text: import numpy as np
  import cv2
  
  # Load the Cascade Classifier Xml file
  face_cascade = cv2.CascadeClassifier("cascade/mallick_haarcascade_frontalface_default.xml")
  
  # Specifying minimum and maximum size parameters
  MIN_FACE_SIZE = 100
  MAX_FACE_SIZE = 300
  
  #Create a VideoCapture object
  cap = cv2.VideoCapture(0)
   
  # Check if camera opened successfully
  if (cap.isOpened() == False): 
    print("Unable to read camera feed")
  
  while(True):
    # Reading each frame
    ret, frameBig = cap.read()
   
    # If frame opened successfully
    if ret == True: 
       
      # Fixing the scaling factor
      scale = 640.0/frameBig.shape[1]
      
      # Resizing the image
      frame = cv2.resize(frameBig,None,fx = scale,fy = scale, interpolation = cv2.INTER_LINEAR)
    
      # Converting to grayscale
      frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  
      # Detect faces
      faces = face_cascade.detectMultiScale(frameGray, scaleFactor=1.1, minNeighbors=5,flags=0, minSize=(MIN_FACE_SIZE,MIN_FACE_SIZE),maxSize=(MAX_FACE_SIZE,MAX_FACE_SIZE))
    
      # Loop over each detected face
      for i in xrange (0,len(faces)):
      
        # Dimension parameters for bounding rectangle for face
        x,y,width,height = faces[i];
  
        # Calculating the dimension parameters for eyes from the dimensions parameters of the face
        ex,ey,ewidth,eheight = int(x + 0.125*width), int(y + 0.25 * height), int(0.75 * width), int(0.25 * height)             
        
        # Drawing the bounding rectangle around the face
        cv2.rectangle(frame, (ex,ey),(ex+ewidth,ey+eheight),(128,255,0), 2)    
  
      # Display the resulting frame    
      cv2.imshow('Ninja Eye Detector',frame)
   
      # Press ESC on keyboard to stop tracking
      key = cv2.waitKey(1)
   
      if (key==27):
  
        break
   
    # Break the loop
    else:
      break 
  # Release VideoCapture object
  cap.release()
  
  # Closes all the frames
  cv2.destroyAllWindows() 
  