How accurate is the extraction?

Reasonable for design / moodboard use; not pixel-perfect. The histogram approach picks the most-frequent COLOR BUCKETS — so a sky that's mostly blue with subtle gradient gets reduced to one or two blue swatches. For pixel-perfect dominant colors, k-means clustering or median-cut quantization is more accurate; those are slower but used in tools like Adobe Color and Coolors.

Why is the image downsampled?

Speed. Processing every pixel of a 4K image takes 16× longer than 800px-wide for ~identical color content. Color histograms aggregate across many pixels; downsampling preserves the color distribution while making computation 16× faster. For palette extraction, the result is visually identical.

Why do my extracted colors look slightly off vs the image?

Quantization rounding. The 4-bit histogram quantizes each color channel to 1 of 16 levels. So #5b8c3a (raw image) might bucket into #5588 33 — the displayed swatch. The differences are typically imperceptible to the naked eye but can matter for color-critical work. For pixel-exact color, use a color picker.

Can I extract a specific section of the image?

Not in this tool — it processes the entire image. For section-specific extraction, crop the image first using an image editor, then run the cropped version through here.

How do I export the palette to my design tool?

The tool offers export formats: JSON (an array of hex strings), CSS variables (`:root { --color-1: #abc; ...; }`), Tailwind config snippet (`colors: { brand: '#abc', ... }`). For Figma, paste the hex codes individually or use a Figma plugin to import a JSON palette.

Why are some swatches very similar?

Because the source image has subtle color variation in those tones — sky gradients, skin tones, similar blues in clothing. To get a more diverse palette, increase the histogram bucket size (which would merge similar colors) or process a different image with more visual variety.

Developer Utilities · Free tool

Color Palette Extractor

Extract dominant colors from any image using histogram analysis, all in your browser. Click swatches to copy hex codes instantly — free tool, no signup.

Updated June 2026

Number of colors (8)

Extracts the dominant colors from any image using a 4-bit-per-channel histogram. Image is downsampled to 800px on the long edge for speed. Runs entirely in your browser; the image never leaves your device. Click any swatch to copy the hex.

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What it does

Drop an image and the tool extracts the dominant colors as a clickable palette. Each swatch shows the hex code and percentage of the image; click to copy. The image is downsampled to 800px wide for speed (perceptually identical color content to the original at 800px+; larger downsampling would change colors in subtle ways). Adjust the number of palette colors (typically 5-12).

Common use cases: moodboard creation from inspiration photos for branding, design, fashion, interior design; website / app theming — extract a palette from a hero photo and use those colors for accent / typography across the site; brand-color identification from a competitor’s logo or marketing material; painting / craft references — match the colors in a reference photo for a painting or quilt; data visualization palette generation — pick colors that already work together harmoniously from a relevant image.

The extraction algorithm is a 4-bit-per-channel histogram: each pixel gets quantized into one of 16×16×16 = 4,096 color buckets, the most-populous buckets become the palette. Fast (~0.1s for an 800px image), deterministic, decent results for most images. For better-quality palette extraction, professional tools use k-means clustering (lloyd’s algorithm) or median-cut quantization — these are slightly slower but produce more visually coherent palettes. For most casual moodboard / branding use cases, the histogram approach is plenty.

Embed this tool on your siteShow snippet

Paste this snippet into any page. Loads on-demand (lazy), no tracking scripts, and sized to most dashboards. Replace the height to fit your layout.

<iframe src="https://freetoolarena.com/embed/color-palette-extractor" width="100%" height="720" frameborder="0" loading="lazy" title="Color Palette Extractor" style="border:1px solid #e2e8f0;border-radius:12px;max-width:720px;"></iframe>

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How to use it

Drop an image — JPG, PNG, WebP all work.
The palette extracts immediately. Default 6 colors; adjust the count slider for more or fewer (1-12 range typical).
Hover any swatch to see the hex code; click to copy to clipboard.
Optionally export the full palette as JSON, CSS variables, or a Tailwind config snippet for direct use in your project.
If the extracted palette feels muted, try a different image — extraction reflects the source. Saturated, brand-color-rich images give more vibrant palettes; muted photographs give muted palettes.

When to use this tool

Building a moodboard for design / branding from inspiration imagery.
Extracting brand colors from competitor logos or marketing materials.
Generating a harmonious data-viz palette from a relevant photograph.
Picking accent colors for a website / app from a hero photo.

When not to use it

Pixel-perfect color identification — for that, use a color picker (sample one specific pixel rather than extract a palette).
Color analysis for printing — print colors (CMYK) don't map directly to screen RGB; for print pipelines work with Pantone references and proof prints.
Brand-equivalent color extraction — many brands use specific Pantone or hex codes that aren't in their public images. Reference brand guidelines (look for press kits / brand pages).

Common use cases

Onboarding a colleague who needs the same calculation/conversion
Verifying a number or output before passing it on
Quick use during a typical workday
Pre-decision sanity-check on inputs and outputs

Frequently asked questions

How accurate is the extraction?: Reasonable for design / moodboard use; not pixel-perfect. The histogram approach picks the most-frequent COLOR BUCKETS — so a sky that's mostly blue with subtle gradient gets reduced to one or two blue swatches. For pixel-perfect dominant colors, k-means clustering or median-cut quantization is more accurate; those are slower but used in tools like Adobe Color and Coolors.
Why is the image downsampled?: Speed. Processing every pixel of a 4K image takes 16× longer than 800px-wide for ~identical color content. Color histograms aggregate across many pixels; downsampling preserves the color distribution while making computation 16× faster. For palette extraction, the result is visually identical.
Why do my extracted colors look slightly off vs the image?: Quantization rounding. The 4-bit histogram quantizes each color channel to 1 of 16 levels. So #5b8c3a (raw image) might bucket into #5588 33 — the displayed swatch. The differences are typically imperceptible to the naked eye but can matter for color-critical work. For pixel-exact color, use a color picker.
Can I extract a specific section of the image?: Not in this tool — it processes the entire image. For section-specific extraction, crop the image first using an image editor, then run the cropped version through here.
How do I export the palette to my design tool?: The tool offers export formats: JSON (an array of hex strings), CSS variables (`:root { --color-1: #abc; ...; }`), Tailwind config snippet (`colors: { brand: '#abc', ... }`). For Figma, paste the hex codes individually or use a Figma plugin to import a JSON palette.
Why are some swatches very similar?: Because the source image has subtle color variation in those tones — sky gradients, skin tones, similar blues in clothing. To get a more diverse palette, increase the histogram bucket size (which would merge similar colors) or process a different image with more visual variety.

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