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 - The web framework used
• Maven - Dependency Management
• ROME - Used to generate RSS Feeds

Contributing

Please read 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
• Nicolas Ducros - Initial work - Website
• Sebastien Crombez - Initial work - [Website]

License

This project is licensed under the Creative Commons Attribution Share Alike 4.0 - see the LICENSE.md file for details

Acknowledgments

• Nicolas Barbey for his Fast Hadamard Transform implementation in python
• Jin LI for his implementation of Convolutional Gated Recurrent Units for PyTorch
• Erik Lindernoren for his processing of the UCF-101 Dataset.