import numpy
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import pickle
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import cv2
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#图片预处理
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def pretreatment(image):
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
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return thresh
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#提取图片特征
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def feature(A):
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midx=int(A.shape[1]/2)+1
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midy=int(A.shape[0]/2)+1
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A1=cv2.mean(A[0:midy,0:midx])
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A2=cv2.mean(A[midy:A.shape[0],0:midx])
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A3=cv2.mean(A[0:midy,midx:A.shape[1]])
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A4=cv2.mean(A[midy:A.shape[0],midx:A.shape[1]])
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A5=cv2.mean(A)
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AF=[A1,A2,A3,A4,A5]
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return AF
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#训练已知图片的特征
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def training():
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train_set={}
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for i in range(11):
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value=[]
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for j in range(10):
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print('./'+str(i)+'/'+str(i)+'-'+str(j)+'.jpg')
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ima=cv2.imread('./'+str(i)+'/'+str(i)+'-'+str(j)+'.jpg',0)
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if ima is None:
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print('image is none !')
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print(ima.shape)
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AF = feature(ima)
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value.append(AF)
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#print(value)
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train_set[i]=value
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#把训练结果存为永久文件，以备下次使用
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output=open('./train_set.pkl','wb')
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pickle.dump(train_set,output)
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output.close()
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return train_set
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#计算两向量的距离
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def distance(v1,v2):
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vector1=numpy.array(v1)
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vector2=numpy.array(v2)
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Vector=(vector1-vector2)**2
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distance = Vector.sum()**0.5
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return distance
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#用最近邻算法识别单个数字
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def knn(train_set,V,k):
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key_sort=[11]*k
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value_sort=[11]*k
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for key in range(11):
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for value in train_set[key]:
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d=distance(V,value)
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for i in range(k):#从小到大排序
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if d<value_sort[i]:
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for j in range(k-2,i-1,-1):
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key_sort[j+1]=key_sort[j]
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value_sort[j+1]=value_sort[j]
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key_sort[i]=key
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value_sort[i]=d
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break
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max_key_count=-1
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key_set=set(key_sort)
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for key in key_set: #统计每个类别出现的次数
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if max_key_count<key_sort.count(key):
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max_key_count=key_sort.count(key)
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max_key=key
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return max_key
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#合并一副图片的所有数字
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def identification(train_set,AF,k):
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result=''
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for i in AF:
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key=knn(train_set,i,k)
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if key==10:
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key='.'
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result=result+str(key)
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return float(result)
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#识别文件夹中的所有图片上的数据并输出到EXCELL中
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def main(k,trained=None):
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# k：knn算法的的k，即取训练集中最相邻前k个样本进行判别
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#trained：trained=0则程序会开始训练出训练集，否则会用已保存的训练集
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if trained==0:
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train_set=training()
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else:
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pkl_file=open('train_set.pkl','rb')
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train_set=pickle.load(pkl_file)
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pkl_file.close()
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#识别所有图片的数字并输出到EXCELL中
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for i in range(1,21):
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ima=cv2.imread('./test/'+str(i)+'.jpg')
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#im=pretreatment(ima)
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AF=feature(ima)
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result=identification(train_set,AF,k)
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main(4,0) #表示：4近邻，还没有训练集备份，还没有昨日数据备份
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