# anopara/genetic-drawing

A genetic algorithm toy project for drawing

Repository: https://github.com/anopara/genetic-drawing
Canonical: https://ross.abutalabs.com/products/genetic-drawing
Language: Python
License: MIT
License Family: permissive
Last push: 2020-06-26T07:34:08+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2280, "days_push": 2259, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2226, forks 194 (observed 2026-08-28T04:06:28.778094+00:00)

## What it is
A Python toy project that uses a genetic algorithm to progressively draw a target image with brushstrokes, producing animated drawing sequences. It runs in Jupyter Notebook and supports user-defined sampling masks to focus detail in specific regions.

## Use cases
- generate an animated drawing of a photo
- recreate an image using evolutionary brushstrokes
- experiment with genetic algorithms for image generation
- create art from images with a genetic algorithm
- focus brushstroke detail on specific image regions with masks

## When to choose
- you want a simple, visual genetic algorithm example to learn from
- you need to generate evolutionary drawing animations from images
- you want a hackable Jupyter-based art toy

## When to avoid
- you need production-quality image stylization or fast performance
- you need a maintained library with an API rather than a notebook
- you need GPU-accelerated or large-scale image synthesis

## Facets
- artifact type: application
- maturity: maintenance
- function: image-processing, machine-learning, graphics
- domain: artificial-intelligence, graphics, image-processing
- platform: python, cross-platform
- tags: genetic-algorithm, evolutionary-art, jupyter-notebook, toy-project, opencv

## Member repositories
- anopara/genetic-drawing (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:28.778094+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 2026-08-30T02:44:52.582477+00:00, confidence not recorded.
  - readme: https://github.com/anopara/genetic-drawing (fetched 2026-08-28T04:06:28.778094+00:00, sha f1a33147f570)
- Data as of 2026-08-30T08:39:29.467469+00:00.