How to Extract Color Palettes from Images with Python’s Haishoku Library

When creating data visualizations in Python, choosing an appealing color scheme is crucial; you can derive palettes directly from images using the Haishoku library.

Install Haishoku

pip install haishoku

Save the target image locally (e.g., test.png) and load it:

from haishoku.haishoku import Haishoku
image = 'test.png'
haishoku = Haishoku.loadHaishoku(image)

Retrieve the dominant color and the full palette:

print(haishoku.dominant)  # (R, G, B) tuple
import pprint
pprint.pprint(haishoku.palette)  # list of up to 8 (R, G, B) tuples

Visualize the results:

Haishoku.showDominant(image)   # opens a preview of the dominant color
Haishoku.showPalette(image)   # opens a preview of the palette

Since version 1.1.4, Haishoku can also load images directly from URLs:

imagepath = 'https://img-blog.csdnimg.cn/20190222215216318.png'
haishoku = Haishoku.loadHaishoku(imagepath)

Applying the Palette to Visualizations

After extracting a palette (e.g., from the Google logo), you can use the colors in Matplotlib, Seaborn, Plotly, etc., to create charts that match the source image’s aesthetic.

These steps demonstrate that extracting and reusing image‑based color schemes in Python requires only a few lines of code.