Metadata-Version: 2.1
Name: spyrit
Version: 0.13.4
Summary: Demo package
Home-page: https://gitlab.in2p3.fr/antonio-tomas.lorente-mur/spyrit
Author: Antonio Tomas Lorente Mur, Nicolas Ducros, Sebastien Crombez
Author-email: Nicolas.Ducros@insa-lyon.fr
License: Attribution-ShareAlike 4.0 International
Description: # Spyrit Version 0.1
        
        Spyrit Toolbox aims to provide all the necessary tools for single-pixel imaging. Starting from simulation, reconstruction, and interface with DMD and spectrometers.
        The aim of this toolbox is to cover all aspects of single-pixel imaging : from simulation to experimental, we aim to provide tools to make realistic measurements and provide reconstruction algorithms. 
            
        ## Getting Started
        
        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.
        
        ### Under Linux
        ```
        git clone --recurse-submodules https://github.com/openspyrit/spyrit.git
        ```
        
        ### Prerequisites
        
        All the necessary packages and libraries are contained within the ```setup.py ``` file.
        
        - numpy (>1.3.0)',
        - matplotlib (>2.2.4)',
        - scipy (>1.1.0)',
        - torch (>1.1.0)',
        - torchvision (>0.2.2)',
        - PIL (>5.3.0)',
        - cv2 (>4.0.0)',
        - imutils (>0.5.3)',
        - pywt (>1.0.1)',
        - fht=['https://github.com/nbarbey/fht'] (included as a submodule in spyrit/fht),
        
        
        ### Installing
        
        A step by step series of examples that tell you how to get a development env running
        
        Say what the step will be
        
        ```
        python3 setup.py
        ```
        
        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);
        ```
        
        
        ## Running the tests
        
        Explain how to run the automated tests for this system
        
        ### Break down into end to end tests
        
        Explain what these tests test and why
        
        ```
        Give an example
        ```
        
        ### And coding style tests
        
        Explain what these tests test and why
        
        ```
        Give an example
        ```
        
        ## Deployment
        
        Add additional notes about how to deploy this on a live system
        
        ## Built With
        
        * [Dropwizard](http://www.dropwizard.io/1.0.2/docs/) - The web framework used
        * [Maven](https://maven.apache.org/) - Dependency Management
        * [ROME](https://rometools.github.io/rome/) - Used to generate RSS Feeds
        
        ## 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
