SPyRiT is a PyTorch-based deep image reconstruction package primarily designed for single-pixel imaging.
The spyrit package is available for Linux, MacOs and Windows. We recommend to use a virtual environment.
(user mode)
pip install spyrit
(developper mode)
git clone https://github.com/openspyrit/spyrit.git
cd spyrit
pip install -e .
On Windows you may need to install PyTorch first. It may also be necessary to run the following commands using administrator rights (e.g., starting your Python environment with administrator rights).
Adapt the two examples below to your configuration (see here for the latest instructions)
(CPU version using pip
)
pip3 install torch torchvision torchaudio
(GPU version using conda
)
conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia
Then, install SPyRiT using pip
:
(user mode)
pip install spyrit
(developper mode)
git clone https://github.com/openspyrit/spyrit.git
cd spyrit
pip install -e .
To check the installation, run in your python terminal:
import spyrit
To start, check the documentation tutorials. These tutorials must be runned from tutorial
folder (they load image samples from spyrit/images/
):
cd spyrit/tutorial/
More advanced reconstruction examples can be found in spyrit-examples/tutorial. Run advanced tutorial in colab:
https://spyrit.readthedocs.io/
When using SPyRiT in scientific publications, please cite the following paper:
When using SPyRiT specifically for the denoised completion network, please cite the following paper:
This project is licensed under the LGPL-3.0 license - see the LICENSE.md file for details