CIL-Demos is a collection of jupyter notebooks, designed to introduce you to the Core Imaging Library (CIL).
The demos can be found in the demos folder, and the README.md in this folder provides some info about the notebooks, including the additional datasets which are required to run them.
To open and run the notebooks interactively in an executable environment, please click the Binder link above.
Note: In the Binder interface, there is no GPU available.
Note: In the Google Cloud platform, there is free GPU (16Gb). However, you need to install CIL manually.
To install via conda
, create a new environment using:
conda create --name cil -c conda-forge -c https://software.repos.intel.com/python/conda -c ccpi cil=24.2.0 ipp=2021.12 astra-toolbox=*=cuda* tigre ccpi-regulariser tomophantom ipykernel ipywidgets scikit-image
where,
astra-toolbox
will allow you to use CIL with the ASTRA toolbox projectors (GPLv3 license).
tigre
will allow you to use CIL with the TIGRE toolbox projectors (BSD license).
ccpi-regulariser
will give you access to the CCPi Regularisation Toolkit.
tomophantom
Tomophantom will allow you to generate phantoms to use as test data.
cudatoolkit
If you have GPU drivers compatible with more recent CUDA versions you can modify this package selector (installing tigre via conda requires 9.2).
ipywidgets
will allow you to use interactive widgets in our jupyter notebooks.
CIL's optimised FDK/FBP recon
module requires:
- the Intel Integrated Performance Primitives Library (license) which can be installed via conda from the
https://software.repos.intel.com/python/conda
channel. - TIGRE, which can be installed via conda from the
ccpi
channel.
-
Activate your environment using:
conda activate cil-demos
. -
Clone the
CIL-Demos
repository and move into theCIL-Demos
folder. -
Run:
jupyter-notebook
on the command line. -
Navigate into
demos/1_Introduction
The best place to start is the 01_intro_walnut_conebeam.ipynb
notebook.
However, this requires installing the walnut dataset.
To test your notebook installation, instead run 03_preprocessing.ipynb
, which uses a dataset shipped with CIL, which will
have automatically been installed by conda.
Instead of using the jupyter-notebook
command, an alternative is to run the notebooks in VSCode
.
For more advanced general imaging and tomography demos, please visit the following repositories: