Photo Organizer

An intelligent photo classification system that automatically sorts your photos into different categories:

• Documents: Screenshots, scanned documents, text-heavy images
• Selfies: Self-portraits with a single person's face prominently displayed
• People: Photos containing people (groups, portraits, etc.)
• Nature: Landscape photos, sky, greenery, outdoor scenes
• Unknown: Photos that don't fit into other categories

Features

• Computer Vision Based: Uses OpenCV for face detection and image analysis
• Automatic Classification: No manual tagging required
• Batch Processing: Processes entire folders at once
• Safe Operations: Copies files (doesn't move them) to preserve originals
• Conflict Resolution: Handles duplicate filenames automatically

Installation

Option 1: Quick Setup

# Install required packages
pip install opencv-python numpy Pillow

# Run directly
python photo_classifier.py /path/to/your/photos

Option 2: Full Installation

# Clone or download this project
cd photo-organizer

# Install in development mode
pip install -e .

# Or install requirements manually
pip install -r requirements.txt

Usage

Basic Usage

# Organize photos from a source folder
python photo_classifier.py /path/to/your/photos

# Specify custom output folder
python photo_classifier.py /path/to/your/photos -o /path/to/organized_photos

Advanced Usage

from photo_classifier import PhotoClassifier

# Create classifier instance
classifier = PhotoClassifier(
    source_folder="/path/to/your/photos",
    output_folder="/path/to/organized_photos"
)

# Process all images
classifier.process_images()

How It Works

The classifier uses several computer vision techniques:

Document Detection

• Analyzes image for rectangular patterns
• Detects horizontal lines (typical of text)
• Checks aspect ratios common in documents

Face Detection

• Uses Haar cascade classifiers for face detection
• Distinguishes between selfies and group photos based on:
  • Number of faces
  • Face size relative to image
  • Face position in frame

Nature Detection

• Analyzes color distribution in HSV color space
• Identifies predominant green (vegetation) and blue (sky) regions
• Classifies based on color percentages

Supported Formats

• JPEG (.jpg, .jpeg)
• PNG (.png)
• BMP (.bmp)
• TIFF (.tiff, .tif)
• WebP (.webp)

Output Structure

After processing, your photos will be organized like this:

organized_photos/
├── documents/
├── selfies/
├── people/
├── nature/
└── unknown/

Requirements

• Python 3.7+
• OpenCV (opencv-python)
• NumPy
• Pillow

Limitations

• Classification accuracy depends on image quality and content
• May misclassify ambiguous images
• Requires good lighting for face detection
• Document detection works best with clear, high-contrast text

Tips for Better Results

1. Good Quality Images: Higher resolution images generally classify better
2. Clear Lighting: Well-lit photos improve face detection accuracy
3. Review Results: Check the "unknown" folder for misclassified images
4. Manual Adjustment: Feel free to move images between folders after processing

Troubleshooting

Common Issues

1. No faces detected: Check image quality and lighting
2. Documents misclassified: Ensure documents have clear text and good contrast
3. Nature photos in wrong category: Adjust color thresholds if needed

Error Messages

• "Could not load image": File might be corrupted or unsupported format
• "Source folder does not exist": Check the path to your photos folder

Example

# Process photos from Downloads folder
python photo_classifier.py ~/Downloads/Photos

# Output will be created in organized_photos/ folder

Sample output:

==================================================
CLASSIFICATION SUMMARY
==================================================
Documents: 15 images
Selfies: 8 images
People: 23 images
Nature: 31 images
Unknown: 12 images
Total processed: 89 images

Output folder: /Users/ajitreddy/organized_photos