Extract Colors from an Image (Dominant Color Extractor)
Upload an image to automatically extract its dominant colors — the representative colors used in a photo or illustration. All processing happens entirely in your browser; the image is never sent to a server.
Usage Tips
- Click the copy button below each swatch to copy its HEX code to the clipboard — ready to paste directly into CSS or your design tool.
- Choose 4, 8, or 12 colors to extract. Pick 4 for a simple logo palette, or 12 to capture the subtle variations in a detailed photo.
- Transparent areas in PNG images are automatically excluded, so the extracted colors focus on the actual subject rather than the background.
- All processing runs locally in your browser using the Canvas API — the image itself is never sent to any server, so it's safe to use with sensitive images.
- Colors are listed from most to least frequent. The colors shown first occupy the largest area in the image.
Frequently Asked Questions
Side Note — The work of finding brand colors from photos
Extracting the colors used in a photo or product package is a routine task in web and graphic design work. When building a brand's visual guidelines, designers often start by pulling representative colors out of existing photos or logos, then use those as candidates for a corporate or accent color. What used to be done by eye with an eyedropper tool, one color at a time, can now be automated with algorithms like the dominant color extraction used here.
There are many algorithms for color extraction. The bucket-quantization-and-frequency approach used by this tool rounds each pixel's RGB value into a coarse group and counts how often each group appears — a simple technique that's cheap to compute and runs fast. More advanced approaches, such as k-means clustering, group colors based on distance in color space and can produce more natural-looking groupings, at the cost of heavier computation.
One interesting quirk is how differently the same image can look depending on how many colors you extract. Pulling just 4 colors reveals the broad tone of an image — whether it leans warm or cool — while extracting 12 starts to pick up subtle variations from shadows and reflected light. Adjusting the count lets you pull different levels of detail out of the very same image.