Image Color Palette Extractor

Workflow & Usage

1. Upload an image
   Drag & drop or click to select a single JPG, PNG, or WebP file.
   As soon as the image loads, the tool extracts its color palette automatically.

2. View the extracted palette
   You’ll immediately see:

   • A preview of the image
   • A list of semantic swatches (Vibrant, Muted, DarkVibrant, etc.)
   • HEX and RGB values
   • A corresponding CSS variable name for each color

3. Copy CSS or JSON
   Two code boxes appear on the right:

   • CSS Variables — copy a ready-to-paste :root block
   • JSON Tokens — ideal for design systems or style dictionaries

4. Refine or replace
   Click Clear to remove the current image and load another.
   The palette regenerates instantly.

Use Cases

• Theme & UI design
  Get natural color sets for buttons, backgrounds, and accents based directly on your hero images or screenshots.

• Branding & identity work
  Turn photos, product shots, or logos into consistent color palettes.

• Social & marketing design
  Build cohesive visuals based on extracted brand-aligned colors.

• Front-end development
  Copy CSS variables or import JSON tokens into Tailwind, React apps, Astro projects, or design token pipelines.

• Photography & creative workflows
  Extract dominant tones for editing decisions, LUTs, or style guides.

Tips for Best Results

• Use images with clear color regions.
  Vibrant works best when there are strong subjects or distinct areas.

• Avoid tiny or noisy images.
  Larger, cleaner pictures create more accurate palette clusters.

• Use semantic roles intentionally.

  • Vibrant often makes a strong accent
  • Muted is good for backgrounds
  • DarkMuted and DarkVibrant can support contrast themes

• Copy CSS variables for theme overrides.
  Works perfectly with Tailwind’s theme.extend.colors.

How It Works

This tool is fast and fully privacy-preserving, powered by modern browser APIs:

• Canvas rendering
  The uploaded image is decoded and analyzed pixel-by-pixel inside your browser.

• node-vibrant color clustering
  Uses MMCQ (median cut quantization) to determine perceptually important colors.

• Semantic swatch mapping
  Colors are intelligently labeled (Vibrant, Muted, etc.) to make the palette more design-ready.

• On-device execution
  No servers, no uploads, no tracking — everything happens locally using Canvas, Web APIs, and optimized color algorithms.

• Export-friendly output
  CSS variables and JSON tokens are generated automatically for easy reuse in modern design systems.