text: #this file is use to train the images captured by the 01_face_dataset.py
  #after execution of this progtam triner.yml will be created
  
  import cv2
  import numpy as np
  from PIL import Image
  import os
  
  # Path for face image database
  path = 'dataset'
  
  #it is use to classify the faces 
  recognizer = cv2.face.LBPHFaceRecognizer_create()
  #open cv's predefined classifire
  detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");
  
  # function to get the images and label data
  def getImagesAndLabels(path):
      # path of the image
      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 gray color
  
          img_numpy = np.array(PIL_img,'uint8')
          #for collecting the faces having same id
          id = int(os.path.split(imagePath)[-1].split(".")[1])
          faces = detector.detectMultiScale(img_numpy)
          #starting points of face and height & width 
          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)
  recognizer.train(faces, np.array(ids))
  
  # Save the model into trainer/trainer.yml
  recognizer.write('trainer/trainer.yml')
  
  # Print the numer of faces trained and end program
  print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))