text: import numpy as np
  import cv2
  import pickle
  
  face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
  recognizer = cv2.face.LBPHFaceRecognizer_create()
  recognizer.read("trainer.yml")
  
  labels = {0}
  with open("labels.pickle", 'rb') as f:
      og_labels = pickle.load(f)
      labels = {v:k for k,v in og_labels.items()}
  cap = cv2.VideoCapture(0)
  
  while(True):
      ret, frame = cap.read()
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
      faces = face_cascade.detectMultiScale(gray,scaleFactor=1.2, minNeighbors=1)
      for (x,y,w,h) in faces:
          roi_gray = gray[y: y+h, x: x+h] #region of interest
          roi_color = frame[y: y+h, x: x+h]
  
          #recognize ? deep learning model 
          id_,conf = recognizer.predict(roi_gray)
          if conf >= 55:
              font = cv2.FONT_HERSHEY_SIMPLEX
              name = labels[id_]         
              color = (255, 255, 255) #BGR
              stroke = 2
              cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)
          
          #img_item = "7.png"
          #cv2.imwrite(img_item,roi_color)
          color = (255, 0, 0) #BGR
          stroke = 2
          end_cord_x = x+w
          end_cord_y = y+h
          cv2.rectangle(frame, (x,y), (end_cord_x, end_cord_y), color, stroke)
  
      cv2.imshow('frame',frame)
      if cv2.waitKey(20) & 0xFF == ord('q'):
          break
  
  cap.release()
  cap.destroyAllWindows()
  
  