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9a807ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | import cv2
import numpy as np
from keras.models import load_model
model=load_model('C:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/model.keras')
faceDetect=cv2.CascadeClassifier('c:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/haarcascade_frontalface_default.xml')
labels_dict={0:'Angry',1:'Disgust', 2:'Fear', 3:'Happy',4:'Neutral',5:'Sad',6:"Smirk",7:'Surprise'}
# len(number_of_image), image_height, image_width, channel
frame=cv2.imread("face_emotion1.jpg")
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces= faceDetect.detectMultiScale(gray, 1.3, 3)
for x,y,w,h in faces:
sub_face_img=gray[y:y+h, x:x+w]
resized=cv2.resize(sub_face_img,(48,48))
normalize=resized/255.0
reshaped=np.reshape(normalize, (1, 48, 48, 1))
result=model.predict(reshaped)
label=np.argmax(result, axis=1)[0]
print(label)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 1)
cv2.rectangle(frame,(x,y),(x+w,y+h),(50,50,255),2)
cv2.rectangle(frame,(x,y-40),(x+w,y),(50,50,255),-1)
cv2.putText(frame, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
cv2.imshow("Frame",frame)
cv2.waitKey(0)
cv2.destroyAllWindows() |