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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2322) Video labeling using existing ObjectRecognition
Date Fri, 28 Apr 2017 05:04:04 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15988207#comment-15988207
] 

ASF GitHub Bot commented on TIKA-2322:
--------------------------------------

chrismattmann commented on issue #168: fix for TIKA-2322 contributed by msharan@usc.edu
URL: https://github.com/apache/tika/pull/168#issuecomment-297907725
 
 
   OK I was able to build your latest Docker @smadha from https://github.com/apache/tika/pull/168/commits/434736be63373e8caa85fd8c9bd117e6edbec555
https://github.com/apache/tika/pull/168/commits/58a116c2123d9c01ba054969121244364059c0d2 and
and found the following:
   
   == Running the Tika App Client Command
   ```
   LMC-053601:smadha-tika mattmann$ java -jar tika-app/target/tika-app-1.15-SNAPSHOT.jar --config=tika-parsers/src/test/resources/org/apache/tika/parser/recognition/tika-config-tflow-video-rest.xml
./tika-parsers/src/test/resources/test-documents/testVideoMp4.mp4
   WARN  JBIG2ImageReader not loaded. jbig2 files will be ignored
   INFO  Available = true, API Status = HTTP/1.0 200 OK
   INFO  minConfidence = 0.015, topN=4
   INFO  Recogniser = org.apache.tika.parser.recognition.tf.TensorflowRESTVideoRecogniser
   INFO  Recogniser Available = true
   WARN  Status = HTTP/1.0 500 INTERNAL SERVER ERROR
   WARN  Response = <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">
   <title>500 Internal Server Error</title>
   <h1>Internal Server Error</h1>
   <p>The server encountered an internal error and was unable to complete your request.
 Either the server is overloaded or there is an error in the application.</p>
   
   WARN  NO objects
   LMC-053601:smadha-tika mattmann$ 
   ```
   
   == Results (from Tensorflow Video Docker Server)
   ```
    * Running on http://0.0.0.0:8764/ (Press CTRL+C to quit)
   10.0.2.2 - - [28/Apr/2017 05:01:26] "GET /inception/v4/ping HTTP/1.1" 200 -
   [2017-04-28 05:01:26,287] ERROR in app: Exception on /inception/v4/classify/video [POST]
   Traceback (most recent call last):
     File "/opt/conda/lib/python2.7/site-packages/flask/app.py", line 1982, in wsgi_app
       response = self.full_dispatch_request()
     File "/opt/conda/lib/python2.7/site-packages/flask/app.py", line 1614, in full_dispatch_request
       rv = self.handle_user_exception(e)
     File "/opt/conda/lib/python2.7/site-packages/flask/app.py", line 1517, in handle_user_exception
       reraise(exc_type, exc_value, tb)
     File "/opt/conda/lib/python2.7/site-packages/flask/app.py", line 1612, in full_dispatch_request
       rv = self.dispatch_request()
     File "/opt/conda/lib/python2.7/site-packages/flask/app.py", line 1598, in dispatch_request
       return self.view_functions[rule.endpoint](**req.view_args)
     File "/usr/bin/inceptionapi.py", line 489, in classify_video
       classids, classnames, confidence = zip(*classes)
   ValueError: need more than 0 values to unpack
   10.0.2.2 - - [28/Apr/2017 05:01:26] "POST /inception/v4/classify/video?mode=fixed&ext=.mp4
HTTP/1.1" 500 
   ```
   
 
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> Video labeling using existing ObjectRecognition
> -----------------------------------------------
>
>                 Key: TIKA-2322
>                 URL: https://issues.apache.org/jira/browse/TIKA-2322
>             Project: Tika
>          Issue Type: Improvement
>          Components: parser
>            Reporter: Madhav Sharan
>            Assignee: Chris A. Mattmann
>              Labels: memex
>             Fix For: 1.15
>
>
> Currently TIKA supports ObjectRecognition in Images. I am proposing to extend this to
support videos. 
> Idea is -
> 1. Extract frames from video and run IncV3 to get labels for these frames. 
> 2. We average confidence scores of same labels for each frame. 
> 3. Return results in sorted order of confidence score. 
> I am writing code for different modes of frame extractions -
> 1. Extract center image.
> 2. Extract frames after every fixed interval.
> 3. Extract N frames equally divided across video.
> We used this approach in [0]. Code in [1]
> [0] https://github.com/USCDataScience/hadoop-pot
> [1] https://github.com/USCDataScience/video-recognition



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