Tools FREE Color Extractor

Color Palette Extractor

Upload any image and instantly extract its dominant colors — hex, RGB, and HSL codes ready to copy or export.

🔒 Runs 100% in your browser No upload · No account Up to 32 colors 6 export formats Paste from clipboard
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Drop an image here
or click to browse  ·  PNG, JPG, WebP, GIF, SVG, AVIF
You can also paste an image from your clipboard (Ctrl / ⌘ + V)
Uploaded image preview
image.png
6
Export palette as

What is a color palette extractor?

A color palette extractor analyzes an image and identifies its most visually dominant colors, returning them as usable color codes. Designers use it to pull brand colors from a logo, match an illustration's color scheme, or build a cohesive palette from a photograph — without guessing or manually sampling pixels.

How does this tool work?

This tool runs entirely in your browser using JavaScript and the HTML5 Canvas API. Your image is never uploaded to any server — processing happens locally on your device. It samples pixels from the image and applies a k-means clustering algorithm to group similar colors and surface the most visually significant palette. Results are returned in your preferred format: HEX, RGB, or HSL.

Frequently Asked Questions

No. Everything runs in your browser using JavaScript and the HTML5 Canvas API. Your image never leaves your device. This also makes the tool faster than server-based alternatives — there's no upload wait time.
Any format your browser can display: PNG, JPG/JPEG, WebP, GIF (first frame), SVG, BMP, and AVIF. You can also paste an image directly from your clipboard using Ctrl+V (or ⌘+V on Mac).
Click any color swatch to copy its code. Use the Figma Tokens export for the Tokens Studio plugin. Use CSS Variables for stylesheets, Tailwind Config for tailwind.config.js, and Adobe ASE to import into Photoshop, Illustrator, or InDesign via the Swatches panel.
Between 2 and 32 colors using the slider. The default of 6 suits most design work. For logos or flat illustrations, 2–4 produces a cleaner result. For complex photographs, 12–20 gives richer palette coverage.
K-means clustering uses random initialization, which can produce slightly different groupings on each run — especially for images with gradual transitions. For most images the results are very consistent. If you get an unexpected result, try re-processing the same image.

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