EMOJI_PREDICTOR

Recognizes the facial emotion and overlays emoji, equivalent to the emotion, on the persons face.

Some results First!

Getting Started

1. Get the code:

   • Using SSH: git clone [email protected]:sp-502/facial_emotion_recognition__EMOJIFIER.git
     OR
   • Using HTTP: git clone https://github.com/sp-502/facial_emotion_recognition__EMOJIFIER.git

2. Setup the Virtual Environment (Recommended):

   • Create the virtual environment
     • python3 -m venv --system-site-packages /path/to/venv
   • Activate your virtual-environment
     • Linux: source /path/to/venv/bin/activate
     • Windows: cd /path/to/venv then .\Scripts\activate
   • Install the requirements
     • cd root-dir-of-project
     • `pip install -I -r requirements.txt

     Install any missing requirement with pip install package-name

     That's all for the setup ! 😃

Making it work for you:

There are 4 steps from nothing (not even a single image) to getting the result as shown above.

And you don't need anything extra than this repo.

• STEP 1 - generating the facial images

  1. cd /to/repo/root/dir
  2. run python3 src/face_capture.py emotion-name num-of-images-to-capture
     -- example: python3 src/face_capture.py cry 200

  This will open the cam and all you need to do is give the cry emotion from your face.

  • Do this step for all the different emotions in different lighting conditions.
  • I used 300 images for each emotions captured in 3 different light condition (100 each).
  • You can see your images inside the 'images' folder which will contain different folder for different emotion images.

• STEP 2 - creating the dataset out of it

  1. run python3 src/dataset_creator.py
  • This will create the ready-to-use dataset as a python pickled file and save it in the dataset folder.

  Edit the emoji-dict inside the code if your 'emotion-list' is not the same as defined there.

• STEP 3 - training the model on the dataset and saving it

  1. run python3 src/model.py
  • This will start the model-training and upon the training it will save the tensorflow model in the 'model-checkpoints' folder.
  • It has the parameters that worked well for me, feel free to change it and explore.

• STEP 4 - using the trained model to make prediction

  1. run python3 src/predictor.py
  • this will open the cam, and start taking the video feed -- NOW YOU HAVE DONE IT ALL. 👏

Its time to show your emotions ❤️

P.S. -- The model was trained on my facial images only, but was able to detect the expressions of my brother as well.