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我在mediepipe上运行了文件但file_list错误怎么办？请帮我看看错误：

for idx, file in enumerate(file_list):

这个完整的代码媒体管道：

 import cv2
    import mediapipe as mp
    mp_drawing = mp.solutions.drawing_utils
    mp_holistic = mp.solutions.holistic
    
    # For static images:
    with mp_holistic.Holistic(static_image_mode=True) as holistic:
      for idx, file in enumerate(file_list):
        image = cv2.imread(file)
        image_height, image_width, _ = image.shape
        # Convert the BGR image to RGB before processing.
        results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    
        if results.pose_landmarks:
          print(
              f'Nose coordinates: ('
              f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
              f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
          )
        # Draw pose, left and right hands, and face landmarks on the image.
        annotated_image = image.copy()
        mp_drawing.draw_landmarks(
            annotated_image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
        mp_drawing.draw_landmarks(
            annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        mp_drawing.draw_landmarks(
            annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        # Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when
        # upper_body_only is set to True.
        mp_drawing.draw_landmarks(
            annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
        cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
    
    # For webcam input:
    cap = cv2.VideoCapture(0)
    with mp_holistic.Holistic(
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5) as holistic:
      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
    
        # Flip the image horizontally for a later selfie-view display, and convert
        # the BGR image to RGB.
        image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
        # To improve performance, optionally mark the image as not writeable to
        # pass by reference.
        image.flags.writeable = False
        results = holistic.process(image)
    
        # Draw landmark annotation on the image.
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        mp_drawing.draw_landmarks(
            image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
        mp_drawing.draw_landmarks(
            image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
        cv2.imshow('MediaPipe Holistic', image)
        if cv2.waitKey(5) & 0xFF == 27:
          break
    cap.release()
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