Submission to JOSS

This release include improvements based on review feedback from our JOSS submission. We want to thank our reviewers Carl Simon Adorf, Matthew Feickert and our editor Daniel S. Katz for their valuable contributions.

Add API Reference to doc pages

Add API Reference to doc pages.
This release also contains some minor bug fixes.

Cross Platform Support for Tutorials

• Add missing tqdm dependency.
• Support notebooks and tutorials across platform by replacing Linux commands by python equivalents.

New sphinx based Documentation Theme

• New fit_canvas option to adjust images to the size of the canvas by Ítalo Epifânio.
• Annotator step (explore, create, improve) can be recovered when using Ipyannotator API by Ítalo Epifânio.
• New documentation theme and structure by Carlos Cerqueira and Ítalo Epifânio.

Usability and Code Improvements

This release adds a large range of usability and code improvements.

A large fraction of annotation code has been rewritten providing the basis for a range of directly user impacting
improvements. The use a ipyannotator as standalone app has been simplified by updating the voila dependency.
The overall code quality has been improved by becoming flak8 compliant and the gradual adding of typing checked by mypi.

Support Multi-Object-Tracking (MOT) Annotation for Videos

This release adds video annotation support. This means you can now explore and create multi object tracking datasets right in your jupyter notebook. Many other features have been included to make this possible (bbox labeling, mulit-bbox support, and many more).
Take a look at the following jupyter notebook

• nbs/01d_tutorial_video_annotator.ipynb

to explore our new video annotation support.

Event Driven Architecture

This release provides a comprehensive refactoring of ipyannotator that allows for much better decoupling of individual annotation components. The new event driven architecture is based on the awesome PyPubSub library.
The following jupyter notebook

• nbs/11-build-annotator-tutorial.ipynb

provides a tutorial that demonstrates how to build new annotators based on the new architecture.

Explore, Improve and Create

This release adds support and tutorials for explore, create and improve image classification and bounding box annotation.

• 01b_tutorial_image_classification.ipynb
• 01c_tutorial_bbox.ipynb

Maintenance Release

v0.3.0

Merge remote-tracking branch 'github/main' into github-master