Reference to 《learning opencv3 computer vision with python》
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
# take first frame of the video
ret, frame = cap.read()
# setup initial location of window
r, h, c, w = 300, 200, 400, 300
track_window = (c, r, w, h)
roi = frame[r:r+h, c:c+w]
hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((100., 30, 32.)),
np.array((180., 120, 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
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)
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
img2 = cv2.polylines(frame, [pts], True, 255, 2)
cv2.imshow('Capture', img2)
k = cv2.waitKey(60) & 0xff
if k == 27:
break
else:
break
cv2.destroyAllWindows()
cap.release()