Image HEX & Palette Extractor
The problem: Finding exact brand and dominant colors from screenshots or logos requires opening heavy design software like Photoshop.
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Image HEX & Palette Extractor: the complete guide
An Image HEX and Color Palette Extractor analyzes any uploaded graphic or photograph and identifies its dominant color scheme, providing exact HEX, RGB, and percentage values with 1-click clipboard copy. UI designers, graphic artists, and web developers frequently need to extract color palettes from brand logos, product photography, or inspirational moodboards without launching heavy desktop editors like Photoshop or Figma. This tool uses HTML5 Canvas pixel sampling and color quantization entirely on your device's GPU.
Color Quantization and Dominance Sampling
High-resolution photographs contain millions of distinct colors due to lighting gradations and anti-aliasing. The extractor maps pixels into quantized color clusters, grouping neighboring shades together and sorting clusters by frequency to extract the true visual signature of the image rather than arbitrary pixel noise.
WCAG Accessibility and Dark/Light Detection
Each extracted color is evaluated for perceived luminance using the ITU-R BT.709 standard. This indicates whether a color is naturally dark or light, helping designers determine appropriate typography pairings and ensuring background/foreground combinations satisfy WCAG accessibility guidelines.
Step by step: how to use Palette Extractor
- 1
Upload or drag & drop any image file (PNG, JPG, WEBP, or SVG).
- 2
The tool renders the image to an offscreen HTML5 Canvas.
- 3
Pixel data is sampled and quantized into dominant color clusters.
- 4
Inspect the top dominant palette chips with HEX and RGB codes.
- 5
Click any color chip to copy its individual HEX value.
- 6
Click 'Copy All HEX' to copy the entire palette as a comma-separated list.
Security & privacy
Uploaded images frequently contain proprietary brand assets, unpublished client designs, or personal photos. This tool processes images 100% locally via the browser's Canvas API. The file is never uploaded to any server or cloud storage.