R language to cluster image colors by using k-means clustering

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description: cluster colors in a flower image through k-means

downloading and reading image file

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library("jpeg")
imageurl<-"https://thumbs-prod.si-cdn.com/WRp3AC1A1q3d4_lCe_fzfMmIu0o=/256x256/https://public-media.smithsonianmag.com/accounts/avatars/IMG_E1531.JPG"
dFile <- download.file(imageurl, "lotus.jpg") # download file to workspace
img <- readJPEG("lotus.jpg") # read local image

processing image data

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imgDm <- dim(img)
imgDm # [1]  879 1200    3
imgRGB <- data.frame(
  x = rep(1:imgDm[2], each = imgDm[1]), 
  y = rep(imgDm[1]:1, imgDm[2]), 
  R = as.vector(img[,,1]),
  G = as.vector(img[,,2]),
  B = as.vector(img[,,3])
)
imgRGB # X 1 y 879 R .. G .. B ..

set a theme for drawing the image

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library(ggplot2)
plotTheme <- function(){
  theme(
    panel.background = element_rect(
      size = 3,
      colour = "black",
      fill = "white"
    ),
    axis.ticks = element_line(
      size = 2
    ),
    panel.grid.major = element_line(
      colour = "gray80",
      linetype = "dotted"
    ),
    panel.grid.minor = element_line(
      colour = "gray90",
      linetype = "dashed"
    ),
    axis.title.x = element_text(
      size = rel(1.2),
      face = "bold"
    ),
    axis.title.y = element_text(
      size = rel(1.2),
      face = "bold"
    ),
    plot.title = element_text(
      size = 20,
      face = "bold",
      vjust = 1.5
    )
  )
}

image clustering using K-means clustering

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kClusters <- 3 
kMeans <- kmeans(imgRGB[,c("R","G","B")], centers  = kCluster)
kColours <- rgb(kMeans$centers[kMeans$cluster,])

ggplot(data = imgRGB, aes(x = x, y = y)) +
  geom_point(colour = kColours) + 
  labs(title = paste("k-Means Clustering of", kClusters, "Colours")) + 
  xlab("x") + 
  ylab("y") + 
  plotTheme()

outcome