Code.txt

import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

#Inputing test data ie fer2013

with open("C:/Users/VISHNU A K/Desktop/PROJECT/Dataset/fer2013.csv") as f:
    temp = f.readlines()
lines = np.array(temp)
num= lines.size

x_train, y_train, x_test, y_test = [], [], [], []

for i in range(1,num):
    try:
        emotion, img, usage = lines[i].split(",")
          
        val = img.split(" ")
            
        pixels = np.array(val, 'float32')
        
        emotion = keras.utils.to_categorical(emotion, 7)
    
        if 'Training' in usage:
            y_train.append(emotion)
            x_train.append(pixels)
        elif 'PublicTest' in usage:
            y_test.append(emotion)
            x_test.append(pixels)
    except:
           print("",end="")


x_train = np.array(x_train, 'float32')
y_train = np.array(y_train, 'float32')
x_test = np.array(x_test, 'float32')
y_test = np.array(y_test, 'float32')

x_train /= 255 #normalize inputs between [0, 1]
x_test /= 255

x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)
x_train = x_train.astype('float32')
x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)
x_test = x_test.astype('float32')

#Constructing CNN Model wirh different layers


model = Sequential()


model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))
model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

model.add(Flatten())

model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(7, activation='softmax'))

gen = ImageDataGenerator()
train_generator = gen.flow(x_train, y_train, batch_size=256)

model.compile(loss='categorical_crossentropy'
    , optimizer=keras.optimizers.Adam()
    , metrics=['accuracy']
)

#Evaluating the different epochs with weights given
fit = True

if fit == True:

    model.fit_generator(train_generator, steps_per_epoch=256, epochs=5) 
else:
    model.load_weights('C:/Users/VISHNU A K/Desktop/PROJECT/Weights/facial_expression_model_weights.h5') 

#Function to display graph
def emotion_analysis(emotions):
    objects = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')
    y_pos = np.arange(len(objects))
    plt.bar(y_pos, emotions, align='center', alpha=0.5)
    plt.xticks(y_pos, objects)
    plt.ylabel('PREDICTION')
    plt.title('EMOTION')
    plt.show()

#Evaluating different test cases

img = image.load_img("C:/Users/VISHNU A K/Desktop/PROJECT/Test cases/sad1.jpg", grayscale=True, target_size=(48, 48))

x = image.img_to_array(img)
x = np.expand_dims(x, axis = 0)
x /= 255
custom = model.predict(x)
emotion_analysis(custom[0])
x = np.array(x, 'float32')
x = x.reshape([48, 48]);
plt.gray()
plt.imshow(x)
plt.show()