Skip to content

Changelog

All notable changes to the Hyperseed project.

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

0.1.0-alpha.3 - 2025-01-29

Added

  • Initial release of Hyperseed - Hyperspectral Seed Image Analysis Tool
  • Core functionality for hyperspectral image processing and analysis
  • Support for ENVI format hyperspectral data files
  • Comprehensive preprocessing pipeline with multiple methods:
  • Standard Normal Variate (SNV)
  • Smoothing (Savitzky-Golay, Gaussian, Moving Average, Median)
  • Baseline correction (Polynomial, Rubberband, ASLS)
  • Derivative computation (1st, 2nd order)
  • Normalization (Min-Max, Max, Area, Vector, Peak)
  • Multiplicative Scatter Correction (MSC)
  • Detrending
  • Advanced segmentation algorithms for seed detection:
  • Threshold-based segmentation (Otsu, adaptive, manual)
  • Watershed segmentation
  • Connected components analysis
  • Combined approach for robust detection
  • Spectral extraction and analysis capabilities:
  • Extract mean spectra from segmented regions
  • Statistical analysis of spectral signatures
  • Export to CSV and HDF5 formats
  • Reflectance calibration with white and dark references
  • Command-line interface (CLI) with multiple commands:
  • analyze: Full pipeline analysis
  • segment: Segmentation only
  • batch: Batch processing
  • config: Configuration management
  • info: System information
  • Comprehensive test suite with ~80% code coverage
  • Visualization tools for segmentation results
  • Support for batch processing of multiple datasets
  • Configuration system with presets (minimal, standard, advanced)
  • Validation metrics for segmentation quality (IoU, Dice, F1-score)

Features

  • Performance: Optimized for large hyperspectral datasets
  • Flexibility: Modular architecture allowing custom pipelines
  • Extensibility: Plugin-ready architecture for custom algorithms
  • Documentation: Comprehensive documentation and examples
  • Testing: Extensive test coverage ensuring reliability

Dependencies

  • Python >=3.10
  • NumPy, SciPy, scikit-learn, scikit-image
  • OpenCV for image processing
  • Pandas for data manipulation
  • Matplotlib for visualization
  • Click for CLI interface
  • Rich for enhanced terminal output

Known Issues

  • Some empty module directories reserved for future features
  • Visualization requires display environment (not suitable for headless servers without proper configuration)

Contributors

  • Nishad Thalhath - Lead Developer
  • Deepa Kasaragod - Contributor

Note

This is an alpha release (alpha.3). The API and features are still under active development and may change in future releases.