cap = cv2.VideoCapture(0)
while True: # Grab a single frame of video ret, frame = cap.read() gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = face_classifier.detectMultiScale(gray,1.3,5) for (x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) roi_gray = gray[y:y+h,x:x+w] roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA) # rect,face,image = face_detector(frame) if np.sum([roi_gray])!=0: roi = roi_gray.astype('float')/255.0 roi = img_to_array(roi) roi = np.expand_dims(roi,axis=0) # make a prediction on the ROI, then lookup the class preds = classifier.predict(roi)[0] label=class_labels[preds.argmax()] label_position = (x,y) cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3) else: cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3) cv2.imshow('Emotion Detector',frame) if cv2.waitKey(1) & 0xFF == ord('q'): break
