python 目标跟踪算法_Python OpenCV学习笔记之：Meanshift算法目标跟踪

# -*- coding: utf-8 -*-

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Meanshift算法目标跟踪

Meanshift算法可以参考：http://blog.csdn.net/carson2005/article/details/7337432

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import numpy as np

import cv2

cap = cv2.VideoCapture(0)

# 读取摄像头第一帧图像

ret, frame = cap.read()

while True:

ret, frame = cap.read()

if ret == True:

break

# 初始化位置窗口

r,h,c,w = 250,90,400,125 # simply hardcoded the values

track_window = (c,r,w,h)

# 设置所要跟踪的ROI

roi = frame[r:r+h, c:c+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)

# 设置终止条件

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.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(10) & 0xff

if k == 27:

break

else:

# cv2.imwrite(chr(k)+\".jpg\",img2)

else:

break

cv2.destroyAllWindows()

cap.release()