| import cv2
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| import numpy as np
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| from keras.models import load_model
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|
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| model=load_model('C:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/model.keras')
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| faceDetect=cv2.CascadeClassifier('c:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/haarcascade_frontalface_default.xml')
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|
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| labels_dict={0:'Angry',1:'Disgust', 2:'Fear', 3:'Happy',4:'Neutral',5:'Sad',6:"Smirk",7:'Surprise'}
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|
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| frame=cv2.imread("face_emotion1.jpg")
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| gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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| faces= faceDetect.detectMultiScale(gray, 1.3, 3)
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| for x,y,w,h in faces:
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| sub_face_img=gray[y:y+h, x:x+w]
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| resized=cv2.resize(sub_face_img,(48,48))
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| normalize=resized/255.0
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| reshaped=np.reshape(normalize, (1, 48, 48, 1))
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| result=model.predict(reshaped)
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| label=np.argmax(result, axis=1)[0]
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| print(label)
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| cv2.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 1)
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| cv2.rectangle(frame,(x,y),(x+w,y+h),(50,50,255),2)
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| cv2.rectangle(frame,(x,y-40),(x+w,y),(50,50,255),-1)
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| cv2.putText(frame, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
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| cv2.imshow("Frame",frame)
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| cv2.waitKey(0)
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| cv2.destroyAllWindows() |
