# fogleman/Quads

Computer art based on quadtrees.

Repository: https://github.com/fogleman/Quads
Canonical: https://ross.abutalabs.com/products/quads
Homepage: http://www.michaelfogleman.com/static/quads/
Language: Python
License Family: other
Last push: 2022-12-26T21:16:44+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4493, "days_push": 1346, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1232, forks 151 (observed 2026-08-28T04:04:04.412993+00:00)

## What it is
A Python program that generates abstract computer art from input images using quadtree subdivision. Each quadrant is filled with an averaged color, and the quadrant with the largest error is recursively split to progressively refine the image.

## Use cases
- generate quadtree art from a photo
- create abstract stylized versions of images
- animate the iterative refinement of an image
- produce breadth-first traversal animations of a quadtree
- turn pictures into low-poly style color block art

## When to choose
- you want generative/abstract art from images via quadtree subdivision
- you want a simple Python script to experiment with image approximation algorithms
- you want to create animations of the refinement process

## When to avoid
- you need general-purpose image editing or photo manipulation
- you need a maintained library with a license for production use
- you need high-performance or GPU-accelerated image processing

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: image-processing, graphics
- domain: graphics, image-processing, media
- platform: python, cross-platform
- tags: quadtree, generative-art, computer-art, animation

## Member repositories
- fogleman/Quads (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.412993+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:21.454380+00:00, confidence not recorded.
  - readme: https://github.com/fogleman/Quads (fetched 2026-08-28T04:04:04.412993+00:00, sha e8ad04133dc9)
  - homepage: http://www.michaelfogleman.com/static/quads/ (fetched 2026-08-29T12:22:16.685281+00:00, sha ec27060918b1)
- Data as of 2026-08-30T08:39:29.467469+00:00.