File size: 6,800 Bytes
d2ce7dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | """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()
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