35.camshift.py

#Camshift in OpenCV

#We need to adapt the window size with size and rotation of the target. Once again, the solution came from “OpenCV Labs” and 
#it is called CAMshift (Continuously Adaptive Meanshift)


#It applies meanshift first. Once meanshift converges, it updates the size of the window as, s = 2 × M/256 . It also
#calculates the orientation of best fitting ellipse to it. Again it applies the meanshift with new scaled search window
#and previous window location. The process is continued until required accuracy is met.

 
#It is almost same as meanshift, but it returns a rotated rectangle (that is our result) and box parameters (used to be
#passed as search window in next iteration)

import numpy as np
import cv2
cap = cv2.VideoCapture('slow.flv')
# take first frame of the video
ret,frame = cap.read()
# setup initial location of window
r,h,c,w = 250,90,400,125 # simply hardcoded the values
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 atleast 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.CamShift(dst, track_window, term_crit)
		# Draw it on image
		pts = cv2.boxPoints(ret)
		pts = np.int0(pts)
		img2 = cv2.polylines(frame,[pts],True, 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()