, block_size, total_size):
- sys.stdout.write('\r>> Downloading %s %.1f%%' % (
- filename, float(count * block_size) / float(total_size) * 100.0))
- sys.stdout.flush()
- filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
- print()
- statinfo = os.stat(filepath)
- print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-
-
-def util_download_tar(url, dest_directory):
- """Downloads a file and extracts it.
-
- Args:
- url: URL to download the file from.
- dest_directory: Destination directory
- Returns:
- Nothing
- """
- filename = url.split('/')[-1]
- filepath = os.path.join(dest_directory, filename)
-
- def _progress(count, block_size, total_size):
- sys.stdout.write('\r>> Downloading %s %.1f%%' % (
- filename, float(count * block_size) / float(total_size) * 100.0))
- sys.stdout.flush()
- filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
- print()
- statinfo = os.stat(filepath)
- print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
- tarfile.open(filepath, 'r:gz').extractall(dest_directory)
-
-
-def maybe_download_and_extract():
- """Download and extract model tar file."""
- dest_directory = FLAGS.model_dir
- if not tf.gfile.Exists(dest_directory):
- tf.gfile.MakeDirs(dest_directory)
- if not tf.gfile.Exists(os.path.join(dest_directory, 'inception_v4.ckpt')):
- util_download_tar(DATA_URL, dest_directory)
- # pylint: disable=line-too-long
- if not tf.gfile.Exists(os.path.join(dest_directory,
'imagenet_lsvrc_2015_synsets.txt')):
- util_download(
-
'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_lsvrc_2015_synsets.txt',
dest_directory)
- if not tf.gfile.Exists(os.path.join(dest_directory,
'imagenet_metadata.txt')):
- util_download(
-
'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_metadata.txt',
dest_directory)
- # pylint: enable=line-too-long
-
-
-def main(_):
- maybe_download_and_extract()
- image = FLAGS.image_file
- run_inference_on_image(image)
-
-
-if __name__ == '__main__':
- tf.app.run()
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inception_v4.py
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inception_v4.py
new file mode 100644
index 000000000..b6644160f
--- /dev/null
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inception_v4.py
@@ -0,0 +1,362 @@
+#!/usr/bin/env python
+# 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.
+
+"""Contains the definition of the Inception V4 architecture.
+
+As described in http://arxiv.org/abs/1602.07261.
+
+ Inception-v4, Inception-ResNet and the Impact of Residual Connections
+ on Learning
+ Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def block_inception_a(inputs, scope=None, reuse=None):
+ """Builds Inception-A block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionA', [inputs],
reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 96, [1, 1],
scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 64, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 96, [3, 3],
scope='Conv2d_0b_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 64, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 96, [3, 3],
scope='Conv2d_0b_3x3')
+ branch_2 = slim.conv2d(branch_2, 96, [3, 3],
scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3],
scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 96, [1, 1],
scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2,
branch_3])
+
+
+def block_reduction_a(inputs, scope=None, reuse=None):
+ """Builds Reduction-A block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockReductionA', [inputs],
reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2,
padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 192, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 224, [3, 3],
scope='Conv2d_0b_3x3')
+ branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2,
padding='VALID',
+ scope='MaxPool_1a_3x3')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_b(inputs, scope=None, reuse=None):
+ """Builds Inception-B block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionB', [inputs],
reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 384, [1, 1],
scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 192, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 224, [1, 7],
scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 256, [7, 1],
scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 192, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 192, [7, 1],
scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, 224, [1, 7],
scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, 224, [7, 1],
scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, 256, [1, 7],
scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3],
scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 128, [1, 1],
scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2,
branch_3])
+
+
+def block_reduction_b(inputs, scope=None, reuse=None):
+ """Builds Reduction-B block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockReductionB', [inputs],
reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 192, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 256, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 256, [1, 7],
scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 320, [7, 1],
scope='Conv2d_0c_7x1')
+ branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2,
padding='VALID',
+ scope='MaxPool_1a_3x3')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+
+
+def block_inception_c(inputs, scope=None, reuse=None):
+ """Builds Inception-C block for Inception v4 network."""
+ # By default use stride=1 and SAME padding
+ with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d],
+ stride=1, padding='SAME'):
+ with tf.variable_scope(scope, 'BlockInceptionC', [inputs],
reuse=reuse):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(inputs, 256, [1, 1],
scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(inputs, 384, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'),
+ slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')])
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(inputs, 384, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, 448, [3, 1],
scope='Conv2d_0b_3x1')
+ branch_2 = slim.conv2d(branch_2, 512, [1, 3],
scope='Conv2d_0c_1x3')
+ branch_2 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'),
+ slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')])
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(inputs, [3, 3],
scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, 256, [1, 1],
scope='Conv2d_0b_1x1')
+ return tf.concat(axis=3, values=[branch_0, branch_1, branch_2,
branch_3])
+
+
+def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
+ """Creates the Inception V4 network up to the given final endpoint.
+
+ Args:
+ inputs: a 4-D tensor of size [batch_size, height, width, 3].
+ final_endpoint: specifies the endpoint to construct the network up to.
+ It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+ 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
+ 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e',
+ 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c',
+ 'Mixed_7d']
+ scope: Optional variable_scope.
+
+ Returns:
+ logits: the logits outputs of the model.
+ end_points: the set of end_points from the inception model.
+
+ Raises:
+ ValueError: if final_endpoint is not set to one of the predefined values,
+ """
+ end_points = {}
+
+ def add_and_check_final(name, net):
+ end_points[name] = net
+ return name == final_endpoint
+
+ with tf.variable_scope(scope, 'InceptionV4', [inputs]):
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ # 299 x 299 x 3
+ net = slim.conv2d(inputs, 32, [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ if add_and_check_final('Conv2d_1a_3x3', net): return net,
end_points
+ # 149 x 149 x 32
+ net = slim.conv2d(net, 32, [3, 3], padding='VALID',
+ scope='Conv2d_2a_3x3')
+ if add_and_check_final('Conv2d_2a_3x3', net): return net,
end_points
+ # 147 x 147 x 32
+ net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3')
+ if add_and_check_final('Conv2d_2b_3x3', net): return net,
end_points
+ # 147 x 147 x 64
+ with tf.variable_scope('Mixed_3a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.max_pool2d(net, [3, 3], stride=2,
padding='VALID',
+ scope='MaxPool_0a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 96, [3, 3], stride=2,
padding='VALID',
+ scope='Conv2d_0a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_3a', net): return net, end_points
+
+ # 73 x 73 x 160
+ with tf.variable_scope('Mixed_4a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 64, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_0 = slim.conv2d(branch_0, 96, [3, 3],
padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, 64, [1, 1],
scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, 64, [1, 7],
scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, 64, [7, 1],
scope='Conv2d_0c_7x1')
+ branch_1 = slim.conv2d(branch_1, 96, [3, 3],
padding='VALID',
+ scope='Conv2d_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_4a', net): return net, end_points
+
+ # 71 x 71 x 192
+ with tf.variable_scope('Mixed_5a'):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, 192, [3, 3], stride=2,
padding='VALID',
+ scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.max_pool2d(net, [3, 3], stride=2,
padding='VALID',
+ scope='MaxPool_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1])
+ if add_and_check_final('Mixed_5a', net): return net, end_points
+
+ # 35 x 35 x 384
+ # 4 x Inception-A blocks
+ for idx in range(4):
+ block_scope = 'Mixed_5' + chr(ord('b') + idx)
+ net = block_inception_a(net, block_scope)
+ if add_and_check_final(block_scope, net): return net,
end_points
+
+ # 35 x 35 x 384
+ # Reduction-A block
+ net = block_reduction_a(net, 'Mixed_6a')
+ if add_and_check_final('Mixed_6a', net): return net, end_points
+
+ # 17 x 17 x 1024
+ # 7 x Inception-B blocks
+ for idx in range(7):
+ block_scope = 'Mixed_6' + chr(ord('b') + idx)
+ net = block_inception_b(net, block_scope)
+ if add_and_check_final(block_scope, net): return net,
end_points
+
+ # 17 x 17 x 1024
+ # Reduction-B block
+ net = block_reduction_b(net, 'Mixed_7a')
+ if add_and_check_final('Mixed_7a', net): return net, end_points
+
+ # 8 x 8 x 1536
+ # 3 x Inception-C blocks
+ for idx in range(3):
+ block_scope = 'Mixed_7' + chr(ord('b') + idx)
+ net = block_inception_c(net, block_scope)
+ if add_and_check_final(block_scope, net): return net,
end_points
+ raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v4(inputs, num_classes=1001, is_training=True,
+ dropout_keep_prob=0.8,
+ reuse=None,
+ scope='InceptionV4',
+ create_aux_logits=True):
+ """Creates the Inception V4 model.
+
+ Args:
+ inputs: a 4-D tensor of size [batch_size, height, width, 3].
+ num_classes: number of predicted classes.
+ is_training: whether is training or not.
+ dropout_keep_prob: float, the fraction to keep before final layer.
+ reuse: whether or not the network and its variables should be reused. To
be
+ able to reuse 'scope' must be given.
+ scope: Optional variable_scope.
+ create_aux_logits: Whether to include the auxiliary logits.
+
+ Returns:
+ logits: the logits outputs of the model.
+ end_points: the set of end_points from the inception model.
+ """
+ end_points = {}
+ with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as
scope:
+ with slim.arg_scope([slim.batch_norm, slim.dropout],
+ is_training=is_training):
+ net, end_points = inception_v4_base(inputs, scope=scope)
+
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d,
slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ # Auxiliary Head logits
+ if create_aux_logits:
+ with tf.variable_scope('AuxLogits'):
+ # 17 x 17 x 1024
+ aux_logits = end_points['Mixed_6h']
+ aux_logits = slim.avg_pool2d(aux_logits, [5, 5],
stride=3,
+ padding='VALID',
+ scope='AvgPool_1a_5x5')
+ aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
+ scope='Conv2d_1b_1x1')
+ aux_logits = slim.conv2d(aux_logits, 768,
+ aux_logits.get_shape()[1:3],
+ padding='VALID',
scope='Conv2d_2a')
+ aux_logits = slim.flatten(aux_logits)
+ aux_logits = slim.fully_connected(aux_logits,
num_classes,
+ activation_fn=None,
+ scope='Aux_logits')
+ end_points['AuxLogits'] = aux_logits
+
+ # Final pooling and prediction
+ with tf.variable_scope('Logits'):
+ # 8 x 8 x 1536
+ net = slim.avg_pool2d(net, net.get_shape()[1:3],
padding='VALID',
+ scope='AvgPool_1a')
+ # 1 x 1 x 1536
+ net = slim.dropout(net, dropout_keep_prob,
scope='Dropout_1b')
+ net = slim.flatten(net, scope='PreLogitsFlatten')
+ end_points['PreLogitsFlatten'] = net
+ # 1536
+ logits = slim.fully_connected(net, num_classes,
activation_fn=None,
+ scope='Logits')
+ end_points['Logits'] = logits
+ end_points['Predictions'] = tf.nn.softmax(logits,
name='Predictions')
+ return logits, end_points
+
+
+def inception_v4_arg_scope(weight_decay=0.00004,
+ use_batch_norm=True,
+ batch_norm_decay=0.9997,
+ batch_norm_epsilon=0.001):
+ """Defines the default arg scope for inception models.
+ Args:
+ weight_decay: The weight decay to use for regularizing the model.
+ use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+ batch_norm_decay: Decay for batch norm moving average.
+ batch_norm_epsilon: Small float added to variance to avoid dividing by
zero
+ in batch norm.
+ Returns:
+ An `arg_scope` to use for the inception models.
+ """
+ batch_norm_params = {
+ # Decay for the moving averages.
+ 'decay': batch_norm_decay,
+ # epsilon to prevent 0s in variance.
+ 'epsilon': batch_norm_epsilon,
+ # collection containing update_ops.
+ 'updates_collections': tf.GraphKeys.UPDATE_OPS,
+ }
+ if use_batch_norm:
+ normalizer_fn = slim.batch_norm
+ normalizer_params = batch_norm_params
+ else:
+ normalizer_fn = None
+ normalizer_params = {}
+ # Set weight_decay for weights in Conv and FC layers.
+ with slim.arg_scope([slim.conv2d, slim.fully_connected],
+ weights_regularizer=slim.l2_regularizer(weight_decay)):
+ with slim.arg_scope(
+ [slim.conv2d],
+ weights_initializer=slim.variance_scaling_initializer(),
+ activation_fn=tf.nn.relu,
+ normalizer_fn=normalizer_fn,
+ normalizer_params=normalizer_params) as sc:
+ return sc
+
+
+default_image_size = 299
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
index a72900b49..09d830c0d 100755
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/inceptionapi.py
@@ -15,111 +15,129 @@
# under the License.
"""
-Image classification with Inception.
+ Image classification with Inception.
-This script exposes the tensorflow's inception classification service over
REST API.
+ This script exposes the tensorflow's inception classification service over
REST API.
-For more details, visit:
- https://tensorflow.org/tutorials/image_recognition/
+ For more details, visit:
+ https://tensorflow.org/tutorials/image_recognition/
-Requirements :
- Flask
- tensorflow
- numpy
- requests
+ Requirements :
+ Flask
+ tensorflow
+ numpy
+ requests
+ pillow
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import os.path
-import re
-import sys
-import tarfile
+import os
+import tempfile
+import json
+import logging
+import requests
+
+from flask import Flask, request, Response, jsonify
+from io import BytesIO
+from logging.handlers import RotatingFileHandler
+from PIL import Image
+from time import time
+
+import tensorflow as tf
+
+from inception_v4 import default_image_size, inception_v4_arg_scope,
inception_v4
try:
- #This import is placed inside here to ensure that video_util and OpenCV is
not required for image recognition APIs
+ # This import is placed inside here to ensure that video_util and OpenCV
is not required for image recognition APIs
from video_util import get_center_frame, get_frames_interval, get_n_frames
except:
print("Can't import video libraries, No video functionality is available")
-
-import numpy as np
-from six.moves import urllib
-import tensorflow as tf
-from datasets import imagenet, dataset_utils
-from nets import inception
-from preprocessing import inception_preprocessing
+json.encoder.FLOAT_REPR = lambda o: format(o, '.2f') # JSON serialization of
floats
slim = tf.contrib.slim
-
-import requests
-import json
-json.encoder.FLOAT_REPR = lambda o: format(
- o, '.2f') # JSON serialization of floats
-from time import time
-from PIL import Image
-from io import BytesIO
-import tempfile
-import flask
-
-
FLAGS = tf.app.flags.FLAGS
-# inception_v4.ckpt
-# Inception V4 checkpoint file.
-# imagenet_metadata.txt
-# Map from synset ID to a human readable string.
-# imagenet_lsvrc_2015_synsets.txt
-# Text representation of a protocol buffer mapping a label to synset ID.
-tf.app.flags.DEFINE_string(
- 'model_dir', '/tmp/imagenet',
- """Path to inception_v4.ckpt, """
- """imagenet_lsvrc_2015_synsets.txt, and """
- """imagenet_metadata.txt.""")
-tf.app.flags.DEFINE_integer('port', '8764', """Server PORT, default:8764""")
-tf.app.flags.DEFINE_string('log', 'inception.log',
+tf.app.flags.DEFINE_string('model_dir',
+
'/usr/share/apache-tika/models/dl/image-video/recognition/',
+ """Path to inception_v4.ckpt & meta files""")
+tf.app.flags.DEFINE_integer('port',
+ '8764',
+ """Server PORT, default:8764""")
+tf.app.flags.DEFINE_string('log',
+ 'inception.log',
"""Log file name, default: inception.log""")
-# pylint: disable=line-too-long
-DATA_URL =
'http://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz'
-# pylint: enable=line-too-long
-
-
-def create_readable_names_for_imagenet_labels():
- """Create a dict mapping label id to human readable string.
+def preprocess_image(image, height, width, central_fraction=0.875, scope=None):
+ """Prepare one image for evaluation.
+ If height and width are specified it would output an image with that size
by
+ applying resize_bilinear.
+ If central_fraction is specified it would crop the central fraction of the
+ input image.
+ Args:
+ image: 3-D Tensor of image. If dtype is tf.float32 then the range should
be
+ [0, 1], otherwise it would converted to tf.float32 assuming that the
range
+ is [0, MAX], where MAX is largest positive representable number for
+ int(8/16/32) data type (see `tf.image.convert_image_dtype` for
details).
+ height: integer
+ width: integer
+ central_fraction: Optional Float, fraction of the image to crop.
+ scope: Optional scope for name_scope.
Returns:
- labels_to_names: dictionary where keys are integers from to 1000
- and values are human-readable names.
-
- We retrieve a synset file, which contains a list of valid synset labels
used
- by ILSVRC competition. There is one synset one per line, eg.
- # n01440764
- # n01443537
- We also retrieve a synset_to_human_file, which contains a mapping from
synsets
- to human-readable names for every synset in Imagenet. These are stored in a
- tsv format, as follows:
- # n02119247 black fox
- # n02119359 silver fox
- We assign each synset (in alphabetical order) an integer, starting from 1
- (since 0 is reserved for the background class).
-
- Code is based on
-
https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py#L463
+ 3-D float Tensor of prepared image.
"""
+ with tf.name_scope(scope, 'eval_image', [image, height, width]):
+ if image.dtype != tf.float32:
+ image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+ # Crop the central region of the image with an area containing 87.5% of
+ # the original image.
+ if central_fraction:
+ image = tf.image.central_crop(image,
central_fraction=central_fraction)
+
+ if height and width:
+ # Resize the image to the specified height and width.
+ image = tf.expand_dims(image, 0)
+ image = tf.image.resize_bilinear(image, [height, width],
+ align_corners=False)
+ image = tf.squeeze(image, [0])
+ image = tf.subtract(image, 0.5)
+ image = tf.multiply(image, 2.0)
+ return image
- # pylint: disable=line-too-long
+
+def create_readable_names_for_imagenet_labels():
+ """
+ Create a dict mapping label id to human readable string.
+ Returns:
+ labels_to_names: dictionary where keys are integers from to 1000
+ and values are human-readable names.
+
+ We retrieve a synset file, which contains a list of valid synset
labels used
+ by ILSVRC competition. There is one synset one per line, eg.
+ # n01440764
+ # n01443537
+ We also retrieve a synset_to_human_file, which contains a mapping from
synsets
+ to human-readable names for every synset in Imagenet. These are stored
in a
+ tsv format, as follows:
+ # n02119247 black fox
+ # n02119359 silver fox
+ We assign each synset (in alphabetical order) an integer, starting
from 1
+ (since 0 is reserved for the background class).
+
+ Code is based on
+
https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py
+ """
dest_directory = FLAGS.model_dir
- synset_list = [s.strip() for s in open(os.path.join(
- dest_directory, 'imagenet_lsvrc_2015_synsets.txt')).readlines()]
+ synset_list = [s.strip() for s in open(os.path.join(dest_directory,
'imagenet_lsvrc_2015_synsets.txt')).readlines()]
num_synsets_in_ilsvrc = len(synset_list)
assert num_synsets_in_ilsvrc == 1000
- synset_to_human_list = open(os.path.join(
- dest_directory, 'imagenet_metadata.txt')).readlines()
+ synset_to_human_list = open(os.path.join(dest_directory,
'imagenet_metadata.txt')).readlines()
num_synsets_in_all_imagenet = len(synset_to_human_list)
assert num_synsets_in_all_imagenet == 21842
@@ -141,105 +159,48 @@ def create_readable_names_for_imagenet_labels():
return labels_to_names
-def util_download(url, dest_directory):
- """Downloads the file.
-
- Args:
- url: URL to download the file from.
- dest_directory: Destination directory
- Returns:
- Nothing
+def get_remote_file(url, success=200, timeout=10):
"""
- filename = url.split('/')[-1]
- filepath = os.path.join(dest_directory, filename)
-
- def _progress(count, block_size, total_size):
- sys.stdout.write('\r>> Downloading %s %.1f%%' % (
- filename, float(count * block_size) / float(total_size) * 100.0))
- sys.stdout.flush()
- filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
- print()
- statinfo = os.stat(filepath)
- print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-
-
-def util_download_tar(url, dest_directory):
- """Downloads a file and extracts it.
-
- Args:
- url: URL to download the file from.
- dest_directory: Destination directory
- Returns:
- Nothing
+ Given HTTP URL, this api gets the content of it
+ returns (Content-Type, image_content)
"""
- filename = url.split('/')[-1]
- filepath = os.path.join(dest_directory, filename)
-
- def _progress(count, block_size, total_size):
- sys.stdout.write('\r>> Downloading %s %.1f%%' % (
- filename, float(count * block_size) / float(total_size) * 100.0))
- sys.stdout.flush()
- filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
- print()
- statinfo = os.stat(filepath)
- print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
- tarfile.open(filepath, 'r:gz').extractall(dest_directory)
-
-
-def maybe_download_and_extract():
- """Download and extract model tar file."""
- dest_directory = FLAGS.model_dir
- if not tf.gfile.Exists(dest_directory):
- tf.gfile.MakeDirs(dest_directory)
- if not tf.gfile.Exists(os.path.join(dest_directory, 'inception_v4.ckpt')):
- util_download_tar(DATA_URL, dest_directory)
- # pylint: disable=line-too-long
- if not tf.gfile.Exists(os.path.join(dest_directory,
'imagenet_lsvrc_2015_synsets.txt')):
- util_download(
-
'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_lsvrc_2015_synsets.txt',
dest_directory)
- if not tf.gfile.Exists(os.path.join(dest_directory,
'imagenet_metadata.txt')):
- util_download(
-
'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/imagenet_metadata.txt',
dest_directory)
- # pylint: enable=line-too-long
+ try:
+ app.logger.info("GET: %s" % url)
+ auth = None
+ res = requests.get(url, stream=True, timeout=timeout, auth=auth)
+ if res.status_code == success:
+ return res.headers.get('Content-Type',
'application/octet-stream'), res.raw.data
+ except:
+ pass
+ return None, None
def current_time():
- """
- Returns current time in milli seconds
- """
+ """Returns current time in milli seconds"""
+
return int(1000 * time())
-class Classifier(flask.Flask):
- '''
- Classifier Service class
- '''
+class Classifier(Flask):
+ """Classifier Service class"""
def __init__(self, name):
super(Classifier, self).__init__(name)
- maybe_download_and_extract()
- import logging
- from logging.handlers import RotatingFileHandler
- file_handler = RotatingFileHandler(
- FLAGS.log, maxBytes=1024 * 1024 * 100, backupCount=20)
+ file_handler = RotatingFileHandler(FLAGS.log, maxBytes=1024 * 1024 *
100, backupCount=20)
file_handler.setLevel(logging.INFO)
- formatter = logging.Formatter(
- "%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+ formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s
- %(message)s")
file_handler.setFormatter(formatter)
self.logger.addHandler(file_handler)
- self.names = imagenet.create_readable_names_for_imagenet_labels()
- self.image_size = inception.inception_v4.default_image_size
+ self.names = create_readable_names_for_imagenet_labels()
+ self.image_size = default_image_size
self.image_str_placeholder = tf.placeholder(tf.string)
image = tf.image.decode_jpeg(self.image_str_placeholder, channels=3)
- processed_image = inception_preprocessing.preprocess_image(
- image, self.image_size, self.image_size, is_training=False)
+ processed_image = preprocess_image(image, self.image_size,
self.image_size)
processed_images = tf.expand_dims(processed_image, 0)
- # Create the model, use the default arg scope to configure the
- # batch norm parameters.
- with slim.arg_scope(inception.inception_v4_arg_scope()):
- logits, _ = inception.inception_v4(
- processed_images, num_classes=1001, is_training=False)
+ # create the model, use the default arg scope to configure the batch
norm parameters.
+ with slim.arg_scope(inception_v4_arg_scope()):
+ logits, _ = inception_v4(processed_images, num_classes=1001,
is_training=False)
self.probabilities = tf.nn.softmax(logits)
dest_directory = FLAGS.model_dir
@@ -250,49 +211,36 @@ def __init__(self, name):
self.sess = tf.Session()
init_fn(self.sess)
- def classify(self, image_string, topk):
- eval_probabilities = self.sess.run(self.probabilities, feed_dict={
- self.image_str_placeholder:
image_string})
+ def classify(self, image_string, topn, min_confidence):
+ eval_probabilities = self.sess.run(self.probabilities,
feed_dict={self.image_str_placeholder: image_string})
eval_probabilities = eval_probabilities[0, 0:]
- sorted_inds = [i[0] for i in sorted(
- enumerate(-eval_probabilities), key=lambda x:x[1])]
+ sorted_inds = [i[0] for i in sorted(enumerate(-eval_probabilities),
key=lambda x: x[1])]
+
+ if topn is None:
+ topn = len(sorted_inds)
- if topk == None:
- topk = len(sorted_inds)
-
res = []
- for i in range(topk):
+ for i in range(topn):
index = sorted_inds[i]
score = float(eval_probabilities[index])
- res.append((index, self.names[index], score))
+ if min_confidence is None:
+ res.append((index, self.names[index], score))
+ else:
+ if score >= min_confidence:
+ res.append((index, self.names[index], score))
+ else:
+ # the scores are in sorted order, so we can break the loop
whenever we get a low score object
+ break
return res
-from flask import Flask, request, abort, g, Response, jsonify
app = Classifier(__name__)
-def get_remotefile(url, success=200, timeout=10):
- """
- Given HTTP URL, this api gets the content of it
- returns (Content-Type, image_content)
- """
- try:
- app.logger.info("GET: %s" % url)
- auth = None
- res = requests.get(url, stream=True, timeout=timeout, auth=auth)
- if res.status_code == success:
- return res.headers.get('Content-Type',
'application/octet-stream'), res.raw.data
- except:
- pass
- return None, None
-
-
@app.route("/")
def index():
- """
- The index page which provide information about other API end points
- """
+ """The index page which provide information about other API end points"""
+
return """
<div>
<h1> Inception REST API </h1>
@@ -302,21 +250,22 @@ def index():
<li> <code>/inception/v4/ping </code> - <br/>
<b> Description : </b> checks availability of the service.
returns "pong" with status 200 when it is available
</li>
- <li> <code>/inception/v4/classify</code> - <br/>
+ <li> <code>/inception/v4/classify/image</code> - <br/>
<table>
<tr><th align="left"> Description </th><td> This is a
classifier service that can classify images</td></tr>
<tr><td></td> <td>Query Params : <br/>
- <code>topk </code>: type = int : top classes to get;
default : 10 <br/>
+ <code>topn </code>: type = int : top classes to get;
default : 5 <br/>
+ <code>min_confidence </code>: type = float : minimum
confidence that a label should have to exist in topn; default : 0.015 <br/>
<code>human </code>: type = boolean : human readable class
names; default : true <br/>
</td></tr>
<tr><th align="left"> How to supply Image Content </th></tr>
<tr><th align="left"> With HTTP GET : </th> <td>
Include a query parameter <code>url </code> which is an
http url of JPEG image <br/>
- Example: <code> curl
"localhost:8764/inception/v4/classify?url=http://xyz.com/example.jpg";</code>
+ Example: <code> curl
"localhost:8764/inception/v4/classify/image?url=http://xyz.com/example.jpg";</code>
</td></tr>
<tr><th align="left"> With HTTP POST :</th><td>
POST JPEG image content as binary data in request body.
<br/>
- Example: <code> curl -X POST
"localhost:8764/inception/v4/classify?topk=10&human=false" --data-binary
@example.jpg </code>
+ Example: <code> curl -X POST
"localhost:8764/inception/v4/classify/image?topn=5&min_confidence=0.015&human=false"
--data-binary @example.jpg </code>
</td></tr>
</table>
</li>
@@ -324,7 +273,8 @@ def index():
<table>
<tr><th align="left"> Description </th><td> This is a
classifier service that can classify videos</td></tr>
<tr><td></td> <td>Query Params : <br/>
- <code>topk </code>: type = int : top classes to get;
default : 10 <br/>
+ <code>topn </code>: type = int : top classes to get;
default : 5 <br/>
+ <code>min_confidence </code>: type = float : minimum
confidence that a label should have to exist in topn; default : 0.015 <br/>
<code>human </code>: type = boolean : human readable class
names; default : true <br/>
<code>mode </code>: options = <code>{"center", "interval",
"fixed"}</code> : Modes of frame extraction; default : center <br/>
  <code>"center"</code> - Just one frame in center.
<br/>
@@ -332,7 +282,7 @@ def index():
  <code>"fixed"</code> - Extract fixed number of
frames.<br/>
<code>frame-interval </code>: type = int : Interval for
frame extraction to be used with INTERVAL mode. If frame_interval=10 then every
10th frame will be extracted; default : 10 <br/>
<code>num-frame </code>: type = int : Number of frames to
be extracted from video while using FIXED model. If num_frame=10 then 10 frames
equally distant from each other will be extracted; default : 10 <br/>
-
+
</td></tr>
<tr><th align="left"> How to supply Video Content </th></tr>
<tr><th align="left"> With HTTP GET : </th> <td>
@@ -342,7 +292,7 @@ def index():
<tr><th align="left"> With HTTP POST :</th><td>
POST video content as binary data in request body. If
video can be decoded by OpenCV it should be fine. It's tested on mp4 and avi on
mac <br/>
Include a query parameter <code>ext </code>this extension
is needed to tell OpenCV which decoder to use, default is ".mp4" </br>
- Example: <code> curl -X POST
"localhost:8764/inception/v4/classify/video?topk=10&human=false" --data-binary
@example.mp4 </code>
+ Example: <code> curl -X POST
"localhost:8764/inception/v4/classify/video?topn=5&min_confidence=0.015&human=false"
--data-binary @example.mp4 </code>
</td></tr>
</table>
</li>
@@ -353,28 +303,28 @@ def index():
@app.route("/inception/v4/ping", methods=["GET"])
def ping_pong():
- """API to do health check. If this says status code 200, then healthy
- """
+ """API to do health check. If this says status code 200, then healthy"""
+
return "pong"
-@app.route("/inception/v4/classify", methods=["GET", "POST"])
+@app.route("/inception/v4/classify/image", methods=["GET", "POST"])
def classify_image():
- """
- API to classify images
- """
+ """API to classify images"""
+
image_format = "not jpeg"
st = current_time()
- topk = int(request.args.get("topk", "10"))
+ topn = int(request.args.get("topn", "5"))
+ min_confidence = float(request.args.get("min_confidence", "0.015"))
human = request.args.get("human", "true").lower() in ("true", "1", "yes")
if request.method == 'POST':
image_data = request.get_data()
else:
url = request.args.get("url")
- c_type, image_data = get_remotefile(url)
+ c_type, image_data = get_remote_file(url)
if not image_data:
- return flask.Response(status=400, response=jsonify(error="Couldnot
HTTP GET %s" % url))
+ return Response(status=400, response=jsonify(error="Could not HTTP
GET %s" % url))
if 'image/jpeg' in c_type:
image_format = "jpeg"
@@ -397,11 +347,14 @@ def classify_image():
read_time = current_time() - st
st = current_time() # reset start time
try:
- classes = app.classify(image_string=jpg_image, topk=topk)
+ classes = app.classify(image_string=jpg_image, topn=topn,
min_confidence=min_confidence)
except Exception as e:
app.logger.error(e)
return Response(status=400, response=str(e))
classids, classnames, confidence = zip(*classes)
+
+ print(classnames, confidence)
+
classifier_time = current_time() - st
app.logger.info("Classifier time : %d" % classifier_time)
res = {
@@ -417,104 +370,100 @@ def classify_image():
res['classnames'] = classnames
return Response(response=json.dumps(res), status=200,
mimetype="application/json")
-CENTER = "center"
-INTERVAL = "interval"
-FIXED = "fixed"
-
-ALLOWED_MODE = set([CENTER ,INTERVAL , FIXED])
@app.route("/inception/v4/classify/video", methods=["GET", "POST"])
def classify_video():
"""
- API to classify videos
- Request args -
- url - PATH of file
- topk - number of labels
- human - human readable or not
- mode - Modes of frame extraction {"center", "interval", "fixed"}
- "center" - Just one frame in center. <Default option>
- "interval" - Extracts frames after fixed interval.
- "fixed" - Extract fixed number of frames.
- frame-interval - Interval for frame extraction to be used with INTERVAL
mode. If frame_interval=10 then every 10th frame will be extracted.
- num-frame - Number of frames to be extracted from video while using FIXED
model. If num_frame=10 then 10 frames equally distant from each other will be
extracted
-
- ext - If video is sent in binary format, then ext is needed to tell
OpenCV which decoder to use. eg ".mp4"
+ API to classify videos
+ Request args -
+ url - PATH of file
+ topn - number of top scoring labels
+ min_confidence - minimum confidence that a label should have to exist
in topn
+ human - human readable or not
+ mode - Modes of frame extraction {"center", "interval", "fixed"}
+ "center" - Just one frame in center. <Default option>
+ "interval" - Extracts frames after fixed interval.
+ "fixed" - Extract fixed number of frames.
+ frame-interval - Interval for frame extraction to be used with
INTERVAL mode. If frame_interval=10 then every 10th frame will be extracted.
+ num-frame - Number of frames to be extracted from video while using
FIXED model. If num_frame=10 then 10 frames equally distant from each other
will be extracted
+
+ ext - If video is sent in binary format, then ext is needed to tell
OpenCV which decoder to use. eg ".mp4"
"""
st = current_time()
- topk = int(request.args.get("topk", "10"))
+ topn = int(request.args.get("topn", "5"))
+ min_confidence = float(request.args.get("min_confidence", "0.015"))
human = request.args.get("human", "true").lower() in ("true", "1", "yes")
-
- mode = request.args.get("mode", CENTER).lower()
- if mode not in ALLOWED_MODE:
+
+ mode = request.args.get("mode", "center").lower()
+ if mode not in {"center", "interval", "fixed"}:
'''
Throw invalid request error
'''
- return flask.Response(status=400, response=jsonify(error="not a valid
mode. Available mode %s" % str(ALLOWED_MODE)))
-
+ return Response(status=400, response=jsonify(error="not a valid mode.
Available mode %s" % str(ALLOWED_MODE)))
+
frame_interval = int(request.args.get("frame-interval", "10"))
num_frame = int(request.args.get("num-frame", "10"))
-
+
if request.method == 'POST':
video_data = request.get_data()
ext = request.args.get("ext", ".mp4").lower()
-
+
temp_file = tempfile.NamedTemporaryFile(suffix=ext)
temp_file.file.write(video_data)
temp_file.file.close()
-
+
url = temp_file.name
-
else:
url = request.args.get("url")
-
+
read_time = current_time() - st
- st = current_time() # reset start time
-
- if mode == CENTER:
+ st = current_time() # reset start time
+
+ if mode == "center":
image_data_arr = [get_center_frame(url)]
- elif mode == INTERVAL:
+ elif mode == "interval":
image_data_arr = get_frames_interval(url, frame_interval)
else:
image_data_arr = get_n_frames(url, num_frame)
-
+
classes = []
for image_data in image_data_arr:
try:
- _classes = app.classify(image_data , topk=None)
+ _classes = app.classify(image_data, topn=None, min_confidence=None)
except Exception as e:
app.logger.error(e)
return Response(status=400, response=str(e))
-
+
_classes.sort()
if len(classes) == 0:
classes = _classes
else:
- for idx,_c in enumerate(_classes):
+ for idx, _c in enumerate(_classes):
c = list(classes[idx])
c[2] += _c[2]
classes[idx] = tuple(c)
-
-
- # avg out confidence score
- for idx,c in enumerate(classes):
+
+ top_classes = []
+ for c in classes:
c = list(c)
- c[2] = c[2]/len(image_data_arr)
-
- classes[idx] = tuple(c)
-
- classes = sorted(classes, key=lambda tup: tup[2])[-topk:][::-1]
+ # avg out confidence score
+ avg_score = c[2] / len(image_data_arr)
+ c[2] = avg_score
+ if avg_score >= min_confidence:
+ top_classes.append(tuple(c))
+
+ top_classes = sorted(top_classes, key=lambda tup: tup[2])[-topn:][::-1]
+
+ classids, classnames, confidence = zip(*top_classes)
- classids, classnames, confidence = zip(*classes)
-
-
classifier_time = current_time() - st
app.logger.info("Classifier time : %d" % classifier_time)
res = {
- 'classids' : classids,
+ 'classids': classids,
'confidence': confidence,
'time': {
- 'read' : read_time,
+ 'read': read_time,
'classification': classifier_time,
'units': 'ms'
}
@@ -523,10 +472,12 @@ def classify_video():
res['classnames'] = classnames
return Response(response=json.dumps(res), status=200,
mimetype="application/json")
+
def main(_):
if not app.debug:
print("Serving on port %d" % FLAGS.port)
app.run(host="0.0.0.0", port=FLAGS.port)
+
if __name__ == '__main__':
tf.app.run()
diff --git
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
index 453f61f0f..a4c208bf9 100644
---
a/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
+++
b/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/video_util.py
@@ -1,40 +1,39 @@
#!/usr/bin/env python
-#
-# 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.
+# 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.
import cv2
-import os
-import sys
import ntpath
import numpy as np
-print "cv2.__version__", cv2.__version__
+print("cv2.__version__", cv2.__version__)
CV_FRAME_COUNT = None
-if hasattr(cv2,"cv"):
+if hasattr(cv2, "cv"):
CV_FRAME_COUNT = cv2.cv.CV_CAP_PROP_FRAME_COUNT
else:
CV_FRAME_COUNT = cv2.CAP_PROP_FRAME_COUNT
+
def _get_image_from_array(image_array):
- #JPG to support tensorflow
- byte_arr = cv2.imencode(".jpg", image_array )[1]
+ # JPG to support tensorflow
+ byte_arr = cv2.imencode(".jpg", image_array)[1]
return "".join(map(chr, byte_arr))
-
+
+
def _path_leaf(path):
"""
Returns file name from path. Path should not end with slash(/)
@@ -42,72 +41,73 @@ def _path_leaf(path):
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
+
def get_center_frame(video_path):
"""
Traverse till half of video and saves center snapshot
@param video_path: Path to video file on system
"""
cap = cv2.VideoCapture(video_path)
-
+
length = int(cap.get(CV_FRAME_COUNT))
-
- success,image = cap.read()
+
+ success, image = cap.read()
count = 0
-
- while(success and count < length/2):
- success,image = cap.read()
- count += 1
-
+
+ while success and count < length / 2:
+ success, image = cap.read()
+ count += 1
+
return _get_image_from_array(image)
-
+
+
def get_frames_interval(video_path, frame_interval):
"""
- Selects one frames after every frame_interval
+ Selects one frames after every frame_interval
@param video_path: Path to video file on system
@param frame_interval: Interval after which frame should be picked. If
frame_interval=10 then every 10th frame will be extracted
"""
cap = cv2.VideoCapture(video_path)
-
+
length = int(cap.get(CV_FRAME_COUNT))
-
- success,image = cap.read()
+
+ success, image = cap.read()
count = 0
-
+
image_arr = []
- while(success and count < length):
- success,image = cap.read()
+ while success and count < length:
+ success, image = cap.read()
if count % frame_interval == 0:
- image = _get_image_from_array(image )
+ image = _get_image_from_array(image)
image_arr.append(image)
-
+
count += 1
-
+
return image_arr
-
+
+
def get_n_frames(video_path, num_frame):
"""
- Get N frames equidistant to each other in a video
+ Get N frames equidistant to each other in a video
@param video_path: Path to video file on system
@param num_frame: Number of frames to be extracted from video. If
num_frame=10 then 10 frames equally distant from each other will be extracted
"""
cap = cv2.VideoCapture(video_path)
-
+
length = int(cap.get(CV_FRAME_COUNT))
-
- op_frame_idx = set(np.linspace(0, length - 2, num_frame, dtype=int))
-
+
+ op_frame_idx = set(np.linspace(0, length - 2, num_frame, dtype=int))
+
success, image = cap.read()
count = 0
-
+
image_arr = []
- while(success and count < length):
+ while success and count < length:
success, image = cap.read()
if success and count in op_frame_idx:
-
- image = _get_image_from_array(image )
+ image = _get_image_from_array(image)
image_arr.append(image)
- count += 1
-
- return image_arr
+ count += 1
+ return image_arr
diff --git
a/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest.xml
b/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest.xml
index b0e5099b1..69a65d076 100644
---
a/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest.xml
+++
b/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-rest.xml
@@ -23,6 +23,7 @@
<mime>image/png</mime>
<mime>image/gif</mime>
<params>
+ <param name="apiBaseUri"
type="uri">http://localhost:8764/inception/v4</param>
<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>
diff --git
a/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
b/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
index c6c50ed83..eb634a391 100644
---
a/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
+++
b/tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
@@ -21,11 +21,11 @@
<parser
class="org.apache.tika.parser.recognition.ObjectRecognitionParser">
<mime>video/mp4</mime>
<params>
- <param name="topN" type="int">4</param>
+ <param name="apiBaseUri"
type="uri">http://localhost:8764/inception/v4</param>
+ <param name="topN" type="int">2</param>
<param name="minConfidence" type="double">0.015</param>
+ <param name="mode" type="string">fixed</param>
<param name="class"
type="string">org.apache.tika.parser.recognition.tf.TensorflowRESTVideoRecogniser</param>
- <param name="healthUri"
type="uri">http://localhost:8764/inception/v4/ping</param>
- <param name="apiUri"
type="uri">http://localhost:8764/inception/v4/classify/video?mode=fixed</param>
</params>
</parser>
</parsers>
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> Standardizing current Object Recognition REST parsers
> -----------------------------------------------------
>
> Key: TIKA-2400
> URL: https://issues.apache.org/jira/browse/TIKA-2400
> Project: Tika
> Issue Type: Sub-task
> Components: parser
> Reporter: Thejan Wijesinghe
> Priority: Minor
> Fix For: 1.17
>
>
> # This involves adding apiBaseUris and refactoring current Object Recognition
> REST parsers,
> # Refactoring dockerfiles related to those parsers.
> # Moving the logic related to checking minimum confidence into servers
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