python-ofiq

Python bindings for OFIQ (Open Source Face Image Quality) — the reference implementation for ISO/IEC 29794-5.

Features

• One-time initialization, process many images efficiently
• Unified quality score [0-100] and 25+ individual quality metrics
• Custom exception hierarchy for precise error handling
• Full type hint support

Installation

pip install python-ofiq

Download OFIQ models and config (~400 MB):

python-ofiq setup

This stores data in ~/.ofiq/data. Set OFIQ_DATA_DIR to use a custom directory.

Quick Start

import cv2
from ofiq import OFIQ, FaceDetectionError

ofiq = OFIQ()

bgr = cv2.imread("face.png")
rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)

try:
    score = ofiq.scalar_quality(rgb)
    print(f"Quality score: {score}")
except FaceDetectionError:
    print("No face detected")

measures = ofiq.vector_quality(rgb)
print(measures)
# {'UnifiedQualityScore': 85.2, 'Sharpness': 92.1, ...}

measures_with_raw = ofiq.vector_quality(rgb, include_raw=True)
print(measures_with_raw["Sharpness"])
# {'scalar': 92.1, 'raw': 0.847}

Exception Handling

from ofiq import (
    OFIQ,
    OFIQError,
    ConfigError,
    QualityError,
    FaceDetectionError,
    LandmarkError,
    SegmentationError,
)

try:
    score = ofiq.scalar_quality(image)
except FaceDetectionError:
    print("No face in image")
except LandmarkError:
    print("Could not extract facial landmarks")
except QualityError as e:
    print(f"Quality assessment failed: {e}")
except OFIQError as e:
    print(f"OFIQ error: {e}")

Thread Safety

OFIQ instances are not thread-safe. The underlying C++ library uses shared mutable state without synchronization:

• Use one OFIQ instance per thread, or
• Serialize access with a lock, or
• Use multiprocessing instead of threading

Scalar vs Raw Scores

Scalar scores are normalized to [0, 100] where higher is always better. Raw scores are the native OFIQ values before normalization — their range and direction vary per measure. Use include_raw=True when you need the original measurement values.

Available Quality Measures

All measures return scalar values in [0, 100] where higher is better, or None if the measure failed:

Measure	Description
UnifiedQualityScore	Overall quality score
Sharpness	Image sharpness/focus
BackgroundUniformity	Background consistency
IlluminationUniformity	Lighting evenness
LuminanceMean	Average brightness
LuminanceVariance	Brightness variation
DynamicRange	Contrast range
CompressionArtifacts	JPEG artifact detection
NaturalColour	Color naturalness
HeadPoseYaw	Face yaw angle
HeadPosePitch	Face pitch angle
HeadPoseRoll	Face roll angle
EyesOpen	Eye openness
MouthClosed	Mouth closure
EyesVisible	Eye visibility
ExpressionNeutrality	Neutral expression
SingleFacePresent	Single face detection
InterEyeDistance	Distance between eyes
HeadSize	Face size in frame
...	and more

API Reference

OFIQ(config_dir="", config_file="ofiq_config.jaxn")

Initialize OFIQ. If config_dir is omitted, uses OFIQ_DATA_DIR env var or ~/.ofiq/data.

scalar_quality(image_rgb) -> float

Compute unified quality score. image_rgb: NumPy uint8 array of shape (H, W, 3) in RGB.

vector_quality(image_rgb, include_raw=False) -> dict

Compute all quality measures. Returns None for failed individual measures.

ofiq.setup(force_download=False) -> str

Download models and config. Returns the data directory path.

ofiq.resolve_data_dir() -> str

Return the resolved data directory.

CLI

python-ofiq setup                    # download models and config
python-ofiq setup --force-download   # re-download even if present
OFIQ_DATA_DIR=/custom/path python-ofiq setup

Version Compatibility

python-ofiq	OFIQ C++
0.1.x	1.1.2

Development

Build from source

git clone https://github.com/unicef/python-ofiq.git
cd python-ofiq

docker build -f docker/Dockerfile -t python-ofiq-test .
docker run -it python-ofiq-test python -c "from ofiq import OFIQ; print('OK')"

Run tests

pip install pytest numpy

pytest tests/ -v -m "not integration"

# Integration tests require models
pytest tests/ -v

License

MIT — see LICENSE.

This project wraps the OFIQ C++ library which is also MIT licensed.

Acknowledgments

• BSI for developing OFIQ
• pybind11 for the bindings framework