The purpose of this project is to track and manage member attendance by using face recognition. You can try out a live demo of this project at test.musaiclub.com!

To run this website, first install Django:

python -m pip install -r requirements.txt

(Note: You MUST use python 3! I had multiple versions on my computer, so I had to replace python with python3. In certain cases, the python command may be named py. It will probably depend on your exact setup.)

After the modules have been installed, you can configure the project using these commands:

python manage.py migrate
python manage.py createsuperuser

Follow the prompts when given.

Once you have Django installed, open a terminal in this directory and run the following command:

python manage.py runserver

That will start the local server. Once that command is running, you will be able to view the site at http://localhost:8000/

(When you do this, your terminal is acting like a web server, and your browser is accessing that local server.)

Once the server is running, visit http://localhost:8000/admin to view the admin panel. Sign in with the username test and password test. (You can also create a new account from the terminal.)

If you want to change the data models which appear in the admin panel, edit them in attendanceapp/models.py. Then run the following two commands in order:

python manage.py makemigrations attendanceapp
python manage.py migrate

Not appearing? If you create any new models, you will need to update attendanceapp/admin.py to make them appear in the admin panel.

We will be creating a local environment under the project directory, either using venv or conda. Since there are more than one branches in this project, we will have to switch from environment to environment when checking out different branches. So in this tutorial, we have avoided using generic name for our local environment, such as env/ or venv/; please use env_interaction/.

Make sure you are not using the pip within conda, otherwise you might encounter an SSLError.

For Windows:

python -m venv .\env_interaction             # create environment
.\env_interaction\Scripts\activate           # activate environment
python -m pip install -r requirements.txt    # install dependencies

For macOS/Unix:

python -m venv ./env_interaction            # create environment
source ./env_interaction/bin/activate       # activate environment
python -m pip install -r requirements.txt   # install dependencies

More about pip: https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/.

For Windows:

(gcm conda.exe).Path   # you should see something, meaning that conda is activated
conda env create --prefix .\env_interaction --file environment.yml # create environment
conda activate .\env_interaction                                   # activate environment

For macOS/Unix:

which conda     # you should see something, meaning that conda is activated
conda env create --prefix ./env_interaction --file environment.yml # create environment
conda activate ./env_interaction                                   # activate environment

More about conda: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

The face_images file contains images the app will try to recognize. It contains a placeholder picture of Barack Obama. Add a picture of yourself to the face_images file and name it to your name.

Make sure a camera is connected to the computer, then run the following command:

python recognition.py

Currently, only five gestures are supported:

• One (index finger up)
• Two (index finger and middle finger up)
• Three (index finger, middle finger and ring finger up)
• Thumbs up
• Thumbs down

This is still under development. For now, we have a very simple model (Support Vector Machine) that trains on ~300 labeled landmarks; see landmarked.csv. The trained model is saved as model.pkl. To (re)train the model, run the following command:

python train.py

Landmark, or hand landmark, is a point on the hand that is used for hand gesture recognition. In this project, we use the hand tracking solutions by MediaPipe to detect the 21 landmarks. Then we train our own model to classify the hand gesture using the (x, y, z) coordinates of the landmarks. Below is a figure showing the indices of the landmarks, which are stored in landmark-index.json. Credit: MediaPipe