text: import face_recognition
  import cv2
  
  # This is a demo of running face recognition on a video file and saving the results to a new video file.
  #
  # 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.
  
  # Open the input movie file
  input_movie = cv2.VideoCapture("hamilton_clip.mp4")
  length = int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT))
  
  # Create an output movie file (make sure resolution/frame rate matches input video!)
  fourcc = cv2.VideoWriter_fourcc(*'XVID')
  output_movie = cv2.VideoWriter('output.avi', fourcc, 29.97, (640, 360))
  
  # Load some sample pictures and learn how to recognize them.
  lmm_image = face_recognition.load_image_file("lin-manuel-miranda.png")
  lmm_face_encoding = face_recognition.face_encodings(lmm_image)[0]
  
  al_image = face_recognition.load_image_file("alex-lacamoire.png")
  al_face_encoding = face_recognition.face_encodings(al_image)[0]
  
  known_faces = [
      lmm_face_encoding,
      al_face_encoding
  ]
  
  # Initialize some variables
  face_locations = []
  face_encodings = []
  face_names = []
  frame_number = 0
  
  while True:
      # Grab a single frame of video
      ret, frame = input_movie.read()
      frame_number += 1
  
      # Quit when the input video file ends
      if not ret:
          break
  
      # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
      rgb_frame = frame[:, :, ::-1]
  
      # Find all the faces and face encodings in the current frame of video
      face_locations = face_recognition.face_locations(rgb_frame)
      face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
  
      face_names = []
      for face_encoding in face_encodings:
          # See if the face is a match for the known face(s)
          match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)
  
          # If you had more than 2 faces, you could make this logic a lot prettier
          # but I kept it simple for the demo
          name = None
          if match[0]:
              name = "Lin-Manuel Miranda"
          elif match[1]:
              name = "Alex Lacamoire"
          print(name)
          face_names.append(name)
  
      # Label the results
      for (top, right, bottom, left), name in zip(face_locations, face_names):
          if not name:
              continue
  
          # 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 - 25), (right, bottom), (0, 0, 255), cv2.FILLED)
          font = cv2.FONT_HERSHEY_DUPLEX
          cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)
  
      # Write the resulting image to the output video file
      print("Writing frame {} / {}".format(frame_number, length))
      output_movie.write(frame)
  
  # All done!
  input_movie.release()
  cv2.destroyAllWindows()
  