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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2262) Supporting Image-to-Text (Image Captioning) in Tika for Image MIME Types
Date Fri, 09 Jun 2017 19:43:18 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2262?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16044905#comment-16044905
] 

ASF GitHub Bot commented on TIKA-2262:
--------------------------------------

thammegowda commented on a change in pull request #180: Fix for TIKA-2262: Supporting Image-to-Text
(Image Captioning) in Tika
URL: https://github.com/apache/tika/pull/180#discussion_r121207360
 
 

 ##########
 File path: tika-parsers/src/main/java/org/apache/tika/parser/recognition/ObjectRecognitionParser.java
 ##########
 @@ -117,55 +130,68 @@ public synchronized void parse(InputStream stream, ContentHandler handler,
Metad
         }
         metadata.set(MD_REC_IMPL_KEY, recogniser.getClass().getName());
         long start = System.currentTimeMillis();
-        List<RecognisedObject> objects = recogniser.recognise(stream, handler, metadata,
context);
+        List<? extends RecognisedObject> objects = recogniser.recognise(stream, handler,
metadata, context);
+
         LOG.debug("Found {} objects", objects != null ? objects.size() : 0);
         LOG.debug("Time taken {}ms", System.currentTimeMillis() - start);
+
         if (objects != null && !objects.isEmpty()) {
+            int count;
+            List<RecognisedObject> acceptedObjects = new ArrayList<RecognisedObject>();
+            List<String> xhtmlIds = new ArrayList<String>();
+            String xhtmlStartVal = null;
+
+            if (recogniser instanceof TensorflowRESTRecogniser || recogniser instanceof TensorflowImageRecParser)
{
 
 Review comment:
   :-1:  There is a better way to handle this.
   
   In Model and Services terminalogy, we have
    `TensorflowRESTRecogniser`, `TensorflowImageRecParser`, and `TensorflowRESTCaptioner`
as services
   `RecognisedObject` and `CaptionObject` as models.
   
   The problem:
   the condition is on the service, i.e. `recogniser instanceof TensorflowRESTRecogniser`
   What if we add a new awesome service tomorrow? We need to change this code right?
   
   The solution:
     Make your decision based on the model object
   
   i.e. check 
   ```java
   for (RecognisedObject object: objects) {
     if (object instanceof CaptionObject`) {
       This result is a caption
     } else {
       this is from something else, the default one
     }
   }
   ```
   As long as the new services return data in the same model, the code will work.
   Let me know if this needs more explanation!
 
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> Supporting Image-to-Text (Image Captioning) in Tika for Image MIME Types
> ------------------------------------------------------------------------
>
>                 Key: TIKA-2262
>                 URL: https://issues.apache.org/jira/browse/TIKA-2262
>             Project: Tika
>          Issue Type: Improvement
>          Components: parser
>            Reporter: Thamme Gowda
>              Labels: deeplearning, gsoc2017, machine_learning
>
> h2. Background:
> Image captions are a small piece of text, usually of one line, added to the metadata
of images to provide a brief summary of the scenery in the image. 
> It is a challenging and interesting problem in the domain of computer vision. Tika already
has a support for image recognition via [Object Recognition Parser, TIKA-1993| https://issues.apache.org/jira/browse/TIKA-1993]
which uses an InceptionV3 model pre-trained on ImageNet dataset using tensorflow. 
> Captioning an image is a very useful feature since it helps text based Information Retrieval(IR)
systems to "understand" the scenery in images.
> h2. Technical details and references:
> * Google has long back open sourced their 'show and tell' neural network and its model
for autogenerating captions. [Source Code| https://github.com/tensorflow/models/tree/master/im2txt],
[Research blog| https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html]
> * Integrate it the same way as the ObjectRecognitionParser
> ** Create a RESTful API Service [similar to this| https://wiki.apache.org/tika/TikaAndVision#A2._Tensorflow_Using_REST_Server]

> ** Extend or enhance ObjectRecognitionParser or one of its implementation
> h2. {skills, learning, homework} for GSoC students
> * Knowledge of languages: java AND python, and maven build system
> * RESTful APIs 
> * tensorflow/keras,
> * deeplearning
> ----
> Alternatively, a little more harder path for experienced:
> [Import keras/tensorflow model to deeplearning4j|https://deeplearning4j.org/model-import-keras
] and run them natively inside JVM.
> h4. Benefits
> * no RESTful integration required. thus no external dependencies
> * easy to distribute on hadoop/spark clusters
> h4. Hurdles:
> * This is a work in progress feature on deeplearning4j and hence expected to have lots
of troubles on the way! 



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