plot_superpixels: Test super pixel segmentation

Description

The segmentation of an image into superpixels are an important step in generating explanations for image models. It is both important that the segmentation is correct and follows meaningful patterns in the picture, but also that the size/number of superpixels are appropriate. If the important features in the image are chopped into too many segments the permutations will probably damage the picture beyond recognition in almost all cases leading to a poor or failing explanation model. As the size of the object of interest is varying it is impossible to set up hard rules for the number of superpixels to segment into - the larger the object is relative to the size of the image, the fewer superpixels should be generated. Using plot_superpixels it is possible to evaluate the superpixel parameters before starting the time consuming explanation function.

Usage

plot_superpixels(
  path,
  n_superpixels = 50,
  weight = 20,
  n_iter = 10,
  colour = "black"
)

Value

A ggplot object

Arguments

path: The path to the image. Must be readable by magick::image_read()
n_superpixels: The number of superpixels to segment into
weight: How high should locality be weighted compared to colour. High values leads to more compact superpixels, while low values follow the image structure more
n_iter: How many iterations should the segmentation run for
colour: What line colour should be used to show the segment boundaries

Examples

Run this code

image <- system.file('extdata', 'produce.png', package = 'lime')

# plot with default settings
plot_superpixels(image)

# Test different settings
plot_superpixels(image, n_superpixels = 100, colour = 'white')

Run the code above in your browser using DataCamp Workspace