semantiva-imaging 0.2.1

Image extension of Semantiva framework.

Semantiva Imaging

Overview

Semantiva Imaging is a comprehensive image processing extension for the Semantiva framework that provides production-ready tools for computer vision and scientific imaging workflows. Built with type safety and pipeline composability at its core, it offers seamless integration with industry-standard libraries like OpenCV while maintaining transparent, auditable processing chains.

The extension delivers enterprise-grade capabilities including multi-format I/O, OpenCV integration, N-channel image support, and advanced factory patterns for rapid processor development—all within Semantiva's dual-channel architecture that processes data and metadata simultaneously.

Links:

• Semantiva Imaging Repository
• Semantiva Framework

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Features

OpenCV Integration

• Factory-Generated Processors: Automatically generates OpenCV-based processors using the _create_opencv_processor factory
• Smart Channel Conversion: Handles BGR/RGB channel reordering transparently for OpenCV functions
• Signature Synthesis: Dynamically creates processors from OpenCV function signatures with proper parameter mapping
• Tuple Return Handling: Manages complex OpenCV return types with automatic unpacking and conversion

Comprehensive Data Types

• Single Channel: SingleChannelImage for 2D grayscale images and SingleChannelImageStack for 3D stacks
• Multi-Channel: RGBImage, RGBAImage, and generic NChannelImage with customizable channel metadata
• Stack Support: All image types available as stack variants for temporal or Z-axis data
• Smart Casting: Auto-cast 12-/16-bit arrays to float32 by default (disable with auto_cast=False)

Multi-Format I/O

• Standard Formats: PNG, JPEG, TIFF, NPZ with dedicated loader/saver classes
• Video Support: AVI video loading and saving with VideoRGBImageStackLoader and VideoRGBImageStackSaver
• Animated Media: Animated GIF support for both single-channel and RGBA image stacks
• Network Loading: HTTP/HTTPS image loading via UrlLoader factory pattern
• Parametric Generation: Synthetic image generation with TwoDGaussianSingleChannelImageGenerator

Advanced Processing

• Factory Systems: _create_nchannel_processor and _create_opencv_processor for dynamic processor generation
• Built-in Operations: Image arithmetic, filtering, normalization, cropping, and stack projections
• Scientific Analysis: Gaussian fitting probes including TwoDGaussianFitterProbe and TwoDTiltedGaussianFitterProbe
• Pre-built OpenCV Processors: Ready-to-use filters, edge detection, and morphological operations

Interactive Visualization

• Cross-Section Viewer: Dynamic exploration of 2D image cross-sections (ImageCrossSectionInteractiveViewer)
• Stack Animation: Animate image sequences with SingleChannelImageStackAnimator
• Projection Views: X-Y intensity projections with ImageXYProjectionViewer
• Standard Display: Both static and interactive image viewers for Jupyter notebook integration

Pipeline Integration

• Seamless Workflow: Full integration with Semantiva's YAML-based pipeline system
• Context-Aware Processing: Metadata flows alongside data for parameter-driven operations
• Modular Design: Mix and match processors in complex multi-stage workflows

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Installation

pip install semantiva semantiva-imaging

Quick Start: Parametric Image Generation Pipeline

Semantiva Imaging demonstrates advanced capabilities through parametric workflows that generate synthetic data, extract features, and perform model fitting—all within a unified pipeline architecture.

The following example generates a time-varying 2D Gaussian signal with parametric position, standard deviation, and orientation changes:

Run the complete pipeline:

semantiva run tests/pipeline_parametric_gaussian_fit.yaml -v

Pipeline highlights:

• Parametric Generation: Creates image stacks using symbolic expressions for signal parameters
• Feature Extraction: Extracts Gaussian parameters from each frame using TwoDTiltedGaussianFitterProbe
• Model Fitting: Fits polynomial models to temporal parameter evolution
• Context Flow: Metadata and parameters flow seamlessly alongside image data

This demonstrates Semantiva's dual-channel processing where data and metadata evolve together, enabling dynamic, parameter-driven workflows ideal for research and production environments.

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Codec-Dependent Classes

Some loader/saver classes in Semantiva Imaging depend on system-specific codecs, which may not be available or consistent across all environments. For detailed information about dependencies, risks, and recommendations, please refer to the Codec Dependencies Documentation.

License

Semantiva-imaging is released under the Apache License 2.0.