emotion_detection.py

df1 = pd.read_csv("fer2013.csv") #make sure the file is in the root location

# Preprocessing
x_train=[]
x_test=[]
y_train=[]
y_test=[]
for i,row in df1.iterrows():
    k=row['pixels'].split(" ")
    if(row['Usage']=='Training'):
        x_train.append(np.array(k))
        y_train.append(row['emotion'])
    elif(row['Usage']=='PublicTest'):
        x_test.append(np.array(k))
        y_test.append(row['emotion'])

x_train=np.array(x_train)
x_test=np.array(x_test)
y_train=np.array(y_train)
y_test=np.array(y_test)

x_train=x_train.reshape(x_train.shape[0],48,48)
x_test=x_test.reshape(x_test.shape[0],48,48)
y_train=tf.keras.utils.to_categorical(y_train,num_classes=7)
y_test=tf.keras.utils.to_categorical(y_test,num_classes=7)