Metadata-Version: 2.4
Name: fingerprint-fqa
Version: 0.1.2
Summary: A comprehensive Python library for Fingerprint Quality Assessment (FQA)
Author-email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/yourusername/fingerprint_fqa
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fingerprint_feature_extractor
Requires-Dist: numpy
Requires-Dist: opencv-python
Requires-Dist: scipy
Requires-Dist: scikit-image
Requires-Dist: Pillow
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: imageio
Requires-Dist: xgboost
Requires-Dist: joblib
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# Fingerprint FQA

A Python library for comprehensive Fingerprint Quality Assessment (FQA).
This library consolidates logic for FDA, LCS, OCL, OFL, RVU, MU, MMB, ROI OMCS, DFIQI, and LFIQ features, providing a unified `extract_all_metrics_single_image` interface.

## Installation

You can install this library in editable mode from the source code:

```bash
cd fingerprint_fqa
pip install -e .
```

## Usage

```python
from fingerprint_fqa import extract_all_metrics_single_image
from fingerprint_fqa import predict_quality_score_from_dict

img_path = "path/to/fingerprint.jpg"

# Extract all features
features = extract_all_metrics_single_image(img_path)
print("Extracted Features:", features)

# Predict score if you have models available
# Ensure that weights_dir is correctly set to your joblib models
# rf_score, xgb_score = predict_quality_score_from_dict(features, weights_dir="path/to/weights")
```
