Dominant Color Extractor
Pull the main colours and a palette out of any image.
How to use
- 1 Drop an image onto the box, or click to choose a file.
- 2 Set how many colours you want in the palette.
- 3 Adjust the grouping precision to merge or separate similar shades.
- 4 Copy individual hex values or the whole palette at once.
About Dominant Color Extractor
The Dominant Color Extractor scans an image and tells you which colours actually dominate it, returning a tidy palette of hex swatches with the percentage share each one covers.
It is the fast way to lift a brand colour off a logo, derive a theme from a photo, or check that a design leans the way you think it does.
Drop in a JPEG, PNG, WebP or GIF and it is decoded, sampled and analysed entirely inside your browser.
Under the hood the tool quantises every pixel into colour buckets and counts them, so visually similar shades are grouped instead of fragmenting into hundreds of near-duplicates.
Two sliders put you in control.
The colour count chooses how many swatches to return, from a single hero colour up to a full twelve-stop palette.
The grouping precision decides how tightly colours merge: a low setting collapses gentle gradients into a few bold blocks, while a high setting preserves subtle distinctions for photographs with rich tonal range.
Each swatch shows its exact hex value and its share of the image, and one click copies the whole palette as a comma-separated list ready for CSS, a design token file or a style guide.
To stay quick even on large photos the image is downsampled before analysis, which does not change the dominant colours but keeps results instant.
Everything happens on your device with the Canvas API, so nothing is uploaded, stored or logged, and the tool works offline once loaded.
FAQ
Is my image uploaded anywhere?
No. The picture is decoded and analysed with the Canvas API inside your browser, so it never leaves your device.
What does grouping precision do?
It controls how aggressively near-identical colours merge. Lower precision gives a few bold blocks; higher precision keeps subtle tonal differences apart.
Why are the percentages approximate?
Large images are downsampled before analysis for speed, so shares are a faithful estimate of the full image rather than an exact per-pixel count.