photo-quality-analyzer-core 0.6.0

The Python SDK for Local Computer Vision & Signal Processing

photo-quality-analyzer

Intelligent technical assessment for digital photography.

photo-quality-analyzer is a local-first Python SDK and CLI tool that uses signal processing and computer vision to objectively score photographic quality. It normalizes metrics against a database of 147+ camera models to account for sensor-specific physics like diffraction limits and dynamic range baselines.

From PyPI

pip install photo-quality-analyzer-core

From GitHub (Source)

For developers or users who want the latest changes directly from the source:

pip install git+https://github.com/prasadabhishek/photo-quality-analyzer.git

Metrics

The engine evaluates technical quality through a multi-dimensional lens:

• Sharpness: FFT-based acutance, invariant to rotation and noise.

• Exposure: Ansel Adams Zone System analysis for clipping detection.

• Focus: ROI-specific sharpness on the main subject (auto-detected).

• Noise: Statistical variance estimation for ISO-related grain.

• Dynamic Range: Tonal entropy and sensor-aware potential.

• Color Balance: Neutral pixel selection for finding color casts.

• Color Balance: Neutral pixel selection for finding color casts.

For more information, see our documentation:

• 📖 USAGE.md: Practical examples and CLI guides.
• ⚙️ API.md: Technical reference for Python developers.
• 🔬 SCIENCE.md: Deep dive into the underlying physics and algorithms.

Usage

CLI

Analyze an entire folder and optionally move files based on quality:

python analyzer.py --folder_path /path/to/photos --move

SDK

from photo_quality_analyzer_core.analyzer import evaluate_photo_quality

# Works with JPEGs and RAW files
result = evaluate_photo_quality("photo.arw")
print(result['judgement']) # "Excellent", "Good", etc.

See USAGE.md for more advanced examples (AI toggling, metric filtering, etc).

How it works

The engine uses a hybrid approach to distinguish between artistic intent and technical failure:

1. FFT Anisotropy: Measures purely optical acutance, invariant to rotation. Adjusted for Aperture-aware diffraction.
2. Zone System Histogram: Analyzes luminance using Ansel Adams' Zone System to detect destructive clipping.
3. Neural ROI: Leverages YOLOv11 to identify main subjects, ensuring metrics are calculated on the subject rather than the background.
4. Sensor Normalization: Benchmarks images against the known limits of the specific camera sensor (Full Frame vs APS-C vs 1-inch).

evaluate_photo_quality(file_path, ...)

The primary entry point. It returns a dictionary containing scores, qualitative labels, and AI-generated scene descriptions.

See API.md for full function signatures and return types.

Contributing

Contributions are welcome! Please run the test suite before submitting:

PYTHONPATH=. python3 -m unittest discover tests

License

MIT