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README.md

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# E-COV
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A Desktop app that detects face, face mask, and alerts authorities of people not wearing face mask on prohibited areas. It uses a deep learning model to build the face and face mask detection, and a face recognition library to recognize people not wearing a face mask.
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Tools and Technologies used: Deep learning, Flask, Pyinstaller, Electron, Node.Js, Docker, HTML, CSS, Azure(Machine Learning Studio)
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E-COV is a desktop app that detects face, face mask, and alerts authorities of people not wearing face mask on prohibited areas. It uses a deep learning model to build the face and face mask detection, and a face recognition library to recognize people not wearing a face mask. It is built to slow down the spread of the Corona pandemic.
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<!-- <img src="./static/assets/img/demo.png" height="200" width="400"> -->
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**version 1.0.0**
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## Getting Started
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These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
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## Prerequisites (Linux)
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- Python
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```bash
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sudo add-apt-repository ppa:jonathonf/python-3.7
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sudo apt-get update
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sudo apt-get install python3.7
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```
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- pip3
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```bash
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sudo apt update
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sudo apt install python3-pip
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```
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- Pyinstaller
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```bash
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pip3 install pyinstaller
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```
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## Installing
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### For Development
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1. Clone the Repo
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```bash
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git clone https://github.com/jedisam/E-COV.git
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```
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1. cd into repo
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```bash
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cd E-COV
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```
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1. Install Requirements
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```bash
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pip3 install requirements.txt
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```
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1. Run the App
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```bash
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flask run
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```
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### To Package & Build
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1. Build the Python app using pyinstaller (This might take some time).
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```bash
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pyinstaller main.py
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```
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Go into dist director and run the app
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```bash
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cd dist/
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./main
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```
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1. cd into Electron folder and install dependencies
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```bash
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cd ../Electron && npm install
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```
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For Linux
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```bash
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npm run package-linux
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```
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For Windows
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```bash
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npm run package-win
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```
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For Mac
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```bash
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npm run package-mac
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```
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1. Run the Desktop app
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Get into the build directory (for linux)
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```bash
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cd e-cov-linux-x64
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./e-cov
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```
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## Tools and Technologies used
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- Deep learning
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- Flask
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- Pyinstaller
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- Electron
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- Node.Js (Email)
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- Docker
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- HTML, CSS
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- Azure(Machine Learning Studio)
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## Authors
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- **Yididya Samuel** [Website](jedisam.github.io)

static/assets/img/demo.png

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