This commit introduces several new files to enhance project organization and developer onboarding. The `.cursorignore` and `.cursorrules` files provide guidelines for Cursor AI, while `CONTRIBUTING.md` outlines contribution procedures. Additionally, `IMPORT_FIX_SUMMARY.md`, `RESTRUCTURE_SUMMARY.md`, and `STATUS.md` summarize recent changes and project status. The `README.md` has been updated to reflect the new project focus and structure, ensuring clarity for contributors and users. These additions aim to improve maintainability and facilitate collaboration within the PunimTag project.
1.8 KiB
1.8 KiB
Meeting Notes
2025-10-15: Project Restructuring
Attendees
- Development Team
Discussion
- Agreed to restructure project for better organization
- Adopted standard Python project layout
- Separated concerns: core, gui, utils, tests
- Created .notes directory for project management
Decisions
- Move all business logic to
src/core/ - Move all GUI components to
src/gui/ - Move utilities to
src/utils/ - Consolidate tests in
tests/ - Move documentation to
docs/ - Archive legacy code instead of deleting
Action Items
- Create new directory structure
- Move files to appropriate locations
- Create init.py files for packages
- Create project notes
- Update import statements
- Test all functionality
- Update documentation
2025-10-15: DeepFace Migration Planning
Attendees
- Development Team
Discussion
- Analyzed test_deepface_gui.py results
- DeepFace shows better accuracy than face_recognition
- ArcFace model recommended for best results
- RetinaFace detector provides best face detection
Decisions
- Migrate from face_recognition to DeepFace
- Use ArcFace model (512-dim encodings)
- Use RetinaFace detector as default
- Support multiple detector backends
- No backward compatibility - fresh start
Action Items
- Document migration plan
- Create architecture document
- Update database schema
- Implement DeepFace integration
- Create migration script
- Test with demo photos
Technical Notes
- Encoding size: 128 → 512 dimensions
- Similarity metric: Euclidean → Cosine
- Location format: tuple → dict
- Tolerance adjustment: 0.6 → 0.4
Future Topics
- Web interface design
- Cloud storage integration
- Performance optimization
- Multi-user support
- Mobile app development