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https://issues.apache.org/jira/browse/TIKA-2322?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15973267#comment-15973267
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ASF GitHub Bot commented on TIKA-2322:
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ThejanW commented on issue #168: fix for TIKA-2322 contributed by
msha...@usc.edu
URL: https://github.com/apache/tika/pull/168#issuecomment-294943747
Hi @smadha, this is interesting. Since you are proposing this as an
improvement to existing ObjectRecognition parser, why not update the
InceptionRestDockerfile[1] to install OpenCV within the container, otherwise
one should have Tensorflow, Flask, Requests, OpenCV all installed in his
system. Needless to say that there can be version conflicts of these
dependencies with the versions the user has.
[1]
https://github.com/apache/tika/blob/master/tika-parsers/src/main/resources/org/apache/tika/parser/recognition/tf/InceptionRestDockerfile
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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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