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November 21, 2021

over 2 years ago by Grace H

We’re helping you search images with specific criteria and import annotations with more accuracy with this release. We also added a small but helpful feature in the Video Editor. Let’s go!

Advanced search

Advanced Search allows users to search images in Vector Projects using more specific criteria than just image names.

To use Advanced Search for the first time in a project:

  1. In Data, click Advanced (β).
  2. Click Sync data.
  3. After your data is synced successfully, type a query in the query bar.

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Advanced Search works in Vector Projects only. To use Advanced Search on images uploaded with Python SDK, images with attached URLs, or frames, you need to annotate them first. If you don’t annotate them, they won’t appear in the advanced search results.

Here are a few query examples:

  • To find images by image name: data->'metadata'->>'name'='imageName.jpg'
  • To find images by the name of the assigned QA: data->'metadata'->>'qaName'='Taylor Brown'
  • To find images by the class name: data-> 'instancesMetadata'-> 'classes' ->> 'pedestrian' > '2'
  • To find images with resolved comments: exists(select from jsonb_array_elements(data -> 'comments') as comments where comments ->> 'resolved' = 'true' HAVING COUNT(*)>0)

Custom time length to jump forward and backward

To adjust the time length to jump forward or backward in the Video Editor:

  1. In the top panel of the Video Editor, click the settings button.
  2. Type a number in the boxes corresponding to Jump forward and Jump backward.
  3. Click outside the dropdown menu to apply the changes.

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You can type decimals.

Validate annotations JSON

SuperAnnotate supports specific annotation formats that you need to take into consideration when uploading annotations.

We added annotation schema validators to help you evaluate the validity of the annotations you want to upload and detect mistakes. Learn more.

Dataframe aggregations for video annotations

We enhanced the aggregate_annotations_as_df function to work on Video Projects. So, we made it more generic to work with all project types.

annotations_df = sa.aggregate_annotations_as_df(
        project_root = “./exports/video_annotations”, 
        project_type = “Video”, 
        folder_names = [“test”, “train”])

The columns of the returned dataframe will vary depending on the project type. Learn more.

What do you think of the updates? We’d love to hear your feedback.

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