text: # Importing libraries
  import cv2
  import numpy as np
  import pickle 
  import os
  import argparse
  import face_recognition
  
  # Argument Parser
  ap =argparse.ArgumentParser()
  ap.add_argument("-e", "--encoding",default =os.path.join(os.getcwd(),"encoding.pickle"), help= "path to the encoding")
  ap.add_argument("-i", "--image",default=os.path.join(os.getcwd(),"index.png"), help= "path to the input image")
  ap.add_argument("-d", "--detection-method", default="cnn", help="type of model")
  
  # Getting the data
  args = vars(ap.parse_args())
  print("[INFO]!! Testing the image !!")
  data = pickle.loads(open(args["encoding"],"rb").read())
  image = cv2.imread(os.path.join(os.getcwd(),"mk.jpg"))
  rgb = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
  boxes= face_recognition.face_locations(rgb,model= "cnn")
  encodings = face_recognition.face_encodings(rgb,boxes)
  names=[]
  
  for encoding in encodings:
  	matches = face_recognition.compare_faces(data["encoding"],encoding)
  	name = "Unknown"
  	if True in matches:
  		match_idx = [i for (i,b) in enumerate(matches) if b]
  		counts= {}
  		for i in match_idx:
  			name = data["names"][i]
  			counts[name] =counts.get(name,0)+1
  		name = max(counts, key= counts.get)
  	names.append(name)
  for ((top,right,bottom,left),name) in zip(boxes,names):
  	cv2.rectangle(image,(left,top),(right,bottom),(0,255,0),2)
  	y = top-15 if top-15>15 else top+15
  	cv2.putText(image,name,(left,y),cv2.FONT_HERSHEY_SIMPLEX,0.45,(0,255,0),2)
  cv2.imshow("Image",image)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  	
  