Blur raster data — step

The function step_blur() creates a specification of a recipe step that will induce Gaussian blur in numerical arrays. The input and output must be list-columns.

step_blur(
  recipe,
  ...,
  role = NA_character_,
  trained = FALSE,
  xmin = 0,
  xmax = 1,
  blur_sigmas = NULL,
  skip = FALSE,
  id = rand_id("blur")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose variables for this step. See selections() for more details.
role: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
xmin, xmax, blur_sigmas: Parameters passed to blur().
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

The gaussian blur step deploys blur(). See there for definitions and references.

TODO: Explain the importance of blur for PH of image data.

Tuning Parameters

This step has 1 tuning parameter(s):

blur_sigmas: Gaussian Blur std. dev.s (type: double, default: NULL)

Examples

topos <- data.frame(pix = I(list(volcano)))

blur_rec <- recipe(~ ., data = topos) %>% step_blur(pix)
blur_prep <- prep(blur_rec, training = topos)
blur_res <- bake(blur_prep, topos)

tidy(blur_rec, number = 1)
#> # A tibble: 1 × 3
#>   terms value id        
#>   <chr> <dbl> <chr>     
#> 1 pix      NA blur_4yCRh
tidy(blur_prep, number = 1)
#> # A tibble: 1 × 3
#>   terms value id        
#>   <chr> <dbl> <chr>     
#> 1 pix      NA blur_4yCRh

with_sigmas <- recipe(~ ., data = topos) %>% step_blur(pix, blur_sigmas = 10)
with_sigmas <- bake(prep(with_sigmas, training = topos), topos)

ops <- par(mfrow = c(1, 3))
image(topos$pix[[1]])
image(blur_res$pix[[1]])
image(with_sigmas$pix[[1]])

par(ops)