emotions_Deepface.py

from deepface import DeepFace
import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

cap = cv2.VideoCapture(0)

while cap.isOpened():

        #Read from camera
       ret, frame = cap.read() 

       #Get the result from deepface
       result = DeepFace.analyze(img_path=frame,actions=['emotion'],enforce_detection=False)

       #Get only the emotions
       emotions = result["emotion"]

        #Get the emotions seperatedly 
       angry = "Angry: "+ str(round(emotions["angry"],2))
       #disgust = round(emotions["disgust"],2)
       #fear = round(emotions["fear"],2)
       happy = "Happy: "+str(round(emotions["happy"],2))
       sad = "Sad: "+str(round(emotions["sad"],2))
       surprise = "Surprise: "+str(round(emotions["surprise"],2))
       neutral = "Neutral: "+str(round(emotions["neutral"],2))

        #Apply the Haar-future to get the face coordinates
        #Works only with B&W images that is why we need to convert the image
       gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
       faces = face_cascade.detectMultiScale(gray,1.1,4)

        #Draw the rectangle around the face and print the emotions
       for(x,y,w,h) in faces:
                cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
                cv2.putText(frame,angry,(x+w+10,y+10),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)
                cv2.putText(frame,happy,(x+w+10,y+50),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)
                cv2.putText(frame,sad,(x+w+10,y+100),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)
                cv2.putText(frame,surprise,(x+w+10,y+150),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)
                cv2.putText(frame,neutral,(x+w+10,y+200),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)

       cv2.imshow('frame', frame)

       key = cv2.waitKey(25)
       if key == ord('q'):
        break

## TODO: Fix the exit.
cap.release()
cv2.destroyAllWindows()