Track objects with Camshift using OpenCV (original) (raw)
`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()
`