def init():
global model, graph, session
model = Sequential()
model.add(Dense(32, activation='relu', input_shape=(6, )))
model.add(Dense(32, activation='relu'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
model.load_weights("training_1/cp.ckpt")
model._make_predict_function()
session = K.get_session()
graph = tf.get_default_graph()
class FacialExpressionModel(object):
emotion_dict = [
"Angry", "Disgusted", "Fearful", "Happy", "Neutral", "Sad", "Surprised"
]
def __init__(self, model_weights_file):
self.loaded_model = Sequential()
self.loaded_model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=(48, 48, 1)))
self.loaded_model.add(Conv2D(64, kernel_size=(3, 3),
activation='relu'))
self.loaded_model.add(MaxPooling2D(pool_size=(2, 2)))
self.loaded_model.add(Dropout(0.25))
self.loaded_model.add(
Conv2D(128, kernel_size=(3, 3), activation='relu'))
self.loaded_model.add(MaxPooling2D(pool_size=(2, 2)))
self.loaded_model.add(
Conv2D(128, kernel_size=(3, 3), activation='relu'))
self.loaded_model.add(MaxPooling2D(pool_size=(2, 2)))
self.loaded_model.add(Dropout(0.25))
self.loaded_model.add(Flatten())
self.loaded_model.add(Dense(1024, activation='relu'))
self.loaded_model.add(Dropout(0.5))
self.loaded_model.add(Dense(7, activation='softmax'))
self.loaded_model.load_weights(model_weights_file)
self.loaded_model._make_predict_function()
def predict_emotion(self, img):
self.preds = self.loaded_model.predict(img)
return FacialExpressionModel.emotion_dict[int(np.argmax(self.preds))]
