Code and example images from: recolorize: An R package for flexible color segmentation of biological images

Color pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing color pattern geometries across images requires color segmentation, where pixels in an image are assigned to one of a set of color classes shared by all images. Manual methods for color segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human-subjective color segmentation with functions for batch-processing low-variation image sets and additional tools for handling images from diverse (high variation) sources. The package also includes export options for a variety of formats and color analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing, and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output.

Methods
The included dataset is a copy of the code and images found in the recolorize_examples GitHub repository as of January 2024: https://github.com/hiweller/recolorize_examples

The code includes all steps necessary to recreate the analyses presented in the paper. Images were sourced as follows:

Figure 1: Images of Chrysochroa beetles by Nathan P. Lord (coauthor).

Figure 2: Pygoplites diacanthus image from John E. Randall/Bishop Museum (http://pbs.bishopmuseum.org/images/JER/detail.asp?size=i&cols=10&ID=1432937402).

Figure 3: Images of Neolamprologus fishes taken by Ad Konings, used with his kind permission.

Figure 4: Images of Polistes fuscatus wasps taken by James Tumulty, and are a subset of the images used for analysis in Tumulty et al. (2023): https://doi.org/10.1016/j.cub.2023.11.032

Figure 5: Images of Diglossa birds taken by Anna E. Hiller (coauthor).