text: from builtins import int, round, str
  
  import cv2
  import numpy as np
  import os
  
  recognizer = cv2.face.LBPHFaceRecognizer_create()
  recognizer.read('trainer/trainer.yml')
  cascadePath = "Cascades/haarcascade_frontalface_alt.xml"
  faceCascade = cv2.CascadeClassifier(cascadePath)
  
  font = cv2.FONT_HERSHEY_SIMPLEX
  
  # Iniciate id counter
  id = 0
  
  # names related to ids: example ==> Marcelo: id=1,  etc
  names = ['None', 'Haroutyun']
  
  # Initialize and start realtime video capture
  cam = cv2.VideoCapture(0)
  cam.set(3, 640) # set video width
  cam.set(4, 480) # set video height
  
  # Define min window size to be recognized as a face
  minW = 0.1 * cam.get(3)
  minH = 0.1 * cam.get(4)
  
  while True:
      ret, img = cam.read()
      #img = cv2.flip(img, -1)  # Flip vertically
  
      #print(img)
      gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  
      faces = faceCascade.detectMultiScale(
          gray,
          scaleFactor = 1.2,
          minNeighbors = 5,
          minSize = (int(minW), int(minH))
      )
  
      for (x, y, w, h) in faces:
          cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
          id, confidence = recognizer.predict(gray[y: y + h, x: x + w])
  
          # Check if confidence is less than 100 ==> "0" is perfect match
          if (confidence < 100):
              id = names[id]
              confidence = "  {0}%".format(round(100 - confidence))
          else:
              id = "unknown"
              confidence = "  {0}%".format(round(100 - confidence))
  
          cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255), 2)
          cv2.putText(img, str(confidence), (x + 5, y + h - 5), font, 1, (255, 255, 0), 1)
  
      cv2.imshow('camera', img)
  
      k = cv2.waitKey(10) & 0xff  # Press 'ESC' for exiting video
      if k == 27:
          break
  
  # Do a bit of cleanup
  print("\n [INFO] Exiting Program and cleanup stuff")
  cam.release()
  cv2.destroyAllWindows()