This commit introduces an `install.sh` script that automates the installation of system dependencies, Python packages, and frontend dependencies for the PunimTag Web application. The script checks for required software versions, installs PostgreSQL and Redis on Ubuntu/Debian systems, sets up databases, creates a Python virtual environment, and installs necessary dependencies. Additionally, the README.md is updated to include installation instructions and prerequisites, highlighting the new automated installation option. This enhancement simplifies the setup process for users.
This commit introduces a new API for managing pending tag linkages, allowing admins to review and approve or deny user-suggested tags. The frontend has been updated with a new User Tagged Photos page for displaying pending linkages, including options for filtering and submitting decisions. Additionally, the Layout component has been modified to include navigation to the new page. Documentation has been updated to reflect these changes.
This commit introduces new API endpoints for cleaning up files and records related to pending photos. The frontend has been updated to include buttons for admins to trigger cleanup operations, allowing for the deletion of files from shared space and records from the pending_photos table. Additionally, the README has been updated with instructions for granting DELETE permissions on auth database tables, and a script has been added to automate this process. Documentation has been updated to reflect these changes.
This commit introduces PostgreSQL as the default database for the PunimTag application, along with a new `.env.example` file for configuration. A setup script for PostgreSQL has been added to automate the installation and database creation process. The README has been updated to reflect these changes, including instructions for setting up PostgreSQL and using the `.env` file for configuration. Additionally, the database session management has been enhanced to support PostgreSQL connection pooling. Documentation has been updated accordingly.
This commit introduces several enhancements to the photo identification and tagging functionalities. The Identify component now supports filtering by photo IDs, allowing users to view faces from specific photos. Additionally, the Tags component has been updated to include an unidentified face count for each photo, improving user awareness of untagged faces. The API has been modified to accommodate these new parameters, ensuring seamless integration with the frontend. Documentation has been updated to reflect these changes.
This commit enhances the README with detailed instructions on the automatic database initialization and schema compatibility between the web and desktop versions. It also introduces new API endpoints for managing unidentified faces and people, including listing, creating, and identifying faces. The schemas for these operations have been updated to reflect the new data structures. Additionally, tests have been added to ensure the functionality of the new API features, improving overall coverage and reliability.
This commit introduces the DeepFace integration for face processing, allowing users to configure detector backends and models through the new Process tab in the GUI. Key features include batch processing, job cancellation support, and real-time progress tracking. The README has been updated to reflect these enhancements, including instructions for automatic model downloads and handling of processing-intensive tasks. Additionally, the API has been expanded to support job management for face processing tasks, ensuring a robust user experience.
This commit enhances the README with detailed instructions for installing and starting Redis, including commands for various operating systems. It clarifies the automatic startup of the RQ worker with the FastAPI server and updates the project status for Phase 2 features. Additionally, a new script `run_api_with_worker.sh` is introduced to streamline the process of starting the FastAPI server alongside the RQ worker, ensuring a smoother setup for users. The worker now has a unique name to prevent conflicts during execution.
This commit introduces a new confidence calibration system that converts DeepFace distance values into actual match probabilities, addressing previous misleading confidence percentages. Key changes include the addition of calibration methods in `FaceProcessor`, updates to the `IdentifyPanel` and `AutoMatchPanel` to utilize calibrated confidence, and new configuration settings in `config.py`. The README has been updated to document these enhancements, ensuring users see more realistic match probabilities throughout the application.
This commit introduces a comprehensive EXIF orientation handling system to improve face processing accuracy. Key changes include the addition of an `exif_orientation` field in the database schema, updates to the `FaceProcessor` class for applying orientation corrections before face detection, and the implementation of a new `EXIFOrientationHandler` utility for managing image orientation. The README has been updated to document these enhancements, including recent fixes for face orientation issues and improved face extraction logic. Additionally, tests for EXIF orientation handling have been added to ensure functionality and reliability.
This commit introduces a quality filtering feature in the Identify Panel, allowing users to filter faces based on a quality score (0-100%). The panel now includes a slider for adjusting the quality threshold and displays the current quality percentage. Additionally, navigation functions have been updated to skip to the next or previous face that meets the quality criteria, improving the user experience during identification. The README has been updated to reflect these new features and enhancements.
This commit finalizes the migration from face_recognition to DeepFace across all phases. It includes updates to the database schema, core processing, GUI integration, and comprehensive testing. All features are now powered by DeepFace technology, providing superior accuracy and enhanced metadata handling. The README and documentation have been updated to reflect these changes, ensuring clarity on the new capabilities and production readiness of the PunimTag system. All tests are passing, confirming the successful integration.
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.
This commit improves the README documentation by detailing the new path handling capabilities, including support for both absolute and relative paths, and automatic path normalization. It also introduces a new section on the Dashboard GUI, highlighting features such as visual folder selection, path validation, and cross-platform compatibility. These updates aim to enhance user experience and clarify the functionality of the photo tagging application.
This commit updates the README to detail new functionalities in the Search GUI, including the ability to search for photos by name, date, tags, and to identify photos without faces or tags. A collapsible filters area has been added, featuring a folder location filter, browse and clear buttons, and tooltips for user guidance. The results display now includes sortable columns and additional interactive features such as a tag help icon and calendar picker, improving the overall user experience and search capabilities.
This commit updates the README to reflect significant enhancements in the Search GUI, including new functionalities for photo selection, tagging, and sorting. Users can now view and manage tags through a dedicated popup, select multiple photos for bulk tagging, and utilize sortable columns for better navigation. The workflow for searching and managing photos has been clarified, providing a comprehensive guide for users to efficiently utilize the updated features.
This commit introduces the DashboardGUI class, providing a user-friendly interface for launching core features of the PunimTag application. The dashboard includes placeholder buttons for scanning, processing, and identifying faces, along with options for folder input and batch processing. The PhotoTagger class is updated to integrate the new dashboard functionality, and the README is revised to include usage instructions for the new dashboard command. This enhancement aims to streamline user interactions and improve overall accessibility to key features.
This commit introduces the SearchGUI class, allowing users to search for photos by name through a user-friendly interface. The new functionality includes options to view search results, open photo locations, and display relevant information about matched individuals. The PhotoTagger class is updated to integrate this feature, and the README is revised to include usage instructions for the new search-gui command. Additionally, the search_faces method in SearchStats is enhanced to return detailed results, improving the overall search experience.
This commit introduces significant enhancements to the TagManagerGUI, including the ability to add and manage bulk tags for all photos in a folder. Users can now link tags in bulk, with clear distinctions between single and bulk linkage types. The GUI also features improved handling of pending tag changes, allowing for better tracking and management of tag states. Additionally, the README has been updated to reflect these new functionalities and provide usage instructions.
This commit introduces the TagManagerGUI class, which provides a comprehensive interface for managing photo tags within the PhotoTagger application. The new GUI preserves the legacy functionality while integrating into the refactored architecture, allowing users to add, edit, and delete tags efficiently. The PhotoTagger class is updated to utilize this new feature, streamlining the tag management process. Additionally, relevant documentation in the README has been updated to reflect these changes and provide usage instructions.
This commit introduces a comprehensive auto-identification feature within the PhotoTagger application, allowing users to automatically match unidentified faces against identified ones using a graphical user interface. The implementation includes database queries to fetch identified faces, a user-friendly display of potential matches, and options for selecting and saving identified faces. The GUI is designed for optimal performance and usability, ensuring a seamless experience. Additionally, the README has been updated to reflect this new functionality and provide usage instructions.
This commit introduces a new IdentifyGUI class for handling face identification within the PunimTag application. The GUI allows users to identify faces interactively, with features such as batch processing, date filtering, and a user-friendly interface for entering person details. The PhotoTagger class is updated to integrate this new functionality, enabling seamless face identification directly from the tagging interface. Additionally, enhancements to the calendar dialog and improved quit validation are included, ensuring a smoother user experience. The README is updated to reflect these new features and usage instructions.
This update introduces a new photo icon in the PhotoTagger interface, allowing users to click the camera icon on face images to open the original photo in their default image viewer. The feature includes cross-platform support for Windows, macOS, and Linux, ensuring proper window sizing and multiple viewer options. Tooltips are added for enhanced user guidance, and the README has been updated to reflect this new functionality and its usage.
This update introduces a comprehensive system for managing photo tags, including the ability to add and remove tags with pending changes tracked until saved. A new tag management dialog allows users to link tags intuitively, featuring a linkage icon for easy access. The interface now supports real-time updates and visual indicators for saved and pending tags, improving user experience. Additionally, the README has been updated to reflect these enhancements and provide clear documentation on the new functionalities.
Add functionality for deduplicating tags, parsing tag strings, and managing tag IDs within the PhotoTagger GUI. Introduce a tag management dialog for adding, editing, and deleting tags, ensuring a user-friendly interface for tag operations. Update the internal logic to utilize tag IDs for improved performance and reliability, while enhancing the README to reflect these significant changes in tag handling and management.
Enhance the tagging system by introducing a normalized structure with a separate `tags` table for unique tag definitions and a `phototaglinkage` table to manage the many-to-many relationship between photos and tags. Update the logic for inserting and retrieving tags to improve data integrity and prevent duplicates. Additionally, update the README to reflect these changes and document the new folder view features.
Introduce a new tag management interface with a file explorer-like design, allowing users to manage photo tags efficiently. Features include multiple view modes (list, icons, compact), resizable columns, and column visibility management through a right-click context menu. Update README to document the new functionality and usage instructions.
