近邻法对0-9进行数字识别程序.docxVIP

子程序

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%%特征生成方法1

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functionfeature=Get_Feature(A)

A=im2bw(A,0.5);%%设置阈值为0.5,使用阈值变换法把灰度图像转化为二值图像

%%本方法把二值化的图片分为6部分

a1=A(1:18,1:10);

a2=A(1:18,11:20);

a3=A(19:36,1:10);

a4=A(19:36,11:20);

f1=nnz(a1);%%计算a1的非零元素总数

f2=nnz(a2);%%计算a2的非零元素总数

f3=nnz(a3);%%计算a3的非零元素总数

f4=nnz(a4);%%计算a4的非零元素总数

%%%特征生成%%%%

feature=[f1f2f3f4]';%%feature是一个4行1列的矩阵

主程序

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%%使用1近邻和3近邻法对0-9个数字进行判别

%%每个文件夹中的前30个为训练样本，后10个测试样本

%%得到：sum为正确率

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%%%%%清除运算空间

clc;

clear;

%根据目录读取文件

Path='E:\数字识别\';%目录名

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%训练过程

%训练样本特征提取，得到10类各30个样本的特征值

%将所有的特征值合成一个300*4的矩阵

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forj=1:10

TypeA=strcat(Path,num2str(j-1),'\');%获取训练样本目录

PicDirA=dir([TypeA,'*.bmp']);%%dir函数

fori=1:30

A=imread([TypeA,PicDirA(i).name]);%依次读入每个文件夹下的每个图片

Feature(:,i)=Get_Feature(A);%获取训练样本特征

end

feature(1+(j-1)*30:j*30,:)=Feature';

end%

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%测试部分

%将每类中剩余的10个样本提取特征

%计算每个待测样本与所有训练样本的欧式距离

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disp('1近邻');

sum1=0;sum2=0;sum3=0;sumz=0;sum3=0;sum4=0;sum5=0;sum6=0;sum7=0;sum8=0;sum9=0;sum0=0;

forj=1:10

TypeA=strcat(Path,num2str(j-1),'\');%获取待测样本目录读0的文件夹（转成字符串型）

PicDirA=dir([TypeA,'*.bmp']);%%dir函数列出所有0文件夹中为bmp的文件

fori=31:40

A=imread([TypeA,PicDirA(i).name]);%获取待测样本文件名

Feature=Get_Feature(A);%获取待测样本特征值

fork=1:300

dis(k)=sqrt(sum((Feature'-feature(k,:)).^2));%求取待测样本与各类所有点距离

end

[new,Ind]=sort(dis);%将距离排序将距离给了new，把之前的序号给了Ind

class0=0;class1=0;class2=0;class3=0;class4=0;class5=0;class6=0;class7=0;class8=0;class9=0;%计数清零

if