text: import face_recognition
  import cv2
  import numpy as np
  from math import sin, cos, radians
  import pickle
  import os
  
  # 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]
  
  # Create arrays of known face encodings and their names
  known_face_encodings = [
      obama_face_encoding,
      biden_face_encoding
  ]
  known_face_names = [
      "Barack Obama",
      "Joe Biden"
  ]
  
  # Initialize some variables
  face_locations = []
  face_encodings = []
  face_names = []
  process_this_frame = True
  
  ArquivoEncoding = "Codings"
  ArquivoNome = "Nomes"
  
  # Test if there are already files to load
  # In case there is, it will just load
  # Otherwise, a base will be created, converted to bits and loaded into the script
  
  if not(os.path.isfile("Codings")):
      with open(ArquivoEncoding, 'wb') as fp:
          pickle.dump(known_face_encodings, fp)
  with open (ArquivoEncoding, 'rb') as fp:
      known_face_encodings = pickle.load(fp)
  
  if not(os.path.isfile("Nomes")):
      with open(ArquivoNome, 'wb') as fp:
          pickle.dump(known_face_names, fp)
  with open (ArquivoNome, 'rb') as fp:
      known_face_names = pickle.load(fp)
  
  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(33) & 0xFF == ord('q'):
          break
  
      # Press 'c' to capture face and assing a name to it
      if cv2.waitKey(33) & 0xFF == ord('c'):
          # Capture the picture
          ret, frame = video_capture.read()
          frameRGB = frame[:,:,::-1]
          # Recognize and store face's measures
          known_face_encodings.append(face_recognition.face_encodings(frameRGB)[0])
          # Assign a name to the captured face
          nome = input("Type captured face's name: ")
          known_face_names.append(nome)
          # Save the image for future use
          cv2.imwrite('opencv '+nome+'.png', frame)
  
      # Press 's' to save everything into bits
      if cv2.waitKey(33) & 0xFF == ord('s'):
          if bool(input("Do you wish to save all the names and encodings? (1|0):\n")):
              with open(ArquivoEncoding, 'wb') as fp:
                  pickle.dump(known_face_encodings, fp)
              with open (ArquivoEncoding, 'rb') as fp:
                  known_face_encodings = pickle.load(fp)
              with open(ArquivoNome, 'wb') as fp:
                  pickle.dump(known_face_names, fp)
              with open (ArquivoNome, 'rb') as fp:
                  known_face_names = pickle.load(fp)
              print("Names' and encodings' lists converted in binary and saved!")
  
  # Release handle to the webcam
  
  video_capture.release()
  cv2.destroyAllWindows()
  