Datasets:
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# -*- coding: utf-8 -*-
"""
Unified preprocessing for 7 fundus-image classification datasets.
Per dataset it produces:
<dataset_root>/
train/<label>/<img>
val/<label>/<img>
test/<label>/<img>
labels.csv (columns: split,filepath,label,class_name,orig_id)
and DELETES everything classification-irrelevant (segmentation masks, disc/fovea
localization, image-quality columns, extra preprocessing versions, other modalities,
upload templates, helper code, original nested folders ...).
Split policy (decided with the user):
* Datasets WITH a complete official labeled split -> keep it:
AIROGS (train/val/test), APTOS (train/val/test), DeepDRiD (train/val/test)
* Datasets WITHOUT one -> stratified 7:1:2 (seed=42):
MMAC (official has no test -> pool train+val and re-split),
ADAM, IDRiD, PAPILA(grouped by patient)
Class encodings:
MMAC 0..4 myopic-maculopathy grade
ADAM 0=Non-AMD 1=AMD
AIROGS 0=NRG 1=RG (release-crop version only)
PAPILA 0=healthy 1=glaucoma (diagnosis==2 "suspect" dropped; split by patient)
IDRiD 0..4 DR grade
APTOS 0..4 DR grade
DeepDRiD 0..4 DR grade (regular fundus only; per-eye label)
Run dry-run first (prints the plan, moves nothing), then with --execute.
"""
import os, sys, csv, random, shutil
from collections import defaultdict
import pandas as pd
ROOT = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image/Dataset"
SEED = 42
RATIOS = (0.7, 0.1, 0.2)
EXECUTE = "--execute" in sys.argv
KEEP = {"train", "val", "test", "labels.csv"}
# ----------------------------------------------------------------------------- helpers
def walk_index(root, exts=(".jpg", ".jpeg", ".png", ".tif", ".tiff", ".bmp")):
"""Map basename-without-extension -> absolute path for every image under root."""
idx = {}
for dp, _, files in os.walk(root):
for f in files:
if f.lower().endswith(exts):
idx[os.path.splitext(f)[0]] = os.path.join(dp, f)
return idx
def stratified_split(pairs, ratios=RATIOS, seed=SEED):
"""pairs: list[(key,label)] -> dict key->split, stratified per label."""
by = defaultdict(list)
for k, l in pairs:
by[l].append(k)
out, rnd = {}, random.Random(seed)
for l in sorted(by, key=str):
ks = sorted(by[l])
rnd.shuffle(ks)
n = len(ks)
ntr = int(round(n * ratios[0]))
nva = int(round(n * ratios[1]))
# guarantee non-empty test when a class is large enough
if n >= 3 and ntr + nva >= n:
nva = max(0, n - ntr - 1)
for i, k in enumerate(ks):
out[k] = "train" if i < ntr else ("val" if i < ntr + nva else "test")
return out
def place(out_dir, records, do_clean_root=True):
"""records: list of dict(src, split, label, class_name, orig_id).
Moves files into out_dir/<split>/<label>/, writes labels.csv, then whitelist-cleans."""
# collision check
seen, dup = set(), 0
for r in records:
key = (r["split"], str(r["label"]), os.path.basename(r["src"]))
if key in seen:
dup += 1
seen.add(key)
counts = defaultdict(int)
for r in records:
counts[(r["split"], r["label"])] += 1
# report
name = os.path.basename(out_dir.rstrip("/"))
total = len(records)
print(f" [{name}] total={total} duplicates={dup}")
for sp in ("train", "val", "test"):
per = {l: c for (s, l), c in sorted(counts.items()) if s == sp}
if per:
print(f" {sp:5s} n={sum(per.values()):5d} by_class={per}")
if dup:
print(f" !! {dup} basename collisions in same split/label — aborting this dataset")
return
if not EXECUTE:
return
rows = []
for r in records:
dd = os.path.join(out_dir, r["split"], str(r["label"]))
os.makedirs(dd, exist_ok=True)
base = os.path.basename(r["src"])
dst = os.path.join(dd, base)
shutil.move(r["src"], dst)
rows.append([r["split"], os.path.join(r["split"], str(r["label"]), base),
r["label"], r["class_name"], r["orig_id"]])
with open(os.path.join(out_dir, "labels.csv"), "w", newline="") as f:
w = csv.writer(f)
w.writerow(["split", "filepath", "label", "class_name", "orig_id"])
w.writerows(sorted(rows))
if do_clean_root:
for item in os.listdir(out_dir):
if item in KEEP:
continue
p = os.path.join(out_dir, item)
shutil.rmtree(p) if os.path.isdir(p) else os.remove(p)
print(f" -> moved & cleaned: {out_dir}")
# ----------------------------------------------------------------------------- datasets
def do_mmac():
out = os.path.join(ROOT, "Myopia", "Classification_of_Myopic_Maculopathy")
base = os.path.join(out, "1. Classification of Myopic Maculopathy")
gt = os.path.join(base, "2. Groundtruths")
label_map = {}
for fn in ["1. MMAC2023_Myopic_Maculopathy_Classification_Training_Labels.csv",
"2. MMAC2023_Myopic_Maculopathy_Classification_Validation_Labels.csv"]:
df = pd.read_csv(os.path.join(gt, fn))
for _, r in df.iterrows():
label_map[str(r["image"])] = int(r["myopic_maculopathy_grade"])
idx = walk_index(os.path.join(base, "1. Images"))
recs, pairs = [], []
for img, g in label_map.items():
key = os.path.splitext(img)[0]
if key not in idx:
continue
pairs.append((key, g))
sp = stratified_split(pairs)
for key, g in pairs:
recs.append(dict(src=idx[key], split=sp[key], label=g,
class_name=f"grade_{g}", orig_id=key))
return out, recs
def do_adam():
out = os.path.join(ROOT, "AMD", "adamdataset")
base = os.path.join(out, "ADAM", "Training400")
pairs, srcs = [], {}
for sub, lab, cname in [("Non-AMD", 0, "Non-AMD"), ("AMD", 1, "AMD")]:
for dp, _, files in os.walk(os.path.join(base, sub)):
for f in files:
if f.lower().endswith((".jpg", ".jpeg", ".png")):
k = os.path.splitext(f)[0]
pairs.append((k, lab))
srcs[k] = (os.path.join(dp, f), lab, cname)
sp = stratified_split(pairs)
recs = [dict(src=srcs[k][0], split=sp[k], label=srcs[k][1],
class_name=srcs[k][2], orig_id=k) for k, _ in pairs]
return out, recs
def do_airogs():
out = os.path.join(ROOT, "Glaucoma", "eyepacs-airogs-light")
base = os.path.join(out, "release-crop", "release-crop")
split_map = {"train": "train", "validation": "val", "test": "test"}
cls = {"NRG": (0, "NRG"), "RG": (1, "RG")}
recs = []
for osp, nsp in split_map.items():
for c, (lab, cname) in cls.items():
d = os.path.join(base, osp, c)
if not os.path.isdir(d):
continue
for f in os.listdir(d):
if f.lower().endswith((".jpg", ".jpeg", ".png")):
recs.append(dict(src=os.path.join(d, f), split=nsp, label=lab,
class_name=cname, orig_id=os.path.splitext(f)[0]))
return out, recs
def do_papila():
out = os.path.join(ROOT, "Glaucoma", "papila-retinal-fundus-images")
base = os.path.join(out, "PapilaDB-PAPILA-17f8fa7746adb20275b5b6a0d99dc9dfe3007e9f")
idx = walk_index(os.path.join(base, "FundusImages"))
# build per-image label, grouped by patient
eye_lab = {} # image_key -> (label, patient)
for eye, suffix in [("od", "OD"), ("os", "OS")]:
df = pd.read_excel(os.path.join(base, "ClinicalData", f"patient_data_{eye}.xlsx"),
header=None)
for _, r in df.iterrows():
pid = str(r[0]).strip()
if not pid.startswith("#"):
continue
num = int(pid[1:])
diag = r[3]
try:
diag = int(float(diag))
except (ValueError, TypeError):
continue
if diag == 2: # suspect -> drop
continue
lab = 0 if diag == 0 else 1
key = f"RET{num:03d}{suffix}"
if key in idx:
eye_lab[key] = (lab, f"RET{num:03d}")
# patient-level label = glaucoma if any kept eye glaucoma
pat_lab = defaultdict(int)
for k, (lab, pat) in eye_lab.items():
pat_lab[pat] = max(pat_lab[pat], lab)
sp = stratified_split(list(pat_lab.items()))
recs = []
for k, (lab, pat) in eye_lab.items():
recs.append(dict(src=idx[k], split=sp[pat], label=lab,
class_name=("healthy" if lab == 0 else "glaucoma"), orig_id=k))
return out, recs
def do_idrid():
out = os.path.join(ROOT, "DR", "idrid-dataset")
df = pd.read_csv(os.path.join(out, "idrid_labels.csv"))
idx = walk_index(os.path.join(out, "Imagenes"))
pairs, srcs = [], {}
for _, r in df.iterrows():
k = str(r["id_code"]).strip()
if k not in idx:
continue
lab = int(r["diagnosis"])
pairs.append((k, lab))
srcs[k] = lab
sp = stratified_split(pairs)
recs = [dict(src=idx[k], split=sp[k], label=srcs[k],
class_name=f"grade_{srcs[k]}", orig_id=k) for k, _ in pairs]
return out, recs
def do_aptos():
out = os.path.join(ROOT, "DR", "aptos2019")
recs = []
for csvname, sp, imgdir in [("train_1.csv", "train", "train_images"),
("valid.csv", "val", "val_images"),
("test.csv", "test", "test_images")]:
df = pd.read_csv(os.path.join(out, csvname))
idx = walk_index(os.path.join(out, imgdir))
for _, r in df.iterrows():
k = str(r["id_code"]).strip()
if k not in idx:
continue
lab = int(r["diagnosis"])
recs.append(dict(src=idx[k], split=sp, label=lab,
class_name=f"grade_{lab}", orig_id=k))
return out, recs
def do_deepdrid():
out = os.path.join(ROOT, "DR", "deepdrid")
base = os.path.join(out, "DeepDRiD-master", "regular_fundus_images")
def eye_level(row, image_id):
col = "left_eye_DR_Level" if "_l" in image_id else "right_eye_DR_Level"
v = row.get(col)
if pd.isna(v):
v = row.get("patient_DR_Level")
return None if pd.isna(v) else int(v)
recs = []
# train + val from per-image csv
for sub, sp in [("regular-fundus-training", "train"), ("regular-fundus-validation", "val")]:
d = os.path.join(base, sub)
df = pd.read_csv(os.path.join(d, f"{sub}.csv"))
idx = walk_index(os.path.join(d, "Images"))
for _, r in df.iterrows():
iid = str(r["image_id"]).strip()
if iid not in idx:
continue
lab = eye_level(r, iid)
if lab is None or lab < 0 or lab > 4:
continue
recs.append(dict(src=idx[iid], split=sp, label=lab,
class_name=f"grade_{lab}", orig_id=iid))
# test from Online-Challenge labels.xlsx
ev = os.path.join(base, "Online-Challenge1&2-Evaluation")
dfx = pd.read_excel(os.path.join(ev, "Challenge1_labels.xlsx"))
idx = walk_index(os.path.join(ev, "Images"))
col = "DR_Levels" if "DR_Levels" in dfx.columns else dfx.columns[-1]
for _, r in dfx.iterrows():
iid = str(r["image_id"]).strip()
if iid not in idx or pd.isna(r[col]):
continue
lab = int(r[col])
if 0 <= lab <= 4:
recs.append(dict(src=idx[iid], split="test", label=lab,
class_name=f"grade_{lab}", orig_id=iid))
return out, recs
DATASETS = [("MMAC/Myopia", do_mmac), ("ADAM/AMD", do_adam),
("AIROGS/Glaucoma", do_airogs), ("PAPILA/Glaucoma", do_papila),
("IDRiD/DR", do_idrid), ("APTOS/DR", do_aptos), ("DeepDRiD/DR", do_deepdrid)]
def main():
only = [a for a in sys.argv[1:] if not a.startswith("--")]
print(f"=== prepare_datasets ({'EXECUTE' if EXECUTE else 'DRY-RUN'}) seed={SEED} ===")
for name, fn in DATASETS:
if only and name.split("/")[0].lower() not in [o.lower() for o in only]:
continue
print(f"\n### {name}")
out, recs = fn()
place(out, recs)
print("\n=== done ===")
if __name__ == "__main__":
main()
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