content: import cv2
  import sys
  #import matplotlib.pyplot as pt
  import numpy as np
  import numpy.linalg as la
  import math as mt
  
  #Content of out eigens
  <<<<<<< HEAD:face_recognition.py
  #	there would be five images of each person
  #	the collumns would be the frob norm of each type
  #	4 rows for each person
  #	1)Smiling
  #	2)Sad
  #	3)Serious
  #	4)Blank
  #	5)If wearing specs then without specs
  #	6)looking left
  #	7)looking right
  #ournorms = {'Abhishek':[5916.56,6155.725,5835.83,6033.245,5922.402,6207.052,6028.91],
  #			'Akshay':[6268.704,6335.443,6119.169,6277.252,6126.155,6232.754,6294.937],
  #			'Chris':[6479.241,6297.295,6477.624,6463.082,6385.727,6275.596,6200.595],
  #			'Tim':[6507.45,6569.225,6637.975,6731.95,6546.934,6239.888,6529.477]}
  
  ournorms = {'Abhishek':[5866.278,6229.924,6123.536,5988.862,5966.183,5990.367,5661.118],
  			'Akshay':[6748.139,5658.617,6238.200,6671.678,6228.899,6167.573,5830.901],
  			'Chris':[6312.924,6374.821,6465.274,6275.596,6596.240,6382.099,6456.81], #left right serious
  			'Tim':[6226.022,6010.737,6107.618,6107.386,5994.380,5916.834,7052.4.3]}
  
  indbuffervals = {'Abhishek':100,
  				 'Akshay':100,
  <<<<<<< HEAD:face_recognition.py
  				 'Chris':50,
  =======
  				 'Chris':200,
  >>>>>>> origin/master:facial_recognition.py
  				 'Tim':150}
  #hardcode values into ournorms above
  
  imagePath = sys.argv[1]
  
  
  <<<<<<< HEAD:face_recognition.py
  def recognizeFace(image,faces):
  =======
  def recognizeFace(faces, image):
  >>>>>>> origin/master:facial_recognition.py
  	retval = True
  	if(len(faces)>10):
  		print("Fuck it too many faces shoot everyone")
  		return True, 100
  	for i in range(faces.shape[0]):
  		x, y, w, h = faces[i]
  		bufw = (400 - w)/2
  		bufh = (400 - h)/2
  		inmod = image[y-bufw:y+w+bufw,x-bufh:x+h+bufh]
  		retwhat = checker(inmod)
  		retval = retwhat and retval
  	return retval,len(faces)
  
  
  =======
  #there would be five images of each person
  #the collumns would be the frob norm of each type
  #4 rows for each person
  #1)Smiling
  #2)Sad
  #3)Serious
  #4)Blank
  #5)If wearing specs then without specs
  #6)looking left
  #7)looking right
  #ournorms = {'Abhishek':[5916.56,6155.725,5835.83,6033.245,5922.402,6207.052,6028.91],
  #'Akshay':[6268.704,6335.443,6119.169,6277.252,6126.155,6232.754,6294.937],
  #'Chris':[6479.241,6297.295,6477.624,6463.082,6385.727,6275.596,6200.595],
  #'Tim':[6507.45,6569.225,6637.975,6731.95,6546.934,6239.888,6529.477]}
  
  ournorms = {'Abhishek':[5866.278,6229.924,6123.536,5988.862,5966.183,5990.367,5661.118],
              'Akshay':[6748.139,5658.617,6238.200,6671.678,6228.899,6167.573,5830.901],
              'Chris':[6312.924,6374.821,6465.274,6275.596,6596.240,6382.099,6456.81], 
              'Tim':[6226.022,6010.737,6107.618,6107.386,5994.380,5916.834,7052.43]}
  
  indbuffervals = {'Abhishek':100,
                    'Akshay':100,
                    'Chris':50,
                    'Tim':150}
  #hardcode values into ournorms above
  
  #imagePath = sys.argv[1]
  
  
  def recognizeFace(image,faces):
      retval = True
      if(len(faces)>10):
          print("Fuck it too many faces shoot everyone")
          return True, 100
      for i in range(faces.shape[0]):
          x, y, w, h = faces[i]
          bufw = (400 - w)/2
          bufh = (400 - h)/2
          inmod = image[y-bufw:y+w+bufw,x-bufh:x+h+bufh]
          retwhat = checker(inmod)
          retval = retwhat and retval
      return retval,len(faces)
      
      
  >>>>>>> ed4cfb17e7f30b2eda5f67f3661d4598f64953a3:facial_recognition.py
  def checker(inmod):
      tempnorm = la.norm(inmod)
      retval = False
      for name,val in ournorms.iteritems():
          for j in val:
              if(np.abs(j-tempnorm)<indbuffervals[name]):
                  retval = True;
                  print("is")
                  print(name)
                  break
          if(retval):
              break
          if(not retval):
              print("not")
              print(name)
      return retval
  
  # Get values from command line
  def check(image):
      #imagePath = sys.argv[1]
      #cascPath = sys.argv[2]
      imagePath = image
      cascPath = "haarcascade_frontalface_default.xml"
  
  
      # Create the haar cascade
      faceCascade = cv2.CascadeClassifier(cascPath)
  
      # Read the image
      image = cv2.imread(imagePath)
      imnonmod = cv2.imread(imagePath)
      gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  
      # Detect faces in the image
      faces = faceCascade.detectMultiScale(
          gray,
          scaleFactor=1.25,
          minNeighbors=5,
          minSize=(40, 40)    
          )
  
  <<<<<<< HEAD:face_recognition.py
  	# Draw a rectangle around the faces
  	for (x, y, w, h) in faces:
  		cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
  <<<<<<< HEAD:face_recognition.py
  
  	what = True
  
  	if(len(faces)>0):
  		what, number = recognizeFace(image,faces)
  
  =======
  
  	what = True
  	if(len(faces)>0):
  		what, number = recognizeFace(faces, image)
  >>>>>>> origin/master:facial_recognition.py
  	# return what to the arduino
  	if(what is False):
  		print("intruder detected")
  		
  =======
      print "Found {0} faces!".format(len(faces))
  
      # Draw a rectangle around the faces
      for (x, y, w, h) in faces:
          cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
  >>>>>>> ed4cfb17e7f30b2eda5f67f3661d4598f64953a3:facial_recognition.py
  
      what = True
  
  <<<<<<< HEAD:face_recognition.py
  	cv2.imshow("Faces found", image)
  <<<<<<< HEAD:face_recognition.py
  	cv2.waitKey(0)
  =======
  	#cv2.waitKey(0)
  	return what
  =======
      if(len(faces)>0):
          what, number = recognizeFace(image,faces)
          
              # return what to the arduino
      if(what is False):
          print("intruder detected")
  >>>>>>> ed4cfb17e7f30b2eda5f67f3661d4598f64953a3:facial_recognition.py
  
  >>>>>>> origin/master:facial_recognition.py
  
      cv2.imshow("Faces found", image)
      #cv2.waitKey(0)
  
  check(imagePath)
  
  #check(imagePath)
  