Metadata-Version: 2.1
Name: spyrit
Version: 0.14.0
Summary: Demo package
Home-page: https://github.com/openspyrit/spyrit
Author: Antonio Tomas Lorente Mur, Nicolas Ducros, Sebastien Crombez
Author-email: Nicolas.Ducros@insa-lyon.fr
License: Attribution-ShareAlike 4.0 International
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        [![GitHub](https://img.shields.io/github/license/openspyrit/spyrit?style=plastic)](https://github.com/openspyrit/spyrit/blob/master/LICENSE.md)
        [![PyPI pyversions](https://img.shields.io/pypi/pyversions/spyrit.svg)](https://pypi.python.org/pypi/spyrit/)
        [![Docs](https://readthedocs.org/projects/spyrit/badge/?version=latest&style=flat)](https://spyrit.readthedocs.io/en/master/)
        
        # Spyrit
        SPYRIT is a [PyTorch](https://pytorch.org/)-based toolbox for deep image reconstruction. While SPYRIT was originally designed for single-pixel image reconstruction, it can solve any linear reconstruction problem.
            
        ## Getting Started
        
        ### User mode
        
        The spyrit package is available for Linux, MacOs and Windows. You can install it with pypi (we recommend you to use virtual environment).
        
        #### Linux and MacOs
        
        ```
        pip install spyrit
        ```
        
        #### Windows
        
        On Windows you need first to install [torch](https://pytorch.org/get-started/locally/). Adapt to your configuration. Two examples below.
        
        CPU version using `pip `
        
        ```
        pip install requests torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
        ```
        
        GPU version using `conda` 
        
        ``` shell
        conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
        ```
        
        Then install SPyRiT using `pip`
        
        ```shell
        pip install spyrit
        ```
        
        ### Developer mode
        
        These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
        
        First, you need to clone the repository:
        
        ```
        git clone --recurse-submodules https://github.com/openspyrit/spyrit.git
        ```
        
        Then, you can install the spyrit package with python (we recommend you to use virtual environment)
        
        #### Linux and MacOs
        
        ```
        cd spyrit
        pip install -e .
        ```
        
        #### Windows
        
        On Windows you need first to redo the symbolic link to fht inside the spyrit repository and then to install [torch](https://pytorch.org/get-started/locally/). Here it's cpu version, adapt to your configuration. 
        
        NB: It may be necessary to run the following commands using administrator rights (e.g., starting your Python environment with administrator rights).
        
        ```
        cd spyrit
        rm -r -fo fht
        cmd /c mklink /d fht spyrit\fht\fht
        pip install requests torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
        pip install -e .
        ```
        
        #### Versioning
        
        To change the version of the package on [pypi](https://pypi.org/project/spyrit/), you need to:
         - change the version in [setup.py](https://github.com/openspyrit/spyrit/blob/master/setup.py#L45) to new_version
         - ```git commit setup.py -m "Towards new_version"```
         - ```git tag -a new_version -m "new_version"```
         - ```git push --follow-tags```
        
        ## API Documentation
        https://spyrit.readthedocs.io/
        
        ## Prerequisites
        
        All the necessary packages and libraries are contained within the ```setup.py ``` file.
        
        - numpy (==1.19.3)
        - matplotlib
        - scipy
        - torch
        - torchvision
        - Pillow
        - opencv-python
        - imutils
        - PyWavelets
        - wget
        - imageio
        - [fht](https://github.com/nbarbey/fht) (included as a submodule in spyrit/fht),
        
        ## Test
        
        To check that the installation has been a success, try running the following lines in yout python terminal :
        
        ```
        import spyrit
        ```
        
        End with an example of getting some data out of the system or using it for a little demo
        
        ```
        import torch;
        a = torch.randn(64,64);
        ```
        
        A minimal exemple can be found [here](https://github.com/openspyrit/spyrit/blob/master/.github/workflows/example.py). To run it, clone or download the file and you can do:
        
        ```
        python example.py
        ```
        
        ## Contributing
        
        Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
        
        
        ## Authors
        
        * **Antonio Tomas Lorente Mur** - *Initial work* - [Website](https://www.creatis.insa-lyon.fr/~lorente/)
        * **Nicolas Ducros** - *Initial work* - [Website](https://www.creatis.insa-lyon.fr/~ducros/WebPage/index.html)
        * **Sebastien Crombez** - *Initial work* - [Website]
        
        
        ## License
        
        This project is licensed under the Creative Commons Attribution Share Alike 4.0 - see the [LICENSE.md](LICENSE.md) file for details
        
        ## Acknowledgments
        
        * [Nicolas Barbey](https://github.com/nbarbey/fht) for his Fast Hadamard Transform implementation in python  
        * [Jin LI](https://github.com/happyjin/ConvGRU-pytorch) for his implementation of Convolutional Gated Recurrent Units for PyTorch
        * [Erik Lindernoren](https://github.com/eriklindernoren/Action-Recognition) for his processing of the UCF-101 Dataset.
        
Keywords: tutorial package
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
