Reverse-engineer inspiration
Pull the dominant colors out of a moodboard image and start a palette from real reference.
Upload a photo, get the dominant colors as a palette. Part of AI color palette generator.
Quick answer: Color extraction analyzes the pixels in an image to identify its most dominant colors. Algorithms like k-means clustering group similar pixels together.
Drag a photo, screenshot, or artwork onto the upload area.
The 5 most dominant colors appear as swatches.
Click any swatch to copy its hex code.
Color extraction analyzes the pixels in an image to identify its most dominant colors. Algorithms like k-means clustering group similar pixels together and return the centroid of each cluster as a representative color.
This is useful for deriving brand palettes from photography, matching UI colors to product images, or extracting a cohesive palette from inspiration artwork. The extracted colors often need refinement: raw pixel colors may not have sufficient contrast or fit within an accessible color system.
Professional workflows typically extract colors as a starting point, then adjust lightness and saturation to meet design system requirements. A raw photo palette rarely passes WCAG contrast checks without modification.
Run the extracted swatches through the WCAG contrast checker to find the failing pairs, build production-ready ramps with the generate 50 to 950 shade scales, or upgrade to Paletta Pro to lift the whole image into a full design system.
Pull the dominant colors out of a moodboard image and start a palette from real reference.
Match the color story of a campaign visual without manually eyedropping each region.
Drop a logo render and get a 5-swatch starting point for the rest of the system. Translate each swatch with the hex to RGB converter so the values land cleanly in code.
Anchor a UI palette to actual product colors so marketing and app stay coherent.
Paletta turns them into a full design system: shade scales, semantic tokens, WCAG-validated, production-ready.