recolorize (version 0.2.0)

editLayers: Edit multiple color patches using morphological operations

Description

A wrapper for editLayer, allowing for multiple layers to be edited at once, either with the same morphological operation or specified for each layer.

Usage

editLayers(
  recolorize_obj,
  layer_idx = "all",
  operations = "clean",
  px_sizes = 2,
  plotting = TRUE
)

Value

A recolorize object. The sizes, pixel_assignments,, and recolored_img attributes will differ from the input object for the relevant color patches (layers) to reflect their changes.

Arguments

recolorize_obj

A recolorize object from recolorize(), recluster(), or imposeColors().

layer_idx

A numeric vector of layer indices to be edited, or "all" (in which case all layers are edited). Corresponds to the order of the colors in the centers attribute of the recolorize object, and to the indices in the pixel_assignments attribute of the same.

operations

Either a single string OR a character vector of imager morphological operation(s) to perform on the specified layer(s). If this is shorter than layer_idx, it is repeated to match the length of layer_idx.

px_sizes

The size(s) (in pixels) of the elements to filter. Either a single number OR a numeric vector. If shorter than layer_idx, it is repeated to match the length of layer_idx. If operation = "shrink" and px_size = 2, for example, the color patch will be shrunk by a 2-pixel radius.

plotting

Logical. Plot results?

Details

Current imager operations are:

• imager::grow(): Grow a pixset

• imager::shrink(): Shrink a pixset

• imager::fill(): Remove holes in an pixset. Accomplished by growing and then shrinking a pixset.

• imager::clean(): Remove small isolated elements (speckle). Accomplished by shrinking and then growing a pixset.

See Also

editLayer for editing a single layer at a time.

Examples

# load image and recolorize it
img <- system.file("extdata/corbetti.png", package = "recolorize")

# first do a standard color binning
init_fit <- recolorize(img, bins = 2, plotting = FALSE)

# then cluster patches by similarity
re_fit <- recluster(init_fit, cutoff = 40)

# to reset graphical parameters:
current_par <- graphics::par(no.readonly = TRUE)

# examine individual layers:
layout(matrix(1:6, nrow = 2))
layers <- splitByColor(re_fit, plot_method = "color")

# we can clean them all using the same parameters...
edited_fit <- editLayers(re_fit, layer_idx = "all",
                         operations = "clean",
                         px_sizes = 2, plotting = TRUE)
# ...but some of those patches don't look so good

# we can use different px_sizes for each layer:
edited_fit_2 <- editLayers(re_fit, layer_idx = "all",
                           operations = "clean",
                           px_sizes = c(1, 3, 1,
                                        2, 1, 2),
                           plotting = TRUE)

# better yet, we can fill some layers and clean others:
edited_fit_3 <- editLayers(re_fit, layer_idx = "all",
                           operations = c("fill", "clean",
                                          "fill", "fill",
                                          "fill", "clean"),
                           px_sizes = c(2, 3,
                                        2, 2,
                                        4, 2))

# or you could just get weird:
edited_fit_3 <- editLayers(re_fit, layer_idx = c(1:6),
                           operations = c("fill", "clean"),
                           px_sizes = c(10, 20))

# reset graphical parameters:
graphics::par(current_par)