Rerun the sequence of calls used to produce a recolorize object

Evaluates the series of calls in the 'call' element of a recolorize object, either on the original image (default) or on another image. It will almost always be easier (and better practice) to define a new function that calls a series of recolorize function in order than to use this function!

Usage

rerun_recolorize(recolorize_obj, img = "original")

Arguments

recolorize_obj

An object of S3 class 'recolorize'.

img

The image on which to call the recolorize functions. If left as "original" (the default), functions are called on the original image stored in the recolorize object. Otherwise can be an object taken by the img argument of recolorize functions (a path to an image or an image array).

Value

A recolorize object.

Details

This function utilizes eval statements to evaluate the calls that were stored in the call element of the specified recolorize object. This makes it potentially more unpredictable than simply defining your own function, which is preferable.

Examples

# list images
corbetti <- system.file("extdata/corbetti.png", package = "recolorize")
chongi <- system.file("extdata/chongi.png", package = "recolorize")

# fit a recolorize object by running two functions in a row:
rc <- recolorize(corbetti, bins = 2, plotting = FALSE)
#> 
#> Using 2^3 = 8 total bins
rc <- recluster(rc, cutoff = 45)



# check out the call structure (a list of commands that were run):
rc$call
#> [[1]]
#> recolorize(img = corbetti, bins = 2, plotting = FALSE)
#> 
#> [[2]]
#> recluster(recolorize_obj = rc, cutoff = 45)
#> 

# we can rerun the analysis on the same image (bit pointless):
rerun <- rerun_recolorize(rc)
#> 
#> Using 2^3 = 8 total bins



# or, we can rerun it on a new image:
rerun_chongi <- rerun_recolorize(rc, img = chongi)
#> 
#> Using 2^3 = 8 total bins