基于python-openv下的模板跟踪算法的实现

前几周搞了一个基于模板跟踪的算法实现。即在视频中跟踪出模板目标
闲话不多说，直接程序给出：

# -*- coding: utf-8 -*-
"""
Created on Thu Jun 30 09:13:24 2016

@author: liu
"""  
import cv2
import numpy as np

from matplotlib import pyplot as plt
#img = cv2.imread('xucaise.jpg',0)

#template = cv2.imread('xuxu.jpg',0)
template = cv2.imread('ppp.png',0)
w, h = template.shape[::-1]

#s1=cv2.imread('xupanmu.jpg',cv2.IMREAD_GRAYSCALE)
#s1 = cv2.imread(ph1)
#s2 = cv2.imread(ph2)
methods = ['cv2.TM_SQDIFF_NORMED']
#methods = ['cv2.TM_CCOEFF_NORMED']#平方差匹配法，最好的匹配为0，值越大匹配越差；
       
cap = cv2.VideoCapture('double.mov')

ret,frame = cap.read()
c=0
time=10
a=1
while ret:
    
    ret ,frame = cap.read()
    c=c+1
    if ret == True:
        
    
        if (c%time==0):
            for meth in methods:
                
                 method = cv2.TM_SQDIFF_NORMED#eval(meth)
                 cv2.imwrite('xuphoto'+str(c)+'.png',frame)
                 s2=cv2.imread('xuphoto'+str(c)+'.png',0)
                 #s2=frame
                 #img = cv2.imread('xucaise.jpg',0)
                 res = cv2.matchTemplate(s2,template,method)
                 #cv2.imwrite('pp'+str(c)+'.jpg',res)
               
                 min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
                 if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
                     top_left = min_loc
                 else:
                     top_left = max_loc
                 #top_left = max_loc
                 bottom_right = (top_left[0] + w +100, top_left[1] + h +100 )
                 cv2.rectangle(s2,top_left, bottom_right, 255, 2)
                 #cv2.imshow('img2',s2)
                 cv2.imwrite('iphone'+str(c)+'.png',s2)
                 
                 plt.subplot(121),plt.imshow(res,cmap = 'gray')
                 plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
                # cv2.imwrite('ccc'+str(c)+'.jpg',res)
                 plt.subplot(122),plt.imshow(s2,cmap = 'gray')
                 plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
                 
                 
                 
                 plt.suptitle('cv2.TM_SQDIFF_NORMED')
                 #plt.suptitle('cv2.TM_CCOEFF_NORMED')
                 
                 plt.show()
                 
                 
                 #k = cv2.waitKey(60) & 0xff
                 #if k == 27:
                  #   break
                #pic_sub(emptyimg,s1,s2)  
                
                
                 if c>200:
                
                     c=0
    
cv2.destroyAllWindows()
cap.release()

---

程序目前是这些，如有看不懂的地方可以和我交流：[e-mail]
这个实现的结果就是能够跟踪我们选取的模板目标。但是对于模板的选取我们也需要小心点，要不然会跟踪丢失。这个你们用了之后会发现其中的小奥秘。
后期我会使用kinnect传感器来进行对于视觉的处理，请大家耐心等待哦