#!/usr/bin/env python3 """ REFUGE2 adapter — disc/cup segmentation + glaucoma binary label (train only). Inputs: {input_root}/REFUGE2/{train,val,test}/images/*.jpg {input_root}/REFUGE2/{train,val,test}/mask/*.{bmp,png} (pixel 0/128/255 = bg/disc/cup) File naming encodes split AND label: train/ g0001..g0040 = glaucoma, n0001..n0360 = non-glaucoma (mask: .bmp) val/ V0001..V0400 (mask: .png) test/ T0001..T0400 (mask: .bmp) val/test glaucoma label is held-out by the challenge (severity=unknown for those). Outputs (under {output_root}): extracted/public_refuge2_disc_cup/{hash[:2]}/{hash}/ fundus_color.jpg disc_cup_mask.png (preserves 0/128/255 — 3 classes) meta.json manifest/public_refuge2_disc_cup_images.parquet captions/public_refuge2_disc_cup_captions.parquet """ import argparse import json from pathlib import Path import numpy as np import pandas as pd from PIL import Image from public_common import ( IMAGE_SCHEMA_COLUMNS, CAPTION_SCHEMA_COLUMNS, study_hash_for, default_base_fields, caption_l1_public, caption_l3_public, study_dir_for, rel_file_path, write_meta, coerce_image_row, ) COHORT = "public_refuge2_disc_cup" COHORT_PHRASE = "REFUGE2 optic disc and cup segmentation dataset" def _split_and_label(filename: str) -> tuple: """Returns (split, glaucoma) from filename prefix. glaucoma: True / False / None (unknown for val/test).""" p = filename[0] if p == "g": return ("train", True) if p == "n": return ("train", False) if p == "V": return ("val", None) if p == "T": return ("test", None) return ("unknown", None) def _save_disc_cup_mask(src: Path, dst: Path): """Preserve 3 classes (0/128/255). Coerce non-{0,128,255} values to nearest.""" arr = np.array(Image.open(src).convert("L")) out = np.zeros_like(arr) out[(arr > 64) & (arr < 192)] = 128 out[arr >= 192] = 255 Image.fromarray(out, mode="L").save(dst, "PNG", optimize=True) def process_one(image_path: Path, mask_path: Path, out_root: Path, force: bool): basename = image_path.stem # g0001 / n0001 / V0001 / T0001 split, glaucoma = _split_and_label(basename) sh = study_hash_for(COHORT, f"{split}_{basename}") sdir = study_dir_for(out_root, COHORT, sh) sdir.mkdir(parents=True, exist_ok=True) meta_p = sdir / "meta.json" if meta_p.exists() and not force: try: meta = json.loads(meta_p.read_text()) if meta.get("status") == "ok": return _row_and_caps(meta) except Exception: pass img = Image.open(image_path).convert("RGB") w, h = img.size img.save(sdir / "fundus_color.jpg", "JPEG", quality=95) _save_disc_cup_mask(mask_path, sdir / "disc_cup_mask.png") meta = { "status": "ok", "cohort": COHORT, "study_hash": sh, "source_basename": basename, "split": split, "image_height_px": int(h), "image_width_px": int(w), "eye": "unknown", "glaucoma": glaucoma, # True / False / None "has_disc_mask": True, "has_cup_mask": True, } write_meta(sdir, meta) return _row_and_caps(meta) def _row_and_caps(meta: dict): sh = meta["study_hash"] image_id = f"{COHORT}_{sh}_fundus_color" row = default_base_fields(COHORT, sh, eye=meta["eye"]) glaucoma = meta.get("glaucoma") if glaucoma is True: row["diagnosis_group"] = ["glaucoma"] row["severity"] = "unknown" # REFUGE2 gives binary label only, no grade row["diagnosis_source"] = "expert_label" elif glaucoma is False: row["diagnosis_group"] = [] row["severity"] = "none" row["diagnosis_source"] = "expert_label" else: row["diagnosis_source"] = "none" row.update({ "image_id": image_id, "file_path": rel_file_path(COHORT, sh, "fundus_color.jpg"), "file_format": "jpg", "modality": "fundus_color", "anatomy": "optic_disc", "device_technology": "fundus_camera", "scan_protocol": "single_shot", "image_height_px": meta["image_height_px"], "image_width_px": meta["image_width_px"], "has_segmentation": True, "n_layers_visible": 0, "is_valid": True, }) caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"]) parts = [f"A color fundus photograph from the REFUGE2 dataset ({meta['split']} split)", "with optic disc and optic cup segmentation masks"] if glaucoma is True: parts.append("glaucomatous eye") elif glaucoma is False: parts.append("non-glaucomatous eye") l3 = ", ".join(parts) + "." caps.append(caption_l3_public(image_id, l3, "manifest_fields+disc_cup_mask")) return row, caps def main(): ap = argparse.ArgumentParser() ap.add_argument("--input-root", required=True, help="Path to .../Generation/REFUGE2 (contains REFUGE2/{train,val,test}/...)") ap.add_argument("--output-root", required=True) ap.add_argument("--force", action="store_true") ap.add_argument("--limit", type=int, default=None) args = ap.parse_args() in_root = Path(args.input_root) out_root = Path(args.output_root) rows, caps = [], [] for split in ("train", "val", "test"): img_dir = in_root / "REFUGE2" / split / "images" mask_dir = in_root / "REFUGE2" / split / "mask" # mask extension differs by split: train/test = .bmp, val = .png for ip in sorted(img_dir.glob("*.jpg")): stem = ip.stem mp = mask_dir / f"{stem}.bmp" if not mp.exists(): mp = mask_dir / f"{stem}.png" if not mp.exists(): print(f" skip {ip.name}: no mask found") continue row, cap = process_one(ip, mp, out_root, args.force) rows.append(row) caps.extend(cap) if args.limit and len(rows) >= args.limit: break if args.limit and len(rows) >= args.limit: break if not rows: print(f"[{COHORT}] no rows") return mdir = out_root / "manifest"; cdir = out_root / "captions" mdir.mkdir(parents=True, exist_ok=True); cdir.mkdir(parents=True, exist_ok=True) imgs_df = pd.DataFrame([coerce_image_row(r) for r in rows])[IMAGE_SCHEMA_COLUMNS] imgs_df.to_parquet(mdir / f"{COHORT}_images.parquet", index=False) caps_df = pd.DataFrame(caps)[CAPTION_SCHEMA_COLUMNS] caps_df.to_parquet(cdir / f"{COHORT}_captions.parquet", index=False) print(f"[{COHORT}] wrote {len(imgs_df)} images, {len(caps_df)} captions") print(imgs_df.groupby(["severity"]).size().to_string()) if __name__ == "__main__": main()