R is a cool image editor!

[This article was first published on Statistic on aiR, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Here I present some functions I wrote to recreate some of the most common image effect available in all image editor.
They require the library rimage.
To load the image, use:

y <- read.jpeg("path")


To display the image, use:

plot(y)




Original image







Sepia tone


rgb2sepia <- function(img){
 iRed <- img[,,1]*255
 iGreen <- img[,,2]*255
 iBlue <- img[,,3]*255
 
 oRed <- iRed * .393 + iGreen * .769 + iBlue * .189
 oGreen <- iRed * .349 + iGreen * .686 + iBlue * .168
 oBlue <- iRed * .272 + iGreen * .534 + iBlue * .131
 
 qw <- array( c(oRed/255 , oGreen/255 , oBlue/255), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2sepia(y))






Negative


rgb2neg <- function(img){
 iRed <- img[,,1]
 iGreen <- img[,,2]
 iBlue <- img[,,3]
 
 oRed <- (1 - iRed)
 oGreen <- (1 - iGreen)
 oBlue <- (1 - iBlue)
 
 qw <- array( c(oRed, oGreen, oBlue), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2neg(y))






Pixelation


pixmatr <- function(a, n){
 aa <- seq(1,dim(a)[1],n)
 ll <- seq(1,dim(a)[2],n)
 
 for(i in 1:(length(aa)-1) ){
  for(j in 1:(length(ll)-1) ){
   sub1 <- a[aa[i]:(aa[i+1]-1),ll[j]:(ll[j+1]-1)]
   k <- mean(sub1)
   sub1m <- matrix( rep(k, n*n), n, n)
   a[aa[i]:(aa[i+1]-1),ll[j]:(ll[j+1]-1)] <- sub1m
   }
  }
 
 for(j in 1:(length(ll)-1) ){
  sub1 <- a[max(aa):dim(a)[1],ll[j]:(ll[j+1]-1)]
  k <- mean(sub1)
  sub1m <- matrix( rep(k, nrow(sub1)*ncol(sub1)), nrow(sub1), ncol(sub1))
  a[max(aa):dim(a)[1],ll[j]:(ll[j+1]-1)] <- sub1m
 }
 
 for(i in 1:(length(aa)-1) ){
  sub1 <- a[aa[i]:(aa[i+1]-1),max(ll):dim(a)[2]]
  k <- mean(sub1)
  sub1m <- matrix( rep(k, nrow(sub1)*ncol(sub1)), nrow(sub1), ncol(sub1))
  a[aa[i]:(aa[i+1]-1),max(ll):dim(a)[2]] <- sub1m
 }
 
 sub1 <- a[max(aa):dim(a)[1], max(ll):dim(a)[2]]
 k <- mean(sub1)
 sub1m <- matrix( rep(k, nrow(sub1)*ncol(sub1)), nrow(sub1), ncol(sub1))
 a[max(aa):dim(a)[1], max(ll):dim(a)[2]] <- sub1m
 
a
}
 
rgb2pix <- function(img,n){
 iRed <- img[,,1]*255
 iGreen <- img[,,2]*255
 iBlue <- img[,,3]*255
 
 oRed <- pixmatr(iRed,n)
 oGreen <- pixmatr(iGreen,n)
 oBlue <- pixmatr(iBlue,n)
 
 qw <- array( c(oRed/255 , oGreen/255 , oBlue/255), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2pix(y, 6))
plot(rgb2pix(y, 10))







Remove red


rgb2blu <- function(img){
 iRed <- img[,,1]
 iGreen <- img[,,2]
 iBlue <- img[,,3]
 
 oRed <- matrix(0, dim(iRed)[1], dim(iRed)[2])
 oGreen <- iGreen
 oBlue <- iBlue
 
 qw <- array( c(oRed, oGreen, oBlue), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2blu(y))






Remove green


rgb2vio <- function(img){
 iRed <- img[,,1]
 iGreen <- img[,,2]
 iBlue <- img[,,3]
 
 oRed <- iRed
 oGreen <- matrix(0, dim(iRed)[1], dim(iRed)[2])
 oBlue <- iBlue
 
 qw <- array( c(oRed, oGreen, oBlue), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2vio(y))






Remove blue


rgb2yel <- function(img){
 iRed <- img[,,1]
 iGreen <- img[,,2]
 iBlue <- img[,,3]
 
 oRed <- iRed
 oGreen <- iGreen
 oBlue <- matrix(0, dim(iRed)[1], dim(iRed)[2])
 
 qw <- array( c(oRed, oGreen, oBlue), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2yel(y))






Adjust brightness


rgb2bri <- function(img, n){
 iRed <- img[,,1]
 iGreen <- img[,,2]
 iBlue <- img[,,3]
 
 oRed <- iRed + (iRed * n)
 oGreen <- iGreen + (iGreen * n)
 oBlue <- iBlue + (iBlue * n)
 
 qw <- array( c(oRed, oGreen, oBlue), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2bri(y, +0.5))
plot(rgb2bri(y, -0.5))







Truncate colors into bands (posterize)


rgb2ban <- function(img, n){
 iRed <- img[,,1]*255
 iGreen <- img[,,2]*255
 iBlue <- img[,,3]*255
 
 band_size <- trunc(255/n)
 
 oRed <- band_size * trunc(iRed / band_size)
 oGreen <- band_size * trunc(iGreen / band_size)
 oBlue <- band_size * trunc(iBlue / band_size)
 
 qw <- array( c(oRed/255, oGreen/255, oBlue/255), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2ban(y, 5))
plot(rgb2ban(y, 10))







Solarize


rgb2sol <- function(img){
 iRed <- img[,,1]*255
 iGreen <- img[,,2]*255
 iBlue <- img[,,3]*255
 
 for(i in 1:dim(iRed)[1]){
  for(j in 1:dim(iRed)[2]){
   if(iRed[i,j]<128) iRed[i,j] <- 255-2*iRed[i,j]
   else iRed[i,j] <- 2*(iRed[i,j]-128)
  }
 }
 
 for(i in 1:dim(iGreen)[1]){
  for(j in 1:dim(iGreen)[2]){
   if(iGreen[i,j]<128) iGreen[i,j] <- 255-2*iGreen[i,j]
   else iGreen[i,j] <- 2*(iGreen[i,j]-128)
  }
 }
 
 for(i in 1:dim(iBlue)[1]){
  for(j in 1:dim(iBlue)[2]){
   if(iBlue[i,j]<128) iBlue[i,j] <- 255-2*iBlue[i,j]
   else iBlue[i,j] <- 2*(iBlue[i,j]-128)
  }
 }
 
 qw <- array( c(iRed/255, iGreen/255, iBlue/255), dim=c(dim(iRed)[1],dim(iRed)[2],3) )
 
 imagematrix(qw, type="rgb")
}
 
plot(rgb2sol(y))




To leave a comment for the author, please follow the link and comment on their blog: Statistic on aiR.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)