text: import mediapipe as mp
  import cv2
  
  
  mp_drawing = mp.solutions.drawing_utils
  mp_hands = mp.solutions.hands
  
  hands = mp_hands.Hands(
      min_detection_confidence=0.7,
      min_tracking_confidence=0.5,
  )
  
  cap = cv2.VideoCapture(0)
  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 = hands.process(image)
      
      # Draw the hand annotations on the image.
      image.flags.writeable = True
      image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
      image_width, image_height = image.shape[1], image.shape[0]
  
      if results.multi_hand_landmarks:
          for hand_landmarks in results.multi_hand_landmarks:
              #print('Handedness:', results.multi_handedness)
              mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
              cv2.imshow('MediaPipe Hands', image)
      if cv2.waitKey(5) & 0xFF == 27:
          break
  hands.close()
  cap.release()
          