Skip to content

Latest commit

 

History

History
132 lines (94 loc) · 3.33 KB

README.md

File metadata and controls

132 lines (94 loc) · 3.33 KB
Raw

JAMMM - A COMP3278 Project

Link To Demo Video: https://youtu.be/5eBuB9qOC-Q

Contributors:

Author: Masood Ahmed
Email: 'masood20@connect.hku.hk'
UID: 3035812127

Author: Abdulwadood Ashraf Faazli
Email: 'awaf2001@connect.hku.hk'
UID: 3035832751

Author: Muhammad Mubeen
Email: 'u3583178@connect.hku.hk'
UID: 3035831783

Author: Li Hoi Kit
Email: 'u3574503@connect.hku.hk'
UID: 3035745037

Author: Chan Kwok Cheung
Email: 'u3582155@connect.hku.hk'
UID: 3035821556

Descriptions:

This is a course project where we are building a full stack web application using MySQL as database, React.js as Frontend, and Flask as Backend. In this project, a person can login via face recognition and after logging in, you can see a personal timetable, the classes scheduled in the coming one hour, and course details.

Setup Details:

Backend Setup:

For Backend, first enter the jamm-backend director and create a data directory:

cd jamm-backend
mkdir data

Now Let's setup the face recognition system:

Setting Environment

Create virtual environment using Anaconda.

conda create -n face python=3.x
conda activate face
pip install -r requirements.txt

MySQL Install

[Mac] https://dev.mysql.com/doc/mysql-osx-excerpt/5.7/en/osx-installation-pkg.html

[Ubuntu] https://dev.mysql.com/doc/mysql-linuxunix-excerpt/5.7/en/linux-installation.html

[Windows] https://dev.mysql.com/downloads/installer/

To Collect Face Data

"""
user_name = "Jack"   # the name
NUM_IMGS = 400       # the number of saved images
"""
python face_capture.py

The camera will be activated and the captured images will be stored in data/Jack folder.
Note: Only one person’s images can be captured at a time.

Now Train a Face Recognition Model

python train.py

train.yml and labels.pickle will be created at the current folder.

Now Let's Check If The Is Trained Correctly:

Go to either faces_gui.py or faces_.py and run the following the command:

python faces.py

The camera will be activated and recognize your face using the pretrained model. If output is like ('Hello ', {name}, 'You did attendance today') then the face is trained correctly.

Importing Database

Open mysql server and import the file RealFinal.sql.

# login the mysql command
mysql -u root -p

# create database.  'mysql>' indicates we are now in the mysql command line

# import from sql file. Replace the filename `RealFinal.sql` with the path to RealFinal.sql file on your local system
mysql> source RealFinal.sql

Finally Running the Backend:

Go into jamm-backend directory and run main.py

python main.py

Settting Up The Frontend:

Firstly, change the directory to Jamm/jamm-frontend then run following command:

npm install

Then run the following command:

npm start

Important Note:

While running, if some of the libraries are missing, do install them via pip install {library-name} or npm install {library-name}.

Feedback

Pull requests are welcome. For feedback and suggestions, please reach out to Group 21.

License

COMP3278 Group 21 2022 © The University of Hong Kong

Thank you for reading. Enjoy the app! Stay happy and stay safe :)