color-extract 0.0.3

Extract dominant colors from images using various K-Means clustering approaches

color-extract

A toolkit to extract dominant colors from images using various K-Means clustering approaches.

Example

Features

Extraction Methods

• Original K-Means: Standard K-Means clustering approach
• Aggressive Weighting: K-Means with aggressive saturation weighting to emphasize vibrant colors
• Vibrant Separate: Separate clustering for vibrant minority colors and base colors
• LAB Enhanced: LAB color space with saturation-weighted sampling for perceptually uniform clustering
• Multi-stage: Multi-stage extraction: vibrant colors first, then distinct base colors

Sorting

• Spatial sorting (left-to-right or top-to-bottom)
• Frequency-based sorting

Installation

pip install color-extract

Command Line Usage

# Basic extraction with default settings
color-extract image.jpg

# Extract 8 colors using the vibrant method
color-extract image.jpg -c 8 -m vibrant

# Compare all methods and define output folder
color-extract image.jpg -m all -o ./my-folder

CLI Options

usage: color-extract [options] image

Arguments:
  image                Path to the input image

Options:
  -h, --help           Show help message
  --colors, -c         Number of colors to extract (default: 6)
  --method, -m         Extraction method (default: lab)
  --output, -o         Output file path (default: ./output)
  --no-plot            Disable plot generation
  --sort               Color sorting: (default: x-axis)
  --max-dimension      Max dimension for downscaling (default: 64)
  --dpi                DPI for output plots (default: 150)

Output Examples

Console

┌─────────────────────────────────────┐
│ LAB Enhanced                        │
└─────────────────────────────────────┘
┌───────┬──────────┬──────────────────┐
│  ■■■  │ #277595  │ (39, 117, 149)   │
│  ■■■  │ #68b2c6  │ (104, 178, 198)  │
│  ■■■  │ #6c6963  │ (108, 105, 99)   │
│  ■■■  │ #394d4d  │ (57, 77, 77)     │
│  ■■■  │ #782722  │ (120, 39, 34)    │
│  ■■■  │ #102937  │ (16, 41, 55)     │
└───────┴──────────┴──────────────────┘
Result saved to output/colors_image_lab_6.png

Rendered Images

Aggressive Weighting	LAB Enhanced

Multi-stage	K-Means

Python API Usage

import color_extract
import numpy as np
from PIL import Image

# Simple extraction from file
colors = color_extract.extract_colors('image.jpg', method='lab', n_colors=5)
for color in colors:
    print(color_extract.rgb_to_hex(color))

# Use with numpy array
img = Image.open('image.jpg')
img_array = np.array(img)
colors = color_extract.extract_colors(img_array, method='aggressive')

# Advanced usage with visualization
from color_extract import plot_single_result, load_and_prepare_image

img, img_array = load_and_prepare_image('image.jpg')
colors = color_extract.extract_colors_lab_enhanced(img_array, n_colors=6)
sorted_colors = color_extract.sort_colors_by_spatial_position(img_array, colors)

# Generate visualization
plot_single_result(img, img_array, sorted_colors, 'LAB Enhanced', 'output.png')

API Reference

Main Functions

extract_colors(image, method='lab', n_colors=6, sort_by='x-axis')

Main convenience function for color extraction.

Parameters:

• image: File path (str) or numpy array (H, W, 3)
• method: Extraction method name
• n_colors: Number of colors to extract
• sort_by: Sorting method ('x-axis', 'y-axis', 'frequency')

Returns:

• List of RGB tuples

Individual Extraction Methods

Each method can be used directly for more control:

# Original K-Means
colors = extract_colors_kmeans_original(img_array, n_colors=6)

# LAB color space
colors = extract_colors_lab_enhanced(img_array, n_colors=6, saturation_boost=5.0)

# Aggressive saturation weighting
colors = extract_colors_weighted_aggressive(img_array, n_colors=6, saturation_boost=10.0)

# Separate vibrant colors
colors = extract_colors_vibrant_separate(img_array, n_colors=6, n_vibrant=3)

# Multi-stage extraction
colors = extract_colors_multistage(img_array, n_colors=6)

Utility Functions

# Color conversion
hex_color = rgb_to_hex((255, 128, 0))  # Returns '#ff8000'
rgb = hex_to_rgb('#ff8000')  # Returns (255, 128, 0)

# Spatial sorting
sorted_colors = sort_colors_by_spatial_position(img_array, colors, axis='x')

# Calculate statistics
stats = calculate_color_statistics(img_array, colors)

# Normalize arrays (useful for TouchDesigner)
normalized = normalize_image_array(array, input_range=(0, 1), output_range=(0, 255))

Visualization Functions

# Plot single result
fig = plot_single_result(img, img_array, colors, 'Method Name', 'output.png')

# Compare multiple methods
algorithms_dict = {
    'Method 1': colors1,
    'Method 2': colors2
}
fig = plot_comparison(img, img_array, algorithms_dict, 'comparison.png')

# Create simple palette image
palette_array = create_color_palette_image(colors, width=100, height=100)

Further Reading

• New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues
• Dominant Colors with (not just) K-Means