COMP3278 Group Project Group 9

Installation Guide

1. Install anaconda for creating an environment (Link)

2. Create a virtual environment (using either conda/ venv) with python=3.9 and activate it (as tensorflow only supports python 3.6 to 3.9) (Take conda as an example)

   conda create -n NAME_OF_YOUR_ENV python=3.9
   conda activate NAME_OF_YOUR_ENV
   

3. Install mediapipe and tensorflow for facial recognition and identification

   pip install mediapipe
   pip install tensorflow
   

4. Install nodejs and flask and supporting packages for frontend

   conda install -c anaconda flask
   conda install -c conda-forge flask-mail
   conda install -c conda-forge nodejs
   pip install python-dotenv
   conda install -c anaconda flask-cors
   

   MySQL setup

5. Install mysql-connector-python and python-dotenv for connecting to the database

   pip install mysql-connector-python
   pip install python-dotenv
   

6. Install XAMPP to your computer (Link)

7. create .env file under folder /flask/

   Type the following. (The YOUR_PASSWORD can be found in the following steps.)

   MYSQL_PASSWORD='YOUR_PASSWORD'
   

   If MySQL is launched with a port other than 3306, Type the following.

   MYSQL_PORT='PORT_NUMBER'
   

8. Launch XAMPP. Go to phpMyAdmin. On the top launch bar, click 'User accounts'. Search for the column with 'User name' = 'root' and 'Host name' = 'localhost'. Look at the password column, if the Password column said 'No', then leave the YOUR_PASSWORD empty (i.e. an empty string). Otherwise, copy the value to YOUR_PASSWORD.

   Then save the .env file.

9. Create a database named 'project' (all lowercase). Import the .sql file in discord to 'project' database in phpMyAdmin.

Start the application

1. Activate the environment, cd to the web folder and install packages for nodejs

cd web
npm install

2. Start the frontend

npm start

After that, the app will open a tab for you automatically. If not, open a web browser and go to http://localhost:3000/

3. Open another terminal, activate the environment. cd to the flask folder and run the backend

cd flask
python app.py

4. Use the Application

• Login using Facial Recognition or Email and Password
• If you have a Lecture/Tutorial in the next 60 minutes, you will see a notification at the left side of the Home Page and you can naviagte to the "Upcoming Course" Page and see the course Info. Else, you can always go to the "TimeTable" Page to view your personal timetable in the current week.
• To view all your login records, head to the Records Page, you will find all your login records there, including login ID, IP Address and Login Duration.

Appendix

Facial recognition testing

The model is a bit rough but still performs better than opencv default one.

(Skip step 1 and 2 if you have followed the installation Setup above)

1. Create a virtual environment (using either conda/ venv) with python=3.9 (as tensorflow only upports python 3.6 to 3.9) Take conda as an example:

   conda create -n NAME_OF_YOUR_ENV python=3.9
   

2. Install mediapipe and tensorflow

   pip install mediapipe
   pip install tensorflow
   

3. run the inference.py in folder face_recognition

   cd face_recognition
   python inference.py