nb-img-classes: Number and brightness image classes.
In nandb: Number and Brightness Image Analysis

Description Usage Arguments Details Value

The number_img and brightness_img classes are designed to hold objects which are images calculated from the number and brightness technique.

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number_img(img, def, thresh, swaps, filt)

brightness_img(img, def, thresh, swaps, filt)

`img`	The calculated number or brightness image.
`def`	The number or brightness definition used.
`thresh`	A positive integer, possibly an object of class autothresholdr::th. If the different channels of the image had different thresholds, this argument may be specified as a vector or list (of positive integers, possibly objects of class autothresholdr::th), one element for each channel.
`swaps`	A non-negative integer with an attribute `auto`. If the different channels of the image had different `swaps`, this argument may be specified as a list (of non-negative integers with attributes `auto`), one element for each channel. For undetrended images, set `swaps = NA`.
`filt`	A string, the filtering method used. Must be either `"mean"` or `"median"`, or `NA` for no filtering. If the different channels of the image had different filters, this may be specified as a character vector, one element for each channel.

An object of class number_img or brightness_img is a 4-dimensional array of real numbers in the mould of an ijtiff_img (indexed as img[y, x, channel, frame]) with 4 attributes:

def: Are we using the "N" or "n" definition of number, or the "B" or "epsilon" definition of brightness?
thresh: A positive integer, possibly an object of class autothresholdr::th detailing which threshold and thresholding method was used in preprocessing (in the multi-channel case, one threshold per channel is given).
swaps: A non-negative integer indicating the number of swaps Robin Hood detrending, with an attribute auto which is a logical indicating whether or not the parameter was chosen automatically (in the multi-channel case, one threshold per channel is given).
filt: Was mean or median filtering used in postprocessing?