README.md

Face mask monitoring system for kiot campus

This project is based on https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch

Title: Face Mask Monitoring at KIOT Campus Gate

A project Submitted to Department of Software Engineering for Partial Fulfillment of the Requirement for the Degree of Bachelor in Software in Engineering.

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

pip install -U -r requirements.txt

All dependencies are included in the associated docker images. Docker requirements are:

• nvidia-docker
• Nvidia Driver Version >= 440.44

Before you run the tracker

1. Clone the repository recursively:

git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git

If you already cloned and forgot to use --recurse-submodules you can run git submodule update --init

2. Github block pushes of files larger than 100 MB (https://help.github.com/en/github/managing-large-files/conditions-for-large-files). Hence you need to download two different weights: the ones for yolo and the ones for deep sort

• download the yolov5 weight from the latest realease https://github.com/ultralytics/yolov5/releases. Place the downlaoded .pt file under yolov5/weights/
• download the deep sort weights from https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6. Place ckpt.t7 file underdeep_sort/deep/checkpoint/

Tracking

Tracking can be run on most video formats

python3 track.py --source ...

• Video: --source video.mp4
• Webcam: --source 0
• RTSP stream: `--source rtsp://0.0.0.0/**
• HTTP stream: --source http://**

MOT compliant results can be saved to inference/output by

python3 track.py --source ... --save-txt

Other information

For more detailed information about the algorithms and their corresponding lisences used in this project access their official github implementations.

How to strart

1 run recognition: python3 e-track.py --source config/cam.txt --img-size 32 --weights /home/black/Downloads/best.pt 2 run streaming servers: python try/socket/catchserver.py and python try/socket/catchserver2.py 2 run server: python run_server

Deployment Diagram

Prepared By Elyas Abate

Advisor: Ashenafi Workie (MSc.) Submitted Date: July