Improve search widget for image data

[phurwicz]

Feb 24 2022: currently using vector search.

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Image search is rather unclear compared with text. Things to consider:

• what kind of Bokeh widget to use. Could be FileInput for uploads or TextInput for urls
• what kind of search is appropriate (as in, making semantic sense and trying to be independent from the vectorizer/dimensionality reduction)
  • look into Structural Similarity
  • more research would be helpful

[phurwicz]

More on image matching

[phurwicz]

Work in progress: first implement a vector-based search. Then try to upgrade to structural similarity. If structural similarity works well, we can do the same for audio in its MFCC format.

[haochuanwei]

Update: consider an abstract vector search engine, or an abstract similarity-based search engine.

[FlorianBertonBrightClue]

Hi, I'm using hover to annote a dataset of image, and I encounter a small issue regarding the visualization of the image.

If the resolution is higher than the row width or row height define for the table we don't visualize very well.

To fix this you can just change in hover/core/local_config.py Line 60: template="""<img src=<%= value %>>""", To: template="""<img src=<%= value % width="200" height="200">>""",

It will automatically resize the image in a 200x200 and so you can see image with for instance a resolution of 720x720

I can do a PR for that if you want but the modifications is very small

[haochuanwei]

Hi, @FlorianBertonBrightClue thank you for using hover and bringing this up!

In some use cases 200x200 can be difficult to see clearly. Actually we can make it configurable on the user side.

The code below should work for the upcoming 0.8.0 version, which is likely within a week.

hover.config["visual"]["table_image_width"] = 200
hover.config["visual"]["table_image_height"] = 200

Does this look good?

[FlorianBertonBrightClue]

And so at line 57/58 in local.config.py, you will put this ? feature_col_kwargs["formatter"] = HTMLTemplateFormatter( template=f'<img src=<%= value %> width="{hover.config["visual"]["table_image_width"]}" height="{hover.config["visual"]["table_image_height"]} >', )

If yes, it should work and the user could configure it as ha wants by setting the hover.config.

I also have two questions for you :

• It seems for now that we can't set the parameters for the DimensionalityReducer, would it be possible later ? Like for instance in umap you can choose the number of neighbor or the minimum distance.

• Can we change the label for data that are already in train ? In my case I did some prelabelling and sometimes I want to change the label because two clusters are close one to each other and finally and want to merge them together

[haochuanwei]

And so at line 57/58 in local.config.py, you will put this ? feature_col_kwargs["formatter"] = HTMLTemplateFormatter( template=f'<img src=<%= value %> width="{hover.config["visual"]["table_image_width"]}" height="{hover.config["visual"]["table_image_height"]} >', )

Basically yes. This line reads the config only once though, so be sure to configure immediately after import hover.

Customize DimensionalityReducer

• It seems for now that we can't set the parameters for the DimensionalityReducer, would it be possible later ? Like for instance in umap you can choose the number of neighbor or the minimum distance.

Technically you can. With dataset.compute_nd_embedding() you can pass in keyword arguments that umap accepts. Hover attempts to “translate” crucial kwargs for compatibility (like “dimension” to different equivalents in umap and ivis) but will forward the rest.

This could be much better documented though.

Edit committed labels

• Can we change the label for data that are already in train ? In my case I did some prelabelling and sometimes I want to change the label because two clusters are close one to each other and finally and want to merge them together

You can do this a few ways depending on which one is convenient:

• in the selection table (the one where large images don’t show well right now), make edits in the label column and save the edits.
• access the underlying dataframe with dataset.dfs["train"].
• export to file, edit and load back.

You cannot change train labels directly the same way you label raw data in the scatter plot. “Commit” locks in the subset and label unless you take the “backdoors” above. This is to prevent mis-relabeling labeled data that happen to be (often for good reasons) mixed into in a selection.