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