clc
clear
x1=imread('1.pgm');
x2=imread('4.pgm');
x3=imread('7.pgm');
x4=imread('10.pgm');
x5=imread('13.pgm');
x6=imread('16.pgm');
x7=imread('19.pgm');
x8=imread('22.pgm');
x9=imread('25.pgm');
x10=imread('28.pgm');
for i=1:10
if(i==1)
x0=x1;
end
if(i==2)
x0=x2;
end
if(i==3)
x0=x3;
end
if(i==4)
x0=x4;
end
if(i==5)
x0=x5;
end
if(i==6)
x0=x6;
end
if(i==7)
x0=x7;
end
if(i==8)
x0=x8;
end
if(i==9)
x0=x9;
end
if(i==10)
x0=x10;
end
re=imresize(x0,[30 30]);
BW2 = edge(re,'canny');
BW3=reshape(BW2,900,1);
for t=1:30
u=1;
for j=1:30
if(BW2(t,j)==1)
BW4(t,u)=1;
u=u+1;
end
end
end
D{i}=std(BW4,0,2);
if(i==1)
FFV1=D{1};
end
if(i==2)
FFV2=D{2};
end
if(i==3)
FFV3=D{3};
end
if(i==4)
FFV4=D{4};
end
if(i==5)
FFV5=D{5};
end
if(i==6)
FFV6=D{6};
end
if(i==7)
FFV7=D{7};
end
if(i==8)
FFV8=D{8};
end
if(i==9)
FFV9=D{9};
end
if(i==10)
FFV10=D{10};
end
end
%Training Neural network
inputs=[FFV1 FFV2 FFV3 FFV4 FFV5 FFV6 FFV7 FFV8 FFV9 FFV10];
targets=[0 0 0 0 0 0 0 1 1 1
0 0 0 1 1 1 1 0 0 0
0 1 1 0 0 1 1 0 0 1
1 0 1 0 1 0 1 0 1 0];
numHiddenNeurons = 10;
net = newff(inputs,targets,numHiddenNeurons,{},'trainscg'); %scale Conjugate gradient
net.trainParam.show = 50;
net.trainParam.lr = 0.001;
net.trainParam.epochs = 1000;
net.trainParam.goal = .001;
[net,tr] = train(net,inputs,targets);
testm=imread('test1.pgm');
test=testm;
resi=imresize(test,[30 30]);
B2= edge(resi,'canny');
B3=reshape(B2,900,1);
for x=1:30
y=1;
for z=1:30
if(B2(x,z)==1)
B4(x,y)=1;
y=y+1;
end
end
end
test1=std(B4,0,2);
a = sim(net,[test1])
plotperf(tr);
