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README.md

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+# PunimTag
+
+A minimal face tagging proof-of-concept that automatically groups similar faces in your photo collection using face recognition and clustering.
+
+## What it does
+
+PunimTag scans a folder of photos, detects all faces, and automatically groups similar faces together. It:
+
+1. **Walks through your photos folder** - Processes all `.jpg` and `.png` files
+2. **Detects faces** - Finds all faces in each image using dlib's face detection
+3. **Creates face encodings** - Generates 128-dimensional face embeddings for each detected face
+4. **Clusters similar faces** - Uses HDBSCAN clustering to group similar faces together
+5. **Stores results in SQLite** - Saves everything to a `faces.db` database for easy querying
+
+## Prerequisites
+
+- Python 3.8+
+- CMake (required for dlib installation)
+- A `photos/` folder with your images
+
+## Installation
+
+1. Clone this repository:
+
+```bash
+git clone <repository-url>
+cd PunimTag
+```
+
+2. Create and activate a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install CMake if not already installed:
+
+```bash
+# Ubuntu/Debian
+sudo apt-get install cmake
+
+# macOS
+brew install cmake
+
+# Windows
+# Download from https://cmake.org/download/
+```
+
+4. Install Python dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+1. Place your photos in the `photos/` folder (subdirectories are supported)
+
+2. Run the script:
+
+```bash
+python punimtag.py
+```
+
+3. The script will process all images and create a `faces.db` SQLite database
+
+## Database Schema
+
+The script creates three tables:
+
+### `images` table
+
+- `id`: Primary key
+- `path`: File path to the image
+
+### `faces` table
+
+- `id`: Primary key
+- `image_id`: Foreign key to images table
+- `location`: Face bounding box coordinates as string
+- `encoding`: 128-dimensional face encoding (stored as BLOB)
+- `cluster_id`: Foreign key to clusters table (NULL for unclustered faces)
+
+### `clusters` table
+
+- `id`: Primary key
+- `label`: Cluster label (e.g., "Cluster 0", "Cluster 1")
+
+## Querying the Database
+
+You can explore the results using any SQLite client:
+
+```bash
+sqlite3 faces.db
+```
+
+Example queries:
+
+```sql
+-- Count faces per image
+SELECT i.path, COUNT(f.id) as face_count
+FROM images i
+LEFT JOIN faces f ON i.id = f.image_id
+GROUP BY i.path;
+
+-- Find all images containing faces from a specific cluster
+SELECT DISTINCT i.path
+FROM images i
+JOIN faces f ON i.id = f.image_id
+WHERE f.cluster_id = 1;
+
+-- Count faces per cluster
+SELECT c.label, COUNT(f.id) as face_count
+FROM clusters c
+JOIN faces f ON c.id = f.cluster_id
+GROUP BY c.id;
+```
+
+## How It Works
+
+1. **Face Detection**: Uses HOG-based face detection from dlib to find face locations
+2. **Face Encoding**: Generates a 128-dimensional vector for each face using a pre-trained neural network
+3. **Clustering**: HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) groups similar face encodings together
+   - Faces with similar encodings are grouped into the same cluster
+   - Faces that don't match any cluster well are marked as noise (cluster_id = NULL)
+
+## Limitations
+
+- This is a proof-of-concept with minimal error handling
+- Face detection may miss faces in poor lighting or at extreme angles
+- Clustering quality depends on having multiple photos of the same person
+- No GUI - results must be queried from the database
+
+## Next Steps
+
+This minimal implementation can be extended with:
+
+- A web interface for viewing clustered faces
+- Better error handling and logging
+- Support for more image formats
+- Face recognition (matching against known individuals)
+- Incremental processing of new photos
+- Export functionality for organized photo albums
+
+## License
+
+[Your chosen license]