ofiqpy 0.1.1

A faithful Python port of OFIQ v1.1.0 (ISO/IEC 29794-5 face image quality), matching the reference within ±1.

ofiqpy — a faithful Python port of OFIQ v1.1.0

A behavior-preserving Python reimplementation of the BSI OFIQ ISO/IEC 29794-5 face image quality library. ofiqpy reuses OFIQ's own model files and reproduces its exact .cpp algorithms, matching the live OFIQ reference to within ±1 per component (the ISO/IEC 29794-5 Annex A.2 conformance criterion).

📖 Docs: https://avhbac.github.io/ofiqpy/

Status: full coverage + near-exact parity. All 27 ISO components + UnifiedQualityScore, an OFIQ-compatible CSV writer, a CLI, and a parallel batch runner — all gated against live OFIQSampleApp. Validated on 1,000+ real CelebA images: 27/28 components fully conformant (±1 on every image, most bit-exact); ~99.99% of all component-image pairs within ±1. The rare per-image residuals are numerical boundaries of discrete/learned models (RTrees vote, AdaBoost score, round tie), not algorithm gaps.

Install & run

pip install ofiqpy
export OFIQPY_OFIQ_DATA=/path/to/OFIQ-Project/data   # OFIQ's models (not bundled)

ofiqpy -i face.jpg -o out.csv                        # CLI

from ofiqpy import assess
scores = assess("face.jpg")            # {component: (raw, scalar)}
print(scores["UnifiedQualityScore"])   # (magnitude, 0-100)

See Installation and Quickstart.

Design

• Same weights. Models are loaded directly from the reference checkout OFIQ-Project/data/models/ via config.py (SSD caffemodel, ADNet-98 ONNX, 3DDFA-V2 ONNX, BiSeNet parsing ONNX, occlusion-seg ONNX, ssim-248 ONNX, MagFace ONNX). No re-training, no substitute backbones.
• Same math. Detection, ADNet landmark crop/denorm, the 5-point LMEDS similarity alignment to 616×616, the landmarked-region mask, tmetric, the generic h·(a+s·sigmoid(x;x0,w)) scalar mapping, and each measure are ported line-faithfully from OFIQ's C++ (see per-module docstrings for file:line provenance).
• Same versions. The isolated .venv pins OpenCV 4.5.5 and onnxruntime 1.18.1 to match OFIQ's build (libofiq_lib.so links OpenCV 4.5.5; bundles onnxruntime 1.18.1).
• Gated, not asserted. tests/verify_ofiq.py runs live OFIQ and checks |port_scalar − ofiq_scalar| ≤ 1 per image; tests/gate_slice.py reports it.

Conformance (1,000+ real CelebA images, port vs live OFIQ, ISO Annex A ±1)

Validated on 1,197 real CelebA images: 27 of 28 components fully conformant (±1 on every image), 24 of them bit-exact (maxΔ=0). ~99.99% of all component-image pairs are within ISO ±1 (mean |Δ| ≤ 0.02 for every component).

The rare per-image residuals:

• Sharpness — 1 image at Δ=2: OFIQ's RTrees vote count differs by exactly 2 trees at a split-threshold knife-edge (a sub-LSB feature difference flips 2 borderline votes through the step-function forest). Bit-exact on the rest.
• BackgroundUniformity / ExpressionNeutrality / NoHeadCoverings — one ±1 image each, a single sigmoid/round boundary.

Sharpness (RTrees) and ExpressionNeutrality (dual EfficientNet + AdaBoost) run OFIQ's own cv2.ml / ONNX models; UnifiedQualityScore runs OFIQ's MagFace ONNX. These four residuals are numerical boundaries of discrete/learned models, not algorithm gaps.

How parity was reached (the residual was a bug, not a build limit)

An earlier version of this port was only ~96% conformant, and the residual was wrongly attributed to a build-level OpenCV float difference. To test that, ctypes bridges were built against OFIQ's own conan OpenCV static libs (native/ofiq_cv.cpp, ofiq_ssd.cpp) and used to compare OFIQ's compiled estimateAffinePartial2D, warpAffine, resize, and the SSD dnn forward pass against the pip opencv-python wheel. Every OpenCV operation was bit-identical — which disproved the build-level theory and localized the divergence to the ADNet landmark back-projection: OFIQ scales landmarks back with squareBox.height / 256 (adnet_landmarks.cpp:313), but makeSquareBoundingBox's floor/ceil can leave the box 1px non-square, and the port had used the width. On exactly-square detector boxes it matched; otherwise it drifted ~1px, propagating (via the alignment source points → affine → whole aligned face) into every landmark-sensitive measure. One-character fix (width→height); all 27 non-model components went bit-exact.

The bridges in native/ are diagnostic only — the runtime uses pip cv2, which is bit-identical to OFIQ's OpenCV. No source build of OpenCV was needed.

Layout

ofiqpy/
  config.py            JAXN loader + OFIQ model resolver + sigmoid params
  sigmoid.py           OFIQ ScalarConversion (Measure.h:271-285)
  session.py           shared preprocessing products
  pipeline.py          detect -> pose -> landmarks -> align -> parse -> occlusion -> region
  detectors/ssd.py     SSD (OpenCV DNN, Caffe)
  landmarks/adnet.py   ADNet-98 + square-crop helpers
  align.py             616x616 alignment, landmarked region (GetFaceMask), tmetric, luminance
  pose/tddfa.py        3DDFA-V2 pose
  segmentation/        BiSeNet parsing, face-occlusion seg
  measures/
    core.py            model cache + dispatch; C03/C09/C17/C20, unified, HeadPose (slot swap)
    geometry.py        C11,C12,C13,C19,C24-C27
    pixel.py           C01,C02,C04(var),C05,C06,C07,C10
    models.py          C08 Sharpness (RTrees), C14/C15/C16 occlusion, C18 Expression
    helpers.py         get_distance/get_middle, c_round, landmark index maps
  output.py            OFIQ-format CSV (named cols, raw + .scalar)
  cli.py               OFIQSampleApp-compatible CLI
tests/
  verify_ofiq.py       runs live OFIQ, ±1 gate
  gate_slice.py        full-coverage conformance runner

Run

export OFIQPY_OFIQ_DATA=/path/to/OFIQ-Project/data

ofiqpy -i <image|dir> -o out.csv               # single / small runs (OFIQ-format CSV)
python -m ofiqpy.batch -i <dir> -o out.csv -w 8 --resume   # parallel batch

# reproduce the conformance gate (needs a built OFIQSampleApp)
export OFIQPY_OFIQ_ROOT=/path/to/OFIQ-Project
export OFIQPY_TEST_IMAGES=/path/to/images
python tests/gate_slice.py 1000

Full documentation: https://avhbac.github.io/ofiqpy/.

License & attribution

ofiqpy is released under the MIT License.

It is a faithful port of OFIQ (Open Source Face Image Quality), developed by the German Federal Office for Information Security (BSI), Copyright © 2024, MIT-licensed (https://github.com/BSI-OFIQ/OFIQ-Project). Please acknowledge OFIQ when using ofiqpy.

Models are not bundled. ofiqpy loads OFIQ's own model files at runtime; they ship with OFIQ and may be licensed separately (see OFIQ's LICENSE.md). Obtain them from an OFIQ install and set OFIQPY_OFIQ_DATA. See NOTICE and the licensing docs.