ipysegment

Approaching usefulness! Still no way to extract the labelling data - also currently only allows for a single class.

Controls

• left click to lasso

• right/middle click to pan

• scroll to zoom

ipysegment

manual image segmentation in jupyter

Installation

You can install using pip:

pip install ipysegment

Or if you use jupyterlab:

pip install ipysegment
jupyter labextension install @jupyter-widgets/jupyterlab-manager

If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:

jupyter nbextension enable --py [--sys-prefix|--user|--system] ipysegment

Contributing

Resources for learning how to make a jupyter widget

1. official docs
   • you gotta scroll down to get to the good stuff. I would start at widget skeleton
2. custom-ipwidget-howto
   • written by me so not super official
   • All the stuff I've figured out that isn't in the official docs - e.g. a complete(i hope) listing of all the methods availiable to widgets
3. ipycanvas
   • general canvas widget for jupyter. Good source of inspiration for this extension

Development Install

# First install the python package. This will also build the JS packages.
pip install -e ".[test, examples]"

When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For lab, this is done by the command:

jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
jupyter labextension install .

For classic notebook, you can run:

jupyter nbextension install --sys-prefix --symlink --overwrite --py <your python package name>
jupyter nbextension enable --sys-prefix --py <your python package name>

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

How to see your changes

Typescript:

To continuously monitor the project for changes and automatically trigger a rebuild, start Jupyter in watch mode:

jupyter lab --watch

And in a separate session, begin watching the source directory for changes:

npm run watch

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

Python:

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.