rm(list=ls())
library(ReadImages)
library(rimage)
library(spatstat)

	#ramp <- colorRamp(c("white", "black"))
	#rgb( ramp(seq(0, 1, length = 0.5)), max = 1)
	imgArray <- read.jpeg('testR.jpg')  # Type: "rgb", Class: "array", Dim: "130 x 134 x 3"
	imgArray.grey<-rgb2grey(imgArray)
	#plot(imgArray.grey)
	#M<-normalize(imgArray)
	M<-imgArray.grey
	#M<-imgArray
	M<-round(M,digits=3)

# Find the most frequently occuring numbers in image matrix
	#table(M)
	names(sort(-table(M)))[c(1,2,3,4)]
	H<-table(M)
	#barplot(H,length(H))
	altM <- matrix(M, nrow=(nrow(M)*ncol(M)), ncol=1);
	MeanAltM<-apply(altM,2,mean)
	#MeanAltM
	stdDevAltM<-apply(altM,2,sd)
	#stdDevAltM

# Find differences between values in names(sort(-table(M)))[c(1,2,3)] the plot histogram
	imageMatrix<-as.matrix(imgArray)
	modes<-(sort(as.numeric(sort(-table(M)))))[c(1,2,3,4,5)]
	modes<-as.numeric(names(modes))
	orderedModes<-(-(sort(-as.numeric(names(sort(-table(M))[c(1,2,3,4)]))))[c(1,2,3,4)]) # Could also probably use "factor()" for the below sorting
	modeBinMarkers<-c(mean(c(orderedModes[1],orderedModes[2])),mean(c(orderedModes[2],orderedModes[3])),mean(c(orderedModes[3],orderedModes[4])))
	discretizedM<-(M[]>(modeBinMarkers[1]-stdDevAltM))*1
	discretizedM<-(discretizedM + ((M[]<=modeBinMarkers[1]-stdDevAltM) & M[]>modeBinMarkers[2])*0)
	discretizedM<-(discretizedM + (M[]<=modeBinMarkers[2] & M[]>modeBinMarkers[3])*0)
	standardizedM<-discretizedM
	standardizedM[as.logical(discretizedM)==FALSE]<-1
	standardizedM[as.logical(discretizedM)==TRUE]<-0
	#plot(imagematrix(standardizedM))
	#table(discretizedM)

# Find distribution of row pixels
	rowDist<-rep(1,nrow(standardizedM))
	for (row in 1:nrow(standardizedM))
	{
	rowDist[row]<-(sum(standardizedM[(nrow(standardizedM)-row),])/nrow(standardizedM))
	}
	#barplot(rowDist, horiz = TRUE)

# Find distribution of column pixels
	colDist<-rep(1,ncol(standardizedM))
	for (col in 1:ncol(standardizedM))
	{
	colDist[col]<-(sum(standardizedM[,col])/ncol(standardizedM))
	}
	#barplot(colDist, horiz = FALSE)

# Calculate row and column statistics	
	meanRowDist<-mean(rowDist)
	medianRowDist<-median(rowDist)
	modeRowDist<-as.numeric(names(sort(-table(rowDist)))[c(1)])
	stdDevRowDist<-sd(rowDist)
	meanColDist<-mean(colDist)
	medianColDist<-median(colDist)
	modeColDist<-as.numeric(names(sort(-table(colDist)))[c(1)])
	stdDevColDist<-sd(colDist)

# Teach character
	CharacterSym <- readline("What character is this? ")
#CharacterSym<-LETTERS[sample(1:26,replace=TRUE)[7]]

#fun <- function() {
#  	CharacterSym <- readline("What character is this? ")
#}
#if(interactive()) fun()


LearnedData<-c(meanRowDist,medianRowDist,modeRowDist,stdDevRowDist,meanColDist,medianColDist,modeColDist,stdDevRowDist,CharacterSym)

f <- read.table(file = "test.txt", sep=",", header=TRUE, quote="", col.names=c("LineNum","colDist_Mean","colDist_Median","colDist_Mode","colDist_StdDev","rowDist_Mean","rowDist_Median","rowDist_Mode","rowDist_StdDev","CharacterSymbol"), colClasses=list("integer","numeric","numeric","numeric","numeric","numeric","numeric","numeric","numeric","character"))
a<-cbind(((dim(f)[1])+1),t(LearnedData))
F<-f[,seq(1,10)]
F[dim(f)[1]+1,]<-a
#attach(F)
write.table(F, file = "test.txt", quote=FALSE, sep=",", col.names=names(f),row.names=FALSE)