cluster-colors

Cluster Colors (or other vectors)

Processing and clustering colors from images presents some challenges:

• Even a small (800x600) image will have up to 480,000 colors.
• Solutions like PIL's Image.quantize, on the other hand, make the colors sample too coarse.
• Even after reducing color variety, you're still dealing with 480,000 color instances.
• Solutions like scikit-learn's KMeans handle some of these challenges, but are non-deterministic and not flexible in the ways that I'd like.

I provide three steps here:

Pool colors

Average similar colors. Specifically, this maps an 8-bit color space to an n-bit color space then averages colors in each bin. An argument, nbits, specifies the number of bits to use for each color channel. The default is 6, which reduces 17-million-ish possible colors to 300-thousand-ish possible colors. The downside is that the boundaries between n-bit bins are arbitrary. Heavy concentrations of near-identical colors will be split if a boundary passes through them.

Pooling colors from an image path will write a cache to your temp directory.

Cut colors

Reduce the number of colors by recursively splitting the color space along the longest axis. This is a median cut algorithm, but it's not constrained to x, y, or z axes. The longest axis is determined by the standard deviation of the colors in the cluster. I've made the cut just a little bit smarter than standard median cut, but this is essentially k-medoids without the re-distribution step, so it's more efficient, but not the best we can do. An argument, num, specifies the number of colors to reduce to. 512 is a good number, but if you're still missing some nuance, you can increase it.

Divisive and Agglomerative clustering

• Both are deterministic.
• Both handle frequency, weight, transparency.
• Both allow a user-defined proximity matrix, so you can use whatever delta function you like as long as delta(a,a) is 0 and delta(a,b) is never 0. Common choices are Euclidean, squared Euclidean, and delta-e.
• Divisive uses a variation of median cut followed by a kmediods-like reassignment step to conversion.
• Agglomerative uses complete linkage.
• Divisive is more robust to outliers and will give more even-sized clusters.
• Divisive child clusters will not necessarily contain (or only contain) the members of the parent.
• Agglomerative is more likely to separate outliers.
• Agglomerative is heirarchical.

Divisive clustering is typically better for, "What are the five dominant colors in this image?"

Agglomerative clustering is typically better for, "How many colors do I need to represent this image with no more than delta==3 between any two cluster members?"

Installation

pip install cluster_colors

Basic usage

from cluster_colors import get_image_clusters

# find the five most dominant colors in an image
clusters = get_image_clusters(image_filename)
clusters.split_to_n(5)
exemplars = clusters.get_as_vectors()

# to save the cluster exemplars as an image file
show_clusters(split_clusters, "open_file_to_see_clusters")