The ultimate image annotation tool in a Jupyter Notebook!

Follows:

• How to install
• How to use
• Examples

Installation instructions

This project requires Python>=3.9, npm and nodejs (LTS, version 16). Their installation is on you.

If you want to create a fresh new virtual environment, open a terminal in this same place (ipyfan). run (from inside ipyfan): bash install.sh.

install.sh contains simple installation steps, thus using an already existing virtual environment is as simple as commenting the first few lines.

note: this works for bash. Yet, you can customize install.sh so it works with fish too. If it doesn't work, please check that the appropiate versions of Python and Node are in use and open an issue.

test with example_single_image.ipynb

source env_ipyfan/bin/activate  # activate the installation environment
cd example
jupyter notebook example_single_image.ipynb

If everything goes well, you should be able to Restart and run all in the example notebook and start annotating the demo images.

What is currently available?

Tool	Left click (hold)	Right click (hold)	Wheel	Middle click (hold)	Tool slider	Description
Lasso	(Draw contour)	(Pan)	Zoom in/out	(Pan)	-	-
Brush	Paint around cursor	(Pan)	Zoom in/out	(Pan)	Change brush size	-
Eraser	Erase around cursor	(Pan)	Zoom in/out	(Pan)	Change eraser size	The only way to reduce violet mask
IIS	Positive click	Negative click	-	-	-	-
Superpixels	Add positive click/(scribble)	Add negative click/(scribble)	-	-	Change superpixel scale	A segment is proposed if #pos > #neg
Clustering	Add positive click/(scribble)	Add negative click/(scribble)	-	-	Change number of clusters	A segment is proposed if #pos > #neg
Cosine	Add positive click/(scribble)	-	-	-	Change mean cosine similarity threshold	-
Gaussian	Add positive click/(scribble)	Add negative click/(scribble) if #neg < #pos-1	-	-	Change threshold on Mahalanobis distance	Using large #neg causes inestability

Remarks

• Final masks are violet.
• Masks that are not violet are proposals (or reference).
• Proposals (reference) are made violet via the "Use proposal" ("Use reference") button.
• All modifications (except eraser) are only additive.
• Methods are run after a tool slider or selected tool change.
• For more detail, read the user_guide.md

Buzzwords

This artificial intelligence (AI) assisted image annotation tool works locally and is awesome. It integrates great interactive image segmentation algorithms and a superpixel based segmentation. It is highly customizable by using Python (or Typescript). The objective is to quickly annotate segmentations or masks in images so to train deep learning computer vision models. We use Pytorch, Scikit-learn, scikit image, timm, ipywidgets and traitlets.

Acknowledgment

Inspired by Ian Hunt-Isaak's annotation tool

Dev

How to see your Typescript change:

To continuously monitor the project for changes and automatically:

npm run watch

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

How to see your Python change:

Restart the kernel of the notebook.

Changelog

• 2022.08.30: make gaussian estimation more robust
• 2022.08.30: increase display size by passing layout parameter
• 2022.08.30: add GIF

To-do / feature requests

• allow pan and zoom while using IIS (and more generally, advanced tools)
• add DINO feature extractor
• display image filename
• start with the full image
• add another layer for:
  • eraser siluette
• undo button
• load previously annotated mask as reference
• create universal read_image function
• avoid crashes if neg pos clicking the same place