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- import numpy as np
- import cv2
- cap = cv2.VideoCapture('videos/slow_traffic_small.mp4')
- # take first frame of the video
- ret,frame = cap.read()
- # setup initial location of window
- # r,h,c,w - region of image
- # simply hardcoded the values
- r,h,c,w = 200,20,300,20
- track_window = (c,r,w,h)
- # set up the ROI for tracking
- roi = frame[r:r+h, c:c+w]
- hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
- mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
- roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
- cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
- # Setup the termination criteria, either 10 iteration or move by at least 1 pt
- term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
- while(1):
- ret ,frame = cap.read()
- if ret == True:
- hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
- dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
- # apply meanshift to get the new location
- ret, track_window = cv2.meanShift(dst, track_window, term_crit)
- # Draw it on image
- x,y,w,h = track_window
- img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
- cv2.imshow('img2',img2)
- k = cv2.waitKey(60) & 0xff
- if k == 27:
- break
- else:
- cv2.imwrite(chr(k)+".jpg",img2)
- else:
- break
- cv2.destroyAllWindows()
- cap.release()
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