ImagenesRLevano

library(ripa)
library(jpeg)
library(neuralnet)

img1 <- readJPEG('E:\\Documentos\\Scripts\\1.jpg')
img2 <- readJPEG('E:\\Documentos\\Scripts\\2.jpg')
img3 <- readJPEG('E:\\Documentos\\Scripts\\3.jpg')
img4 <- readJPEG('E:\\Documentos\\Scripts\\4.jpg')
img5 <- readJPEG('E:\\Documentos\\Scripts\\5.jpg')
x1 = imagematrix(img1[,,1])
x2 = imagematrix(img2[,,1])
x3 = imagematrix(img3[,,1])
x4 = imagematrix(img4[,,1])
x5 = imagematrix(img5[,,1])
a = as.matrix(x1)
b = as.matrix(x2)
c = as.matrix(x3)
d = as.matrix(x4)
e = as.matrix(x5)
for(i in 1:20){
  for(j in 1:20){
    if(a[i,j]<=0.6)
      a[i,j]=1
    else
      a[i,j]=0
  }
}
for(i in 1:20){
  for(j in 1:20){
    if(b[i,j]<=0.6)
      b[i,j]=1
    else
      b[i,j]=0
  }
}
for(i in 1:20){
  for(j in 1:20){
    if(c[i,j]<=0.6)
      c[i,j]=1
    else
      c[i,j]=0
  }
}
for(i in 1:20){
  for(j in 1:20){
    if(d[i,j]<=0.6)
      d[i,j]=1
    else
      d[i,j]=0
  }
}
for(i in 1:20){
  for(j in 1:20){
    if(e[i,j]<=0.6)
      e[i,j]=1
    else
      e[i,j]=0
  }
}
write.table(a,file="ima1.txt",col.names=FALSE,row.names=FALSE)
write.table(b,file="ima1.txt",col.names=FALSE,row.names=FALSE)
write.table(c,file="ima1.txt",col.names=FALSE,row.names=FALSE)
write.table(d,file="ima1.txt",col.names=FALSE,row.names=FALSE)
write.table(e,file="ima1.txt",col.names=FALSE,row.names=FALSE)

a1 = as.vector(a)
a2 = as.vector(b)
a3 = as.vector(c)
a4 = as.vector(d)
a5 = as.vector(e)
data1 <-data.frame(a1,a2,a3,a4,a5)
t1<-rep(1,400)
t2<-rep(0,400)
datos<-data.frame(data1,t1,t2)

red2 <- neuralnet(datos$t1+datos$t2~datos$a1+datos$a2+datos$a3+datos$a4+datos$a5,data=datos,hidden=9,rep=5)
plot(red2,rep="best")
nSalida <-compute(red2,covariate=matrix(c(datos$a1,datos$a2,datos$a3,datos$a4,datos$a5),byrow=TRUE,ncol=5))
print(nSalida)
salidaTEST <-cbind(datos,as.data.frame(nSalida$net.result))
colnames(salidaTEST)<-c("E1","E2","E3","E4","E5","S1 ANN","S2 ANN","Resultado S1","Resultado S2")
print(salidaTEST)