Face To Face With Marilyn Monroe

February 24, 2014
By

(This article was first published on Ripples, and kindly contributed to R-bloggers)

Symmetry is what we see at a glance (Blaise Pascal)

Ladies and gentlement, the beautiful Marilyn Monroe:

marilyn-monroe3

There are several image processing packages in R. In this experiment I used biOps, which turns images into 3D matrices. The third dimension is a 3-array corresponding to (r, g, b) color of pixel defined by two other dimensions. Since images are defined by matrices, you can do simple operations to produce interesting images from the original one. For example, this is what happens swaping last half of columns by first one and preserving its order:

IMG01-Interchange

It is also very simple to generate two artificial symmetrical faces matching each half with itself as a mirror. By the way, I prefer the first one: maybe two sexy moles are better than just one.

IMG03-Symmetric2 IMG02-Symmetric1

Let’s introduce a bit of randomness. This is what happens when you take an uniform sample of rows and columns (first image) or an uniform sample of pixels of the image (second one):

IMG04-Uniform1 IMG05-Uniform2

And this is what happens when you divide image into blocks and mix them randomly:

IMG07-Mosaic

There is a funny and useful function called jitter which adds a small amount of noise to a numeric vector. What happens when you jitter every pixel of the image? As you can see, It becomes very vintage:

IMG06-Jitter

What if you transpose matrix? What if you change every color by another one? What if you change only a small range of them? What if you sum two images? What if you translate rgb colors into a grey scale? What if …? I answered some of these questions already and results are nice as well. After all, Marilyn can be represented as a simple matrix. Or maybe not.

Make your own experiments:

library("biOps")
library("abind")
rm(list=ls())
#############################################################
#Read Original Image
#############################################################
x     <- readJpeg("marilyn-monroe3.jpg")
plot(x)
#############################################################
#1. Swap
#############################################################
plot(imagedata(abind(x[,(ncol(x)/2):ncol(x),], x[,1:(ncol(x)/2),] , along=2)))
dev.copy(jpeg,filename="IMG01-Swap.jpg");
dev.off ();
#############################################################
#2. Artificial Symmetrical faces
#############################################################
plot(imagedata(abind(x[,1:(ncol(x)/2),], x[,(ncol(x)/2):1,] , along=2)))
dev.copy(jpeg,filename="IMG02-Symmetric1.jpg");
dev.off ();
plot(imagedata(abind(x[,ncol(x):(ncol(x)/2),], x[,(ncol(x)/2):ncol(x),] , along=2)))
dev.copy(jpeg,filename="IMG03-Symmetric2.jpg");
dev.off ();
#############################################################
#3. Uniform sampling over axis points
#############################################################
x2   <- aperm(array(255, dim = c(3, ncol(x), nrow(x))))
rows <- sample(1:nrow(x), round(nrow(x)*0.80), replace = FALSE)
cols <- sample(1:ncol(x), round(ncol(x)*0.80), replace = FALSE)
for (i in 1:length(rows)) 
{
  for (j in 1: length(cols)) 
    {
    x2[rows[i], cols[j],1]<-x[rows[i], cols[j],1] 
    x2[rows[i], cols[j],2]<-x[rows[i], cols[j],2] 
    x2[rows[i], cols[j],3]<-x[rows[i], cols[j],3] 
  }
}
plot(imagedata(x2))
dev.copy(jpeg,filename="IMG04-Uniform1.jpg");
dev.off ();
#############################################################
#4. Uniform sampling over pixels
#############################################################
m2 <- matrix(rbinom(nrow(x)*ncol(x),1,0.5),nrow(x),ncol(x))
x4<- do.call(abind, c(list(x[,,1]*m2+(m2==0)*255,x[,,2]*m2+(m2==0)*255,x[,,3]*m2+(m2==0)*255), along = 3))
plot(imagedata(x4))
dev.copy(jpeg,filename="IMG05-Uniform2.jpg");
dev.off ();
#############################################################
#6. Jittering
#############################################################
x1<-mapply(as.matrix(x[,,1]), FUN=function(x) 
  {z<-round(x+jitter(0, amount=50)) 
  if(z<0|z>255) x else z})
x1 <- matrix(x1, nrow = nrow(x),ncol = ncol(x))
x2<-mapply(as.matrix(x[,,2]), FUN=function(x) 
{z<-round(x+jitter(0, amount=50)) 
 if(z<0|z>255) x else z})
x2 <- matrix(x2, nrow = nrow(x),ncol = ncol(x))
x3<-mapply(as.matrix(x[,,3]), FUN=function(x) 
{z<-round(x+jitter(0, amount=50)) 
 if(z<0|z>255) x else z})
x3 <- matrix(x3, nrow = nrow(x),ncol = ncol(x))
x4<- do.call(abind, c(list(x1,x2,x3), along = 3))
plot(imagedata(x4))
dev.copy(jpeg,filename="IMG06-Jitter.jpg");
dev.off ();
#############################################################
#7. Mosaic
#############################################################
sptr <- 6 #Row splits
rnkr <- sample(1:sptr, size = sptr, replace = FALSE)
wthr <- floor(nrow(x)/sptr) #Splits width (row)
rnkr <- as.vector(sapply(rnkr, function (x) rep(x,wthr)))
rnkr <- rnkr*10E6+seq(1, length(rnkr), by=1)
rnkr <- rank(rnkr)
sptc <- round(ncol(x)/wthr)
rnkc <- sample(1:sptc, size = sptc, replace = FALSE)
wthc <- floor(ncol(x)/sptc) #Splits width (row)
rnkc <- as.vector(sapply(rnkc, function (x) rep(x,wthc)))
rnkc <- rnkc*10E6+seq(1, length(rnkc), by=1)
rnkc <- rank(rnkc)
x2<-x[1:length(rnkr),1:length(rnkc),]
x2<-x[rank(rnkr),rank(rnkc),]
plot(imagedata(x2))
dev.copy(jpeg,filename="IMG07-Mosaic.jpg");
dev.off ();

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