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Source code for HappyNet
Real-Time Human Emotion Recognition with Convolutional Neural Nets
Demo video: https://youtu.be/MDHtzOdnSgA

Authors:

    Dan Duncan
    Gautam Shine
    Chris English
          
Stanford CS231n - March 2016

About HappyNet:

    HappyNet takes pictures or webcam video as input. It detects all faces in each frame, and then
    classifies which emotion each face is expressing.

    HappyNet then replaces each face with an emoji corresponding to that emotion.

    Recognized emotions:

        1. Neutral
        2. Happy
        3. Sad
        4. Angry
        5. Surprise
        6. Fear

    Due to legacy reasons, HappyNet tries to classify a seventh emotion: disgust. We usually combine
    and angry, as HappyNet couldn't tell these two apart. With more training, maybe it could distinguish.


    Training accuracy was 91% and test accuracy was 57%, with the following requirements:
        - User's facial expression must be strong / exaggerated
        - Lighting must be good (no shadows on face)
        - Camera is at eye level or slightly above eye level

The scripts in this repository allow you to:

    - Run HappyNet
    - Retrain HappyNet on new data
    - Automatically generate your own training set


Credits:

    This was built using the Caffe and OpenCV libraries

    We used the Emotions in the Wild (EitW) Caffe model as our starting point
        - This model is available on Caffe Model Zoo
        - EitW is itself based on VGG_S Net

    We retrained EitW on new data:
        Cohn-Kanade Plus (CK+) dataset (available for online download)
        Japanese Female Facial Expressions (JAFFE) dataset (also available online)
        Our own data, generated using our 'gather_training_data' script

Requirements:
    > 2 GB of memory
    Caffe and OpenCV installed
    Webcam
        Note: Webcam currently does not work on virtual machines.
        Try using a native Mac or Linux system. Don't try on a virtual machine running on Windows.
    
    If you want to run on GPU:
        2GB or more VRAM
        CUDA and CuDNN libraries installed
        Caffe must be compiled with these CUDA and CuDNN selected


Description of files:

Main scripts: 
    gather_training_data.py - Use this to generate a custom training set
    process_dataset.py - Read in an entire training set and calculate accuracy over the set
    process_image.py   - Read in a single image, add the correct emoji, and write to file
    video_generate.py  - Run HappyNet in real-time and save output to video
    video_test.py      - Run HappyNet in real-time; does not save to video

Scripts for retraining the network on new data:

    These 5 scripts are to be run in numerical order
    Note you'll need to modify them with your own paths

    Generate Caffe-compatible database of input images:
        execute_0_create_file_list
        exceute_1_create_lmdb_databse

    Generate a mean image (mean.binaryproto file) from input dataset:
        execute_2_create_mean_image

    Retrain an existing caffe model with the new inputs:
        execute_3_train_custom_model

        * Note, this needs to be modified if you are running on GPU. Our network was
        VGG_S net, which requires 2 GB of GPU memory, so we ran on CPU.
        The modification is just an extra flag, something like "-gpu 0"

    Delete all unnecessary files
        execute_4_cleanup_training_data

        This deletes the output files from scripts 0, 1, and 2. 
        Run this when you are getting ready to start over from file 0.
        Don't run it until then though - you might want to reuse the info in those files!

Utility functions:
    caffe_functions.py - anything dealing primarily with caffe
    opencv_functions.py - anything dealing primarily with opencv
    utility_functions.py - General functions mostly related to file I/O

Datasets:
    Only contains the emojis we used.
    Cohn-Kanade Plus (CK+) and Japanese Female Facial Expressions (JAFFE) can be downloaded online.

Models:
    deploy.prototxt - Architecture of our model (this file should not need to be changed)
    solver.prototxt - This configures the retraining process.
    train.prototxt - This configures the architecture during training. 
                     Mainly used to add layer-specific learning rates.
    loss_history.txt - Log file from our last retraining on our dataset

    NOT INCLUDED: EmotiW_VGG_S.caffemodel - 
        This is the file with all the weights. It is 500 MB and cannot be archived. 
        However, you can download the Emotios in the Wild model from Caffe Model Zoo. 
        Retrain it on new data for a day or two, and you can get similar numbers to our model.

    We trained on:
        Homebrewed dataset of 2000 images of 5 people making all 6 emotions
        All data was generated with the script generate_training_data.py
    We trained for:
        About 24 hours
        A bigger dataset could be collected in a couple hours and would
        likely greatly improve performance.