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Changelog

Source: NEWS.md

ClassiPyR (development version)

Features

Sample Management

  • Load samples from ROI files with automatic year/month filtering
  • Support for validation mode (existing classifications) and annotation mode (new samples)
  • Resume previous annotations from saved MAT files
  • Navigate between samples with previous/next/random buttons
  • Filter samples by classification status (all/classified/annotated/unannotated)
  • Samples with both manual annotations AND auto-classifications can switch between modes

Classification Loading

  • Load classifications from CSV files (recursive folder search)
  • Load classifications from MATLAB classifier output (.mat files)
  • Option to apply classification threshold for MATLAB results
  • Automatic sample status indicators in dropdown:
    • ✎ = Has manual annotation
    • ✓ = Has auto-classification
    • ✎✓ = Has both (can switch between modes)
      • = Unannotated
  • Paginated image display (50/100/200/500 images per page)
  • Images grouped by class on consecutive pages for efficient review
  • Filter images by class
  • Click to select/deselect individual images
  • Drag-select to select multiple images at once
  • Visual indicators for selected and relabeled images
  • Unmatched class detection with yellow warning highlighting

Annotation Tools

  • Relabel selected images to any class
  • Select all / deselect all buttons
  • Quick class search in relabel dropdown
  • Changes tracked and displayed in statistics tab

Class List Management

  • Load class lists from .mat or .txt files
  • Create class lists from scratch directly in the app
  • Edit class names (with warnings about index preservation for ifcb-analysis)
  • Add new classes to end of list
  • Sort class list by ID or alphabetically (view only)
  • Export class list as .mat or .txt
  • Visual warnings for classes in classifications not in class2use list

Output

  • Save annotations as MATLAB-compatible .mat files (using iRfcb)
  • Save validation statistics as CSV (in validation_statistics/ subfolder)
  • Organize output PNGs by class folder (for CNN training)
  • Auto-save when navigating between samples

Settings & Persistence

  • Configurable folder paths via settings modal
  • Settings persisted between sessions (.classipyr_settings.json)
  • Class list file path remembered and auto-loaded on startup
  • Annotator name tracking for statistics

User Interface

  • Clean, modern interface using bslib (Flatly theme)
  • Mode indicator showing current sample and progress/accuracy
  • Validation Statistics tab shows appropriate content based on mode
  • Switch between annotation/validation modes for dual-mode samples

Technical Notes

  • Requires Python with scipy for MAT file writing (optional - only for ifcb-analysis compatibility)
  • Uses iRfcb package for IFCB data handling
  • Session cache preserves work when switching samples
  • Security: Input validation, XSS prevention, path traversal protection

Development

This application was developed through human-AI collaboration: - Anders Torstensson: Project vision, requirements, testing, and guidance - Claude Code (Anthropic): Implementation, code generation, and iterative refinement