text: import mediapipe as mp
  mp_drawing = mp.solutions.drawing_utils
  mp_drawing_styles = mp.solutions.drawing_styles
  mp_face_mesh = mp.solutions.face_mesh
  
  # For static images:
  IMAGE_FILES = []
  drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
  with mp_face_mesh.FaceMesh(
      static_image_mode=True,
      max_num_faces=1,
      refine_landmarks=True,
      min_detection_confidence=0.5) as face_mesh:
    for idx, file in enumerate(IMAGE_FILES):
      image = cv2.imread(file)
      # Convert the BGR image to RGB before processing.
      results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
  
      # Print and draw face mesh landmarks on the image.
      if not results.multi_face_landmarks:
        continue
      annotated_image = image.copy()
      for face_landmarks in results.multi_face_landmarks:
        print('face_landmarks:', face_landmarks)
        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_TESSELATION,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_tesselation_style())
        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_CONTOURS,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_contours_style())
        mp_drawing.draw_landmarks(
            image=annotated_image,
            landmark_list=face_landmarks,
            connections=mp_face_mesh.FACEMESH_IRISES,
            landmark_drawing_spec=None,
            connection_drawing_spec=mp_drawing_styles
            .get_default_face_mesh_iris_connections_style())
      cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
  
  # For webcam input:
  drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
  cap = cv2.VideoCapture(0)
  with mp_face_mesh.FaceMesh(
      max_num_faces=1,
      refine_landmarks=True,
      min_detection_confidence=0.5,
      min_tracking_confidence=0.5) as face_mesh:
    while cap.isOpened():
      success, image = cap.read()
      if not success:
        print("Ignoring empty camera frame.")
        # If loading a video, use 'break' instead of 'continue'.
        continue
  
      # To improve performance, optionally mark the image as not writeable to
      # pass by reference.
      image.flags.writeable = False
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      results = face_mesh.process(image)
  
      # Draw the face mesh annotations on the image.
      image.flags.writeable = True
      image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
      if results.multi_face_landmarks:
        for face_landmarks in results.multi_face_landmarks:
          mp_drawing.draw_landmarks(
              image=image,
              landmark_list=face_landmarks,
              connections=mp_face_mesh.FACEMESH_TESSELATION,
              landmark_drawing_spec=None,
              connection_drawing_spec=mp_drawing_styles
              .get_default_face_mesh_tesselation_style())
          mp_drawing.draw_landmarks(
              image=image,
              landmark_list=face_landmarks,
              connections=mp_face_mesh.FACEMESH_CONTOURS,
              landmark_drawing_spec=None,
              connection_drawing_spec=mp_drawing_styles
              .get_default_face_mesh_contours_style())
          mp_drawing.draw_landmarks(
              image=image,
              landmark_list=face_landmarks,
              connections=mp_face_mesh.FACEMESH_IRISES,
              landmark_drawing_spec=None,
              connection_drawing_spec=mp_drawing_styles
              .get_default_face_mesh_iris_connections_style())
      # Flip the image horizontally for a selfie-view display.
      cv2.imshow('MediaPipe Face Mesh', cv2.flip(image, 1))
      if cv2.waitKey(5) & 0xFF == 27:
        break
  cap.release()
  