🎨 Image Color Extractor (Palette from Image)
By Shihab Mia · Updated 2026-07-30
PNG, JPG, GIF, WebP or BMP. The image is read in your browser and never leaves your device.
Choose an image to extract its color palette.
This image color extractor reads an image you upload and pulls out its most common (dominant) colors as a clean palette of hex codes. Drop in a photo, logo, screenshot or piece of artwork, set how many colors you want, and click any swatch to copy its hex value. The whole process runs in your browser using the Canvas API, so your image is never uploaded to a server and nothing leaves your device.
What is the Image Color Extractor?
A digital image is just a grid of pixels, and each pixel carries a color described by three channels: red, green and blue, each from 0 to 255. A single photo can contain tens of thousands of slightly different colors, far too many to be useful as a palette. Extracting a palette means grouping all those near-identical colors together and reporting the handful that appear most often, so you end up with the few tones that actually define the look of the image.
This tool does that with a method called frequency bucketing on quantized RGB. First the image is drawn onto a small canvas (downscaled so the work stays fast), then every sampled pixel has its red, green and blue values rounded down into coarser bands. Rounding to 32 levels per channel, for example, merges colors that are visually almost the same into one bucket. The tool counts how many pixels fall into each bucket, averages the true colors inside the most-populated buckets, and shows those averages as your palette, ordered by how much of the image they cover.
Because the result is frequency based, the colors you see reflect area, not attention. A large plain background will dominate the palette even if a small bright logo is the part you care about. That is expected behaviour for this kind of extractor and is usually what you want for things like matching a theme to a photo. If a tiny accent color matters more to you than a big neutral one, increase the number of colors so the smaller regions still make the list, or crop the image to the region you care about before uploading.
Quantized bucketing is not the only way to build a palette. Some tools use an eyedropper, where you click one exact pixel and read its single color, and others use k-means clustering, which iteratively repositions cluster centers until they best fit the spread of colors in the image. An eyedropper is precise but only ever gives you one color at a time and requires you to guess where the interesting pixels are. K-means gives visually pleasing clusters but is slower and can shift slightly between runs on the same image. Quantized bucketing sits between the two: it is fast enough to run entirely in the browser on a full photo, deterministic (the same image always produces the same palette), and it reports a real coverage percentage for every swatch, which neither a simple eyedropper nor most k-means implementations expose by default.
Once you have a palette, the next step is usually deciding how the colors work together. In color theory, complementary colors sit opposite each other on the color wheel and create strong contrast, analogous colors sit next to each other and feel calm and cohesive, and triadic colors are spaced evenly around the wheel for a balanced but vibrant look. An extracted palette from a real photo will not always map neatly onto one of these schemes, but knowing the relationship between your top two or three swatches (are they close in hue, or nearly opposite) tells you whether the image reads as harmonious or high-contrast, and helps you decide which extracted tone to use as a background versus which to reserve for an accent or call-to-action button.
When to use it
- Building a website or brand color scheme that matches a hero photo or product shot.
- Pulling the exact hex codes out of a logo or screenshot when you do not have the source file.
- Creating a design palette from a piece of artwork, a moodboard image or a landscape photo.
- Checking which colors dominate a marketing image before choosing accent or text colors.
- Extracting a seasonal or nature-inspired palette from a photo for a mood board, print or packaging project.
- Generating hex codes to plug straight into CSS custom properties or a design tool without opening separate software.
How to use the Image Color Extractor
- Click "Choose an image" and select a PNG, JPG, GIF, WebP or BMP file.
- Set how many colors you want in the palette (2 to 16).
- Pick a sampling quality: high reads every pixel, fast reads fewer for speed.
- Read off the dominant colors with their hex codes and the percentage of the image each covers.
- Click any swatch to copy its hex code to the clipboard.
Formula & method
Worked examples
An image is mostly sky blue with a small patch of grass green.
- The canvas is sampled and each pixel is quantized to 32 levels per channel.
- Most pixels land in a blue bucket, far fewer land in a green bucket.
- Buckets are sorted by pixel count: blue first, green second.
- The true RGB values in the blue bucket are averaged to, say, (110, 170, 230) = #6EAAE6.
- Coverage: if 8,000 of 10,000 sampled pixels are blue, that swatch shows 80.0%.
Result: Palette led by #6EAAE6 at 80.0% coverage, with the green tone listed below it.
Converting a single pixel value of RGB (200, 100, 50) into a bucket key.
- qr = floor(200 ÷ 8) = 25
- qg = floor(100 ÷ 8) = 12
- qb = floor(50 ÷ 8) = 6
- key = (25 x 1024) + (12 x 32) + 6 = 25600 + 384 + 6 = 25990
- Any pixel within the same 8-wide band on each channel maps to this same key.
Result: The pixel joins bucket 25990, where it is grouped with visually identical neighbors.
A PNG logo with a solid mark on a transparent background.
- The canvas reads each sampled pixel’s alpha channel along with red, green and blue.
- Pixels with alpha below the opacity threshold (the transparent corners) are skipped entirely.
- Only the opaque pixels that make up the actual logo artwork are quantized and bucketed.
- The two or three ink colors of the logo dominate the result instead of being drowned out by empty space.
Result: A short palette of just the logo’s real colors, for example #E63946 and #1D3557, with no white or gray from the transparent background.
What the sampling quality setting does
| Setting | Pixels read | Best for |
|---|---|---|
| High | Every sampled pixel | Small images or maximum accuracy |
| Medium | 1 in every 2 | A good balance of speed and accuracy (default) |
| Fast | 1 in every 4 | Large images where speed matters most |
Common color formats you can build from an extracted hex code
| Format | Example | Notes |
|---|---|---|
| HEX | #6EAAE6 | Six hex digits, the format this tool copies |
| RGB | rgb(110, 170, 230) | Each channel as a 0 to 255 value |
| HSL | hsl(210, 71%, 67%) | Hue, saturation, lightness |
Basic color harmony rules for using an extracted palette
| Harmony | How the colors relate | Typical use |
|---|---|---|
| Complementary | Two hues roughly opposite on the color wheel | High-contrast accents, buttons, calls to action |
| Analogous | Hues that sit next to each other on the wheel | Calm, cohesive backgrounds and gradients |
| Triadic | Three hues spaced evenly around the wheel | Balanced but vibrant brand palettes |
| Monochromatic | One hue at different lightness or saturation levels | Minimal, understated UI and print layouts |
Common mistakes to avoid
- Expecting the brightest color to top the palette. The palette is ranked by how much area a color covers, not how eye-catching it is. A big muted background will outrank a small vivid accent. Raise the color count or crop to the area you care about.
- Treating an averaged swatch as an exact pixel. Each swatch is the average of many similar pixels, not a single sampled point. It represents a region of the image, so it may differ slightly from any one pixel you eyedrop manually.
- Uploading a huge image and expecting per-pixel reading. The image is downscaled to about 200 pixels on its long side before sampling, which keeps it fast and barely changes the dominant colors. Fine detail in a very large image will not each get its own swatch.
- Forgetting that transparent areas are skipped. Mostly transparent pixels (like the empty corners of a PNG logo) are ignored so they do not pollute the palette with the page background. Only opaque pixels are counted.
- Assuming the extracted hex codes are print-ready. Extracted values are screen RGB, read from how your monitor renders the image, not CMYK ink values. For packaging or offset print, convert the hex to CMYK and check it against a physical proof or Pantone book, since screen and print colors do not always match.
- Using an extracted color as text without checking contrast. A color that looks great as a background swatch can fail accessibility guidelines when used as small text on a light or dark surface. Run any extracted foreground and background pairing through a contrast checker before locking it into a design.
Glossary
- Hex code
- A color written as a hash followed by six hexadecimal digits, two each for red, green and blue, for example #6EAAE6.
- RGB
- The red, green and blue channels that make up a screen color, each ranging from 0 to 255.
- HSL
- A color model expressed as hue (position on the color wheel), saturation and lightness, often easier for humans to reason about than raw RGB.
- Dominant color
- A color that covers a large share of an image, found by counting how many pixels fall near it.
- Quantization
- Rounding precise color values into a smaller set of bands so near-identical colors are treated as one.
- Bucket
- A group that collects all pixels sharing the same quantized color, used here to count frequency.
- Canvas
- The browser drawing surface this tool uses to read an image’s pixels without sending it anywhere.
- Eyedropper tool
- A picker that reads the exact color of one clicked pixel, as opposed to a palette extractor that summarizes many pixels at once.
Frequently asked questions
How does the image color extractor find the dominant colors?
It draws your image onto a small canvas, samples the pixels, and rounds each pixel’s red, green and blue values into coarse bands so near-identical colors group together. It counts how many pixels fall into each group and shows the most common ones as your palette, ranked by how much of the image they cover.
Is my image uploaded to a server?
No. The image is read and analysed entirely in your browser using the FileReader and Canvas APIs. Nothing is sent over the network, so the tool works offline and your photo never leaves your device.
How many colors should I extract?
Start with the default of 8. Use fewer (4 to 6) for a tight, brand-style palette, or more (10 to 16) when an image has many distinct regions and you want smaller accent colors to appear as well.
Why is a color I can clearly see missing from the palette?
The palette is ranked by area, so a small but vivid color can be pushed out by larger, plainer regions. Increase the number of colors, or crop the image down to the part you care about, and the missing tone should appear.
What image formats can I use?
Any common raster format your browser can decode, including PNG, JPG, GIF, WebP and BMP. Animated GIFs are read as their first frame. Some SVG files may be blocked from pixel reading by the browser for security reasons.
Can I copy the colors in RGB or HSL instead of hex?
The tool copies hex codes, which are the most widely supported format. You can convert any copied hex into RGB or HSL with a color converter, since hex, RGB and HSL all describe the same color in different notations.
What is the difference between this extractor and an eyedropper tool?
An eyedropper reads one exact pixel you click on, which is precise but limited to a single point. This extractor samples the whole image, groups similar pixels, and reports the colors that cover the most area, so it summarizes the image rather than reading one spot.
Can I use the extracted colors for print or packaging?
The hex and RGB values describe how the color looks on a screen, not CMYK ink. For print work, convert the extracted hex to CMYK and compare it against a physical proof, since screen colors and printed colors do not always match exactly.
Does it work on screenshots and logos with transparent backgrounds?
Yes. Screenshots are read like any other photo, and for logos or PNGs with transparency, fully or mostly transparent pixels are skipped so only the opaque artwork contributes to the palette.
How do I know if two extracted colors have enough contrast to use together as text and background?
This tool reports color and coverage, not contrast. Take any two hex codes you plan to pair (for example a background and a text color) and check them in a WCAG contrast checker, which will tell you whether the pairing meets accessibility standards for readable text.