語法與前面幾種都一下，只有最下部分有不同而已

import cv2

import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils

mp_holistic = mp.solutions.holistic

# 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

    # 以下開始不一樣，但是大同小異

    results = holistic.process(image)

    # Draw landmark annotation on the image.

    # 使用了face_landmarks,left_hand_landmarks,right_hand_landmarks,pose_landmarks

    # 基本語法與前面幾個都差不多

    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()