for_for.m

基于BP网络的车牌定位程序

for i=0:9
    for j=1:50
        I=imread(strcat('E:\MATLAB7\work\cars\',num2str(i),'-',num2str(j),'.bmp'));
        k=get_feature(I);
        %i=str2num(i);
        %j=str2num(j);
        if ((i==0)&&(j==1))
            P=k;
        else P=[P k];
        end
    end
end
T0=[0 0 0 0 0 0 0 0 0 0]';
T1=[0 0 0 0 0 0 0 0 0 1]';
T2=[0 0 0 0 0 0 0 0 1 0]';
T3=[0 0 0 0 0 0 0 0 1 1]';
T4=[0 0 0 0 0 0 0 1 0 0]';
T5=[0 0 0 0 0 0 0 1 0 1]';
T6=[0 0 0 0 0 0 0 1 1 0]';
T7=[0 0 0 0 0 0 0 1 1 1]';
T8=[0 0 0 0 0 0 1 0 0 0]';
T9=[0 0 0 0 0 0 1 0 0 1]';
T=T0;
for i=1:49
    T=[T T0];
end
for i=1:50
    T=[T T1];
end
for i=1:50
    T=[T T2];
end
for i=1:50
    T=[T T3];
end
for i=1:50
    T=[T T4];
end
for i=1:50
    T=[T T5];
end
for i=1:50
    T=[T T6];
end
for i=1:50
    T=[T T7];
end
for i=1:50
    T=[T T8];
end
for i=1:50
    T=[T T9];
end
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1;0 1];
net=newff(threshold,[31,10],{'tansig','logsig'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=0.01;
LP.lr=0.1;
net=train(net,P,T);
for i=0:9
    for j=11:20
        I=imread(strcat('E:\MATLAB7\work\cars\',num2str(i),'-',num2str(j),'.bmp'));
        k=get_feature(I);
        %i=str2num(i);
        %j=str2num(j);
        if ((i==0)&&(j==11))
            P_test=k;
        else P_test=[P_test k];
        end
    end
end
Y=sim(net,P_test);
Y=round(Y)