Colour Palette Extractor Tool

A Python tool that uses K-Means clustering to extract dominant colours from an image and outputs hex codes with accessibility recommendations.

Given any image, this tool identifies its most dominant colours using K-Means clustering and returns hex codes alongside recommended font colours for accessibility.

This tool identifies the most dominant colours of any image using K-Means clustering and returns hex codes alongside recommended font colours for accessibility, using the Rec. 709 luminance formula.

The purpose of this tool is to give designers a data-driven starting point for building a brand/design identity by extracting the dominant colour palette of any inspiration image.

How It Works

The script loads an image and reshapes it into a flat list of pixels. A K-means clustering model is applied to this data, defining groups of dominant colours. It then converts the RGB values to hex codes and calculates a contrast ratio to indicate whether black or white text is more suitable for each colour.

Example Output

The program will produce a figure showing the most dominant colours and output a table with the hex code, swatch, and recommended font colour.

Left: the original input image. Right: the 5 dominant colours extracted by K-Means, visualised as swatches.

and produces a table like this:

Color (Hex) | Recommended Font Color

#e2dce6 | Black (#000000) #889d76 | Black (#000000) #656336 | White (#FFFFFF) #ecc89d | Black (#000000) #dcad68 | Black (#000000)

Tools Used

  • scikit-learn — K-Means clustering
  • matplotlib — Used for image loading and palette visualisation
  • numpy — Used for pixel matrix reshaping