Skip to content

Latest commit

 

History

History
56 lines (45 loc) · 2.92 KB

README.md

File metadata and controls

56 lines (45 loc) · 2.92 KB
Raw

EMOJI_PREDICTOR

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

Some results First!

res

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.

result