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Social-distancing is an important way to slow down the spread of infectious diseases. People are asked to limit their interactions with each other, reducing the chances of the disease being spread with physical or close contact. In the past decade, AI/Deep Learning has shown promising results on a number of daily life problems. Various daily life tasks have been automated with the help of AI. In this project we will combine deep learning and computer vision to monitor social distancing.

License: MIT License

Python 100.00%

monitoring-social-distancing--using-machine-learning's Introduction

Monitoring-Social-distancing

Github usually doesn't support files larger than 25 Mb.You can have to download Yolo Weight from the official website of YOLO.

For CPU:

To run this code in your terminal:

*Open your terminal
Change directory to where you have downloaded this code
Install python3 if you have not, if installed already then it's ok!
Run python3 -m venv venv to create a virtual environment named venv.
Run source venv/bin/activate to activate your environment!
Write pip install -r requirements.txt to install the python dependencies related to this project like opencv,numpy,scipy etc.
Run the command time python social_distance_detector.py --input pedestrians.mp4 --output output.avi --display 1 to run your social distance detection project

After you run the last line of command,a window will pop up and after execution of the file a `output.avi` file will be showing up in your directory like this:

For GPU:

You can run the same code at your local machine if you have a GPU or in Google Collab (just select "GPU" as Runtime Type).

Contacts:

Created by: Kanishk Sharma

LinkedIn: Kanishk Sharma

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kanishksh4rma / monitoring-social-distancing--using-machine-learning Goto Github PK