File size: 6,225 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 | #!/usr/bin/env python3
"""
Messidor-2 adapter — DR grading + DME + gradability multi-task fundus dataset.
Inputs:
{input_root}/messidor-2/messidor-2/preprocess/{id_code} (1744 PNG files)
{input_root}/messidor_data.csv (id_code, diagnosis, adjudicated_dme, adjudicated_gradable)
Outputs (under {output_root}):
extracted/public_messidor2_dr/{hash[:2]}/{hash}/
fundus_color.jpg
meta.json
manifest/public_messidor2_dr_images.parquet
captions/public_messidor2_dr_captions.parquet
Label mapping:
diagnosis 0..4 -> severity {none, mild, moderate, severe, proliferative}, diagnosis_group += [DR] if >0
adjudicated_dme 0/1 -> diagnosis_group += [DME], lesion_tags += [macular_edema]
adjudicated_gradable 0 -> image_quality_band = "ungradable", is_valid = False
"""
import argparse
import json
from pathlib import Path
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_messidor2_dr"
COHORT_PHRASE = "Messidor-2 diabetic retinopathy screening dataset"
DR_SEVERITY = {0: "none", 1: "mild", 2: "moderate", 3: "severe", 4: "proliferative"}
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"])
dr = meta.get("dr_grade")
dme = bool(meta.get("dme"))
gradable = bool(meta.get("gradable"))
dx, lesions = [], []
if dr is not None and dr > 0:
dx.append("DR")
if dme:
dx.append("DME")
lesions.append("macular_edema")
row["diagnosis_group"] = dx
row["lesion_tags"] = lesions
row["severity"] = DR_SEVERITY.get(dr, "unknown")
row["diagnosis_source"] = "adjudicated_label" if dr is not None else "none"
row["label_confidence"] = "adjudicated" if dr is not None else None
row["image_quality_band"] = "unknown" if gradable else "ungradable"
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": int(meta["image_height_px"]),
"image_width_px": int(meta["image_width_px"]),
"has_segmentation": False,
"n_layers_visible": 0,
"is_valid": gradable,
})
caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"])
parts = ["A color fundus photograph from the Messidor-2 dataset"]
if dr is not None:
parts.append(f"diabetic retinopathy grade {dr} ({DR_SEVERITY[dr]})")
if dme:
parts.append("diabetic macular edema present")
if not gradable:
parts.append("flagged as ungradable")
l3 = ", ".join(parts) + "."
caps.append(caption_l3_public(image_id, l3, "manifest_fields+csv_labels"))
return row, caps
def process_one(image_path: Path, labels: dict, out_root: Path, force: bool):
basename = image_path.stem
sh = study_hash_for(COHORT, basename)
sdir = study_dir_for(out_root, COHORT, sh)
sdir.mkdir(parents=True, exist_ok=True)
meta_path = sdir / "meta.json"
if meta_path.exists() and not force:
try:
meta = json.loads(meta_path.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)
meta = {
"status": "ok",
"cohort": COHORT,
"study_hash": sh,
"source_basename": basename,
"image_height_px": int(h),
"image_width_px": int(w),
"eye": "unknown",
"dr_grade": labels.get("diagnosis"),
"dme": int(labels.get("adjudicated_dme", 0)),
"gradable": int(labels.get("adjudicated_gradable", 1)),
}
write_meta(sdir, meta)
return _row_and_caps(meta)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--input-root", required=True,
help="Path to .../Generation/Messidor2 (contains messidor_data.csv and messidor-2/messidor-2/preprocess/)")
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)
csv_path = in_root / "messidor_data.csv"
img_dir = in_root / "messidor-2" / "messidor-2" / "preprocess"
df = pd.read_csv(csv_path)
print(f"[{COHORT}] CSV rows: {len(df)}, image dir: {img_dir}")
if args.limit:
df = df.head(args.limit)
rows, caps = [], []
missing = 0
for _, lab in df.iterrows():
fname = lab["id_code"]
ip = img_dir / fname
if not ip.exists():
missing += 1
continue
row, cap = process_one(ip, lab.to_dict(), out_root, args.force)
rows.append(row)
caps.extend(cap)
if missing:
print(f"[{COHORT}] WARN: {missing} CSV rows have no matching image file")
if not rows:
print(f"[{COHORT}] no rows produced, aborting")
return
manifest_dir = out_root / "manifest"
captions_dir = out_root / "captions"
manifest_dir.mkdir(parents=True, exist_ok=True)
captions_dir.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(manifest_dir / f"{COHORT}_images.parquet", index=False)
caps_df = pd.DataFrame(caps)[CAPTION_SCHEMA_COLUMNS]
caps_df.to_parquet(captions_dir / 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()
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