photo-quality-analyzer-core 0.8.4

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

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What This Library Is (And Isn't)

This library is a Technical Quality Filter ("Janitor"), not an artistic evaluator ("Curator").

✅ What it WILL do:

• Identify out-of-focus, underexposed, or noisy images
• Filter out broken shots from large photo libraries (10,000+ images)
• Provide objective technical metrics (sharpness, noise, dynamic range)
• Normalize scores against known camera sensor physics

❌ What it WON'T do:

• Judge artistic merit or emotional impact
• Understand intentional creative choices (low-key lighting, film grain, etc.)
• Replace human curation for portfolio selection
• Prefer "interesting" photos over "boring but technically perfect" ones

Use Case: Wedding photographers culling 5,000 shots to eliminate camera-shake blurs and exposure failures—not fine art curation.

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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 (YOLO26): Leverages the latest YOLO26 (January 2026 release) via ONNX Runtime 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).

Technology Stack

• ONNX Runtime: Optimized, lightweight inference engine (replaced PyTorch).
• YOLO26: Transformer-based subject detection (43% faster on CPUs).
• OpenCV (Headless): Efficient image processing without GUI overhead.

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