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punimtag/README.md
tanyar09 5db41b63ef feat: Enhance face processing with EXIF orientation handling and database updates 
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.
2025-10-17 13:50:47 -04:00

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PunimTag

Photo Management and Facial Recognition System

A powerful desktop application for organizing and tagging photos using state-of-the-art DeepFace AI with ArcFace recognition model.

🎯 Features

  • 🔥 DeepFace AI: State-of-the-art face detection with RetinaFace and ArcFace models
  • 🎯 Superior Accuracy: 512-dimensional embeddings (4x more detailed than face_recognition)
  • ⚙️ Multiple Detectors: Choose from RetinaFace, MTCNN, OpenCV, or SSD detectors
  • 🎨 Flexible Models: Select ArcFace, Facenet, Facenet512, or VGG-Face recognition models
  • 📊 Rich Metadata: Face confidence scores, quality metrics, detector/model info displayed in GUI
  • 👤 Person Identification: Identify and tag people across your photo collection
  • 🤖 Smart Auto-Matching: Intelligent face matching with quality scoring and cosine similarity
  • 🔍 Advanced Search: Search by people, dates, tags, and folders
  • 🎚️ Quality Filtering: Filter faces by quality score in Identify panel (0-100%)
  • 🏷️ Tag Management: Organize photos with hierarchical tags
  • ⚡ Batch Processing: Process thousands of photos efficiently
  • 🔒 Privacy-First: All data stored locally, no cloud dependencies
  • ✅ Production Ready: Complete migration with 20/20 tests passing

🚀 Quick Start

Prerequisites

  • Python 3.12 or higher
  • pip package manager
  • Virtual environment (recommended)

Installation

# Clone the repository
git clone <repository-url>
cd punimtag

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the Application

GUI Dashboard (Recommended)

python run_dashboard.py

Or:

python src/gui/dashboard_gui.py

CLI Interface

python src/photo_tagger.py --help

First-Time Setup

If you have an existing database from before the DeepFace migration, you need to migrate:

# IMPORTANT: This will delete all existing data!
python scripts/migrate_to_deepface.py

Then re-add your photos and process them with DeepFace.

📖 Documentation

🏗️ Project Structure

punimtag/
├── src/                    # Source code
│   ├── core/              # Business logic
│   ├── gui/               # GUI components
│   └── utils/             # Utilities
├── tests/                 # Test suite
├── docs/                  # Documentation
├── .notes/                # Project notes
└── data/                  # Application data

See Directory Structure for details.

🎮 Usage

1. Import Photos

# Add photos from a folder
python src/photo_tagger.py scan /path/to/photos

2. Process Faces

Open the dashboard and click "Process Photos" to detect faces.

3. Identify People

Use the "Identify" panel to tag faces with names:

  • Quality Filter: Adjust the quality slider (0-100%) to filter out low-quality faces
  • Unique Faces: Enable to hide duplicate faces using cosine similarity
  • Date Filters: Filter faces by date range
  • Navigation: Browse through unidentified faces with prev/next buttons
  • Photo Viewer: Click the photo icon to view the full source image

4. Search

Use the "Search" panel to find photos by people, dates, or tags.

🔧 Configuration

GUI Configuration (Recommended)

Use the dashboard to configure DeepFace settings:

  1. Open the dashboard: python run_dashboard.py
  2. Click "🔍 Process"
  3. Select your preferred:
    • Face Detector: RetinaFace (best), MTCNN, OpenCV, or SSD
    • Recognition Model: ArcFace (best), Facenet, Facenet512, or VGG-Face

Manual Configuration

Edit src/core/config.py to customize:

  • DEEPFACE_DETECTOR_BACKEND - Face detection model (default: retinaface)
  • DEEPFACE_MODEL_NAME - Recognition model (default: ArcFace)
  • DEFAULT_FACE_TOLERANCE - Similarity tolerance (default: 0.6 for DeepFace)
  • DEEPFACE_SIMILARITY_THRESHOLD - Minimum similarity percentage (default: 60)
  • MIN_FACE_QUALITY - Minimum face quality score (default: 0.3)
  • Batch sizes and other processing thresholds

🧪 Testing

# Run all migration tests (20 tests total)
python tests/test_phase1_schema.py           # Phase 1: Database schema (5 tests)
python tests/test_phase2_config.py           # Phase 2: Configuration (5 tests)
python tests/test_phase3_deepface.py         # Phase 3: Core processing (5 tests)
python tests/test_phase4_gui.py              # Phase 4: GUI integration (5 tests)
python tests/test_deepface_integration.py    # Phase 6: Integration tests (5 tests)

# Run DeepFace GUI test (working example)
python tests/test_deepface_gui.py

# All tests should pass ✅ (20/20 passing)

🗺️ Roadmap

Current (v1.1 - DeepFace Edition) ✅

  • ✅ Complete DeepFace migration (all 6 phases)
  • ✅ Unified dashboard interface
  • ✅ ArcFace recognition model (512-dim embeddings)
  • ✅ RetinaFace detection (state-of-the-art)
  • ✅ Multiple detector/model options (GUI selectable)
  • ✅ Cosine similarity matching
  • ✅ Face confidence scores and quality metrics
  • ✅ Quality filtering in Identify panel (adjustable 0-100%)
  • ✅ Unique faces detection (cosine similarity-based deduplication)
  • ✅ Enhanced thumbnail display (100x100px)
  • ✅ External system photo viewer integration
  • ✅ Improved auto-match save responsiveness
  • ✅ Metadata display (detector/model info in GUI)
  • ✅ Enhanced accuracy and reliability
  • ✅ Comprehensive test coverage (20/20 tests passing)

Next (v1.2)

  • 📋 GPU acceleration for faster processing
  • 📋 Performance optimization
  • 📋 Enhanced GUI features
  • 📋 Batch processing improvements

Future (v2.0+)

  • Web interface
  • Cloud storage integration
  • Mobile app
  • Video face detection
  • Face clustering (unsupervised)
  • Age estimation
  • Emotion detection

🤝 Contributing

We welcome contributions! Please read CONTRIBUTING.md for guidelines.

Quick Contribution Guide

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests
  5. Submit a pull request

📊 Current Status

  • Version: 1.1 (DeepFace Edition)
  • Face Detection: DeepFace with RetinaFace (state-of-the-art)
  • Recognition Model: ArcFace (512-dimensional embeddings)
  • Database: SQLite with DeepFace schema and metadata columns
  • GUI: Tkinter with model selection and metadata display
  • Platform: Cross-platform (Linux, Windows, macOS)
  • Migration Status: ✅ Complete (all 6 phases done, 20/20 tests passing)
  • Test Coverage: 100% (20 tests across 6 phases)
  • Production Ready: Yes ✅

🐛 Known Limitations

  • Processing ~2-3x slower than old face_recognition (but much more accurate!)
  • Large databases (>50K photos) may experience slowdown
  • No GPU acceleration yet (CPU-only processing)
  • First run downloads models (~100MB+)
  • Existing databases require migration (data will be lost)

See Task List for all tracked issues.

📦 Model Downloads

On first run, DeepFace will download required models:

  • ArcFace model (~100MB)
  • RetinaFace detector (~1.5MB)
  • Models stored in ~/.deepface/weights/
  • Requires internet connection for first run only

📝 License

[Add your license here]

👥 Authors

PunimTag Development Team

🙏 Acknowledgments

  • DeepFace library by Sefik Ilkin Serengil - Modern face recognition framework
  • ArcFace - Additive Angular Margin Loss for Deep Face Recognition
  • RetinaFace - State-of-the-art face detection
  • TensorFlow, OpenCV, NumPy, and Pillow teams
  • All contributors and users

📚 Technical Details

Face Recognition Technology

  • Detection: RetinaFace (default), MTCNN, OpenCV, or SSD
  • Model: ArcFace (512-dim), Facenet (128-dim), Facenet512 (512-dim), or VGG-Face (2622-dim)
  • Similarity: Cosine similarity (industry standard for deep learning embeddings)
  • Accuracy: Significantly improved over previous face_recognition library

🔧 Recent Updates

Face Orientation Fix (Latest)

Fixed face orientation issues in the identify functionality

  • ✅ Resolved rotated face display: Faces now show in correct orientation instead of being rotated
  • ✅ Fixed false positive detection: Eliminated detection of clothes/objects as faces for rotated images
  • ✅ Improved face extraction: Fixed blank face crops by properly handling EXIF orientation data
  • ✅ Comprehensive EXIF support: Full support for all 8 EXIF orientation values (1-8)
  • ✅ Consistent processing: Face detection and extraction now use consistent orientation handling

Technical Details:

  • Applied EXIF orientation correction before face detection to prevent false positives
  • Implemented proper coordinate handling for all orientation types
  • Enhanced face extraction logic to work with corrected images
  • Maintained backward compatibility with existing face data

Migration Documentation

📧 Contact

[Add contact information]

⭐ Star History

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Made with ❤️ for photo enthusiasts