recolorize (version 0.2.0)

colorClusters: Generate color clusters from an image

Description

Clusters all the pixels in an image according to the specified method and returns color centers, cluster assignments, and cluster sizes.

Usage

colorClusters(
  bg_indexed,
  method = c("histogram", "kmeans"),
  n = 10,
  bins = 3,
  color_space = "Lab",
  ref_white = "D65",
  bin_avg = TRUE
)

Value

A list with the following elements:

1. pixel_assignments: A vector of color center assignments for each pixel.

2. centers: A matrix of color centers, in RGB color space.

3. sizes: The number of pixels assigned to each cluster.

Arguments

bg_indexed

A list returned by backgroundIndex().

method

Binning scheme to use, one of either kmeans or histogram. Produce very different results (see details).

n

If method = "kmeans", the number of colors to fit.

bins

If method = "histogram", either the number of bins per color channel (if a single number is provided) OR a vector of length 3 with the number of bins for each channel.

color_space

Color space in which to cluster colors, passed to [grDevices]{convertColor}. One of "sRGB", "Lab", or "Luv". Default is "Lab", a perceptually uniform (for humans) color space.

ref_white

Reference white for converting to different color spaces. D65 (the default) corresponds to standard daylight.

bin_avg

Logical. Return the color centers as the average of the pixels assigned to the bin (the default), or the geometric center of the bin?

Details

stats::kmeans() clustering tries to find the set of n clusters that minimize overall distances. Histogram binning divides up color space according to set breaks; for example, bins = 2 would divide the red, green, and blue channels into 2 bins each (> 0.5 and < 0 .5), resulting in 8 possible ranges. A white pixel (RGB = 1, 1, 1) would fall into the R \> 0.5, G \> 0.5, B \> 0.5 bin. The resulting centers represent the average color of all the pixels assigned to that bin.

K-means clustering can produce more intuitive results, but because it is iterative, it will find slightly different clusters each time it is run, and their order will be arbitrary. It also tends to divide up similar colors that make up the majority of the image. Histogram binning will produce the same results every time, in the same order, and because it forces the bins to be dispersed throughout color space, tends to better pick up small color details. Bins are also comparable across images. However, this sometimes means returning empty bins (i.e. the white bin will be empty if clustering a very dark image).

Examples

# make a 100x100 'image' of random colors
img <- array(runif(30000), dim = c(100, 100, 3))
plotImageArray(img)

# make a background index object:
bg_indexed <- backgroundIndex(img, backgroundCondition())

# histogram clustering
hist_clusters <- colorClusters(bg_indexed, method = "hist", bins = 2)
plotColorPalette(hist_clusters$centers)

# we can use a different number of bins for each channel
uneven_clusters <- colorClusters(bg_indexed, method = "hist",
                                 bins = c(3, 2, 1))
plotColorPalette(uneven_clusters$centers)

# using kmeans
kmeans_clusters <- colorClusters(bg_indexed, method = "kmeans",
                                 n = 5)
plotColorPalette(kmeans_clusters$centers)