itk-jupyter-widgets
Interactive Jupyter widgets to visualize images in 2D and 3D.
Key Features:
- Exquisite volume rendering
- Tri-plane volume slicing
- Innovative, powerful opacity transfer function / window / level widget
- Support for
- NumPy Arrays
- itk.Image
- vtk.vtkImageData, vtki.UniformGrid
- Dask Arrays
- ImageJ / Fiji / ImageJ2 images
- Additional NumPy Array-like objects
- Anisotropic voxel spacing supported
- 3D and 2D image support
- Line profiles
- Combine with other ipywidgets to quickly create graphical interfaces to algorithms
These widgets are designed to support image analysis with the Insight Toolkit (ITK), but they also work with other spatial analysis tools in the scientific Python ecosystem.
These widgets are built on itk.js and vtk.js.
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)
Tasks:
- Binder: Compare images with a checkerboard pattern
- Binder: Examine a line profile
- Binder: Interactively explore algorithm parameters
- Binder: Record a video
- Binder: Select a region of interest
- 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 itk-jupyter-widgets
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.
Advanced Usage
The itk-jupyter-widgets are based on ipywidgets. As a consequence, widgets traits can be queried, assigned, or observed with the viewer object returned by the view function. itk-jupyter-widgets can be combined with other ipywidgets to quickly explore algorithm parameters, create graphical interfaces, or create data visualization dashboards.
Mouse Controls
- Left click + drag
- Rotate
- Right click + drag or shift + left click + drag
- Pan
- Mouse wheel or control + left click + drag or pinch
- Zoom
- Alt + left click + drag left-right
- Change color transfer function window
- Shift + left click + drag top-bottom
- Change color transfer function level
- Shift + alt + left click + drag top-bottom
- Change primary Gaussian volume opacity transfer function magnitude
Keyboard Shortcuts
Keyboard shortcuts take effect when the mouse is positioned inside the viewer. All shortcuts are prefixed with Alt+. Corresponding keys for the Dvorak keyboard layout have the same effect.
- Alt + 1
- X-plane mode
- Alt + 2
- Y-plane mode
- Alt + 3
- Z-plane mode
- Alt + 4
- Volume rendering mode
- Alt + q
- Toggle user interface
- Alt + w
- Toggle region of interest (ROI) selection widget
- Alt + e
- Reset ROI
- Alt + r
- Reset camera
- Alt + s
- Toggle slicing planes in volume rendering mode
- Alt + f
- Toggle fullscreen
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)
- VTK vtkImageData
- vtki UniformGrid
or how to:
- Compares images with a checkerboard pattern
- Examine a line profile
- Interatively explore algorithm parameters
- Record a video
- Select a region of interest
- 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/itk-jupyter-widgets.git cd itk-jupyter-widgets 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. To develop for Jupyter Lab, additionally run:
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter labextension install ./js
Warning
This project is under active development. Its API and behavior may change at any time. We mean it.