tools/data_processing/deidentification/deidentify_trial.py

"""Small visual trial for burned-in PII handling.

This script is intentionally sample-only. It never edits source images and never
saves raw OCR text. It writes comparison sheets so the cohort-level policy can be
chosen by eye before a full preprocessing run.
"""

from __future__ import annotations

import argparse
import json
import random
import re
from pathlib import Path
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont

import cohorts as C


IMG_EXTS = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp"}


def parse_ocr_metadata(text: str) -> dict[str, int | str]:
    """Keep only non-identifier fields from OCR text."""
    meta: dict[str, int | str] = {}

    age_match = re.search(r"(?<!\d)(\d{1,3})\s*岁", text)
    if age_match:
        age = int(age_match.group(1))
        if 0 <= age <= 120:
            meta["age"] = age

    if "男" in text:
        meta["sex"] = "M"
    elif "女" in text:
        meta["sex"] = "F"

    eye_match = re.search(r"\b(O[DSU])\b", text.upper())
    if eye_match:
        meta["eye"] = eye_match.group(1)

    return meta


def blackout_top_bar(img: np.ndarray, ratio: float) -> np.ndarray:
    """Mask the top horizontal metadata bar while preserving image size."""
    out = img.copy()
    rows = _rows_from_ratio(img, ratio)
    out[:rows, :] = 0
    return out


def crop_top_bar(img: np.ndarray, ratio: float) -> np.ndarray:
    """Remove the top horizontal metadata bar."""
    rows = _rows_from_ratio(img, ratio)
    return img[rows:, :].copy()


def blackout_frame(img: np.ndarray, *, top: float, bottom: float, right: float) -> np.ndarray:
    """Mask fixed device UI bands while preserving the scan geometry."""
    out = img.copy()
    top_rows = _rows_from_ratio(img, top) if top > 0 else 0
    bottom_rows = _rows_from_ratio(img, bottom) if bottom > 0 else 0
    right_cols = _cols_from_ratio(img, right) if right > 0 else 0
    if top_rows:
        out[:top_rows, :] = 0
    if bottom_rows:
        out[-bottom_rows:, :] = 0
    if right_cols:
        out[:, -right_cols:] = 0
    return out


def deidentify_fundus_circle(
    img: np.ndarray,
    *,
    radius_ratio: float = 0.97,
    threshold: int = 25,
    open_kernel: int = 25,
    fallback_top_ratio: float = 0.25,
) -> tuple[np.ndarray, bool, dict[str, float | int | str]]:
    """Keep the detected retinal circle and black out everything outside it."""
    circle_info = _detect_fundus_circle(img, threshold=threshold, open_kernel=open_kernel)
    if circle_info is None:
        return _fundus_fallback(img, fallback_top_ratio, "no_component")
    cx, cy, radius, area = circle_info
    h, w = img.shape[:2]
    if area < int(h * w * 0.08):
        return _fundus_fallback(img, fallback_top_ratio, "component_too_small")
    radius = float(radius) * radius_ratio
    if radius < min(h, w) * 0.20:
        return _fundus_fallback(img, fallback_top_ratio, "radius_too_small")

    circle = np.zeros((h, w), dtype=np.uint8)
    cv2.circle(circle, (int(cx), int(cy)), int(radius), 255, -1)
    out = np.zeros_like(img)
    out[circle > 0] = img[circle > 0]
    return out, True, {"cx": int(cx), "cy": int(cy), "radius": int(radius), "area": area}


def deidentify_fundus_left_outside_circle(
    img: np.ndarray,
    *,
    radius_ratio: float = 0.985,
    left_ratio: float = 0.55,
    threshold: int = 25,
    open_kernel: int = 25,
    fallback_top_ratio: float = 0.25,
) -> tuple[np.ndarray, bool, dict[str, float | int | str]]:
    """Black out only left-side pixels outside the detected fundus field of view."""
    circle_info = _detect_fundus_circle(img, threshold=threshold, open_kernel=open_kernel)
    if circle_info is None:
        return _fundus_fallback(img, fallback_top_ratio, "no_component")

    cx, cy, radius, area = circle_info
    h, w = img.shape[:2]
    if area < int(h * w * 0.08):
        return _fundus_fallback(img, fallback_top_ratio, "component_too_small")

    radius = float(radius) * radius_ratio
    if radius < min(h, w) * 0.20:
        return _fundus_fallback(img, fallback_top_ratio, "radius_too_small")

    circle = np.zeros((h, w), dtype=np.uint8)
    cv2.circle(circle, (int(cx), int(cy)), int(radius), 255, -1)
    left_limit = max(int(cx), int(round(w * left_ratio)))
    left_limit = min(w, max(1, left_limit))
    left_mask = np.zeros((h, w), dtype=bool)
    left_mask[:, :left_limit] = True

    out = img.copy()
    out[(circle == 0) & left_mask] = 0
    return out, True, {
        "cx": int(cx),
        "cy": int(cy),
        "radius": int(radius),
        "area": area,
        "left_limit": int(left_limit),
    }


def read_image(path: Path) -> np.ndarray:
    data = np.fromfile(str(path), dtype=np.uint8)
    img = cv2.imdecode(data, cv2.IMREAD_COLOR)
    if img is None:
        raise ValueError(f"failed to decode image: {path}")
    return img


def write_image(path: Path, img: np.ndarray, quality: int = 92) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    ext = path.suffix.lower()
    params = []
    if ext in {".jpg", ".jpeg"}:
        params = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
    ok, data = cv2.imencode(ext, img, params)
    if not ok:
        raise ValueError(f"failed to encode image: {path}")
    data.tofile(str(path))


def sample_paths(cohort_key: str, n: int, seed: int, scan_limit: int | None) -> list[Path]:
    cohort = _cohort(cohort_key)
    root = Path(C.read_path(cohort))
    exts = set(cohort.get("exts") or IMG_EXTS)
    files: list[Path] = []
    for path in root.rglob("*"):
        if path.is_file() and path.suffix.lower() in exts:
            files.append(path)
            if scan_limit and len(files) >= scan_limit:
                break
    if not files:
        raise FileNotFoundError(f"no images found for {cohort_key} under {root}")
    rng = random.Random(seed)
    return rng.sample(files, min(n, len(files)))


def maybe_ocr_metadata(img: np.ndarray, reader: object | None) -> dict[str, int | str]:
    if reader is None:
        return {}
    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 load_easyocr_reader(enable: bool) -> object | None:
    if not enable:
        return None
    try:
        import easyocr
    except Exception as exc:  # pragma: no cover - depends on h100 environment
        print(f"[warn] easyocr unavailable; OCR metadata disabled: {exc}")
        return None
    try:
        return easyocr.Reader(["ch_sim", "en"], gpu=True)
    except Exception as exc:  # pragma: no cover - depends on h100 environment
        print(f"[warn] easyocr reader failed; OCR metadata disabled: {exc}")
        return None


def make_sheet(
    groups: list[list[np.ndarray]],
    labels: list[str],
    out_path: Path,
    *,
    cell: int = 256,
    header: int = 24,
) -> None:
    rows = len(groups)
    cols = len(labels)
    sheet = Image.new("RGB", (cols * cell, rows * (cell + header)), (18, 18, 18))
    draw = ImageDraw.Draw(sheet)
    font = ImageFont.load_default()
    for row, imgs in enumerate(groups):
        for col, label in enumerate(labels):
            x = col * cell
            y = row * (cell + header)
            draw.text((x + 5, y + 5), label, fill=(230, 230, 230), font=font)
            thumb = _thumb_bgr(imgs[col], cell)
            sheet.paste(thumb, (x, y + header))
    out_path.parent.mkdir(parents=True, exist_ok=True)
    sheet.save(out_path, quality=92)


def run_fundus_trial(args: argparse.Namespace, metadata_rows: list[dict[str, object]]) -> None:
    reader = load_easyocr_reader(args.ocr)
    paths = sample_paths(args.fundus_cohort, args.n, args.seed, args.scan_limit)
    groups: list[list[np.ndarray]] = []
    for i, path in enumerate(paths):
        img = read_image(path)
        meta = maybe_ocr_metadata(img, reader)
        masked, ok, info = deidentify_fundus_circle(
            img,
            radius_ratio=args.fundus_radius_ratio,
            threshold=args.fundus_threshold,
            open_kernel=args.fundus_open_kernel,
        )
        groups.append([img, masked])
        write_image(args.out / "fundus" / f"sample_{i:03d}_masked.jpg", masked)
        metadata_rows.append(
            {
                "sample_idx": i,
                "cohort": args.fundus_cohort,
                "kind": "fundus",
                "method": "circle_mask",
                "circle_ok": ok,
                "circle_info": info,
                "ocr_meta": meta,
            }
        )
    make_sheet(groups, ["original", "circle_mask"], args.out / "fundus_compare.jpg")


def run_bscan_trial(args: argparse.Namespace, metadata_rows: list[dict[str, object]]) -> None:
    paths = sample_paths(args.bscan_cohort, args.n, args.seed + 1, args.scan_limit)
    ratios = [float(part) for part in args.bscan_ratios.split(",") if part.strip()]
    frame_specs = _parse_frame_specs(args.bscan_frame_specs)

    blackout_groups: list[list[np.ndarray]] = []
    crop_groups: list[list[np.ndarray]] = []
    frame_groups: list[list[np.ndarray]] = []
    blackout_labels = ["original"] + [f"black_{int(r * 100)}pct" for r in ratios]
    crop_labels = ["original"] + [f"crop_{int(r * 100)}pct" for r in ratios]
    frame_labels = ["original"] + [
        f"top{int(top * 100)}_bot{int(bottom * 100)}_right{int(right * 100)}"
        for top, bottom, right in frame_specs
    ]

    for i, path in enumerate(paths):
        img = read_image(path)
        blackout_imgs = [img] + [blackout_top_bar(img, r) for r in ratios]
        crop_imgs = [img] + [crop_top_bar(img, r) for r in ratios]
        frame_imgs = [img] + [
            blackout_frame(img, top=top, bottom=bottom, right=right)
            for top, bottom, right in frame_specs
        ]
        blackout_groups.append(blackout_imgs)
        crop_groups.append(crop_imgs)
        frame_groups.append(frame_imgs)
        for label, out_img in zip(blackout_labels[1:], blackout_imgs[1:], strict=True):
            write_image(args.out / "bscan_blackout" / f"sample_{i:03d}_{label}.jpg", out_img)
        for label, out_img in zip(crop_labels[1:], crop_imgs[1:], strict=True):
            write_image(args.out / "bscan_crop" / f"sample_{i:03d}_{label}.jpg", out_img)
        for label, out_img in zip(frame_labels[1:], frame_imgs[1:], strict=True):
            write_image(args.out / "bscan_frame" / f"sample_{i:03d}_{label}.jpg", out_img)
        metadata_rows.append(
            {
                "sample_idx": i,
                "cohort": args.bscan_cohort,
                "kind": "bscan",
                "methods": {"blackout": ratios, "crop": ratios, "frame": frame_specs},
            }
        )

    make_sheet(blackout_groups, blackout_labels, args.out / "bscan_blackout_compare.jpg")
    make_sheet(crop_groups, crop_labels, args.out / "bscan_crop_compare.jpg")
    make_sheet(frame_groups, frame_labels, args.out / "bscan_frame_compare.jpg")


def main() -> None:
    args = parse_args()
    args.out.mkdir(parents=True, exist_ok=True)
    metadata_rows: list[dict[str, object]] = []

    if args.fundus:
        run_fundus_trial(args, metadata_rows)
    if args.bscan:
        run_bscan_trial(args, metadata_rows)

    with (args.out / "metadata.jsonl").open("w", encoding="utf-8") as f:
        for row in metadata_rows:
            f.write(json.dumps(row, ensure_ascii=False) + "\n")
    print(f"DONE -> {args.out}")


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--out", type=Path, default=Path("/data/team/lisicheng/deid_trial"))
    parser.add_argument("--n", type=int, default=12)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--scan-limit", type=int, default=50000)
    parser.add_argument("--fundus", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--bscan", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--ocr", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--fundus-cohort", default="tongren_95disease_fundus")
    parser.add_argument("--bscan-cohort", default="xiangya_bscan")
    parser.add_argument("--fundus-radius-ratio", type=float, default=0.97)
    parser.add_argument("--fundus-threshold", type=int, default=25)
    parser.add_argument("--fundus-open-kernel", type=int, default=25)
    parser.add_argument("--bscan-ratios", default="0.06,0.08,0.10")
    parser.add_argument("--bscan-frame-specs", default="0.08,0.05,0.02;0.10,0.05,0.02")
    return parser.parse_args()


def _cohort(key: str) -> dict[str, object]:
    for cohort in C.INCLUDE:
        if cohort["key"] == key:
            return cohort
    raise KeyError(f"unknown cohort: {key}")


def _rows_from_ratio(img: np.ndarray, ratio: float) -> int:
    if not 0 < ratio < 1:
        raise ValueError(f"ratio must be between 0 and 1: {ratio}")
    return max(1, min(img.shape[0] - 1, int(round(img.shape[0] * ratio))))


def _cols_from_ratio(img: np.ndarray, ratio: float) -> int:
    if not 0 < ratio < 1:
        raise ValueError(f"ratio must be between 0 and 1: {ratio}")
    return max(1, min(img.shape[1] - 1, int(round(img.shape[1] * ratio))))


def _parse_frame_specs(raw: str) -> list[tuple[float, float, float]]:
    specs: list[tuple[float, float, float]] = []
    for item in raw.split(";"):
        item = item.strip()
        if not item:
            continue
        parts = [float(part) for part in item.split(",")]
        if len(parts) != 3:
            raise ValueError(f"frame spec must be top,bottom,right: {item}")
        specs.append((parts[0], parts[1], parts[2]))
    return specs


def _to_gray(img: np.ndarray) -> np.ndarray:
    if img.ndim == 2:
        return img
    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)


def _detect_fundus_circle(
    img: np.ndarray, *, threshold: int, open_kernel: int
) -> tuple[float, float, float, int] | None:
    gray = _to_gray(img)
    _, th = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
    kernel = np.ones((open_kernel, open_kernel), np.uint8)
    th = cv2.morphologyEx(th, cv2.MORPH_OPEN, kernel)

    n_labels, labels, stats, _ = cv2.connectedComponentsWithStats(th)
    if n_labels <= 1:
        return None

    areas = stats[1:, cv2.CC_STAT_AREA]
    largest = 1 + int(np.argmax(areas))
    area = int(stats[largest, cv2.CC_STAT_AREA])
    mask = (labels == largest).astype("uint8") * 255
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if not contours:
        return None

    contour = max(contours, key=cv2.contourArea)
    (cx, cy), radius = cv2.minEnclosingCircle(contour)
    return cx, cy, radius, area


def _fundus_fallback(
    img: np.ndarray, top_ratio: float, reason: str
) -> tuple[np.ndarray, bool, dict[str, str]]:
    return blackout_top_bar(img, top_ratio), False, {"reason": reason}


def _thumb_bgr(img: np.ndarray, cell: int) -> Image.Image:
    if img.ndim == 2:
        rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    else:
        rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    im = Image.fromarray(rgb)
    im.thumbnail((cell, cell), Image.Resampling.LANCZOS)
    canvas = Image.new("RGB", (cell, cell), (0, 0, 0))
    canvas.paste(im, ((cell - im.width) // 2, (cell - im.height) // 2))
    return canvas


if __name__ == "__main__":
    main()