Examples on Binder

Data types:

• Binder: 2D ITK Images

• Binder: 3D ITK Images

• Binder: Dask Array images

• Binder: Large volumes

• Binder: NumPy array images (processed with SciPy)

• Binder: NumPy array images (processed with scikit-image)

• Binder: NumPy array for image with anisotropic spacing

• Binder: NumPy array point sets

• Binder: ITK Mesh

• Binder: skan segmentation skeleton

Tasks:

• Binder: Compare images with a checkerboard pattern

• Binder: Compare images side by side

• Binder: Examine a line profile

• Binder: Interactively explore algorithm parameters

• Binder: Record a video

• Binder: Select a region of interest

• Binder: Specify camera parameters

• Binder: Specify a colormap

Installation

To install the widgets for the Jupyter Notebook with pip:

pip install itkwidgets

or with conda:

conda install -c conda-forge itkwidgets

For Jupyter Lab, additionally run:

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib jupyterlab-datawidgets itkwidgets

Usage

In Jupyter, import the view function:

from itkwidgets import view

Then, call the view function at the end of a cell, passing in the image to examine:

view(image)

For information on additional options, see the view function docstring:

view?

Other available widgets:

• itkwidgets.line_profile: Plot an intensity line profile.

• itkwidgets.checkerboard: Compare two images in a checkerboard pattern.

Examples

After installation, try the following examples that demonstrate how to visualize:

• 2D ITK Images

• 3D ITK Images

• Dask Array images

• Large volumes

• ImageJ ImgLib2 images (requires conda and a local Fiji installation)

• NumPy array images (processed with SciPy)

• NumPy array images (processed with scikit-image)

• NumPy array for image with anisotropic spacing

• VTK vtkImageData

• pyvista UniformGrid

• NumPy array point sets

• ITK Mesh

• VTK vtkPolyData

• VTK vtkUnstructuredGrid

• pyvista PolyData

• pyvista StructuredGrid

• pyvista UnstructuredGrid

• pyvista LiDAR

• vtkplotter actors and volumes

• skan segmentation skeleton

or how to:

• Compares images with a checkerboard pattern

• Compares images side by side

• Examine a line profile

• Interatively explore algorithm parameters

• Record a video

• Select a region of interest

• Specify camera parameters

• Specify a colormap

Troubleshooting

If you experience the notebook warning:

IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_data_rate_limit`.

Set the notebook configuration value:

jupyter notebook --NotebookApp.iopub_data_rate_limit=1e12

Hacking

Participation is welcome! For a development installation (requires Node.js):

git clone https://github.com/InsightSoftwareConsortium/itkwidgets.git
cd itkwidgets
python -m pip install -r requirements-dev.txt -r requirements.txt
python -m pip install -e .
jupyter nbextension install --py --symlink --sys-prefix itkwidgets
jupyter nbextension enable --py --sys-prefix itkwidgets
jupyter nbextension enable --py --sys-prefix widgetsnbextension
python -m pytest

The above commands will setup your system for development with the Jupyter Notebook. In one terminal, start Jupyter:

cd itkwidgets
jupyter notebook

In another terminal, put Webpack in watch mode to rebuild any Javascript changes when you save a Javascript file:

cd itkwidgets
npm run watch

If Python code is changed, restart the kernel to see the changes. If Javascript code is changed, reload the page after to Webpack has finished building.

To develop for Jupyter Lab, additionally run:

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib jupyterlab-datawidgets jupyter-webrtc
jupyter labextension install ./js
jupyter lab --watch