import numpy as np
import cv2 as cv
# Read the input video cap = cv.VideoCapture( 'sample.mp4' )
# take first frame of the # video ret, frame = cap.read()
# setup initial region of # tracker x, y, width, height = 400 , 440 , 150 , 150
track_window = (x, y,
width, height)
# set up the Region of # Interest for tracking roi = frame[y:y + height,
x : x + width]
# convert ROI from BGR to # HSV format hsv_roi = cv.cvtColor(roi,
cv.COLOR_BGR2HSV)
# perform masking operation mask = cv.inRange(hsv_roi,
np.array(( 0. , 60. , 32. )),
np.array(( 180. , 255. , 255 )))
roi_hist = cv.calcHist([hsv_roi],
[ 0 ], mask,
[ 180 ],
[ 0 , 180 ])
cv.normalize(roi_hist, roi_hist, 0 , 255 , cv.NORM_MINMAX)
# Setup the termination criteria, # either 15 iteration or move by # atleast 2 pt term_crit = ( cv.TERM_CRITERIA_EPS |
cv.TERM_CRITERIA_COUNT, 15 , 2 )
while ( 1 ):
ret, frame = cap.read()
# Resize the video frames.
frame = cv.resize(frame,
( 720 , 720 ),
fx = 0 , fy = 0 ,
interpolation = cv.INTER_CUBIC)
cv.imshow( 'Original' , frame)
# perform thresholding on
# the video frames
ret1, frame1 = cv.threshold(frame,
180 , 155 ,
cv.THRESH_TOZERO_INV)
# convert from BGR to HSV
# format.
hsv = cv.cvtColor(frame1,
cv.COLOR_BGR2HSV)
dst = cv.calcBackProject([hsv],
[ 0 ],
roi_hist,
[ 0 , 180 ], 1 )
# apply Camshift to get the
# new location
ret2, track_window = cv.CamShift(dst,
track_window,
term_crit)
# Draw it on image
pts = cv.boxPoints(ret2)
# convert from floating
# to integer
pts = np.int0(pts)
# Draw Tracking window on the
# video frame.
Result = cv.polylines(frame,
[pts],
True ,
( 0 , 255 , 255 ),
2 )
cv.imshow( 'Camshift' , Result)
# set ESC key as the
# exit button.
k = cv.waitKey( 30 ) & 0xff
if k = = 27 :
break
# Release the cap object cap.release() # close all opened windows cv.destroyAllWindows() |