text: ''''
  Training Multiple Faces stored on a DataBase:
  	==> Each face should have a unique numeric integer ID as 1, 2, 3, etc                       
  	==> LBPH computed model will be saved on trainer/ directory. (if it does not exist, pls create one)
  	==> for using PIL, install pillow library with "pip install pillow"
  '''
  
  
  import cv2
  import numpy as np
  from PIL import Image
  import os
  
  # Path for face image database
  path_data = 'dataset'
  
  recognizer = cv2.face.LBPHFaceRecognizer_create()
  detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
  
  # function to get the images and label data
  def getImagesAndLabels(path):
  
      imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
      faceSamples=[]
      ids = []
  
      for imagePath in imagePaths:
  
          PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
          img_numpy = np.array(PIL_img,'uint8')
  
          id = int(os.path.split(imagePath)[-1].split(".")[1])
          faces = detector.detectMultiScale(img_numpy)
  
          for (x,y,w,h) in faces:
              faceSamples.append(img_numpy[y:y+h,x:x+w])
              ids.append(id)
  
      return faceSamples,ids
  
  #print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
  faces,ids = getImagesAndLabels(path_data)
  recognizer.train(faces, np.array(ids))
  
  # Save the model into trainer/trainer.yml
  recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
  
  # Print the numer of faces trained and end program
  #print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))
  