"""Small QC trial for fundus-like left-corner PII de-identification. Reads samples from the final manifest, masks the detected circular field of view, and writes comparison sheets plus non-identifying metadata. Source images are never modified. """ from __future__ import annotations import argparse import json import math import tarfile from pathlib import Path from typing import Any import cv2 import numpy as np import pandas as pd from PIL import Image from deidentify_trial import ( deidentify_fundus_circle, deidentify_fundus_left_outside_circle, load_easyocr_reader, make_sheet, ) DEFAULT_MANIFEST = Path( "/data/team/lisicheng/new_data_manifest/" "new_data_manifest_dedup_split_labeled_with_wds_deid.parquet" ) DEFAULT_TONGREN_META = Path("/data/team/lisicheng/Dataser/tongren_metadata.csv") DEFAULT_OUT = Path("/data/team/lisicheng/new_data_manifest/fundus_pii_trial_20260702") COHORTS = [ "tongren_fundus", "tongren_external_fundus", "fq_testfundus", "fq_rawfundus", "fq_validfundus", ] def main() -> None: args = parse_args() args.out.mkdir(parents=True, exist_ok=True) reader = load_easyocr_reader(args.ocr) tongren_meta = load_tongren_metadata(args.tongren_metadata) df = pd.read_parquet( args.manifest, columns=["image_id", "cohort", "modality", "train_path", "file_path", "rel_path"], ) df = df[df["cohort"].isin(args.cohorts)].copy() records = [] for cohort, group in df.groupby("cohort", sort=True): sample = group.sample(n=min(args.per_cohort, len(group)), random_state=args.seed) rows = [] for idx, row in enumerate(sample.to_dict("records")): img = read_image_any(str(row.get("train_path") or row.get("file_path"))) meta = metadata_for_row(row, tongren_meta) if not meta and reader is not None: meta = ocr_metadata(img, reader) masked, ok, info = deidentify_fundus_circle( img, radius_ratio=args.radius_ratio, threshold=args.threshold, open_kernel=args.open_kernel, ) left_masked, left_ok, left_info = deidentify_fundus_left_outside_circle( img, radius_ratio=args.left_radius_ratio, left_ratio=args.left_ratio, threshold=args.threshold, open_kernel=args.open_kernel, ) rows.append([img, masked, left_masked]) rec = { "image_id": row["image_id"], "cohort": cohort, "source_path": row.get("train_path") or row.get("file_path"), "circle_ok": ok, "circle_info": info, "left_circle_ok": left_ok, "left_circle_info": left_info, "ocr_age": meta.get("age"), "ocr_sex": meta.get("sex"), "ocr_eye": meta.get("eye"), } records.append(rec) save_rgb(args.out / "images" / f"{cohort}_{idx:02d}_masked.jpg", masked) save_rgb(args.out / "images" / f"{cohort}_{idx:02d}_left_masked.jpg", left_masked) make_sheet( rows, ["source", "full_circle", "left_only_circle"], args.out / f"{cohort}_compare.jpg", cell=args.cell, ) with (args.out / "metadata.jsonl").open("w", encoding="utf-8") as f: for rec in records: f.write(json.dumps(rec, ensure_ascii=False) + "\n") print(f"DONE -> {args.out} ({len(records)} samples)") def load_tongren_metadata(path: Path) -> dict[str, dict[str, Any]]: if not path.exists(): return {} meta = pd.read_csv(path) out: dict[str, dict[str, Any]] = {} for row in meta.itertuples(index=False): sex = {"男": "M", "女": "F"}.get(str(row.sex), None) age = None if pd.isna(row.age) else int(row.age) eye = eye_from_seq(row.eye_seq) out[str(row.filename)] = {"age": age, "sex": sex, "eye": eye} return out def eye_from_seq(value: Any) -> str | None: if value is None or (isinstance(value, float) and math.isnan(value)): return None text = str(int(value)) if isinstance(value, float) else str(value) if text == "1": return "OD" if text == "2": return "OS" return None def metadata_for_row(row: dict[str, Any], tongren_meta: dict[str, dict[str, Any]]) -> dict[str, Any]: name = str(row.get("rel_path") or Path(str(row.get("train_path") or "")).name) return {k: v for k, v in tongren_meta.get(name, {}).items() if v is not None} def ocr_metadata(img: np.ndarray, reader: object) -> dict[str, Any]: from deidentify_trial import parse_ocr_metadata h = img.shape[0] crop = img[: int(h * 0.55), :] rgb = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB) results = reader.readtext(rgb, detail=0) return parse_ocr_metadata(" ".join(str(item) for item in results)) def read_image_any(path: str) -> np.ndarray: if "::" in path: tar_path, member = path.split("::", 1) with tarfile.open(tar_path) as tf: fp = tf.extractfile(member) if fp is None: raise FileNotFoundError(path) data = fp.read() arr = np.frombuffer(data, dtype=np.uint8) else: arr = np.fromfile(path, dtype=np.uint8) img = cv2.imdecode(arr, cv2.IMREAD_COLOR) if img is None: raise ValueError(f"failed to decode image: {path}") return img def save_rgb(path: Path, img: np.ndarray) -> None: path.parent.mkdir(parents=True, exist_ok=True) rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) Image.fromarray(rgb).save(path, quality=92) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--manifest", type=Path, default=DEFAULT_MANIFEST) parser.add_argument("--tongren-metadata", type=Path, default=DEFAULT_TONGREN_META) parser.add_argument("--out", type=Path, default=DEFAULT_OUT) parser.add_argument("--cohorts", nargs="+", default=COHORTS) parser.add_argument("--per-cohort", type=int, default=8) parser.add_argument("--seed", type=int, default=20260702) parser.add_argument("--ocr", action=argparse.BooleanOptionalAction, default=True) parser.add_argument("--cell", type=int, default=320) parser.add_argument("--radius-ratio", type=float, default=0.97) parser.add_argument("--left-radius-ratio", type=float, default=0.985) parser.add_argument("--left-ratio", type=float, default=0.55) parser.add_argument("--threshold", type=int, default=25) parser.add_argument("--open-kernel", type=int, default=25) return parser.parse_args() if __name__ == "__main__": main()