基于OpenCV的meanshift跟踪算法

  代码如下：

import numpy as np
import cv2

cap = cv2.VideoCapture('output_2.avi')
ret, frame = cap.read()  # take first frame of the video
print(frame.shape)

# setup initial location of window
r, h, c, w = 200, 170, 260, 100  # simply hardcoded the values
track_window = (c, r, w, h)

cv2.rectangle(frame, (c, r), (c + w, r + h), 255, 2)
cv2.imshow("frame", frame)
cv2.waitKey(0)

# 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((140., 140., 140.)), np.array((290., 290., 290.)))
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 at least 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)

while (True):
    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.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), 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()