text: from __future__ import print_function
  #!/usr/bin/env python
  
  # Python 2/3 compatibility
  from __future__ import print_function
  # Allows use of print like a function in Python 2.x
  
  # Import the Numby and OpenCV2 Python modules
  import numpy as np
  import cv2
  
  # This section selects the Haar Cascade Classifer File to use
  # Ensure that the path to the xml files are correct
  # In this example, the files have been copied to the local folder
  face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
  eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
  
  # Some Additional Samples - Only one imread should be active. So be sure to comment out all others.
  # Image download URL - http://blogs.intel.com/iot/files/2016/04/ASBRparticpants.jpg
  #img = cv2.imread('ASBRparticpants.jpg')
  # Image download URL - http://blogs.intel.com/iot/files/2016/09/bmw-group-intel-mobileye-3.jpg
  #img = cv2.imread('bmw-group-intel-mobileye-3.jpg')
  # Image download URL - https://upload.wikimedia.org/wikipedia/commons/1/17/Intel_Board_of_Directors.jpg
  #img = cv2.imread('Intel_Board_of_Directors.jpg')
  try:
  	# Image download URL - https://simplecore.intel.com/newsroom/wp-content/uploads/sites/11/brian-krzanich_2.jpg
  	# Load image to analyze
  	img = cv2.imread('brian-krzanich_2.jpg')
  	# Converion to Grayscale
  	gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  
  	# Face detection using Haar Cascades
  	# Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
  	# cv2.CascadeClassifier.detectMultiScale(image[,scaleFactor[,minNeighbors[,flags[,minSize[,maxSize]]]]]) -> objects
  	faces = face_cascade.detectMultiScale(gray, 1.3, 5)
  	# Draw the rectangles around detected Regions of Interest [ROI] - faces
  	# cv2.rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> img
  	for (x,y,w,h) in faces:		
  		cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
      	roi_gray = gray[y:y+h, x:x+w]
      	roi_color = img[y:y+h, x:x+w]
      	# Since eyes are a part of face, limit eye detection to face regions to improve accuracy
      	eyes = eye_cascade.detectMultiScale(roi_gray)
      	for (ex,ey,ew,eh) in eyes:
      		# Draw the rectangles around detected Regions of Interest [ROI] - eyes
          	cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
  
  	# Display the result        
  	cv2.imshow('img',img)
  	# Show image until dismissed using GUI exit window
  	cv2.waitKey(0)
  	# Release all resources used
  	cv2.destroyAllWindows()
  
  
  except cv2.error as e:
  	print('Please correct OpenCV Error')