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import csv
import json
import shutil
import re
import numpy as np
import pandas as pd
from pathlib import Path
from PIL import Image, UnidentifiedImageError
from typing import List, Dict, Any, Set, Tuple
DOWNLOAD_ROOT = "./Skin_Image"
EXTRACT_ROOT = "./Skin_Image"
OUTPUT_IMAGE_DIR = "./Skin_Image/images"
IMAGE_SIZE = 512
MIN_WIDTH = 28
MIN_HEIGHT = 28
DATASET_CONFIGS = [
{
"bench_class": "Isic2018MSBench",
"module": "medsegbench",
"npz_stem": "isic2018_512",
"dataset_name": "isic2018",
},
{
"bench_class": "UWSkinCancerMSBench",
"module": "medsegbench",
"npz_stem": "uwaterlooskincancer_512",
"dataset_name": "uwaterlooskincancer",
},
]
def to_uint8(arr):
arr = np.asarray(arr)
if arr.dtype == np.uint8:
return arr
arr = arr.astype(np.float32)
if arr.size > 0 and arr.min() >= 0 and arr.max() <= 1.0:
arr = arr * 255.0
return np.clip(arr, 0, 255).astype(np.uint8)
def save_image(arr, save_path):
arr = to_uint8(arr)
if arr.ndim == 2:
img = Image.fromarray(arr, mode="L")
elif arr.ndim == 3:
if arr.shape[2] == 1:
img = Image.fromarray(arr[:, :, 0], mode="L")
elif arr.shape[2] == 3:
img = Image.fromarray(arr, mode="RGB")
elif arr.shape[2] == 4:
img = Image.fromarray(arr, mode="RGBA")
else:
raise ValueError(f"Unsupported image shape: {arr.shape}")
else:
raise ValueError(f"Unsupported image shape: {arr.shape}")
img.save(save_path)
def is_binary_mask(arr):
unique_vals = set(np.unique(arr).tolist())
return unique_vals.issubset({0, 1})
def key_to_subdir(key):
parts = key.split("_")
if len(parts) >= 2 and parts[0] in {"train", "val", "valid", "validation", "test"}:
split = parts[0]
rest = "_".join(parts[1:])
return Path(split) / rest
return Path(key)
def mask_to_bbox(mask_arr):
if mask_arr.ndim == 3:
mask_arr = mask_arr[:, :, 0]
ys, xs = np.where(mask_arr > 0)
if len(xs) == 0:
return None
return int(xs.min()), int(ys.min()), int(xs.max()) + 1, int(ys.max()) + 1
def step1_download_datasets():
print("\n" + "=" * 70)
print("STEP 1: Download datasets")
print("=" * 70)
import importlib
for cfg in DATASET_CONFIGS:
mod = importlib.import_module(cfg["module"])
bench_cls = getattr(mod, cfg["bench_class"])
print(f"\n[Download] {cfg['bench_class']} -> {DOWNLOAD_ROOT}")
_ = bench_cls(
root=DOWNLOAD_ROOT,
split="train",
download=True,
size=IMAGE_SIZE,
)
print(f"[Done] {cfg['bench_class']} download/verification complete")
def step2_extract_npz(cfg) -> Path:
npz_path = Path(DOWNLOAD_ROOT) / f"{cfg['npz_stem']}.npz"
if not npz_path.exists():
raise FileNotFoundError(f"NPZ file not found: {npz_path}")
out_root = Path(EXTRACT_ROOT) / cfg["npz_stem"]
out_root.mkdir(parents=True, exist_ok=True)
print(f"\n[Extract] {npz_path}")
data = np.load(str(npz_path), allow_pickle=True)
print(" Keys:")
for key in data.files:
arr = data[key]
print(f" - {key}: shape={arr.shape}, dtype={arr.dtype}")
item_count = 0
for key in data.files:
arr = np.asarray(data[key])
subdir = key_to_subdir(key)
target_dir = out_root / subdir
target_dir.mkdir(parents=True, exist_ok=True)
if arr.ndim == 3 and arr.shape[-1] not in (1, 3, 4):
for i in range(arr.shape[0]):
single = arr[i]
if is_binary_mask(single):
np.save(target_dir / f"{i:05d}.npy", single)
else:
save_image(single, target_dir / f"{i:05d}.png")
item_count += 1
elif arr.ndim == 4 and arr.shape[-1] in (1, 3, 4):
for i in range(arr.shape[0]):
save_image(arr[i], target_dir / f"{i:05d}.png")
item_count += 1
elif arr.ndim == 2 or (arr.ndim == 3 and arr.shape[-1] in (1, 3, 4)):
if is_binary_mask(arr):
np.save(target_dir / "0.npy", arr)
else:
save_image(arr, target_dir / "0.png")
item_count += 1
else:
np.save(target_dir / f"{key}.npy", arr)
item_count += 1
print(f" Extraction complete: {item_count} items -> {out_root}")
return out_root
def step3_crop(cfg, extract_root: Path) -> List[Tuple[str, str]]:
print(f"\n[Crop] Dataset: {cfg['dataset_name']}")
os.makedirs(OUTPUT_IMAGE_DIR, exist_ok=True)
dataset_name = cfg["dataset_name"]
saved_pairs = []
for split_dir in sorted(extract_root.iterdir()):
if not split_dir.is_dir():
continue
split_name = split_dir.name
if split_name.startswith("."):
continue
images_dir = split_dir / "images"
labels_dir = None
for label_candidate in ["labels", "label", "label_C1", "masks", "mask"]:
candidate = split_dir / label_candidate
if candidate.is_dir():
labels_dir = candidate
break
if not images_dir.is_dir():
print(f" [Skip] No images directory: {split_dir}")
continue
if labels_dir is None:
print(f" [Skip] No labels directory: {split_dir}")
continue
image_files = sorted([
f for f in images_dir.iterdir()
if f.suffix.lower() in {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff", ".npy"}
])
print(f" [{split_name}] Image count: {len(image_files)}, labels dir: {labels_dir.name}")
for img_file in image_files:
idx_str = img_file.stem
if img_file.suffix == ".npy":
img_arr = np.load(str(img_file))
img_arr = to_uint8(img_arr)
if img_arr.ndim == 2:
pil_img = Image.fromarray(img_arr, mode="L").convert("RGB")
elif img_arr.ndim == 3 and img_arr.shape[2] == 1:
pil_img = Image.fromarray(img_arr[:, :, 0], mode="L").convert("RGB")
elif img_arr.ndim == 3:
pil_img = Image.fromarray(img_arr, mode="RGB")
else:
continue
else:
try:
pil_img = Image.open(str(img_file)).convert("RGB")
except Exception as e:
print(f" [WARN] Failed to open image: {img_file}, {e}")
continue
mask_arr = None
for ext in [".npy", ".png", ".jpg", ".bmp", ".tif"]:
mask_file = labels_dir / f"{idx_str}{ext}"
if mask_file.exists():
if ext == ".npy":
mask_arr = np.load(str(mask_file))
else:
mask_arr = np.array(Image.open(str(mask_file)).convert("L"))
break
if mask_arr is None:
continue
bbox = mask_to_bbox(mask_arr)
if bbox is None:
continue
x_min, y_min, x_max, y_max = bbox
w, h = pil_img.size
x_min = max(0, min(x_min, w - 1))
y_min = max(0, min(y_min, h - 1))
x_max = max(0, min(x_max, w))
y_max = max(0, min(y_max, h))
if x_max <= x_min or y_max <= y_min:
continue
new_image_name = f"{split_name}_{idx_str}_{dataset_name}.png"
crop_image_name = f"{split_name}_{idx_str}_{dataset_name}_crop.png"
new_image_path = os.path.join(OUTPUT_IMAGE_DIR, new_image_name)
crop_image_path = os.path.join(OUTPUT_IMAGE_DIR, crop_image_name)
try:
pil_img.save(new_image_path, format="PNG")
except Exception as e:
print(f" [WARN] Failed to save original: {new_image_path}, {e}")
continue
crop = pil_img.crop((x_min, y_min, x_max, y_max))
try:
crop.save(crop_image_path, format="PNG")
except Exception as e:
print(f" [WARN] Failed to save crop: {crop_image_path}, {e}")
continue
saved_pairs.append((new_image_path, crop_image_path))
print(f" Saved {len(saved_pairs)} image pairs")
return saved_pairs
def step4_filter_small_images(saved_pairs: List[Tuple[str, str]]):
print(f"\n[Filter] Checking {len(saved_pairs)} image pairs")
deleted_count = 0
for original_path, crop_path in saved_pairs:
remove = False
if os.path.isfile(crop_path):
try:
with Image.open(crop_path) as img:
w, h = img.size
if w < MIN_WIDTH or h < MIN_HEIGHT:
remove = True
print(f" [Small] {os.path.basename(crop_path)}: {w}x{h}")
except (UnidentifiedImageError, OSError) as e:
remove = True
print(f" [Broken] {crop_path}: {e}")
else:
remove = True
if remove:
deleted_count += 1
if os.path.isfile(crop_path):
os.remove(crop_path)
if os.path.isfile(original_path):
os.remove(original_path)
kept = len(saved_pairs) - deleted_count
print(f" Total: {len(saved_pairs)} | Deleted: {deleted_count} | Kept: {kept}")
def main():
step1_download_datasets()
for cfg in DATASET_CONFIGS:
print("\n" + "=" * 70)
print(f"Processing dataset: {cfg['dataset_name']}")
print("=" * 70)
extract_root = step2_extract_npz(cfg)
saved_pairs = step3_crop(cfg, extract_root)
step4_filter_small_images(saved_pairs)
print("\n" + "=" * 70)
print("All processing complete")
print("=" * 70)
print(f"Output image directory: {OUTPUT_IMAGE_DIR}")
if os.path.isdir(OUTPUT_IMAGE_DIR):
all_images = [f for f in os.listdir(OUTPUT_IMAGE_DIR) if f.endswith(".png")]
originals = [f for f in all_images if not f.endswith("_crop.png")]
crops = [f for f in all_images if f.endswith("_crop.png")]
print(f"Final image count: {len(all_images)} (originals: {len(originals)}, crops: {len(crops)})")
if __name__ == "__main__":
main()
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