text: import face_recognition
  import cv2
  import numpy as np
  
  # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
  # other example, but it includes some basic performance tweaks to make things run a lot faster:
  #   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
  #   2. Only detect faces in every other frame of video.
  
  # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
  # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
  # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
  
  # Get a reference to webcam #0 (the default one)
  video_capture = cv2.VideoCapture(0)
  
  # Load a sample picture and learn how to recognize it.
  obama_image = face_recognition.load_image_file("obama.jpg")
  obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
  
  # Load a second sample picture and learn how to recognize it.
  biden_image = face_recognition.load_image_file("biden.jpg")
  biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
  
  # ME
  me_image = face_recognition.load_image_file("me.jpg")
  me_face_encoding = face_recognition.face_encodings(me_image)[0]
  
  
  #   ----------------------------------------------------------------
  #   ---   BETTER TO DO IT THIS WAY, PREVENTS ERRORS IN CASE IT   ---
  #   ---       DOESN'T FIND FACES IN TRAINING IMAGES              ---
  #   ----------------------------------------------------------------
  #unknown_face_encodings = face_recognition.face_encodings(unknown_image)
  #if len(unknown_face_encodings) > 0:
  #   unknown_face_encoding = unknown_face_encodings[0]
  
  
  # Create arrays of known face encodings and their names
  known_face_encodings = [
  	obama_face_encoding,
  	biden_face_encoding,
  	me_face_encoding
  ]
  known_face_names = [
  	"Barack Obama",
  	"Joe Biden",
  	"MEEE"
  
  ]
  
  # Initialize some variables
  face_locations = []
  face_encodings = []
  face_names = []
  process_this_frame = True
  
  while True:
  	# Grab a single frame of video
  	ret, frame = video_capture.read()
  
  	# Resize frame of video to 1/4 size for faster face recognition processing
  	small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
  
  	# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
  	rgb_small_frame = small_frame[:, :, ::-1]
  
  	# Only process every other frame of video to save time
  	if process_this_frame:
  		# Find all the faces and face encodings in the current frame of video
  		face_locations = face_recognition.face_locations(rgb_small_frame)
  		face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
  
  		face_names = []
  		for face_encoding in face_encodings:
  			# See if the face is a match for the known face(s)
  			matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
  			name = "Unknown"
  
  			# # If a match was found in known_face_encodings, just use the first one.
  			# if True in matches:
  			#     first_match_index = matches.index(True)
  			#     name = known_face_names[first_match_index]
  
  			# Or instead, use the known face with the smallest distance to the new face
  			face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
  			best_match_index = np.argmin(face_distances)
  			if matches[best_match_index]:
  				name = known_face_names[best_match_index]
  
  			face_names.append(name)
  
  	process_this_frame = not process_this_frame
  
  
  	# Display the results
  	for (top, right, bottom, left), name in zip(face_locations, face_names):
  		# Scale back up face locations since the frame we detected in was scaled to 1/4 size
  		top *= 4
  		right *= 4
  		bottom *= 4
  		left *= 4
  
  		# Draw a box around the face
  		cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
  
  		# Draw a label with a name below the face
  		cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
  		font = cv2.FONT_HERSHEY_DUPLEX
  		cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
  
  	# Display the resulting image
  	cv2.imshow('Video', frame)
  
  	# Hit 'q' on the keyboard to quit!
  	if cv2.waitKey(1) & 0xFF == ord('q'):
  		break
  
  # Release handle to the webcam
  video_capture.release()
  cv2.destroyAllWindows()
  