python实现基于SVM手写数字识别功能

#!/usr/bin/env python
import numpy as np
import mlpy
import cv2
print 'loading ...'

def getnumc(fn):
 '''返回数字特征'''
 fnimg = cv2.imread(fn) #读取图像
 img=cv2.resize(fnimg,(8,8)) #将图像大小调整为8*8
 alltz=[]
 for now_h in xrange(0,8):
  xtz=[]  
  for now_w in xrange(0,8):
   b = img[now_h,now_w,0]
   g = img[now_h,1]
   r = img[now_h,2]
   btz=255-b
   gtz=255-g
   rtz=255-r
   if btz>0 or gtz>0 or rtz>0:
    nowtz=1
   else:
    nowtz=0
   xtz.append(nowtz) 
  alltz+=xtz
 return alltz
 
#读取样本数字
x=[]
y=[]
for numi in xrange(1,10):
 for numij in xrange(1,5):
  fn='nums/'+str(numi)+'-'+str(numij)+'.png'
  x.append(getnumc(fn))
  y.append(numi)
 
x=np.array(x)
y=np.array(y)
svm = mlpy.LibSvm(svm_type='c_svc',kernel_type='poly',gamma=10)
svm.learn(x,y)
print u"训练样本测试："
print svm.pred(x)
print u"未知图像测试："
for iii in xrange (1,10):
 testfn= 'nums/test/'+str(iii)+'-test.png'
 testx=[]
 testx.append(getnumc(testfn))
 print  
 print testfn+":",print svm.pred(testx)