🎨 Image Color Palette Extractor
Browser-based color palette extractor that analyzes image pixels using Canvas API and k-means clustering to identify 5-10 dominant colors. Perfect for designers, artists, and AI prompt creators who need quick color inspiration.
Color Palette:
How to Use This Image Color Palette Extractor
Upload any image to instantly extract its dominant color palette for design inspiration, AI art prompts, or creative projects.
- Choose your image - Upload PNG, JPG, WebP, or GIF files (under 5MB recommended for speed)
- Select color count - Choose between 5-10 colors based on your needs (6 colors work well for most projects)
- Extract palette - Click "Extract Colors" to analyze your image using k-means clustering
- Use the results - Copy HEX codes for web design or color names for AI image prompts
- Save or share - Download the palette as an image or copy individual color codes
Perfect for designers, artists, and AI prompt creators who need quick color inspiration from existing images.
How It Works
This color palette extractor uses advanced computer vision techniques to analyze your images entirely in your browser.
- Image Processing - Your image is loaded into an HTML5 Canvas element for pixel-level analysis
- Color Sampling - The algorithm strategically samples pixels across the image to represent the full color distribution
- K-Means Clustering - Mathematical clustering groups similar colors together and identifies dominant hues
- Color Optimization - The system selects the most representative color from each cluster for your palette
- Format Output - Results are provided as HEX codes and descriptive color names for various use cases
All processing happens locally in your browser using the Canvas API - no images are uploaded to servers, ensuring your privacy and providing instant results.
When You Might Need This
- • Extract color palettes from photos for interior design projects
- • AI art prompts - get specific color names for Midjourney, DALL-E, Stable Diffusion
- • Web design inspiration by analyzing competitor websites or mood boards
- • Brand color discovery from logo images and marketing materials
- • Digital art reference - match colors from photographs in digital paintings
- • Fashion design coordination using colors extracted from fabric patterns
- • Create CSS color variables from design mockups and UI screenshots
- • Photography post-processing to understand dominant color temperatures
- • Marketing material consistency by extracting brand colors from existing assets
- • Game development environment design using real-world location color palettes
Frequently Asked Questions
How accurate are the extracted colors compared to the original image?
The tool uses k-means clustering on sampled pixels to identify the most statistically dominant colors in your image. Accuracy depends on image quality and complexity - simple images with distinct color areas give more precise results than highly detailed or noisy images. The algorithm samples pixels efficiently to represent the overall color distribution.
What image formats are supported and what's the recommended file size?
The tool supports PNG, JPG, JPEG, WebP, and GIF formats through the browser's Canvas API. For optimal performance, keep images under 5MB. Larger images are automatically processed but may take longer. The tool works entirely in your browser without uploading files to servers.
Can I use these colors directly in AI image generation prompts?
Yes! The tool provides both HEX codes and descriptive color names perfect for AI prompts. For Midjourney, DALL-E, or Stable Diffusion, use the descriptive names like 'sea green' or 'burnt orange' rather than HEX codes, as AI models understand natural color descriptions better than technical codes.
Why might the extracted colors look different from what I see in the image?
The algorithm identifies the most mathematically dominant colors across the entire image, which may not always match colors that appear most prominent to human perception. Areas that seem visually important might be small in terms of total pixels. Additionally, monitor color calibration and lighting conditions can affect how colors appear.
How does the color extraction algorithm work?
The tool uses k-means clustering to group similar pixel colors together. It samples pixels from your image, analyzes their RGB values, and mathematically determines which colors appear most frequently. The algorithm then selects representative colors from each cluster, providing you with the most dominant hues in your image.