♻️ ecoview ♻️

Cloud-based recycling module with object classification

UB Electrical Engineering Capstone Design, Spring 2019

Software Lead: Michael Lawrenson
Image Acquisition Lead: Osama Abaali
Peripherals Lead: Lee Yanting
Microcontroller Lead: Justin Struzik

Overview

Abstract

Raspberry Pi integrated with a compartmentalized recycling station, capable of monitoring deposits in individual totes, measuring the height of tote contents, and relaying this information to our web app via our API. In addition, the user can capture a photo of their recycleable and provide a categorical assignment by examining the symbol located on the underside. This resulting collection of crowdsourced images is aggregated and used as training data for our convolution neural network, or CNN.

Architecture

Content height is measured with ultrasonic sensors, which measure the time needed to echo a trigger signal. Deposits are registed by means of continuosly monitored infrared break beams. At regular time intervals, collected information stored in-memory is used to construct an API request, syncronizing the web app with the physical system. Image files are stored in a heirarchy such that directory names are the ground-truth classification labels.

Setup

There are 3 main program branches:

Firmware, run locally on the Pi
Web app, run in a Docker container
Image Analysis, eventually to be performed with Google Colaboratory

Stack

Website

Framework: Flask
Styling: Bootstrap
Graphics: Chartist.js

Database

MySQL

Image Processing

Tensorflow

Communication

restful API: ecoview.stateData(), ecoview.processData()

Quick Start

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Real-world deployement performed with Docker.

Installing

Clone the repository to a local directory

  git clone https://github.com/eco-view/ecoview.git

Dependencies

Make sure you have all the required python modules

  pip3 install -r requirements.txt

Initialize SQL database

  mysql.server start

Start the flask server

  python3 app.py

By default, the server will run on port 8080

  http://localhost:8080/

Deployment

docker build ecoview:latest .
docker run -d -p 8080:8080 ecoview:latest
echo 'Running [ecoview] @ http://localhost:8080

Built With

Python - Primary language
Flask - Web framework
MySQL - Database
Tensorflow - Image processing
Bootstrap - Visual styling

Contributing

no outside contribution

Versioning

ecoview:latest

Author

Michael Lawrenson - Software Lead - minelminel

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

much love to the folks at StackOverflow

oaabaali2896 / ecoview Goto Github PK

ecoview's Introduction