text: #!/usr/bin/env python
  # coding: utf-8
  
  # In[1]:
  
  
  import cv2
  import numpy as np
  import os 
  
  
  # In[2]:
  
  
  recognizer = cv2.face.LBPHFaceRecognizer_create()
  recognizer.read('trainer/trainer.yml')
  cascadePath = "haarcascade_frontalface_default.xml"
  faceCascade = cv2.CascadeClassifier(cascadePath);
  
  font = cv2.FONT_HERSHEY_SIMPLEX
  
  
  # In[3]:
  
  
  #iniciate id counter
  id = 1
  
  # names related to ids: example ==> Wilson: id=1,  etc
  names = ['','Wilson'] 
  
  
  # In[4]:
  
  
  # Initialize and start realtime video capture
  cam = cv2.VideoCapture(0)
  cam.set(3, 640) # set video widht
  cam.set(4, 480) # set video height
  
  # Define min window size
  minW = 0.1*cam.get(3)
  minH = 0.1*cam.get(4)
  
  while True:
  
      ret, img =cam.read()
  
      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
  
  
  print("\n [INFO] Exiting Program")
  cam.release()
  cv2.destroyAllWindows()
  
  
  # In[ ]:
  
  
  
  
  