Python OpenCV: Meanshift

# Python program to demonstrate
# meanshift 


import numpy as np
import cv2
 

# read video
cap = cv2.VideoCapture('sample.mp4')
 
# retrieve the very first 
# frame from the video
_, frame = cap.read()
 
# set the region for the
# tracking window p, q, r, s
# put values according to yourself
p, q, r, s = 150, 150, 460, 100
track_window = (r, p, s, q)
 
    
# create the region of interest
r_o_i = frame[p:p + q, r:r + s]

# converting BGR to HSV format
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
 
# apply mask on the HSV frame
mask = cv2.inRange(hsv, 
                   np.array((0., 61., 33.)),
                   np.array((180., 255., 255.)))

# get histogram for hsv channel
roi = cv2.calcHist([hsv], [0], mask, 
                   [180], [0, 180])

# normalize the retrieved values
cv2.normalize(roi, roi, 0, 255, 
              cv2.NORM_MINMAX)
 
# termination criteria, either 15 
# iteration or by at least 2 pt
termination = (cv2.TERM_CRITERIA_EPS | 
               cv2.TERM_CRITERIA_COUNT
               , 15, 2 )
 
while(True):
    _, frame = cap.read()
 
    frame = cv2.resize(frame, 
                       (1280, 720), 
                       fx = 0, 
                       fy = 0, 
                       interpolation = cv2.INTER_CUBIC)
 
    # convert BGR to HSV format
    hsv = cv2.cvtColor(frame, 
                       cv2.COLOR_BGR2HSV)
    
    bp = cv2.calcBackProject([hsv], 
                             [0], 
                             roi, 
                             [0, 180], 
                             1)
 
    # applying meanshift to get the new region
    _, track_window = cv2.meanShift(bp, 
                                    track_window, 
                                    termination)
 
    # Draw track window on the frame
    x, y, w, h = track_window
    vid = cv2.rectangle(frame, (x, y), 
                        (x + w, y + h), 
                        255, 2)
    
    # show results
    cv2.imshow('tracker', vid)
 
    k = cv2.waitKey(1) & 0xff
    if k == ord('q'):
        break
        
# release cap object
cap.release()

# destroy all opened windows
cv2.destroyAllWindows()