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Changelog

All notable changes to image_toolkit will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

0.1.0 - 2026-01-17

Added

Core Image Processing

  • Fluent API design for intuitive image manipulation workflows
  • ImageHandler class for single-image operations with method chaining
  • BatchImageHandler class for parallel batch processing of multiple images
  • Context manager support (with statement) for automatic resource cleanup
  • Automatic format detection and validation

Image Transformations

  • resize() - Resize images with custom dimensions
  • resize_aspect() - Aspect-ratio preserving resize with intelligent cropping
  • thumbnail() - Generate thumbnails with multiple sizing strategies (fit, cover, stretch)
  • crop() - Extract image regions with coordinate-based cropping
  • rotate() - Rotate images by arbitrary angles with automatic dimension adjustment
  • flip() - Horizontal and vertical image flipping
  • adjust_brightness() - Brightness adjustment with clamping
  • adjust_contrast() - Contrast enhancement/reduction
  • adjust_saturation() - Color saturation control
  • grayscale() - Color to grayscale conversion
  • blur() - Gaussian blur with configurable radius

Format Conversion & Compression

  • Support for JPEG, PNG, WebP, TIFF, BMP formats
  • convert() - Format conversion with quality control
  • optimize() - File size optimization with configurable quality/compression
  • Progressive JPEG encoding support
  • WebP format support for modern web applications

Analysis & Information

  • get_info() - Extract comprehensive image metadata (dimensions, format, mode, size)
  • analyze_duplicates() - Perceptual hash-based duplicate detection across image sets
  • Statistical analysis capabilities for image properties

Machine Learning Integration

  • to_tensor() - Convert PIL Images to PyTorch tensors with normalization
  • from_tensor() - Convert PyTorch tensors back to PIL Images
  • Support for ImageNet normalization presets
  • Custom normalization parameter support
  • Automatic channel reordering (CHW ↔ HWC)

Performance Features

  • TurboJPEG acceleration for JPEG operations (3-5x speedup over PIL)
  • Parallel batch processing with configurable worker pools
  • Memory-efficient streaming for large image sets
  • Lazy evaluation support for transformation pipelines
  • Intelligent fallback mechanisms when TurboJPEG unavailable

Error Handling & Validation

  • Comprehensive error handling with descriptive messages
  • Input validation for all transformation parameters
  • Graceful degradation when optional dependencies missing
  • Type hints throughout codebase for better IDE support

Performance

  • JPEG Operations: 3-5x speedup using TurboJPEG vs PIL for decode/encode operations
  • Batch Processing: Near-linear scaling with CPU cores for parallel image processing
  • Memory Efficiency: Streaming batch processing prevents memory exhaustion on large datasets
  • Perceptual Hashing: Fast duplicate detection using pHash algorithm (average hashing)

Documentation

  • Comprehensive README.md with quick start guide and feature overview
  • Detailed README_detailed.md with extensive examples and use cases
  • Architecture documentation (docs/ARCHITECTURE.md) explaining design philosophy
  • API reference documentation for all public methods
  • Installation instructions for core and optional dependencies
  • Performance comparison benchmarks
  • Machine learning integration examples
  • Batch processing usage patterns

Testing

  • 15+ test modules covering all major functionality:
    • test_basic.py - Core functionality tests
    • test_transformations.py - Image transformation operations
    • test_batch.py - Batch processing and parallelization
    • test_formats.py - Format conversion and compatibility
    • test_ml_integration.py - PyTorch tensor operations
    • test_analysis.py - Image analysis and duplicate detection
    • test_error_handling.py - Error conditions and edge cases
    • test_context_manager.py - Resource management
    • test_turbojpeg.py - TurboJPEG acceleration
    • test_optimization.py - Image optimization
    • Additional specialized test suites
  • High test coverage across all modules
  • Integration tests for real-world workflows
  • Performance benchmarks

Dependencies

Core Dependencies

  • Python 3.8+
  • Pillow (PIL) 10.0.0+ - Image processing foundation
  • NumPy - Numerical operations and array handling

Optional Dependencies

  • PyTurboJPEG - Hardware-accelerated JPEG operations (3-5x faster)
  • PyTorch - Machine learning tensor integration
  • imagehash - Perceptual hashing for duplicate detection