chiaroscuro-forge 0.2.1

An intelligent image enhancement tool inspired by Renaissance techniques

Chiaroscuro Forge

An intelligent image enhancement tool inspired by Renaissance techniques. Features automatic parameter detection, advanced color preservation, quality metrics, and parallel batch processing. Perfect for photographers and developers seeking to transform ordinary images with artistic precision.

Features

• Intelligent Enhancement: Automatically analyzes image characteristics and applies optimal processing parameters
• Advanced Color Preservation: Maintains color fidelity while enhancing contrast and details
• Multiple Enhancement Methods: LAB, RGB, and ratio-based color processing modes
• Quality Metrics: Calculates SSIM, PSNR, MS-SSIM, and other perceptual quality scores
• Batch Processing: Process multiple images in parallel with detailed reporting
• Preset System: Save and reuse customized enhancement settings
• Application Types: Specialized processing for photography, documents, medical images, and art

Installation

Requirements

• Python 3.7+
• NumPy
• SciPy
• scikit-image

Install from PyPI

pip install chiaroscuro-forge

After installing, the CLI entrypoint is available as:

chiaroscuro-forge --help

Install from source

git clone https://github.com/MichailSemoglou/chiaroscuro-forge.git
cd chiaroscuro-forge
pip install -e .

Quick Start

Process a single image

chiaroscuro-forge input.jpg --output enhanced.jpg

Analyze an image and suggest parameters

chiaroscuro-forge input.jpg --analyze

Process multiple images in batch mode

chiaroscuro-forge "images/*.jpg" --output processed/ --batch

Create and use presets

# Save parameters as preset
chiaroscuro-forge input.jpg --analyze --save-preset my_preset

# Use preset to process images
chiaroscuro-forge input.jpg --output enhanced.jpg --preset my_preset

Command-Line Options

Input/Output

• image_path: Path to input image or glob pattern for batch processing
• --output, -o: Path for output image or directory for batch processing
• --batch, -b: Enable batch processing mode

Processing Parameters

• --application, -a: Application type (general, photography, medical, document, art)
• --preset: Name of a preset to use

Analysis Options

• --analyze: Analyze image and suggest parameters
• --analyze-batch: Analyze multiple images and suggest optimal parameters
• --compare: Compare different processing methods
• --compare-dir: Output directory for comparison results

Preset Management

• --save-preset: Save parameters as a preset
• --list-presets: List all available presets
• --preset-description: Description for the preset

Batch Processing Options

• --workers, -w: Number of parallel workers (default: 4)
• --skip-existing: Skip files that have already been processed
• --report: Generate a JSON report with processing results
• --log-file: Path to log file for batch processing

Examples

Basic Enhancement

chiaroscuro-forge photo.jpg --output enhanced.jpg

Custom Application Type

chiaroscuro-forge document.jpg --output enhanced.jpg --application document

Analyze and Process

chiaroscuro-forge photo.jpg --analyze --output enhanced.jpg

Compare Processing Methods

chiaroscuro-forge photo.jpg --compare

Batch Processing with Report

chiaroscuro-forge "photos/*.jpg" --output enhanced/ --batch --workers 8 --report

Python API

You can use Chiaroscuro Forge directly in your Python code:

Get Image Statistics

from chiaroscuro_forge import get_image_statistics

stats = get_image_statistics("photo.jpg")
print(f"Dimensions: {stats['dimensions']}")
print(f"Brightness: {stats['brightness']:.2f}")
print(f"Dynamic range: {stats['dynamic_range']:.2f}")
print(f"Contrast ratio: {stats['contrast_ratio']:.2f}")

Process an Image

from chiaroscuro_forge import process_image

processed, metrics = process_image(
    "input.jpg",
    output_path="enhanced.jpg",
    application_type="photography"
)
print(f"SSIM: {metrics['ssim']:.4f}")
print(f"PSNR: {metrics['psnr']:.2f} dB")

Analyze Image Characteristics

from chiaroscuro_forge import analyze_image_characteristics

analysis = analyze_image_characteristics("photo.jpg")
print(f"Suggested parameters: {analysis['suggested_params']}")

Development

The project is structured around core image processing functions with a focus on quality and customizability:

• get_image_statistics(): Returns comprehensive image statistics (dimensions, intensity, histogram, dynamic range, contrast)
• analyze_image_characteristics(): Extracts characteristics from images
• process_image(): Main processing function with numerous customizable parameters
• compare_processing_methods(): Compares different enhancement approaches
• batch_process_images(): Handles processing of multiple images

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.