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The Core Imaging Library (CIL) is an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered backprojection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multichannel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimisation framework for prototyping reconstruction methods including sparsity and total variation regularisation, as well as tools for loading, preprocessing and visualising tomographic data.
The documentation for CIL can be accessed here.
Binary installation of CIL can be achieved with conda.
We recommend using either miniconda or miniforge, which are both minimal installers for conda. We also recommend a conda version of at least 23.10 for quicker installation.
Install a new environment using:
conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0To install CIL and the additional packages and plugins needed to run the CIL demos install the environment with:
conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0 astra-toolbox tigre ccpi-regulariser tomophantom "ipywidgets<8"where:
astra-toolbox(requires an NVIDIA GPU) enables CIL support for ASTRA toolbox projectors (GPLv3 license)tigre(requires an NVIDIA GPU) enables support for TIGRE toolbox projectors (BSD license)ccpi-regulariseris the CCPi Regularisation Toolkittomophantomcan generate phantoms to use as test data Tomophantom
CIL's optimised FDK/FBP recon module requires:
- the Intel Integrated Performance Primitives Library (license) which can be installed via conda from the
intelchannel. - TIGRE, which can be installed via conda from the
ccpichannel.
Finally, CIL can be run via a Jupyter Notebook enabled Docker container:
docker run --rm --gpus all -p 8888:8888 -it ghcr.io/tomographicimaging/cil:latestGPU support requires nvidia-container-toolkit and an NVIDIA GPU.
Omit the --gpus all to run without GPU support.
Jupyter Notebooks usage examples without any local installation are provided in Binder. Please click the launch binder icon above. For more information, go to CIL-Demos and https://mybinder.org.
- PyCon DE & PyData Berlin 2022, Apr 2022: Abstract, Video, Material
- Training School for the Synergistic Image Reconstruction Framework (SIRF) and Core Imaging Library (CIL), Jun 2021: Videos, Material
- Synergistic Reconstruction Symposium, Nov 2019: Slides, Videos, Material
In case of development it is useful to be able to build the software directly. You should clone this repository as
git clone --recurse-submodule git@github.com:TomographicImaging/CIL.gitThe use of
8000
--recurse-submodule is necessary if the user wants the examples data to be fetched (they are needed by the unit tests). We have moved such data, previously hosted in this repo at Wrappers/Python/data to the CIL-data repository and linked it to this one as submodule. If the data is not available it can be fetched in an already cloned repository as
git submodule update --init --recursiveTo create a conda environment with all the dependencies for building CIL run the following shell script:
sh scripts/create_local_env_for_cil_development.sh -n NUMPY_VERSION -p PYTHON_VERSION -e ENVIRONMENT_NAMEOr with the CIL build and test dependencies:
sh scripts/create_local_env_for_cil_development_tests.sh -n NUMPY_VERSION -p PYTHON_VERSION -e ENVIRONMENT_NAMEAnd then install CIL in to this environment using CMake.
Alternatively, one can use the scripts/requirements-test.yml to create a conda environment with all the
appropriate dependencies on any OS, using the following command:
conda env create -f scripts/requirements-test.ymlCMake and a C++ compiler are required to build the source code. Let's suppose that the user is in the source directory, then the following commands should work:
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=<install_directory>
cmake --build . --target installIf targeting an active conda environment then the <install_directory> can be set to the CONDA_PREFIX environment variable (e.g. ${CONDA_PREFIX} in Bash, or %CONDA_PREFIX% in the Anaconda Prompt on Windows).
If not installing to a conda environment then the user will also need to set the locations of the IPP library and includes, and the path to CIL.
By default the location of the IPP library and includes is ${CMAKE_INSTALL_PREFIX}/lib and ${CMAKE_INSTALL_PREFIX}/include respectively. To pass the location of the IPP library and headers please pass the following parameters:
cmake .. -DCMAKE_INSTALL_PREFIX=<install_directory> -DIPP_LIBRARY=<path_to_ipp_library> -DIPP_INCLUDE=<path_to_ipp_includes>The user will then need to add the path <install_directory>/lib to the environment variable PATH or LD_LIBRARY_PATH, depending on system OS.
In the repository root, simply update submodules and run docker build:
git submodule update --init --recursive
docker build . -t ghcr.io/tomographicimaging/cil[1] Jørgensen JS et al. 2021 Core Imaging Library Part I: a versatile python framework for tomographic imaging. Phil. Trans. R. Soc. A 20200192. Code. Pre-print
[2] Papoutsellis E et al. 2021 Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography. Phil. Trans. R. Soc. A 20200193. Code. Pre-print