File size: 5,557 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 | #!/usr/bin/env python3
"""
DRIVE adapter — vessel segmentation public dataset.
Inputs:
{input_root}/DRIVE/training/images/NN_training.tif (20 files)
{input_root}/DRIVE/training/1st_manual/NN_manual1.gif (vessel GT)
{input_root}/DRIVE/training/mask/NN_training_mask.gif (FOV mask)
Outputs (under {output_root}):
extracted/public_drive_vessel/{hash[:2]}/{hash}/
fundus_color.jpg (re-encoded from .tif)
vessel_mask.png (binary 0/255, from 1st_manual)
fov_mask.png (binary 0/255, from mask/)
meta.json
manifest/public_drive_vessel_images.parquet
captions/public_drive_vessel_captions.parquet
Test split (also 20 images) intentionally skipped — it lacks vessel GT, only FOV mask.
"""
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_drive_vessel"
COHORT_PHRASE = "DRIVE retinal vessel segmentation dataset"
def _binarize(p: Path) -> np.ndarray:
arr = np.array(Image.open(p).convert("L"))
return ((arr > 127).astype(np.uint8) * 255)
def process_one(image_path: Path, vessel_path: Path, fov_path: Path,
out_root: Path, force: bool):
basename = image_path.stem # "21_training"
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)
Image.fromarray(_binarize(vessel_path), mode="L").save(
sdir / "vessel_mask.png", "PNG", optimize=True)
Image.fromarray(_binarize(fov_path), mode="L").save(
sdir / "fov_mask.png", "PNG", optimize=True)
meta = {
"status": "ok",
"cohort": COHORT,
"study_hash": sh,
"source_basename": basename,
"image_height_px": int(h),
"image_width_px": int(w),
"has_vessel_mask": True,
"has_fov_mask": True,
"eye": "unknown",
}
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"])
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": True,
"n_layers_visible": 0,
"is_valid": True,
})
caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"])
l3 = ("A color fundus photograph from the DRIVE dataset, "
"with manually annotated retinal vessel segmentation mask and field-of-view mask.")
caps.append(caption_l3_public(image_id, l3, "manifest_fields+vessel_mask"))
return row, caps
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--input-root", required=True,
help="Path to .../Generation/DRIVE (contains DRIVE/training/...)")
ap.add_argument("--output-root", required=True,
help="Public manifest root, e.g. .../c3/public_eye_pretrain")
ap.add_argument("--force", action="store_true")
args = ap.parse_args()
in_root = Path(args.input_root)
out_root = Path(args.output_root)
train_dir = in_root / "DRIVE" / "training"
img_dir = train_dir / "images"
vessel_dir = train_dir / "1st_manual"
fov_dir = train_dir / "mask"
img_files = sorted(img_dir.glob("*_training.tif"))
print(f"[{COHORT}] {len(img_files)} training images under {img_dir}")
rows, caps = [], []
for ip in img_files:
stem = ip.stem.replace("_training", "")
vp = vessel_dir / f"{stem}_manual1.gif"
fp = fov_dir / f"{stem}_training_mask.gif"
if not (vp.exists() and fp.exists()):
print(f" skip {ip.name}: vessel={vp.exists()}, fov={fp.exists()}")
continue
row, cap = process_one(ip, vp, fp, out_root, args.force)
rows.append(row)
caps.extend(cap)
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)} image rows, {len(caps_df)} caption rows")
if __name__ == "__main__":
main()
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