1 #coding:utf8
 2 import cv2
 3 import numpy as np
 4 import matplotlib.pyplot as plt
 5 
 6 def load_img():
 7     img=[]
 8     for i in range(40):
 9         for j in range(10):
10             path=‘att_faces\\s‘+str(i+1)+‘\\‘+str(j+1)+‘.pgm‘
11             a=cv2.imread(path,0)
12             a=a.flatten()/255.0
13             img.append(a)
14     return img
15 
16 def dis(A,B,dis_type=0,s=None):
17     if dis_type==1:  # 欧式距离
18         return np.sum(np.square(A-B))
19     elif dis_type==2:  # 马式距离
20         f=np.sqrt(np.dot(np.dot((A-B),s.I),(A-B).T))  # h=16已溢出
21         return f.tolist()[0][0]
22     else:  # 曼哈顿距离
23         return np.sum(abs(A-B))
24 
25 def pca_test(data,h,dis_type=0):
26     q,r=np.linalg.qr(data.T)
27     u,s,v=np.linalg.svd(r.T)
28     fi=np.dot(q,(v[:h]).T)
29     y=np.dot(fi.T,data.T)
30     ym=[np.mean(np.reshape(x,(40,10)),axis=1) for x in y]
31     ym=np.reshape(ym,(h,40))
32     c=[]
33     if dis_type==2:# 计算马氏距离的额外处理"
34         yr=[np.reshape(x,(40,10)) for x in y]
35         yr=[[np.array(yr)[j][k] for j in  range(h)]for k in range(40)]
36         for k in yr:
37             k=np.reshape(k,(h,10))
38             e=np.cov(k)
39             c.append(e)
40     return fi,ym,c
41 
42 def validate(fi,ym,test,label,dis_type=0,c=None):
43     ty=np.dot(fi.T,test.T)
44     correctnum=0
45     testnum=len(test)
46     for i in range(testnum):
47         if dis_type==2:
48             n=len(ym.T)
49             dd=[dis(ty.T[i],ym.T[n_],dis_type,np.mat(c[n_])) for n_ in range(n)]
50         else:
51             dd=[dis(ty.T[i],yy,dis_type) for yy in ym.T]
52         if np.argsort(dd)[0]+1==label[i]:
53             correctnum+=1
54     rate = float(correctnum) / testnum
55     print "Correctnum = %d, Sumnum = %d" % (correctnum, testnum), "Accuracy:%.2f" % (rate)
56     return rate
57 
58 
59 img=load_img()
60 test=img
61 label=[a+1 for a in range(40) for j in range(10)]
62 index=range(400)
63 np.random.shuffle(index)
64 label_=[label[i] for i in index]
65 test_=np.mat([test[i] for i in index])
66 x_=[2**i for i in range(9)]
67 d_=[‘Manhattan Distance‘,‘Euclidean Metric‘, ‘Mahalanobis Distance‘]
68 for j in range(3):
69     y_=[]
70     plt.figure()
71     for i in range(9):
72         fi,ym,c=pca_test(np.mat(img),h=x_[i],dis_type=j)
73         y_.append(validate(fi,ym,test_,label_,dis_type=j,c=c))
74     plt.ylim([0,1.0])
75     plt.plot(x_,y_)
76     plt.scatter(x_,y_)
77     plt.xlabel(‘h‘)
78     plt.ylabel(‘Accuracy‘)
79     plt.title(d_[j])
80 plt.show()