#!/usr/bin/env python3 """ IDRiD adapter — DR grading + lesion segmentation + OD/fovea localization (single cohort). Two image tracks (disjoint, distinguished by basename prefix): Grading + Localization track (516 = 413 train + 103 test): Inputs: B. Disease Grading/1. Original Images/{a|b}. {Training|Testing} Set/IDRiD_NNN.jpg B. Disease Grading/2. Groundtruths/{a|b}. IDRiD_Disease Grading_{Training|Testing} Labels.csv C. Localization/2. Groundtruths/1. Optic Disc Center Location/*.csv C. Localization/2. Groundtruths/2. Fovea Center Location/*.csv Manifest row: DR grade + DME risk; has_segmentation=False. Sidecar parquet: OD + fovea pixel coords + image dims. Segmentation track (81 = 54 train + 27 test): Inputs: A. Segmentation/1. Original Images/{a|b}. {Training|Testing} Set/IDRiD_NN.jpg A. Segmentation/2. All Segmentation Groundtruths/.../{MA,HE,EX,SE,OD}.tif Mask suffix per class: MA=microaneurysms, HE=haemorrhages, EX=hard exudates, SE=soft exudates, OD=optic disc. Manifest row: has_segmentation=True; lesion_tags driven by which masks exist. Outputs (under {output_root}): extracted/public_idrid/{hash[:2]}/{hash}/ fundus_color.jpg lesion_microaneurysms.png (binary 0/255, present only in seg track) lesion_haemorrhages.png lesion_hard_exudates.png lesion_soft_exudates.png optic_disc_mask.png meta.json manifest/public_idrid_images.parquet manifest/public_idrid_sidecar.parquet (localization coords) captions/public_idrid_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_idrid" COHORT_PHRASE = "IDRiD diabetic retinopathy dataset" DR_SEVERITY = {0: "none", 1: "mild", 2: "moderate", 3: "severe", 4: "proliferative"} DME_RISK = {0: None, 1: "macular_edema", 2: "clinically_significant_macular_edema"} LESION_SUFFIX = { "MA": ("microaneurysms", "lesion_microaneurysms.png"), "HE": ("haemorrhages", "lesion_haemorrhages.png"), "EX": ("hard_exudates", "lesion_hard_exudates.png"), "SE": ("soft_exudates", "lesion_soft_exudates.png"), "OD": ("optic_disc", "optic_disc_mask.png"), } def _binarize_save(src: Path, dst: Path): arr = np.array(Image.open(src).convert("L")) Image.fromarray(((arr > 0).astype(np.uint8) * 255), mode="L").save( dst, "PNG", optimize=True) def _save_fundus(src: Path, dst: Path): img = Image.open(src).convert("RGB") img.save(dst, "JPEG", quality=95) return img.size # (w, h) # ============================================================ # Grading + Localization track # ============================================================ def _load_grading_labels(in_root: Path) -> pd.DataFrame: parts = [] for split, fn in [ ("train", "B. Disease Grading/2. Groundtruths/a. IDRiD_Disease Grading_Training Labels.csv"), ("test", "B. Disease Grading/2. Groundtruths/b. IDRiD_Disease Grading_Testing Labels.csv"), ]: df = pd.read_csv(in_root / fn) df = df[[c for c in df.columns if not c.startswith("Unnamed")]] df.columns = [c.strip() for c in df.columns] df = df.rename(columns={ "Image name": "image_name", "Retinopathy grade": "dr_grade", "Risk of macular edema": "dme_risk", }) df = df.dropna(subset=["image_name"]) df["split"] = split parts.append(df) return pd.concat(parts, ignore_index=True) def _load_loc_csv(p: Path, prefix: str) -> dict: df = pd.read_csv(p) df = df[[c for c in df.columns if not c.startswith("Unnamed")]] df.columns = [c.strip() for c in df.columns] df = df.rename(columns={"Image No": "image_name", "X- Coordinate": f"{prefix}_x", "Y - Coordinate": f"{prefix}_y"}) df = df.dropna(subset=["image_name"]) return df.set_index("image_name")[[f"{prefix}_x", f"{prefix}_y"]].to_dict(orient="index") def process_grading(in_root: Path, out_root: Path, force: bool): labels = _load_grading_labels(in_root) od_train = _load_loc_csv(in_root / "C. Localization/2. Groundtruths/1. Optic Disc Center Location/a. IDRiD_OD_Center_Training Set_Markups.csv", "od") od_test = _load_loc_csv(in_root / "C. Localization/2. Groundtruths/1. Optic Disc Center Location/b. IDRiD_OD_Center_Testing Set_Markups.csv", "od") fv_train = _load_loc_csv(in_root / "C. Localization/2. Groundtruths/2. Fovea Center Location/IDRiD_Fovea_Center_Training Set_Markups.csv", "fovea") fv_test = _load_loc_csv(in_root / "C. Localization/2. Groundtruths/2. Fovea Center Location/IDRiD_Fovea_Center_Testing Set_Markups.csv", "fovea") od_all = {**od_train, **od_test} fv_all = {**fv_train, **fv_test} rows, caps, side = [], [], [] for _, lab in labels.iterrows(): name = str(lab["image_name"]).strip() split = lab["split"] src = in_root / f"B. Disease Grading/1. Original Images/{'a. Training Set' if split=='train' else 'b. Testing Set'}/{name}.jpg" if not src.exists(): print(f" [grading] missing image {src}") continue basename = f"grading_{split}_{name}" sh = study_hash_for(COHORT, 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": rows.append(_grading_row(meta)) caps.extend(_grading_caps(meta)) side.append(_sidecar_row(meta)) continue except Exception: pass w, h = _save_fundus(src, sdir / "fundus_color.jpg") od = od_all.get(name, {}) fv = fv_all.get(name, {}) meta = { "status": "ok", "cohort": COHORT, "study_hash": sh, "source_basename": basename, "track": "grading", "split": split, "image_height_px": int(h), "image_width_px": int(w), "eye": "unknown", "dr_grade": int(lab["dr_grade"]) if pd.notna(lab["dr_grade"]) else None, "dme_risk": int(lab["dme_risk"]) if pd.notna(lab["dme_risk"]) else None, "od_x_px": float(od.get("od_x")) if od else None, "od_y_px": float(od.get("od_y")) if od else None, "fovea_x_px": float(fv.get("fovea_x")) if fv else None, "fovea_y_px": float(fv.get("fovea_y")) if fv else None, } write_meta(sdir, meta) rows.append(_grading_row(meta)) caps.extend(_grading_caps(meta)) side.append(_sidecar_row(meta)) print(f"[{COHORT}/grading] {len(rows)} rows") return rows, caps, side def _grading_row(meta: dict) -> dict: sh = meta["study_hash"] image_id = f"{COHORT}_{sh}_fundus_color" row = default_base_fields(COHORT, sh, eye=meta["eye"]) dr = meta.get("dr_grade") dme_risk = meta.get("dme_risk") dx, lesions = [], [] if dr is not None and dr > 0: dx.append("DR") if dme_risk and dme_risk > 0: dx.append("DME") tag = DME_RISK.get(dme_risk) if tag: lesions.append(tag) row["diagnosis_group"] = dx row["lesion_tags"] = lesions row["severity"] = DR_SEVERITY.get(dr, "unknown") row["diagnosis_source"] = "expert_grade" if dr is not None else "none" row["label_confidence"] = "consensus" if dr is not None else None row.update({ "image_id": image_id, "file_path": rel_file_path(COHORT, sh, "fundus_color.jpg"), "file_format": "jpg", "modality": "fundus_color", "anatomy": "macula", "device_technology": "fundus_camera", "scan_protocol": "single_shot", "image_height_px": meta["image_height_px"], "image_width_px": meta["image_width_px"], "has_segmentation": False, "n_layers_visible": 0, "is_valid": True, }) return row def _grading_caps(meta: dict) -> list: sh = meta["study_hash"] image_id = f"{COHORT}_{sh}_fundus_color" caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"]) parts = [f"A color fundus photograph from the IDRiD dataset ({meta['split']} split)"] if meta.get("dr_grade") is not None: parts.append(f"diabetic retinopathy grade {meta['dr_grade']} ({DR_SEVERITY[meta['dr_grade']]})") if meta.get("dme_risk") is not None and meta["dme_risk"] > 0: parts.append(f"macular edema risk {meta['dme_risk']}") l3 = ", ".join(parts) + "." caps.append(caption_l3_public(image_id, l3, "manifest_fields+csv_labels")) return caps def _sidecar_row(meta: dict) -> dict: sh = meta["study_hash"] return { "image_id": f"{COHORT}_{sh}_fundus_color", "split": meta.get("split"), "od_x_px": meta.get("od_x_px"), "od_y_px": meta.get("od_y_px"), "fovea_x_px": meta.get("fovea_x_px"), "fovea_y_px": meta.get("fovea_y_px"), "image_width_px": meta.get("image_width_px"), "image_height_px": meta.get("image_height_px"), } # ============================================================ # Segmentation track # ============================================================ def process_segmentation(in_root: Path, out_root: Path, force: bool): seg_dir = in_root / "A. Segmentation" rows, caps = [], [] for split, sub_img, sub_gt in [ ("train", "a. Training Set", "a. Training Set"), ("test", "b. Testing Set", "b. Testing Set"), ]: img_dir = seg_dir / "1. Original Images" / sub_img gt_dir = seg_dir / "2. All Segmentation Groundtruths" / sub_gt for ip in sorted(img_dir.glob("IDRiD_*.jpg")): name = ip.stem # IDRiD_01 basename = f"seg_{split}_{name}" sh = study_hash_for(COHORT, 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": rows.append(_seg_row(meta)) caps.extend(_seg_caps(meta)) continue except Exception: pass w, h = _save_fundus(ip, sdir / "fundus_color.jpg") has_mask = {} for suffix, (label, fname) in LESION_SUFFIX.items(): subdir_idx = {"MA": "1. Microaneurysms", "HE": "2. Haemorrhages", "EX": "3. Hard Exudates", "SE": "4. Soft Exudates", "OD": "5. Optic Disc"}[suffix] src = gt_dir / subdir_idx / f"{name}_{suffix}.tif" if src.exists(): _binarize_save(src, sdir / fname) has_mask[label] = True else: has_mask[label] = False meta = { "status": "ok", "cohort": COHORT, "study_hash": sh, "source_basename": basename, "track": "segmentation", "split": split, "image_height_px": int(h), "image_width_px": int(w), "eye": "unknown", **{f"has_{k}_mask": v for k, v in has_mask.items()}, } write_meta(sdir, meta) rows.append(_seg_row(meta)) caps.extend(_seg_caps(meta)) print(f"[{COHORT}/seg] {len(rows)} rows") return rows, caps def _seg_row(meta: dict) -> dict: sh = meta["study_hash"] image_id = f"{COHORT}_{sh}_fundus_color" row = default_base_fields(COHORT, sh, eye=meta["eye"]) lesions = [] for label in ("microaneurysms", "haemorrhages", "hard_exudates", "soft_exudates"): if meta.get(f"has_{label}_mask"): lesions.append(label) if lesions: row["diagnosis_group"] = ["DR"] # any DR-related lesion implies DR positive row["lesion_tags"] = lesions row["diagnosis_source"] = "expert_segmentation" if lesions else "none" row.update({ "image_id": image_id, "file_path": rel_file_path(COHORT, sh, "fundus_color.jpg"), "file_format": "jpg", "modality": "fundus_color", "anatomy": "macula", "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, }) return row def _seg_caps(meta: dict) -> list: sh = meta["study_hash"] image_id = f"{COHORT}_{sh}_fundus_color" caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"]) present = [k for k in ("microaneurysms", "haemorrhages", "hard_exudates", "soft_exudates", "optic_disc") if meta.get(f"has_{k}_mask")] parts = [f"A color fundus photograph from the IDRiD dataset ({meta['split']} split, segmentation subset)"] if present: parts.append("with manually annotated " + ", ".join(p.replace("_", " ") for p in present) + " segmentation masks") l3 = ", ".join(parts) + "." caps.append(caption_l3_public(image_id, l3, "manifest_fields+mask_presence")) return caps # ============================================================ # Main # ============================================================ def main(): ap = argparse.ArgumentParser() ap.add_argument("--input-root", required=True, help="Path to .../Generation/IDRiD (contains A./B./C. subdirs)") ap.add_argument("--output-root", required=True) ap.add_argument("--force", action="store_true") ap.add_argument("--skip-grading", action="store_true") ap.add_argument("--skip-segmentation", action="store_true") args = ap.parse_args() in_root = Path(args.input_root) out_root = Path(args.output_root) rows, caps, side = [], [], [] if not args.skip_grading: r, c, s = process_grading(in_root, out_root, args.force) rows += r; caps += c; side += s if not args.skip_segmentation: r, c = process_segmentation(in_root, out_root, args.force) rows += r; caps += c if not rows: print(f"[{COHORT}] no rows produced") 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) if side: pd.DataFrame(side).to_parquet(mdir / f"{COHORT}_sidecar.parquet", index=False) print(f"[{COHORT}] wrote {len(imgs_df)} images, {len(caps_df)} captions, {len(side)} sidecar") print(imgs_df.groupby(["has_segmentation", "severity"]).size().to_string()) if __name__ == "__main__": main()