chepaishibie.zip_plate recognition_字母识别_车牌训练库_车牌识别_车牌识别；神经网络

clc;close all;clear all; %load('P.mat'); %load('T.mat'); [L0,I0]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\0\'); [L1,I1]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\1\'); [L2,I2]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\2\'); [L3,I3]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\3\'); [L4,I4]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\4\'); [L5,I5]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\5\'); [L6,I6]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\6\'); [L7,I7]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\7\'); [L8,I8]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\8\'); [L9,I9]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\9\'); [L10,I10]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\A\'); [L11,I11]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\B\'); [L12,I12]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\C\'); [L13,I13]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\D\'); [L14,I14]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\E\'); [L15,I15]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\F\'); [L16,I16]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\J\'); [L17,I17]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\H\'); [L18,I18]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\J\'); [L19,I19]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\K\'); [L20,I20]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\L\'); [L21,I21]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\M\'); [L22,I22]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\N\'); [L23,I23]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\P\'); [L24,I24]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\Q\'); [L25,I25]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\R\'); [L26,I26]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\S\'); [L27,I27]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\T\'); [L28,I28]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\U\'); [L29,I29]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\V\'); [L30,I30]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\W\'); [L31,I31]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\X\'); [L32,I32]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\Y\'); [L33,I33]=pretreatment('汽车车牌识别图像库\字符模板\font_ra\Z\'); P=[I0',I1',I2',I3',I4',I5',I6',I7',I8',I9',I10',I11',I12',I13',I14',I15',I16',I17',I18',I19',I20',I21',I22',I23',I24',I25',I26',I27',I28',I29',I30',I31',I32',I33']; T1=eye(34,34); T=[repmat(T1(:,1),1,L0),repmat(T1(:,2),1,L1),repmat(T1(:,3),1,L2),repmat(T1(:,4),1,L3),repmat(T1(:,5),1,L4)... repmat(T1(:,6),1,L5),repmat(T1(:,7),1,L6),repmat(T1(:,8),1,L7),repmat(T1(:,9),1,L8),repmat(T1(:,10),1,L9)... repmat(T1(:,11),1,L10),repmat(T1(:,12),1,L11),repmat(T1(:,13),1,L12),repmat(T1(:,14),1,L13),repmat(T1(:,15),1,L14)... repmat(T1(:,16),1,L15),repmat(T1(:,17),1,L16),repmat(T1(:,18),1,L17),repmat(T1(:,19),1,L18),repmat(T1(:,20),1,L19)... repmat(T1(:,21),1,L20),repmat(T1(:,22),1,L21),repmat(T1(:,23),1,L22),repmat(T1(:,24),1,L23),repmat(T1(:,25),1,L24)... repmat(T1(:,26),1,L25),repmat(T1(:,27),1,L26),repmat(T1(:,28),1,L27),repmat(T1(:,29),1,L28),repmat(T1(:,30),1,L29)... repmat(T1(:,31),1,L30),repmat(T1(:,32),1,L31),repmat(T1(:,33),1,L32),repmat(T1(:,34),1,L33)]; net=newff(minmax(P),[1000,200,34],{'logsig','logsig','logsig'},'trainrp');%1000 100 2000,300minmax()求矩阵每一行向量的最大值和最小值 %函数newff建立一个可训练的前馈网络。这需要4个输入参数。 %第一个参数是一个Rx2的矩阵以定义R个输入向量的最小值和最大值； %第二个参数是一个设定每层神经元个数的数组； %第三个参数是包含每层用到的传递函数名称的细胞数组； %最后一个参数是用到的训练函数的名称。 net.inputWeights{1,1}.initFcn='randnr'; net.inputWeights{2,1}.initFcn='randnr'; net.trainparam.epochs=8000; net.trainparam.show=50; net.trainparam.goal=0.001;%0.0000000001; net=init(net);%初始化权重和偏置的工作用命令init来实现。这个函数接收网络对象并初始化权重和偏置后返回网络对象。 [net,tr]=train(net,P,T); i=imread('car1.jpg'); dw=location(i);%车牌定位， [PIN0,PIN1,PIN2,PIN3,PIN4,PIN5,PIN6]=stringsplit(dw);%字符分割，见上篇文章 PIN0=pretreatment2(PIN0); PIN1=pretreatment2(PIN1); PIN2=pretreatment2(PIN2); PIN3=pretreatment2(PIN3); PIN4=pretreatment2(PIN4); PIN5=pretreatment2(PIN5); PIN6=pretreatment2(PIN6); P0=[PIN0',PIN1',PIN2',PIN3',PIN4',PIN5',PIN6']; for i=2:7 T0=sim(net,P0(:,i));%T0为P0(:,i)输入神经网络得到的输出，T0为34x1的列向量 T1=compet(T0);% compet是神经网络的竞争传递函数，用于指出矩阵中每列的最大值。对应最大值的行的值为1，其他行的值都为0 d=find(T1==1)-1; if(d==10) str='A'; elseif(d==11) str='B'; elseif(d==12) str='C'; elseif(d==13) str='D'; elseif(d==14) str='E'; elseif(d==15) str='F'; elseif(d==16) str='G'; elseif(d==17) str='H'; elseif(d==18) str='J'; elseif(d==19) str='K'; elseif(d==20) str='L'; elseif(d==21) str='M'; elseif(d==22) str='N'; elseif(d==23) str='P'; elseif(d==24) str='Q'; elseif(d==25) str='R'; elseif(d==26) str='S'; elseif(d==27) str='T'; elseif(d==28) str='U'; elseif(d==29) str='V'; elseif(d==30) str='W'; elseif(d==31) str='X'; elseif(d==32) str='Y'; elseif(d==33) str='Z'; else str=num2str(d); end switch i case 2 str1=str; case 3 str2=str; case 4 str3=str; case 5 str4=str; case 6 str5=str; otherwise str6=str; end end s=strcat('沪',str1,str2,str3,str4,str5,str6); figure(); imshow(dw),title(s);