File size: 6,894 Bytes
e2f75d1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | #!/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()
|