initial region of interest
keep tracking boat
mean shifted image
#main.py
import numpy as np
import cv2
cap = cv2.VideoCapture("assets/Santa Barbara.mp4")
# take first frame of the video
for i in range (0, 400):
ret,frame = cap.read()
# setup initial location of window
x, y, w, h = 460, 280, 300, 150 # simply hardcoded the values
track_window = (x, y, w, h)
# set up the ROI for tracking
roi = frame[y:y+h, x:x+w]
hsv_roi = cv2.cvtColor(roi, 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)
img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 5)
cv2.imshow('img2', img2)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
cv2.waitKey(-1)
while True:
ret, frame = cap.read()
width = int(cap.get(3))
height = int(cap.get(4))
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), (0, 0, 255), 5)
cv2.imshow('img2', img2)
cv2.imshow('dst', dst)
if cv2.waitKey(1) == ord('q'):
break
if cv2.waitKey(1) == ord('p'):
# wait until any key is pressed
cv2.waitKey(-1)
cap.release()
cv2.destroyAllWindows()
reference:
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