File size: 6,806 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 | #!/usr/bin/env python3
"""
EyePACS adapter — augmented_resized_V2 (143k DR-graded 600x600 fundus images).
Inputs:
{input_root}/augmented_resized_V2/{train,val,test}/{0,1,2,3,4}/*.jpg
Subdir name = DR grade. Filenames like 005b95c28852-600.jpg.
Outputs (under {output_root}):
extracted/public_eyepacs_combo_dr_aug/{hash[:2]}/{hash}/
fundus_color.jpg (copied as-is — already 600x600)
meta.json
manifest/public_eyepacs_combo_dr_aug_images.parquet
captions/public_eyepacs_combo_dr_aug_captions.parquet
143k images → multiprocessed. study_hash basename includes split to avoid hash collisions
if the same id_code appears across splits (it should not, but be defensive).
"""
import argparse
import json
import shutil
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import pandas as pd
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
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_eyepacs_combo_dr_aug"
COHORT_PHRASE = "EyePACS combined diabetic retinopathy screening dataset (augmented 600x600 v2)"
DR_SEVERITY = {0: "none", 1: "mild", 2: "moderate", 3: "severe", 4: "proliferative"}
def process_one(args):
image_path_str, split, dr_grade, out_root_str, force = args
image_path = Path(image_path_str)
out_root = Path(out_root_str)
basename = f"{split}_{image_path.stem}" # e.g. train_005b95c28852-600
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":
return _row_and_caps(meta)
except Exception:
pass
dst_img = sdir / "fundus_color.jpg"
try:
if not dst_img.exists() or force:
shutil.copyfile(image_path, dst_img)
# Verify the copy is a readable image (catches truncated / non-JPG bytes)
with Image.open(dst_img) as im:
im.verify()
with Image.open(dst_img) as im:
w, h = im.size
except Exception as e:
# Delete the partial copy so a future run can re-attempt
try: dst_img.unlink(missing_ok=True)
except Exception: pass
return ("FAIL", basename, type(e).__name__, str(e)[:200])
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",
"dr_grade": int(dr_grade),
}
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"])
dr = meta["dr_grade"]
row["diagnosis_group"] = ["DR"] if dr > 0 else []
row["severity"] = DR_SEVERITY[dr]
row["diagnosis_source"] = "screening_label"
row["label_confidence"] = "single_reader"
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,
})
caps = caption_l1_public(image_id, COHORT_PHRASE, "fundus_color", meta["eye"])
l3 = (f"A color fundus photograph from the EyePACS combined DR screening dataset "
f"({meta['split']} split, augmented 600x600), "
f"diabetic retinopathy grade {dr} ({DR_SEVERITY[dr]}).")
caps.append(caption_l3_public(image_id, l3, "manifest_fields+screening_label"))
return row, caps
def _enumerate_inputs(in_root: Path):
base = in_root / "augmented_resized_V2"
for split in ("train", "val", "test"):
for grade in range(5):
d = base / split / str(grade)
if not d.exists():
continue
for ip in d.glob("*.jpg"):
yield (str(ip), split, grade)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--input-root", required=True,
help="Path to .../Generation/EyePACS (contains augmented_resized_V2/)")
ap.add_argument("--output-root", required=True)
ap.add_argument("--num-workers", type=int, default=8)
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)
inputs = list(_enumerate_inputs(in_root))
print(f"[{COHORT}] enumerated {len(inputs)} images")
if args.limit:
inputs = inputs[:args.limit]
job_args = [(p, s, g, str(out_root), args.force) for (p, s, g) in inputs]
rows, caps = [], []
failures = []
with ProcessPoolExecutor(max_workers=args.num_workers) as ex:
futs = [ex.submit(process_one, a) for a in job_args]
for i, fut in enumerate(as_completed(futs), 1):
try:
result = fut.result()
except Exception as e:
failures.append(("worker", type(e).__name__, str(e)[:200]))
continue
if isinstance(result, tuple) and len(result) == 4 and result[0] == "FAIL":
failures.append(result[1:])
continue
row, cap = result
rows.append(row)
caps.extend(cap)
if i % 5000 == 0:
print(f" ... {i}/{len(futs)} ({len(failures)} failed so far)")
if failures:
print(f"[{COHORT}] {len(failures)} images FAILED to extract:")
for f in failures[:30]:
print(f" {f}")
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()
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