text: # Below is code used to track the Position of a user through usage of the
  # webcam. The specific iteration changes the color of the nose just to
  # showcase a little bit of customization that can be done.
  # please refer to: https://google.github.io/mediapipe/solutions/pose
  # for more details and documentation
  
  # Imports
  import cv2
  import mediapipe as mp
  import time
  
  # OpenCV video capture, Mac/Linux use: cap = cv2.VideoCapture(0)
  # Windows use cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
  # To access a video: cap = cv2.VideoCapture('/pathtovideo.mp4')
  cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
  
  # Calling Pose Detection Modules from Mediapipe
  mpDraw = mp.solutions.drawing_utils # Draw lines between landmarks
  mpPose = mp.solutions.pose
  pose = mpPose.Pose() # write needed parameters, ctrl + click
  
  # frame rate Initialization
  pTime = 0
  
  while True:
      # Read Image Capture
      success, img = cap.read()
  
      # convert image since mediapipe only uses RGB
      imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      results = pose.process(imgRGB)
  
      # Print Landmark Locations
      if results.pose_landmarks:
          # Draw pose Connections
          mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS)
  
          # Loop to get info of Landmark Information
          for id, lm in enumerate(results.pose_landmarks.landmark):
  
              # get image shape
              h, w, c = img.shape
              # convert decimal representations to integers
              cx, cy = int(lm.x * w), int(lm.y *h)
              # print(id, cx, cy) # information on location specific data
  
              # OPTIONAL: Make Nose Blue
              if id == 0:
                  cv2.circle(img, (cx, cy), 5, (255,0,0), cv2.FILLED)
  
      # Frame Rate
      cTime = time.time()
      fps = 1/(cTime - pTime)
      pTime = cTime
  
      # Output fps to image
      cv2.putText(img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3,
                  (255, 0, 0), 3)
  
      # Show Live Image
      cv2.imshow("image", img)
      cv2.waitKey(1)
  
  # clean capture
  cap.release()