File size: 8,696 Bytes
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import io
from pathlib import Path
import pandas as pd
import numpy as np
from PIL import Image
INPUT_PARQUET_PATHS = [
r"./data/fold1-00000-of-00001.parquet",
r"./data/fold2-00000-of-00001.parquet",
r"./data/fold3-00000-of-00001.parquet",
]
SPLIT_CSV_PATH = r"./PanNuke_split.csv"
OUTPUT_TRAIN_ROOT = Path(r"./train_images")
OUTPUT_TEST_ROOT = Path(r"./test_images")
IMAGE_COLUMN = "image"
CLASS_LABEL_COLUMN = "tissue"
IMAGE_EXT = ".png"
TISSUE_ID_TO_NAME: dict[int, str] = {
0: "Adrenal Gland",
1: "Bile Duct",
2: "Bladder",
3: "Breast",
4: "Cervix",
5: "Colon",
6: "Esophagus",
7: "Head and Neck",
8: "Kidney",
9: "Liver",
10: "Lung",
11: "Ovarian",
12: "Pancreatic",
13: "Prostate",
14: "Skin",
15: "Stomach",
16: "Testis",
17: "Thyroid",
18: "Uterus",
}
CLASS_ORDER = list(TISSUE_ID_TO_NAME.values())
def load_split_mapping(csv_path: str) -> dict[str, str]:
df = pd.read_csv(csv_path)
df.columns = df.columns.str.strip()
mapping = {}
for _, row in df.iterrows():
img_path = str(row["image_path"]).strip()
split = str(row["split"]).strip().lower()
mapping[img_path] = split
print(f"[INFO] Loaded split CSV: {len(mapping)} entries "
f"(train: {sum(1 for v in mapping.values() if v == 'train')}, "
f"test: {sum(1 for v in mapping.values() if v == 'test')})")
return mapping
def is_null_like(x):
if x is None:
return True
if isinstance(x, (bytes, bytearray, dict, list, tuple, np.ndarray, Image.Image)):
return False
try:
result = pd.isna(x)
if isinstance(result, (bool, np.bool_)):
return bool(result)
return False
except Exception:
return False
def try_decode_pil_from_bytes(data):
try:
img = Image.open(io.BytesIO(data))
img.load()
return img
except Exception:
return None
def try_decode_image(obj):
if is_null_like(obj):
return None
if isinstance(obj, Image.Image):
return obj
if isinstance(obj, (bytes, bytearray)):
return try_decode_pil_from_bytes(obj)
if isinstance(obj, np.ndarray):
try:
arr = obj.astype(np.uint8) if obj.dtype != np.uint8 else obj
if arr.ndim in (2, 3):
return Image.fromarray(arr)
except Exception:
return None
if isinstance(obj, list):
try:
arr = np.array(obj)
arr = arr.astype(np.uint8) if arr.dtype != np.uint8 else arr
if arr.ndim in (2, 3):
return Image.fromarray(arr)
except Exception:
return None
if isinstance(obj, dict):
if "bytes" in obj and obj["bytes"] is not None:
return try_decode_pil_from_bytes(obj["bytes"])
if "array" in obj and obj["array"] is not None:
try:
arr = np.array(obj["array"])
arr = arr.astype(np.uint8) if arr.dtype != np.uint8 else arr
if arr.ndim in (2, 3):
return Image.fromarray(arr)
except Exception:
pass
if "path" in obj and obj["path"] and os.path.exists(str(obj["path"])):
try:
img = Image.open(obj["path"])
img.load()
return img
except Exception:
pass
return None
def save_image(img: Image.Image, save_path: Path):
save_path.parent.mkdir(parents=True, exist_ok=True)
if img.mode not in ["1", "L", "LA", "P", "RGB", "RGBA", "I", "I;16"]:
img = img.convert("RGB")
img.save(save_path)
def unique_save_path(folder: Path, stem: str, ext: str) -> Path:
p = folder / f"{stem}{ext}"
k = 1
while p.exists():
p = folder / f"{stem}_{k}{ext}"
k += 1
return p
def resolve_class_name(raw_label) -> str | None:
try:
tissue_id = int(float(str(raw_label).strip()))
except (ValueError, TypeError):
return None
return TISSUE_ID_TO_NAME.get(tissue_id, None)
def process_parquet(parquet_path: Path, fold_num: int, split_mapping: dict[str, str]) -> dict:
print(f"\n[INFO] Reading fold{fold_num}: {parquet_path.name}")
df = pd.read_parquet(parquet_path)
print(f"[INFO] Rows: {len(df)} | Columns: {list(df.columns)}")
class_counts: dict[str, dict[str, int]] = {}
saved = 0
failed = 0
no_split = 0
for local_idx, row in enumerate(df.itertuples(index=False)):
stem = f"fold{fold_num}_{local_idx:08d}"
raw_label = getattr(row, CLASS_LABEL_COLUMN)
if is_null_like(raw_label):
print(f" [WARN] {stem} has empty class label, skipped")
failed += 1
continue
class_name = resolve_class_name(raw_label)
if class_name is None:
print(f" [WARN] {stem} label '{raw_label}' is not in mapping table, skipped")
failed += 1
continue
relative_path = f"{class_name}/{stem}{IMAGE_EXT}"
split = split_mapping.get(relative_path, None)
if split is None:
alt_stem = f"fold{fold_num}_{local_idx}"
alt_path = f"{class_name}/{alt_stem}{IMAGE_EXT}"
split = split_mapping.get(alt_path, None)
if split is None:
print(f" [WARN] {relative_path} not found in split CSV, skipped")
no_split += 1
failed += 1
continue
if split == "train":
output_root = OUTPUT_TRAIN_ROOT
elif split == "test":
output_root = OUTPUT_TEST_ROOT
else:
print(f" [WARN] {relative_path} has unknown split '{split}', skipped")
failed += 1
continue
img = try_decode_image(getattr(row, IMAGE_COLUMN))
if img is None:
print(f" [WARN] {stem} failed to decode image, skipped")
failed += 1
continue
out_folder = output_root / class_name
save_path = unique_save_path(out_folder, stem, IMAGE_EXT)
try:
save_image(img, save_path)
if class_name not in class_counts:
class_counts[class_name] = {"train": 0, "test": 0}
class_counts[class_name][split] += 1
saved += 1
except Exception as e:
print(f" [ERROR] Failed to save ({save_path}): {e}")
failed += 1
done = saved + failed
if done % 500 == 0:
print(f" ... Processed {done}/{len(df)} (saved {saved} / failed {failed})")
print(f"[INFO] fold{fold_num} finished: saved {saved} / failed {failed} "
f"(no_split_info: {no_split})")
return class_counts
def print_summary(total_class_counts: dict):
print("\n" + "=" * 70)
print(f"[DONE] Train directory: {OUTPUT_TRAIN_ROOT}")
print(f"[DONE] Test directory: {OUTPUT_TEST_ROOT}")
print(f"[DONE] Class statistics (total {len(total_class_counts)} classes):")
print(f" {'Class':<20s} {'Train':>8s} {'Test':>8s} {'Total':>8s}")
print(f" {'-'*20} {'-'*8} {'-'*8} {'-'*8}")
total_train = 0
total_test = 0
for cls in CLASS_ORDER:
counts = total_class_counts.get(cls, {"train": 0, "test": 0})
train_n = counts.get("train", 0)
test_n = counts.get("test", 0)
total_train += train_n
total_test += test_n
print(f" {cls:<20s} {train_n:>8d} {test_n:>8d} {train_n + test_n:>8d}")
print(f" {'-'*20} {'-'*8} {'-'*8} {'-'*8}")
print(f" {'TOTAL':<20s} {total_train:>8d} {total_test:>8d} {total_train + total_test:>8d}")
print("=" * 70)
def main():
for root in [OUTPUT_TRAIN_ROOT, OUTPUT_TEST_ROOT]:
root.mkdir(parents=True, exist_ok=True)
for cls in CLASS_ORDER:
(root / cls).mkdir(parents=True, exist_ok=True)
split_mapping = load_split_mapping(SPLIT_CSV_PATH)
total_class_counts: dict[str, dict[str, int]] = {}
for fold_num, parquet_path_str in enumerate(INPUT_PARQUET_PATHS, start=1):
parquet_path = Path(parquet_path_str)
if not parquet_path.exists():
raise FileNotFoundError(f"Parquet file not found: {parquet_path}")
class_counts = process_parquet(parquet_path, fold_num, split_mapping)
for cls, counts in class_counts.items():
if cls not in total_class_counts:
total_class_counts[cls] = {"train": 0, "test": 0}
for split_key in ["train", "test"]:
total_class_counts[cls][split_key] += counts.get(split_key, 0)
print_summary(total_class_counts)
if __name__ == "__main__":
main()
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