normalizeRGB: Normalize pixel RGB ratios

In colordistance: Distance Metrics for Image Color Similarity

Description

Converts clusters from raw channel intensity to their fraction of the intensity for that cluster

Usage

normalizeRGB(extractClustersObject)

Arguments

extractClustersObject

A list of color clusters such as those returned by extractClusters or getHistList. List must contain identically sized dataframes with color coordinates (R, G, B or H, S, V) as the first three columns.

Value

A list of the same size and structure as the input list, but with the cluster normalized as described.

Note

This is a useful option if your images have a lot of variation in lighting, but obviously comes at the cost of reducing variation (if darker and lighter colors are meaningful sources of variation in the dataset).

For example, a bright yellow (R=1, G=1, B=0) and a darker yellow (R=0.8, G=0.8, B=0) both have 50% red, 50% green, and 0% blue, so their normalized values would be equivalent.

A similar but less harsh alternative would be to use HSV rather than RGB for pixel binning and color similarity clustering by setting hsv=T in clustering functions and specifying a low number of 'value' bins (e.g. bins=c(8, 8, 2)).

Examples

cluster.list <- colordistance::getKMeansList(c(system.file("extdata",
"Heliconius/Heliconius_A", package="colordistance"), lower=rep(0.8, 3),
upper=rep(1, 3)))
cluster.list <- colordistance::extractClusters(cluster.list)
colordistance:::normalizeRGB(cluster.list)

colordistance documentation built on March 21, 2021, 1:06 a.m.