Advanced color extraction and analysis tool with machine learning capabilities, comprehensive export options, and professional design insights.
This is a complete rewrite and massive upgrade from the original tool, featuring:
- Multiple extraction algorithms: K-means, DBSCAN, Median Cut, Octree, Histogram analysis
- Advanced color analysis: Harmony detection, accessibility scoring, emotional analysis
- Professional insights: Brand personality analysis, color temperature, vibrance metrics
- Modern architecture: Modular design with proper packaging and type hints
- Rich CLI interface with beautiful output and progress tracking
- 20+ export formats: JSON, CSS, SCSS, ASE, ACO, Swift, Kotlin, Python, and more
- Batch processing with parallel execution for multiple images
- Color comparison between different images
- Color suggestions based on harmony rules and accessibility
- Comprehensive analysis: Color blindness simulation, contrast ratios, luminance
- Color clustering with variance analysis
- Harmony detection: Complementary, triadic, analogous, split-complementary
- Accessibility analysis with WCAG compliance checking
- Brand personality insights based on color psychology
- Emotional associations and temperature analysis
# Clone and install directly from GitHub
git clone https://github.com/Kynlos/Python-Colour-Info-Extractor.git
cd Python-Colour-Info-Extractor
pip install -e .
# Install with all optional dependencies
pip install -e ".[gui,export,dev]"
# Or install directly from GitHub URL
pip install git+https://github.com/Kynlos/Python-Colour-Info-Extractor.git- Python 3.8 or higher
- pip (Python package installer)
- Git (for cloning the repository)
# Basic color extraction
pycolour-extract image.jpg
# Advanced analysis with multiple formats
pycolour-extract image.jpg --algorithm kmeans --export-formats json css scss html
# Full analysis with branding insights
pycolour-extract image.jpg --analyze-emotion --analyze-brand --analyze-accessibility
# Batch process a directory
pycolour-extract batch ./images --recursive --export-formats json html
# Compare color palettes
pycolour-extract compare image1.jpg image2.jpg image3.jpg
# Generate color suggestions
pycolour-extract suggest "#FF0000" "#00FF00" --suggestion-type harmonyfrom pycolour_extract import ColorExtractor, ColorAnalyzer
# Extract colors with advanced analysis
extractor = ColorExtractor(algorithm="kmeans", max_colors=16)
result = extractor.extract_colors("image.jpg")
# Access color data
colors = result.color_data.colors
dominant = result.color_data.dominant_color
harmonies = result.color_data.harmonies
# Perform additional analysis
analyzer = ColorAnalyzer()
emotions = analyzer.calculate_color_emotion(colors)
brand_personality = analyzer.analyze_brand_personality(colors)
# Export to various formats
from pycolour_extract.exporters import FormatExporter
from pycolour_extract.models.color_data import ExportFormat
exporter = FormatExporter()
exporter.export(result, ExportFormat.CSS, "colors.css")
exporter.export(result, ExportFormat.JSON, "analysis.json")- Best for: General purpose, balanced results
- Pros: Fast, reliable, good color separation
- Use case: Most images, product photography
- Best for: Images with noise, irregular color distributions
- Pros: Handles outliers well, finds natural groupings
- Use case: Artistic images, complex backgrounds
- Best for: Preserving important colors, uniform distribution
- Pros: Perceptually accurate, maintains color relationships
- Use case: Logos, graphics with important color relationships
- Best for: Fast processing, web applications
- Pros: Very fast, good for real-time applications
- Use case: Batch processing, web services
- Best for: Scientific analysis, peak detection
- Pros: Finds all significant colors, detailed analysis
- Use case: Research, detailed color studies
- Complementary: Colors opposite on the color wheel
- Triadic: Three evenly spaced colors
- Analogous: Adjacent colors on the wheel
- Split-Complementary: Base color + two colors adjacent to complement
- Tetradic: Four colors forming a rectangle
- WCAG Compliance: Automatic contrast ratio checking
- Color Blindness Simulation: Protanopia, Deuteranopia, Tritanopia
- Accessibility Scoring: Overall accessibility rating
- Recommendations: Suggestions for improvement
Analyzes colors to determine brand personality traits:
- Modern vs Traditional
- Professional vs Casual
- Bold vs Subtle
- Masculine vs Feminine
- Youthful vs Mature
Calculates emotional impact based on color psychology:
- Energy & Excitement
- Calmness & Serenity
- Warmth & Comfort
- Sophistication & Luxury
- Trustworthiness & Reliability
- Creativity & Innovation
- CSS: Custom properties and utility classes
- SCSS/LESS/Stylus: Variables and mixins
- ASE: Adobe Swatch Exchange
- ACO: Adobe Color format
- GPL: GIMP Palette
- JavaScript: ES6 modules and CommonJS
- Python: Constants and dictionaries
- Swift: UIColor extensions
- Kotlin: Color constants
- Java: Color class definitions
- JSON: Complete analysis data
- CSV/TSV: Tabular color data
- XML: Structured analysis
- YAML: Human-readable format
- HTML: Interactive color report
- SVG: Scalable palette graphics
- PNG: Palette images
- PDF: Professional reports
from pycolour_extract import ColorExtractor, ColorAnalyzer
from pycolour_extract.models.color_data import ExportFormat
# Configure extractor
extractor = ColorExtractor(
algorithm="kmeans",
max_colors=20
)
# Extract with clustering
result = extractor.extract_colors(
"image.jpg",
n_clusters=5,
cluster_colors=True
)
# Advanced analysis
analyzer = ColorAnalyzer()
# Color relationships
relationships = analyzer.analyze_color_relationships(result.color_data.colors)
# Color blindness impact
cb_analysis = analyzer.calculate_color_blindness_impact(result.color_data.colors)
# Generate suggestions
suggestions = analyzer.generate_color_suggestions(
result.color_data.colors[:3],
suggestion_type="accessibility"
)
print(f"Found {len(result.color_data.colors)} colors")
print(f"Dominant color: {result.color_data.dominant_color.hex}")
print(f"Color temperature: {result.color_data.color_temperature}K")
print(f"Accessibility score: {result.color_data.accessibility_score}/1.0")from pathlib import Path
from concurrent.futures import ThreadPoolExecutor
from pycolour_extract import ColorExtractor
from pycolour_extract.exporters import FormatExporter
def process_image(image_path):
extractor = ColorExtractor(algorithm="kmeans", max_colors=12)
result = extractor.extract_colors(image_path)
# Export multiple formats
exporter = FormatExporter()
output_dir = image_path.parent / f"{image_path.stem}_analysis"
output_dir.mkdir(exist_ok=True)
exporter.export(result, ExportFormat.JSON, output_dir / "colors.json")
exporter.export(result, ExportFormat.CSS, output_dir / "colors.css")
exporter.export(result, ExportFormat.HTML, output_dir / "report.html")
return result
# Process multiple images in parallel
image_dir = Path("./images")
image_files = list(image_dir.glob("*.jpg")) + list(image_dir.glob("*.png"))
with ThreadPoolExecutor(max_workers=4) as executor:
results = list(executor.map(process_image, image_files))
print(f"Processed {len(results)} images")PyColour Extract 2.0 incorporates advanced color theory:
- RGB: Standard display colors
- HSV: Hue, Saturation, Value
- HSL: Hue, Saturation, Lightness
- LAB: Perceptually uniform color space
- LCH: Lightness, Chroma, Hue
- XYZ: CIE color space
- Delta E CIE2000: Most accurate color difference
- Delta E CIE1994: Industry standard
- Delta E CIE1976: Simple Euclidean distance
- RGB Euclidean: Fast approximation
- Color Temperature: Warm/cool analysis in Kelvin
- Vibrance: Saturation intensity measurement
- Luminance: Perceptual brightness
- Contrast Ratios: WCAG accessibility compliance
Create a .pycolour-extract.json config file:
{
"default_algorithm": "kmeans",
"max_colors": 16,
"export_formats": ["json", "css", "html"],
"analyze_emotion": true,
"analyze_brand": true,
"analyze_accessibility": true,
"output_template": "{filename}_analysis"
}from pycolour_extract.config import Config
config = Config(
algorithm="dbscan",
max_colors=20,
enable_clustering=True,
cluster_count=5,
analyze_harmony=True,
calculate_emotions=True
)
extractor = ColorExtractor.from_config(config)# Run tests
pytest
# Run with coverage
pytest --cov=pycolour_extract --cov-report=html
# Run specific test types
pytest tests/test_extraction.py -v
pytest tests/test_analysis.py -v
pytest tests/test_export.py -v- Small images (800x600): ~0.5-2 seconds
- Medium images (1920x1080): ~2-8 seconds
- Large images (4K+): ~8-30 seconds
- Use
algorithm="octree"for fastest processing - Reduce
max_colorsfor better performance - Enable parallel processing for batch operations
- Use
quiet=Trueto disable progress output
- Brand color extraction from logos and marketing materials
- Mood board creation with automatic color harmonies
- Accessibility auditing for web and print design
- Color trend analysis across image collections
- Theme generation for applications and websites
- Asset optimization with dominant color backgrounds
- Color-based image tagging and categorization
- Automated style guide creation
- Color psychology studies with emotional analysis
- Cultural color analysis across different regions
- Product color research for market analysis
- Scientific color measurement and documentation
- Product color extraction for search and filtering
- Automatic color tagging for inventory systems
- Color-based recommendations for similar products
- Brand compliance checking across product lines
| Feature | Original (v1) | PyColour Extract 2.0 |
|---|---|---|
| Installation | Single Python file | Professional package from GitHub |
| Algorithms | 1 (basic pixel analysis) | 5 (advanced ML: K-means, DBSCAN, etc.) |
| Export Formats | 2 (txt, png) | 20+ (CSS, SCSS, JSON, ASE, PDF, etc.) |
| Analysis | Basic RGB stats | Professional color theory & psychology |
| Performance | Single-threaded | Multi-threaded with parallel processing |
| Interface | Drag & drop only | Rich CLI + Python API |
| Color Theory | Basic RGB/HSV | Advanced (LAB, LCH, Delta E, etc.) |
| Accessibility | None | Full WCAG analysis & color blindness |
| Batch Processing | None | Directory processing with progress |
| Documentation | Basic README | Comprehensive docs & examples |
| Code Quality | Script-style | Modern packaging with type hints |
# Clone the repository
git clone https://github.com/Kynlos/Python-Colour-Info-Extractor.git
cd Python-Colour-Info-Extractor
# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install in development mode with all dependencies
pip install -e ".[dev]"
# Install pre-commit hooks (optional)
pre-commit install
# Run tests
pytestIf you're new to the project:
# 1. Make sure you have Python 3.8+ and Git installed
python --version
git --version
# 2. Clone and enter the project directory
git clone https://github.com/Kynlos/Python-Colour-Info-Extractor.git
cd Python-Colour-Info-Extractor
# 3. Install the package
pip install -e .
# 4. Test the installation
pycolour-extract --version
# 5. Try it with an image
pycolour-extract path/to/your/image.jpgsrc/pycolour_extract/
├── __init__.py # Package initialization
├── cli.py # Command-line interface
├── config.py # Configuration management
├── core/ # Core functionality
│ ├── color_extractor.py # Color extraction algorithms
│ ├── color_analyzer.py # Color analysis and theory
│ └── palette_generator.py # Palette generation
├── models/ # Data models
│ └── color_data.py # Color data structures
├── exporters/ # Export functionality
│ └── format_exporter.py # Multi-format export
├── gui/ # GUI components (optional)
│ └── main_window.py # Main GUI application
└── utils/ # Utility functions
├── color_utils.py # Color manipulation utilities
└── image_utils.py # Image processing utilities
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
- New algorithms: Additional color extraction methods
- Export formats: Support for more design tools
- Analysis features: Advanced color theory implementations
- Performance: Optimization and GPU acceleration
- Documentation: Tutorials and examples
- GUI improvements: Enhanced user interface
This project is licensed under the MIT License - see the LICENSE file for details.
- Original PyColour Extract: Foundation and inspiration
- scikit-learn: Machine learning algorithms
- Pillow: Image processing capabilities
- colormath: Color space conversions and theory
- Rich: Beautiful command-line interface
- Color theory research: Academic papers and industry standards
- GUI Application: Desktop application with real-time preview
- Web Interface: Browser-based color extraction tool
- API Service: REST API for integration with other tools
- Mobile App: Color extraction on mobile devices
- AI-Powered Analysis: Machine learning for color trend prediction
- Cloud Processing: Scalable cloud-based extraction
- Real-time Processing: Webcam and video color extraction
- Advanced Clustering: Deep learning-based color grouping
- Multi-modal Analysis: Text, audio, and color correlation
- Augmented Reality: AR color extraction and overlay
- Enterprise Features: Team collaboration and brand management
- API Integrations: Direct integration with design tools
PyColour Extract 2.0 - Transform your color analysis workflow with professional-grade tools and insights.
For support, feature requests, or contributions, visit our GitHub repository.
-
Clone the repository:
git clone https://github.com/Kynlos/Python-Colour-Info-Extractor.git cd Python-Colour-Info-Extractor -
Install the package:
pip install -e . -
Test with an image:
pycolour-extract your-image.jpg
-
Explore advanced features:
pycolour-extract --help
The tool will create an analysis directory with your results in multiple formats!