How does palette extraction work?

We sample your image down to ~200px on the long edge, then group similar pixel colors using the median-cut algorithm — the same approach GIF encoders use to choose a color table. The most populous groups become your dominant colors.

Why do I get slightly different colors than another tool?

Different tools use different algorithms (k-means, octree, median-cut) and different sampling strategies. Median-cut tends to produce balanced palettes; k-means produces more 'central' averages. Both are valid.

Can I extract a palette from a photo?

Absolutely — that's the most common use case. Photos of products, nature, or interiors are great starting points for brand palettes.

What formats can I export?

CSS (with --color-1 through --color-N variables), JSON (machine-readable for design systems), and a PNG swatch sheet for sharing.

Is the image uploaded?

No. Sampling and quantization happen entirely in your browser.

Why are very dark or very white pixels excluded?

Pure black and pure white usually dominate photos but rarely make useful brand colors. We skip near-pure values to surface meaningful tones.

How accurate are the colors?

Colors are averaged within each cluster, so they reflect the dominant tone — not necessarily a single pixel's exact value. For pixel-perfect color picking, use an eyedropper tool instead.

What's the largest image I can analyze?

Up to 50MB. Sampling is downscaled to 200px so analysis is fast regardless of source size.

Color Palette Extractor — Pull Brand-Ready Palettes from Any Image

Designers borrow palettes from photos all the time — sunset shots, vintage posters, hero product images. Eyedropping pixel by pixel is slow and you almost always end up with colors that don't represent the image as a whole. An automatic palette extractor finds the genuinely dominant colors so you get a palette that feels like the image, not just a sample of it.

What is the Color Palette Extractor — Dominant Colors from Image?

A browser-based color palette extractor that uses the median-cut quantization algorithm to find the dominant colors in any image. Outputs hex/RGB swatches with copy-to-clipboard and export to CSS variables, JSON, or a swatch PNG.

Key features

Median-cut quantization for balanced, representative palettes
3, 5, 6, 8, 10, or 12-color extraction
Click any swatch to copy its hex code
Export as CSS custom properties for direct use in stylesheets
Export as JSON for design systems and tooling
Export as a swatch PNG for sharing or moodboards
Auto-skips near-pure white and black for more useful palettes
100% browser-side — no uploads, no API calls

How it works

The image is sampled down to ~200px on the long edge to keep analysis fast. Each remaining pixel is added to a 3D color space (R, G, B). The median-cut algorithm splits this cloud of pixels along its longest axis, then recursively splits the resulting halves until you have N buckets. The average color of each bucket becomes one swatch, and buckets are ordered by population size so the most dominant color comes first.

Why use this tool

Manual eyedropping returns single-pixel values that often don't represent the image. Median-cut returns averaged, balanced colors that genuinely reflect the dominant tones — the colors a viewer perceives, not just whatever happened to be at a single coordinate.

Common use cases

Extracting brand colors from a logo or marketing asset
Building a website color palette from a hero photo
Creating Tailwind / design-system color tokens from a moodboard
Matching UI accent colors to product photography
Generating Pinterest-style color stories from photos
Analyzing a competitor's site colors from a screenshot

How to use this tool

Upload Your Image — Drop a photo, screenshot, or design reference.
Choose How Many Colors — 3 for accents, 5–6 for a full palette, 8–12 for detailed analysis.
Copy or Export — Click any swatch to copy its hex, or export the palette as CSS, JSON, or PNG.

Who should use this

Brand designers building palettes from inspiration. Web and UI designers choosing accent colors. Photographers grading consistent palettes across a series. Marketers building moodboards. Developers turning a brand image into CSS variables.

How to get started

Upload your image, pick a color count, copy the hex codes you like, and export the full palette in your preferred format.

Best practices

Start with 5–6 colors for brand palettes — more is rarely useful
If results look washed out, try a smaller count (3) to surface only the strongest colors
Crop the image first if you only want colors from a specific region
For UI palettes, mix the extracted dominants with neutral grays you choose manually

Pro tips

5 colors is the sweet spot for brand palettes — enough variety, easy to remember.
Photos of nature produce calm palettes; product shots produce sharp brand-ready ones.
Export as CSS variables to drop straight into a Tailwind or design system config.

Expert insights

💡 5 Is Magic

Almost every famous brand uses 5 or fewer core colors. Starting with 5 swatches gives you a brand-ready palette without the cognitive overload of 10+ choices.

🔍 Median-Cut vs K-Means

Median-cut splits color space evenly — great for brand palettes. K-means averages around 'attractor' colors — better for theming. We use median-cut for predictability.

✓ Crop First

If only one part of an image inspires you, use the Crop tool first — extracting from a focused region gives much purer palettes than from the whole frame.

Limitations to be aware of

Returns averaged colors per cluster, not exact pixel values
Skips near-black and near-white by design — use an eyedropper for those
Extraction is single-image; no batch processing
Color accuracy depends on the source image's color profile

Frequently asked questions

How does palette extraction work?: We sample your image down to ~200px on the long edge, then group similar pixel colors using the median-cut algorithm — the same approach GIF encoders use to choose a color table. The most populous groups become your dominant colors.
Why do I get slightly different colors than another tool?: Different tools use different algorithms (k-means, octree, median-cut) and different sampling strategies. Median-cut tends to produce balanced palettes; k-means produces more 'central' averages. Both are valid.
Can I extract a palette from a photo?: Absolutely — that's the most common use case. Photos of products, nature, or interiors are great starting points for brand palettes.
What formats can I export?: CSS (with --color-1 through --color-N variables), JSON (machine-readable for design systems), and a PNG swatch sheet for sharing.
Is the image uploaded?: No. Sampling and quantization happen entirely in your browser.
Why are very dark or very white pixels excluded?: Pure black and pure white usually dominate photos but rarely make useful brand colors. We skip near-pure values to surface meaningful tones.
How accurate are the colors?: Colors are averaged within each cluster, so they reflect the dominant tone — not necessarily a single pixel's exact value. For pixel-perfect color picking, use an eyedropper tool instead.
What's the largest image I can analyze?: Up to 50MB. Sampling is downscaled to 200px so analysis is fast regardless of source size.

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