Image Color Extractor
Fast, accurate, and free online Image Color Extractor tool that runs directly in your browser.
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1Enter data
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2Click the button
The tool will immediately process your data in the browser. -
3Get the result
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Other tools you may find usefulColor extractor from image - palette from any photo
Do you want to know what colors dominate in the photo? The extractor analyzes the image and extracts the N most representative colors. Useful for creating design palettes from photography, branding and generating harmonious color combinations.
Color extraction algorithms
k-means clustering: divides pixels into K clusters of similar colors - cluster centers are the dominant colors. Fast, but results depend on initialization. Median Cut: recursively divides the color space into boxes - deterministic. Octree quantization: octree pixel tree – fast for large images. Result: 3-16 colors depending on settings.
Extraction parameters
Number of colors (K): 3 (dominant palette), 6 (full palette), 12 (detailed). Extreme brightness suppression: Optional exclusion of very bright (white) and very dark (black) pixels. Sampling: 100% pixels (accurate, slow) or 10% random (fast). Color space: RGB (default) or LAB (more perceptual).
Palette export formats
HEX list: #FF5733, #3498DB, #2ECC71. CSS variables: --color-1: #FF5733; --color-2: #3498DB;. JSON: [{"hex":"#FF5733","rgb":[255,87,51],"percentage":32.5}]. SVG palette (visualization). Figma: Copy HEX and paste into Color Styles. Tailwind: config object.
Applications of color extraction
Brand colors with logo: upload your logo and extract official brand colors. Palette from photography: a consistent color scheme for the design. Competitor analysis: what colors company X uses. Auto background: match the page background to the main color of the product photo. Art direction: palette for the entire hero image project.
FAQ
How accurate is the k-means algorithm for colors?
K-means gives good results for K=6-12 and a sufficient number of pixels. Problem: Random initialization may produce different results each time it is run. Solution: k-means++ (deterministic initialization) or run multiple times and choose the best one. For logos (few colors, sharp edges): median cut is better.
How to extract colors from a photo in Python?
from PIL import Image; from sklearn.cluster import KMeans; img = Image.open("photo.jpg").convert("RGB"); pixels = list(img.getdata()); kmeans = KMeans(n_clusters=6).fit(pixels); colors = kmeans.cluster_centers_. The colorthief package (pure Python, similar approach) is easier to use.
Does the tool upload photos to the server?
No – processing is done locally via Canvas API (JavaScript). The photo is loaded into the browser's memory, the pixels are read by ctx.getImageData(). No upload to the server. Privacy: Your photos don't leave your device.
How to use a palette from a photo in Figma?
Copy HEX colors from the tool. In Figma: Resources → Colors → + (add). Or: create rectangles with each color, check → Styles → Create Style. Figma Palette plugin (community) can import directly from URL.
How to select gradients based on extracted colors?
Take 2 adjacent colors from the palette (color 1 and color 2). Use the HEX color blender tool to generate transitions. OKLCH space: a gradient with oklch() in CSS gives a natural transition. Example: linear-gradient(to right, oklch(from #FF5733 l c h), oklch(from #3498DB l c h)).
Related tools: HEX color blender, color scheme tester and color naming tool.