scripts/gen_nfiq2_proxy_scores.py

"""Generate NFIQ2-proxy quality scores using image heuristics.

NFIQ2 is not available as a pip package and requires building from source.
This script approximates it using:
  - Gabor filter energy at fingerprint ridge frequencies (8–16 px/cycle)
  - Block-wise local contrast (RMS of intensity)
  - Laplacian variance (sharpness)

The three signals are fused and rescaled to [0, 100], matching NFIQ2 convention
(higher = better quality).  Scores are written as JSONL with the same schema
expected by run_eval.py:
  {"image_path": "...", "q_score": 73.2}

Usage (reads image paths from an existing scores file, e.g. sifq_scores.jsonl):
  python sifq/scripts/gen_nfiq2_proxy_scores.py \
      --sifq-scores sifq/eval_results/sifq_scores.jsonl \
      --output      /tmp/nfiq2_scores.jsonl

Or scan a directory directly:
  python sifq/scripts/gen_nfiq2_proxy_scores.py \
      --image-dir dataset/302b/images/baseline \
      --output    /tmp/nfiq2_scores.jsonl
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

import cv2
import numpy as np


# ---------------------------------------------------------------------------
# Quality features
# ---------------------------------------------------------------------------

def _gabor_energy(gray: np.ndarray, image_size: int = 224) -> float:
    """Mean Gabor filter energy at typical fingerprint ridge frequencies."""
    img = cv2.resize(gray, (image_size, image_size)).astype(np.float32) / 255.0
    total = 0.0
    count = 0
    for theta_deg in range(0, 180, 30):
        theta = np.deg2rad(theta_deg)
        # wavelength in pixels: 8–16 px for 500 dpi prints at 224px crops
        for wavelength in (8, 12, 16):
            sigma = wavelength * 0.56
            kern = cv2.getGaborKernel(
                (31, 31), sigma, theta, wavelength, gamma=0.5, psi=0,
                ktype=cv2.CV_32F,
            )
            filtered = cv2.filter2D(img, cv2.CV_32F, kern)
            total += float(np.mean(filtered ** 2))
            count += 1
    return total / count if count else 0.0


def _local_contrast(gray: np.ndarray, block: int = 16) -> float:
    """Mean RMS contrast over non-overlapping blocks."""
    img = cv2.resize(gray, (224, 224)).astype(np.float32)
    h, w = img.shape
    vals = []
    for r in range(0, h - block + 1, block):
        for c in range(0, w - block + 1, block):
            patch = img[r : r + block, c : c + block]
            vals.append(float(np.std(patch)))
    return float(np.mean(vals)) if vals else 0.0


def _laplacian_var(gray: np.ndarray) -> float:
    """Variance of Laplacian — sharpness metric."""
    img = cv2.resize(gray, (224, 224))
    lap = cv2.Laplacian(img, cv2.CV_64F)
    return float(np.var(lap))


def _quality_score(image_path: str) -> float | None:
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    if img is None:
        return None
    g_energy = _gabor_energy(img)
    contrast  = _local_contrast(img)
    lap_var   = _laplacian_var(img)
    # Combine: each signal contributes roughly equally after normalisation.
    # Raw typical ranges (empirical, 500-dpi fingerprints at 224 px):
    #   g_energy  ~[0.0001, 0.006]
    #   contrast  ~[5, 60]
    #   lap_var   ~[50, 3000]
    score = (
        np.clip(g_energy / 0.006, 0.0, 1.0) * 0.40 +
        np.clip(contrast  / 60.0,  0.0, 1.0) * 0.35 +
        np.clip(lap_var   / 3000.0, 0.0, 1.0) * 0.25
    )
    return round(float(score) * 100.0, 2)


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Generate NFIQ2-proxy quality scores")
    src = p.add_mutually_exclusive_group(required=True)
    src.add_argument(
        "--sifq-scores", type=str,
        help="Path to SIFQ scores JSONL; image paths are read from 'image_path' field",
    )
    src.add_argument(
        "--image-dir", type=str,
        help="Root directory to scan recursively for .png/.bmp/.wsq images",
    )
    p.add_argument("--output", type=str, default="/tmp/nfiq2_scores.jsonl")
    p.add_argument(
        "--extensions", type=str, default="png,bmp,wsq,jpg,jpeg",
        help="Comma-separated file extensions to scan (only with --image-dir)",
    )
    return p.parse_args()


def collect_paths(args: argparse.Namespace) -> list[str]:
    if args.sifq_scores:
        paths: list[str] = []
        with open(args.sifq_scores, encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line:
                    paths.append(json.loads(line)["image_path"])
        return paths
    # --image-dir
    exts = {f".{e.lstrip('.')}" for e in args.extensions.split(",")}
    return [
        str(p) for p in Path(args.image_dir).rglob("*")
        if p.suffix.lower() in exts
    ]


def main() -> None:
    args = parse_args()
    paths = collect_paths(args)
    print(f"Scoring {len(paths)} images → {args.output}")

    out = Path(args.output)
    out.parent.mkdir(parents=True, exist_ok=True)

    skipped = 0
    with out.open("w", encoding="utf-8") as fout:
        for i, p in enumerate(paths):
            score = _quality_score(p)
            if score is None:
                skipped += 1
                continue
            fout.write(json.dumps({"image_path": p, "q_score": score}) + "\n")
            if (i + 1) % 1000 == 0:
                print(f"  {i + 1}/{len(paths)}  (skipped={skipped})")

    print(f"Done. Written {len(paths) - skipped} records, skipped {skipped}.")
    print(f"Output: {out}")


if __name__ == "__main__":
    main()