This commit introduces several new documentation files for the PunimTag Photo Viewer project, including an Executive Summary, Quick Start Guide, Complete Plan, and Architecture Overview. These documents provide a high-level overview, setup instructions, detailed project plans, and architectural diagrams to assist developers and decision-makers. The README has also been updated to include links to these new resources, ensuring easy navigation and access to essential information for users and contributors.
This commit introduces a new Help page to the PunimTag application, providing users with detailed guidance on various features and workflows. The navigation has been updated to include the Help page, improving accessibility to support resources. Additionally, the user guide has been refined to remove outdated workflow examples, ensuring clarity and relevance. The Dashboard page has also been streamlined for a cleaner interface. Documentation has been updated to reflect these changes.
This commit introduces a new user guide in the documentation, providing detailed instructions on using the PunimTag web application. The guide includes sections on getting started, navigation overview, page-by-page usage, workflow examples, and tips for best practices. This addition aims to enhance user experience by offering clear guidance on application features and functionalities.
This commit introduces a comprehensive analysis of pose modes and face width detection to enhance profile classification accuracy. New scripts have been added to analyze pose data in the database, check identified faces for pose information, and validate yaw angles. The PoseDetector class has been updated to calculate face width from landmarks, which serves as an additional indicator for profile detection. The frontend and API have been modified to include pose mode in responses, ensuring better integration with existing functionalities. Documentation has been updated to reflect these changes, improving user experience and accuracy in face processing.
This commit introduces a comprehensive auto-match automation plan that automates the face matching process in the application. Key features include the ability to automatically identify faces based on pose and similarity thresholds, with configurable options for auto-acceptance. The API has been updated to support new parameters for auto-acceptance and pose filtering, while the frontend has been enhanced to allow users to set an auto-accept threshold and view results. Documentation has been updated to reflect these changes, improving user experience and functionality.
This commit introduces a comprehensive face pose detection system utilizing the RetinaFace library to automatically classify face poses (yaw, pitch, roll) during image processing. The database schema has been updated to store pose information, including pose mode and angles. The face processing pipeline has been modified to integrate pose detection with graceful fallback mechanisms, ensuring compatibility with existing functionality. Additionally, new utility functions for pose detection have been added, along with unit tests to validate the implementation. Documentation has been updated to reflect these changes, enhancing the overall user experience and accuracy in face matching.
This commit introduces the Modify Identified workflow, allowing users to edit person information, view associated faces, and unmatch faces from identified people. The API has been updated with new endpoints for unmatching faces and retrieving faces for specific persons. The frontend includes a new Modify page with a user-friendly interface for managing identified persons, including search and edit functionalities. Documentation and tests have been updated to reflect these changes, ensuring reliability and usability.
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 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.
