text: # import all the library
  # from imutils.video import VideoStream
  from imutils.video import FPS  # it is use for showing the FPS
  import face_recognition  #this library help for pace recognition
  import argparse
  import imutils
  import pickle
  import time
  import cv2
  import os
  
  
  #this var is use for get the name save it to the name.txt file
  Name=''
  
  
  # load the known faces and embeddings along with OpenCV's Haar
  # cascade for face detection
  print("[INFO] loading encodings + face detector...")
  
  #for linux absolute path of the file is provides 
  data = pickle.loads(open('/home/sharwan/Desktop/face_recognition/encodings.pickle', "rb").read())
  detector = cv2.CascadeClassifier('/home/sharwan/Desktop/face_recognition/haarcascade/haarcascade_frontalface_default.xml')
  
  # initialize the video stream and allow the camera sensor to warm up
  print("[INFO] starting video stream...")
  cam=cv2.VideoCapture(0)
  # vs = VideoStream(usePiCamera=True).start() 
  #we can use imulitls videoStreaming for the pi camera
  
  time.sleep(0.5)
  # start the FPS counter
  fps = FPS().start()
  #this is the counter for the while loop so that program exit after after some time
  C=20
  # loop over frames from the video file stream
  while True:
  	C-=1
  	# grab the frame from the threaded video stream and resize it
  	# to 500px (to speedup processing)
  	ret,frame = cam.read()
  #  her we change the sizr of the frame
  	frame = imutils.resize(frame, width=500)
  
  	# convert the input frame from (1) BGR to grayscale (for face
  	# detection) and (2) from BGR to RGB (for face recognition)
  	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
  	# detect faces in the grayscale frame
  	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
  		minNeighbors=5, minSize=(30, 30))
  	# OpenCV returns bounding box coordinates in (x, y, w, h) order
  	# but we need them in (top, right, bottom, left) order, so we
  	# need to do a bit of reordering
  	boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]
  	# compute the facial embeddings for each face bounding box
  	encodings = face_recognition.face_encodings(rgb, boxes)
   
   # this list element contains all the name which we detect at that time
  	names = []
  
  	# loop over the facial embeddings
  	for encoding in encodings:
  		# attempt to match each face in the input image to our known
  		# encodings
  		matches = face_recognition.compare_faces(data["encodings"],
  			encoding)
  		name = "Unknown"
  		# check to see if we have found a match
  		if True in matches:
  			# find the indexes of all matched faces then initialize a
  			# dictionary to count the total number of times each face
  			# was matched
  			matchedIdxs = [i for (i, b) in enumerate(matches) if b]
  			counts = {}
  
  			# loop over the matched indexes and maintain a count for
  			# each recognized face face
  			for i in matchedIdxs:
  				name = data["names"][i]
  				counts[name] = counts.get(name, 0) + 1
  			# determine the recognized face with the largest number
  			# of votes (note: in the event of an unlikely tie Python
  			# will select first entry in the dictionary)
  			name = max(counts, key=counts.get)
  		
  		# update the list of names
  		names.append(name)
  
  	# loop over the recognized faces
  	for ((top, right, bottom, left), name) in zip(boxes, names):
  		# draw the predicted face name on the image
  		cv2.rectangle(frame, (left, top), (right, bottom),
  			(0, 255, 0), 2)
  		y = top - 15 if top - 15 > 15 else top + 15
  		cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
  			0.75, (0, 255, 0), 2)
  	# display the image to our screen
  	# cv2.imshow("Frame", frame)
  	key = cv2.waitKey(1) & 0xFF
  	if C==0:
  		key = ord("q")
  
  	# if the `q` key was pressed, break from the loop
  	if key == ord("q"):
       #here we check that if the names list is empty the it will show Unknown in the terminal.
  		if len(names)==0:
  			Name='UNKNOWN!!!! {upside-down face}'
  		else:
  			Name=names[0]
  		break
  	# update the FPS counter
  	fps.update()
   
  # stop the timer and display FPS information
  fps.stop()
  print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
  print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
  # do a bit of cleanup
  cv2.destroyAllWindows()
  
  #sending the name of the person to file
  f=open("/home/sharwan/Desktop/face_recognition/name.txt","w")
  f.write(Name)
  