From a532c5efb8f20a8eb400fdad0edc00dad50a5c46 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Sun, 2 Jun 2019 13:35:52 +0100 Subject: tests run without pillow --- Readme.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index e831f1a..0e1dc3c 100644 --- a/Readme.md +++ b/Readme.md @@ -4,7 +4,7 @@ |--------|-------------|-------------------| | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit-dev)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit-dev/) | ![conda version](https://anaconda.org/ccpi/ccpi-regulariser/badges/version.svg) ![conda last release](https://anaconda.org/ccpi/ccpi-regulariser/badges/latest_release_date.svg) [![conda platforms](https://anaconda.org/ccpi/ccpi-regulariser/badges/platforms.svg) ![conda dowloads](https://anaconda.org/ccpi/ccpi-regulariser/badges/downloads.svg)](https://anaconda.org/ccpi/ccpi-regulariser) | -**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (CCPi-RGL) toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** +**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (CCPi-RGL) toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.**
@@ -100,9 +100,9 @@ conda install ccpi-regulariser -c ccpi -c conda-forge #### Python (conda-build) ``` - export CIL_VERSION=19.03 - conda build recipe/ --numpy 1.12 --python 3.5 - conda install ccpi-regulariser=${CIL_VERSION} --use-local --force + export CIL_VERSION=19.06 (Unix) / set CIL_VERSION=19.06 (Windows) + conda build recipe/ --numpy 1.15 --python 3.7 + conda install ccpi-regulariser=${CIL_VERSION} --use-local --force-reinstall cd demos/ python demo_cpu_regularisers.py # to run CPU demo python demo_gpu_regularisers.py # to run GPU demo -- cgit v1.2.3