OpenCV（for Python）视频分析 Meanshift 和 Camshift

目标• 本节我们要学习使用 Meanshift 和 Camshift 算法在视频中找到并跟踪目标对象Meanshift 算法Meanshift 算法的基本原理是和很简单的。假设我们有一堆点，和一个小的圆形窗口，Meanshift 算法就是不断移动小圆形窗口，直到找到圆形区域内最大灰度密度处为止。# -*- coding: utf-8 -*-"""Created on Mon Jan 27 08:02:04 2014@author: duan"""

import numpy as np
import cv2

#打开视频文件

cap = cv2.VideoCapture('slow.flv')

#获取视频的第一帧ret,frame = cap.read()# 设置窗口的初始位置r,h,c,w = 250,90,400,125         # 写死的值track_window = (c,r,w,h)# 建立跟踪的ROIroi = frame[r:r+h, c:c+w]

#图片从RGB空间转换为HSV空间hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

#将低亮度的值忽略掉mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))

#计算图像直方图

roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])

#归一化处理cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)

# 设置终止标准，10次迭代或至少移动1 ptterm_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 locationret, track_window = cv2.meanShift(dst, track_window, term_crit)# Draw it on imagex,y,w,h = track_windowimg2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)cv2.imshow('img2',img2)k = cv2.waitKey(60) & 0xffif k == 27:breakelse:cv2.imwrite(chr(k)+".jpg",img2)else:breakcv2.destroyAllWindows()cap.release()