WiSpy is a Flask web application predicting room occupancy across the University College Dublin campus based on historical Wi-Fi log data. It also employs RSSI and audio data alongside face detection methods in rooms with a leaking Wi-Fi signal.
WiSpy is has a live version
Users can:
- View predicted occupancy (both occupancy sensing and a continuous headcount) for any room and period for which we have data.
- Compare the occupancy of different rooms and classes.
- Add new data by dragging and dropping files on the "Upload new data" page.
1 . 3
WiSpy uses a number of open-source projects:
- Python 3.5.x -
- AngularJS - HTML enhanced for web apps
- Flask - Micro-web framework for Python apps
- node-sass - CSS with more features
- SQLite - software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine.
- node.js - used to install front-end dependencies via NPM
- Various Python modules used for statistical modelling, database interaction and data gathering, installed via requirements.txt.
WiSpy requires Node.JS and Python 3 be installed.
To install WiSpy:
$ git clone https://github.com/lukekearney/research-practicumRun the install script, which will prompt you if you need to install any dependencies:
python install.pyPython dependencies are installed via:
pip install -r requirements.txtIf assets are not compiled or installed, switch directory to app/static and run:
npm install to install additional assets and third party libraries
npm run build-css to compile SCSS to CSS
gulp to create minified versions of the relevant assets
MIT
Free Software