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Image Tools

Image Color Extractor

Extract a dominant color palette from any image using quantization — get 5–10 key colors with Hex, RGB, and HSL codes.

Last updated: March 25, 2026

Client-Side Processing
Input Data Stays on Device
Instant Local Execution
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What is Image Color Extractor?

Images contain thousands to millions of unique pixel colors. An image color extractor uses color quantization algorithms to reduce this complexity into a small, representative palette — typically 5–10 dominant colors that best describe the visual character of the image. Unlike the eyedropper tool (which reads one specific pixel), a palette extractor finds the most statistically significant color clusters across the entire image.

The most common algorithm for this is Median Cut or k-Means clustering. Median Cut works by recursively splitting the color space into boxes and averaging the colors within each box. k-Means clustering groups pixels into k clusters by proximity in RGB color space, with each cluster center representing a dominant color. The output palette is useful for creating design systems that match photography, generating brand guidelines from mood boards, creating gradient backgrounds that match a photo's mood, or programmatically matching UI colors to user-uploaded content.

How to Use Image Color Extractor

1

Upload a photo, logo, screenshot, or any image using the "Upload Image" button or drag-and-drop

2

Choose the number of colors to extract (5 is ideal for logos, 8–12 for complex photographs)

3

Wait a moment while the quantization algorithm processes the image

4

View the extracted palette — each swatch shows Hex, RGB, and HSL values

5

Click any color swatch to copy its Hex code, or click "Export Palette" to download the full palette as a CSS, JSON, or ASE file

Common Use Cases

  • Extracting a brand color palette from a company photography set for design system documentation
  • Generating a background gradient that harmonizes with a hero section product photo
  • Analyzing competitor brand colors by uploading their website screenshot
  • Creating a mood board color palette from an inspirational photography collection
  • Matching UI accent colors to a user-uploaded profile photo for a personalized interface
  • Extracting color themes from a movie still or artwork for a creative project
  • Building a Tailwind config color palette based on a product photography shoot
  • Color-coding content categories by extracting representative colors from category imagery

Example Input and Output

Extracting a palette from a sunset photograph (8 colors):

Uploaded: sunset-ocean.jpg
Image: sunset-ocean.jpg
Algorithm: Median Cut
Colors to extract: 8
Extracted 8-color palette
🎨 Extracted Color Palette

Color 1 — Deep Navy    #1A2744    RGB(26, 39, 68)
Color 2 — Ocean Blue   #2E5FA3    RGB(46, 95, 163)
Color 3 — Warm Amber   #E8922A    RGB(232, 146, 42)
Color 4 — Golden Hour  #F4C74D    RGB(244, 199, 77)
Color 5 — Coral Pink   #E86A5A    RGB(232, 106, 90)
Color 6 — Sky Lavender #9A8FBF    RGB(154, 143, 191)
Color 7 — Cloud White  #F0EDE8    RGB(240, 237, 232)
Color 8 — Dark Shadow  #0D1520    RGB(13, 21, 32)

CSS export: 8 custom property variables generated

How This Tool Works

The uploaded image is drawn onto an off-screen Canvas element. ctx.getImageData() reads all pixel RGBA values as a flat Uint8ClampedArray. The Median Cut algorithm (or k-means depending on implementation) processes this array to produce k cluster representative colors. Each cluster color is converted from its averaged RGB to Hex and HSL representations. The palette swatches are rendered and the export formats (CSS, JSON) are assembled from the extracted colors.

Technical Stack

HTML5 Canvas getImageData()Median Cut color quantizationk-Means clusteringRGB to Hex/HSL conversionClient-side only

Total Privacy

Image color extraction runs entirely in your browser using Canvas API pixel reading and JavaScript quantization. Your images — including proprietary photography or confidential product visuals — are never uploaded to our servers.

CSS Variables Export

Export the extracted palette as CSS custom properties: :root { --palette-1: #1A2744; --palette-2: #2E5FA3; ... }. This makes the extracted colors immediately usable in your stylesheet. For Tailwind users, the JSON export can be pasted into the colors section of your tailwind.config.js extend object.

Color Accessibility Check

After extracting a palette, check color pairs for WCAG contrast compliance before using them as foreground/background text combinations. Dark palette colors on light palette colors should achieve at least 4.5:1 contrast ratio (WCAG AA for normal text) or 3:1 (for large text and UI components). Use the WebToolsPlanet Color Contrast Checker to verify accessible color combinations before production use.

Frequently Asked Questions

How does color quantization work?

Color quantization reduces the number of distinct colors in an image to a small representative set. The Median Cut algorithm: (1) Places all pixel colors in a 3D bounding box in RGB color space. (2) Finds the longest dimension (R, G, or B axis). (3) Sorts pixels by that dimension and splits the box at the median value. (4) Recursively applies this to each half until the desired number of boxes is reached. (5) Averages the colors within each box to produce the palette colors.

What is the difference between a dominant color and an average color?

The average color is the mathematical mean of all pixels (additive mix of all colors). For most photos this produces a muddy neutral gray or brown — not a useful design color. Dominant colors are the cluster centers of the most statistically significant color groups in the image. A sunset photo has both red-orange clusters (sky) and blue-gray clusters (ocean water) — both are dominant colors, but neither appears in the average.

Why do similar-looking images produce different palettes?

The palette depends on pixel value distribution, not just visual impression. A photo with a tiny red flower on a large green field produces a palette dominated by green despite the eye being drawn to the flower. Images with very gradual gradients may produce palette colors very close to each other. For design work, manually removing palette duplicates or very similar colors (within ΔE < 5) produces a more useful distinct color set.

How many colors should I extract?

For logos and brand marks: 3–5 colors is ideal (most logos use 2-4 colors), and extracting more will produce noise from shadows and anti-aliasing. For detailed photographs: 8–16 colors captures the full mood without too much granularity. For generating CSS gradients: 3–4 colors is the sweet spot. For generative art or data visualization palettes: 10–20 colors provides a rich but manageable set.

Why are some colors in my logo palette slightly off?

JPEG compression introduces color artifacts around edges and in flat-color regions — colors that appear to be solid #FF0000 may actually vary from #FD0102 to #FF0204 across pixels. For logo color extraction, always use PNG (lossless) source files for accurate results. For web logos exported from Figma or Illustrator, export as PNG before color extraction rather than JPEG.

Can I extract colors from a URL instead of uploading a file?

Extracting colors from a URL requires the image to be fetched and decoded. Browser CORS policies prevent direct fetch of cross-origin images into Canvas. If the image URL is on a server that permits cross-origin access (CORS header: Access-Control-Allow-Origin: *), it can be loaded. Most public image CDNs (Unsplash, Pexels, cloudinary with settings) permit this. For images on restrictive servers, take a screenshot and upload it instead.

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