1import numpy as np
2import cv2 as cv
3import argparse
4
5parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
6                                              The example file can be downloaded from: \
7                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
8parser.add_argument('image', type=str, help='path to image file')
9args = parser.parse_args()
10
11cap = cv.VideoCapture(args.image)
12
13# take first frame of the video
14ret,frame = cap.read()
15
16# setup initial location of window
17x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
18track_window = (x, y, w, h)
19
20# set up the ROI for tracking
21roi = frame[y:y+h, x:x+w]
22hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
23mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
24roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
25cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
26
27# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
28term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
29
30while(1):
31    ret, frame = cap.read()
32
33    if ret == True:
34        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
35        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
36
37        # apply meanshift to get the new location
38        ret, track_window = cv.meanShift(dst, track_window, term_crit)
39
40        # Draw it on image
41        x,y,w,h = track_window
42        img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
43        cv.imshow('img2',img2)
44
45        k = cv.waitKey(30) & 0xff
46        if k == 27:
47            break
48    else:
49        break
50