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kerasmodel.py
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kerasmodel.py
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import numpy as np
import cv2
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from keras.layers.pooling import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
from keras.models import load_model
import speech_recognition as sr # importing speech recognition package from google api
import playsound # to play saved mp3 file
from gtts import gTTS # google text to speech
import os # to save/open files
num = 123
def bot_speaks(output):
global num
num += 1
print("Bot : ", output)
toSpeak = gTTS(text=output, lang='en-US', slow=False)
file = str(num) + ".mp3"
toSpeak.save(file)
playsound.playsound(file, True)
os.remove(file)
def plot_model_history(model_history):
"""
Plot Accuracy and Loss curves given the model_history
"""
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
fig.savefig('plot.png')
plt.show()
def create_model():
model = Sequential()
model.add(Conv2D(32, 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(0.25))
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(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
return model
def train():
train_dir = 'data/train'
val_dir = 'data/test'
num_train = 28709
num_val = 7178
batch_size = 64
num_epoch = 50
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(48,48),
batch_size=batch_size,
color_mode="grayscale",
class_mode='categorical')
validation_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(48,48),
batch_size=batch_size,
color_mode="grayscale",
class_mode='categorical')
# Create the model
model = Sequential()
model.add(Conv2D(32, 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(0.25))
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(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])
model_info = model.fit_generator(
train_generator,
steps_per_epoch=num_train // batch_size,
epochs=num_epoch,
validation_data=validation_generator,
validation_steps=num_val // batch_size)
plot_model_history(model_info)
model.save_model('model1.h5')
def predict():
EmotionsDetected = []
model = create_model()
model.load_weights('model.h5')
# prevents openCL usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)
# dictionary which assigns each label an emotion (alphabetical order)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# start the webcam feed
cap = cv2.VideoCapture(0)
while True:
# Find haar cascade to draw bounding box around face
ret, frame = cap.read()
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
prediction = model.predict(cropped_img)
maxindex = int(np.argmax(prediction))
# cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
EmotionsDetected.append(str(emotion_dict[maxindex]))
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
cap.release()
cv2.destroyAllWindows()
emotion = max(set(EmotionsDetected), key = EmotionsDetected.count)
if emotion == "Angry":
bot_speaks("Thank you for your Interview. You need to be calm while answering questions.")
elif emotion =="Disgusted":
bot_speaks("Thank you for your Interview. You need to be more comfortable while answering questions.")
elif emotion =="Fearful":
bot_speaks("Thank you for your Interview. You need to be more confident while answering questions.")
elif emotion =="Happy":
bot_speaks("Thank you for your Interview.You look happy It was so nice interviewing you.")
elif emotion =="Neutral":
bot_speaks("Thank you for your Interview. You need bit of improvement while answering your questions.")
elif emotion =="Sad":
bot_speaks("Thank you for your Interview. You need to be more confident while answering questions.")
elif emotion =="Surprised":
bot_speaks("Thank you for your Interview. It was so nice interviewing you.")
if __name__ == '__main__':
#predict()
train()