colordistance 0.8.0

An R package with functions for quantifying the differences between colorful objects.

Input: Set(s) of JPEG or PNG images of colorful objects, optionally with backgrounds masked out.

Output: Color clusters, visualizations for color binning and image similarity, and distance matrices quantifying color similarity between images.

Requirements: R >= 3.3.2

Documentation: https://hiweller.github.io/colordistance

Author: Hannah Weller

Contact: hannahiweller@gmail.com

Installation

colordistance is still in development, and you can track it at https://github.com/hiweller/colordistance.

To install the current (largely untested) version of colordistance in R:

  1. Install the devtools package (install.packages("devtools")).

  2. Install colordistance without vignettes (long-form documentation) to save time and space or with vignettes for offline access to help documents.

    # Without vignettes
    devtools::install_github("hiweller/colordistance")
    
    # With vignettes
    devtools::install_github("hiweller/colordistance", build_vignettes=TRUE)
  3. You can access help documents by running help(package="colordistance") and clicking on the html files or, if you set build_vignettes=TRUE during install, run vignette("colordistance-introduction").

Documentation

All of the colordistance vignettes that (optionally) come with the package are also available online at https://hiweller.github.io/colordistance/. I recommend reading at least the introduction before getting started.

Quickstart

To get started with colordistance, you’ll need:

  1. A set of images of objects you want to compare, ideally as consistent with each other as possible in terms of lighting and angle, and with anything you want to ignore masked out with a uniform background color.

  2. R version 3.3.2 or later.

  3. Estimates for the upper and lower RGB bounds for your background color. R reads in pixels channels with a 0-1 intensity range instead of the typical 0-255 (so pure red would be [1, 0, 0], green would be [0, 1, 0], blue would be [0, 0, 1], and so on). Background masking is rarely perfect, so you’ll need to specify an upper and lower threshold for the background cutoff - around 0.2 usually does it. So if your background is white, your lower threshold would be [0.8, 0.8, 0.8] and your upper would be [1, 1, 1]. The default background color for colordistance is bright green, [0, 1, 0].

To run an analysis with all the default settings (bright green background masking, RGB color histograms with 3 bins per channel, and earth mover’s distance for color distance metric – see documentation), just run:

colordistance::imageClusterPipeline("path/to/images/folder")

You’ll get a blue and yellow heatmap with a cluster dendrogram and labels taken from the image names. Yellow cells correspond to dissimilar images; blue cells correspond to more similar images. If those scores don’t look right, try changing the number of bins (bins argument), the distance metric (distanceMethod argument), and making sure you’re masking out the right background color.

Questions?

Email me!