# GreycLab/CImg

The CImg Library is a small and open-source C++ toolkit for image processing

Repository: https://github.com/GreycLab/CImg
Canonical: https://ross.abutalabs.com/products/cimg
Homepage: http://cimg.eu
Language: C++
License: NOASSERTION
License Family: other
Last push: 2026-08-21T09:04:50+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 4114, "days_push": 12, "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 1690, forks 303 (observed 2026-08-28T04:05:22.578069+00:00)

## What it is
CImg is a small, open-source, header-only C++ template library for image processing. It provides a single image class supporting up to 4-dimensional datasets, with loading/saving, filtering, drawing, display, and statistics capabilities and minimal dependencies.

## Use cases
- load and save images in various formats from C++
- apply filters and transformations to images
- draw primitives like text, curves, and 3d objects on images
- process volumetric 3d images and hyperspectral data
- compute image statistics in C++
- build image processing algorithms without heavy dependencies
- display images and handle user interactions in a C++ app

## When to choose
- you need a lightweight, self-contained C++ image processing library with no complex dependencies
- you want a single-header library that is easy to integrate into existing C++ projects
- you need to work with multi-dimensional image data including 3d volumes
- you want a permissive-ish license (CeCILL-C, LGPL-like) usable in commercial applications

## When to avoid
- you need GPU-accelerated or highly optimized large-scale image pipelines
- you prefer a Python or high-level API rather than C++ templates
- you need extensive built-in computer vision algorithms like feature detection or deep learning inference
- you require a permissive license like MIT or BSD for your project

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, graphics, gui
- domain: image-processing, computer-vision, graphics, cross-platform
- platform: windows, cross-platform, cpp
- tags: header-only, single-header, image-filtering, image-display, volumetric-images, cecill-license, linux, macos

## Member repositories
- GreycLab/CImg (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:22.578069+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 2026-08-30T03:38:19.144676+00:00, confidence not recorded.
  - readme: https://github.com/GreycLab/CImg (fetched 2026-08-28T04:05:22.578069+00:00, sha ebec9547a651)
  - homepage: http://cimg.eu (fetched 2026-08-29T11:13:19.419921+00:00, sha 8d2384ba2f28)
  - site_page: https://cimg.eu/reference/index.html (fetched 2026-08-29T11:13:19.446957+00:00, sha e93c234023d1)
- Data as of 2026-08-30T08:39:29.467469+00:00.