13

In order to classify a jpeg image in R, I would like to get the RGB values of each pixel.

My question: Is there a way to extract RGB channels from a jpeg image in R ?

DJack
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  • Jpeg format does not have layers or channels. Furthermore it is typically in a compressed mode. You will need to convert to a raster format and then extract color pixel by pixel. Probably easier in a program designed for this task or a graphics converted application. – IRTFM Apr 23 '13 at 07:48
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    since the `biOps` package is no longer available, I think the solution using `jpeg` should be made the correct answer. – Bastiaan Quast Sep 21 '17 at 09:09
  • Thanks, I have changed it – DJack Sep 21 '17 at 10:03

3 Answers3

19

You have several package to read in JPEG. Here I use package jpeg:

library(jpeg)
img <- readJPEG("Rlogo.jpg")

dim(img)
[1]  76 100   3

As you can see, there is 3 layers: they correspond to your R, G and B values. In each layer, each cell is a pixel.

img[35:39,50:54,]
, , 1

          [,1]      [,2]      [,3]      [,4]      [,5]
[1,] 0.5098039 0.5921569 0.4549020 0.3372549 0.1921569
[2,] 0.5098039 0.6000000 0.4549020 0.3372549 0.1921569
[3,] 0.5137255 0.6000000 0.4549020 0.3450980 0.1921569
[4,] 0.5215686 0.6039216 0.4627451 0.3450980 0.1921569
[5,] 0.5215686 0.6039216 0.4627451 0.3450980 0.1882353

, , 2

          [,1]      [,2]      [,3]      [,4]      [,5]
[1,] 0.5882353 0.6666667 0.5098039 0.3803922 0.2156863
[2,] 0.5882353 0.6627451 0.5098039 0.3803922 0.2156863
[3,] 0.5843137 0.6627451 0.5098039 0.3764706 0.2156863
[4,] 0.5843137 0.6627451 0.5058824 0.3764706 0.2117647
[5,] 0.5843137 0.6627451 0.5058824 0.3764706 0.2156863

, , 3

          [,1]      [,2]      [,3]      [,4]      [,5]
[1,] 0.7254902 0.7921569 0.6156863 0.4588235 0.2705882
[2,] 0.7254902 0.7921569 0.6156863 0.4588235 0.2784314
[3,] 0.7254902 0.7921569 0.6156863 0.4588235 0.2784314
[4,] 0.7176471 0.7921569 0.6156863 0.4666667 0.2862745
[5,] 0.7176471 0.7921569 0.6156863 0.4666667 0.2862745
plannapus
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10

I recommend the biOpspackage for image manipulation.

Here is an example:

library(biOps)
x <- readJpeg(system.file("samples", "violet.jpg", package="biOps"))
plot(x)

r <- imgRedBand(x)
plot(r)
image(x[,,1])

g <- imgGreenBand(x)
plot(g)
image(x[,,2])

b <- imgBlueBand(x)
plot(b)
image(x[,,3])

Visual example:

redPal <- colorRampPalette(c("black", "red"))
greenPal <- colorRampPalette(c("black", "green"))
bluePal <- colorRampPalette(c("black", "blue"))

x11(width=9, height=2.5)
par(mfcol=c(1,3))
image(x=seq(ncol(r)), y=seq(nrow(r)), z=t(r), asp=1, xaxt="n", yaxt="n", bty="n", xlab="", ylab="", main="red channel", col=redPal(256))
image(x=seq(ncol(g)), y=seq(nrow(g)), z=t(g), asp=1, xaxt="n", yaxt="n", bty="n", xlab="", ylab="", main="green channel", col=greenPal(256))
image(x=seq(ncol(b)), y=seq(nrow(b)), z=t(b), asp=1, xaxt="n", yaxt="n", bty="n", xlab="", ylab="", main="blue channel", col=bluePal(256))

enter image description here

Marc in the box
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5

I like the approach via R's biOps package. After loading your data into canvas, you're able to convert your jpg file from imagedata to raster and do some further processing. Here's my code:

# Required packages
library(biOps)
library(raster)

# Load and plot data
data(logo)
jpg <- logo

plot.imagedata(jpg)

# Convert imagedata to raster
rst.blue <- raster(jpg[,,1])
rst.green <- raster(jpg[,,2])
rst.red <- raster(jpg[,,3])

# Plot single raster images and RGB composite
plot(stack(rst.blue, rst.green, rst.red), 
     main = c("Blue band", "Green band", "Red band"))
plotRGB(stack(rst.blue, rst.green, rst.red))
fdetsch
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