moodDetector.py

from json import load
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout

import numpy as np

emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']

def loadModel():
    num_classes = 7
    model = Sequential()

    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))

    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.22))

    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    model.load_weights('./data/weigths.h5')

    return model

def analyze(model, image):
    emotion_predictions = model.predict(image)[0,:]
    return emotion_labels[np.argmax(emotion_predictions)]