Problem Statement

To monitor the presence of face masks and detect coughs in real time and classify individuals into varying risk categories.

Built With

• Flask
• Flask-SocketIO
• Librosa
• Keras
• Tensorflow
• OpenCV
• Media Recorder API
• Bootstrap 4

The Web Application is deployed on Microsoft Azure, and can be accessed via

https://pnuemosenseai.azurewebsites.net/ [Unavailable now]

Troubleshooting

• It is advised to open the link in the incognito mode.
• Latency: latency depends on the GPU of the system on which you are running the web application. For best results, A high performing GPU is required.

How do I deploy the app?

Getting Started

The following package versions must be installed to successfully deploy the model.

• matplotlib 3.1.3
• ffmpeg 3.2
• numpy 1.18.5
• Flask 1.1.2
• gevent 1.4.0
• Keras 2.3.0
• librosa 0.6.3
• numba 0.49.1
• tensorflow 2.1.0
• python 3.7.1
• flask-socketio 4.3.1
• imutils 0.5.3
• opencv-python 4.3.0.36
• six 1.12.0
• scipy 1.4.1
• setuptools 41.0.0

To run the model on your local machine,download this repository as a zip file, clone or pull it by using the command

$ git pull https://github.com/mitali3112/Cough-Detector.git

or

$ git clone https://github.com/mitali3112/Cough-Detector.git

Requirements can be installed using the command (from the command-line) preferably in a virtual environment.

$ pip install -r requirements.txt

Then, navigate to the project folder and execute the command

$ python app.py

to deploy the app locally.

On your web browser go to http://localhost:8000/

Demo

.mp4)

Contributers

• Aparna Ranjith
• Gunveen Batra
• Mansi Parashar
• Mitali Sheth
• Sruti Dammalapati

Acknowledgements

We thank B-Aegis Life Sciences for the opportunity.