code: import numpy as np
  import cv2 as cv
  import argparse
  
  parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
                                                The example file can be downloaded from: \
                                                https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
  parser.add_argument('image', type=str, help='path to image file')
  args = parser.parse_args()
  
  cap = cv.VideoCapture(args.image)
  
  # take first frame of the video
  ret,frame = cap.read()
  
  # setup initial location of window
  x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
  track_window = (x, y, w, h)
  
  # set up the ROI for tracking
  roi = frame[y:y+h, x:x+w]
  hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
  mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
  roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
  cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
  
  # Setup the termination criteria, either 10 iteration or move by atleast 1 pt
  term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
  
  while(1):
      ret, frame = cap.read()
  
      if ret == True:
          hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
          dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
  
          # apply meanshift to get the new location
          ret, track_window = cv.meanShift(dst, track_window, term_crit)
  
          # Draw it on image
          x,y,w,h = track_window
          img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
          cv.imshow('img2',img2)
  
          k = cv.waitKey(30) & 0xff
          if k == 27:
              break
      else:
          break
  
apis: numpy.array