# ksdd2.py — KSDD2 loader (manual-download) # Configs: # - image_only # - classification_from_masks (recommended) # - classification_from_pyb (auto-fallback to masks if all-positive) # - classification_from_pyb_any (union, K=1) from pathlib import Path from typing import Dict, Iterable, Tuple, Optional, Set, List import os import re import collections import datasets try: import pickletools # safe scan except Exception: pickletools = None try: import pickle # fallback except Exception: pickle = None IMG_EXTS = {".png", ".jpg", ".jpeg", ".bmp", ".tif", ".tiff"} MASK_DIR_CANDS = ["masks", "masks_defect", "ground_truth", "gt", "label", "labels"] _DESCRIPTION = "KSDD2: Kolektor Surface-Defect Dataset 2. Binary classification via GT masks or weak splits." _HOMEPAGE = "https://www.vicos.si/resources/kolektorsdd2/" _CITATION = "KSDD2 by ViCoS Lab / Kolektor Group. See official page for citation." def _is_gt_file(p: Path) -> bool: s = p.stem.lower() return s.endswith("_gt") or s.endswith("_mask") def _to_num(s: str) -> Optional[int]: return int(s) if s.isdigit() else None def _extract_numbers(stem: str): for m in re.finditer(r"\d+", stem): yield int(m.group(0)) class KSDD2Config(datasets.BuilderConfig): def __init__(self, mode="masks", min_votes: Optional[int]=None, **kw): super().__init__(version=datasets.Version("2.0.0"), **kw) self.mode = mode self.min_votes = min_votes # used for pyb/pyb_any class KSDD2(datasets.GeneratorBasedBuilder): BUILDER_CONFIG_CLASS = KSDD2Config BUILDER_CONFIGS = [ KSDD2Config(name="image_only", description="Flat train/test, images only", mode="image_only"), KSDD2Config(name="classification_from_masks", description="Label via *_GT.* masks (non-black=>defect). Excludes *_GT.* from inputs.", mode="masks"), KSDD2Config(name="classification_from_pyb", description="Weak splits via split_weakly_*.pyb (vote; fallback to masks on all-positive).", mode="pyb", min_votes=2), KSDD2Config(name="classification_from_pyb_any", description="Weak splits union (K=1).", mode="pyb_any", min_votes=1), ] DEFAULT_CONFIG_NAME = "classification_from_masks" # ---------- info ---------- def _info(self): if self.config.mode == "image_only": feats = {"image": datasets.Image(), "path": datasets.Value("string")} else: feats = { "image": datasets.Image(), "label": datasets.ClassLabel(names=["good", "defect"]), "path": datasets.Value("string"), } return datasets.DatasetInfo(description=_DESCRIPTION, features=datasets.Features(feats), citation=_CITATION, homepage=_HOMEPAGE) # ---------- splits ---------- def _split_generators(self, dl_manager): root = Path(self.config.data_dir or "") if not root.exists(): raise FileNotFoundError(f"Please download KSDD2 and set data_dir. Looked for: {root}") gens = [] for sp in ("train", "test"): if (root/sp).exists(): gens.append(datasets.SplitGenerator( name=getattr(datasets.Split, sp.upper()), gen_kwargs={"root": root, "split": sp})) if not gens: gens.append(datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"root": root, "split": None})) return gens # ---------- helpers ---------- def _iter_images(self, base: Path): for p in sorted(base.rglob("*")): if p.is_file() and p.suffix.lower() in IMG_EXTS: if _is_gt_file(p): continue yield p def _find_mask_for(self, img_path: Path) -> Optional[Path]: for ext in IMG_EXTS: cand = img_path.with_name(img_path.stem + "_GT" + ext) if cand.exists(): return cand parent = img_path.parent for d in MASK_DIR_CANDS: mdir = parent / d if mdir.exists(): for ext in IMG_EXTS: cand = mdir / (img_path.stem + "_GT" + ext) if cand.exists(): return cand return None def _mask_is_defect(self, mask_path: Optional[Path]) -> int: if not mask_path or not mask_path.exists(): return 0 try: from PIL import Image with Image.open(mask_path) as im: im = im.convert("L") ex = im.getextrema() if not ex: return 0 lo, hi = ex return 1 if hi > 0 else 0 except Exception: return 0 # ---- PYB parsing (list of IDs) ---- def _pyb_vote_defects(self, base: Path, min_votes: int, only_files: Optional[List[str]] = None) -> Set[str]: root = base.parent pybs = sorted(root.glob("split_weakly_*.pyb")) if only_files: allow = {x.lower().strip() for x in only_files} pybs = [p for p in pybs if p.name.lower() in allow] names_present: Set[str] = set() num_map: Dict[int, Set[str]] = {} for img in self._iter_images(base): bn = img.name.lower() names_present.add(bn) n = _to_num(img.stem) if n is not None and n >= 1000: num_map.setdefault(n, set()).add(bn) votes = collections.Counter() for f in pybs: data = f.read_bytes() ids: Set[int] = set() used_pickletools = False if pickletools is not None: try: tmp: List[int] = [] cur = [] for op, arg, pos in pickletools.genops(data): if op.name in ("BININT", "BININT1", "BININT2", "LONG1", "LONG4"): try: x = int(arg) if x >= 1000: tmp.append(x) except Exception: pass elif op.name in ("EMPTY_LIST", "APPENDS", "LIST"): pass used_pickletools = True except Exception: used_pickletools = False if not used_pickletools and pickle is not None: try: obj = pickle.loads(data) # [ [(id, True), ...], [(id, True), ...] ] for part in obj: for pair in part: if isinstance(pair, (list, tuple)) and pair: n = int(pair[0]) if n >= 1000: ids.add(n) except Exception: pass hit = set() for n in ids: hit.update(num_map.get(n, [])) for bn in hit: votes[bn] += 1 return {bn for bn, v in votes.items() if v >= max(1, min_votes)} # ---------- generator ---------- def _generate_examples(self, root: Path, split: Optional[str]): base = root / split if split else root # A) images only if self.config.mode == "image_only": for p in self._iter_images(base): yield str(p), {"image": str(p), "path": str(p)} return # B) classification from masks if self.config.mode == "masks": for img in self._iter_images(base): mask = self._find_mask_for(img) label = self._mask_is_defect(mask) yield str(img), {"image": str(img), "label": label, "path": str(img)} return # C1) from pyb if self.config.mode in ("pyb", "pyb_any"): min_votes = self.config.min_votes or (2 if self.config.mode == "pyb" else 1) try: min_votes = int(os.getenv("KSDD2_MIN_VOTES", min_votes)) except Exception: pass only = os.getenv("KSDD2_PYB_FILES", "") only_files = [s for s in only.split(",") if s.strip()] if only else None defect_names = self._pyb_vote_defects(base, min_votes=min_votes, only_files=only_files) total = good = defect = 0 cache = [] for img in self._iter_images(base): bn = img.name.lower() y = 1 if bn in defect_names else 0 cache.append((img, y)) total += 1; defect += y; good += (1 - y) if total > 0 and defect / total > 0.95: for img, _ in cache: mask = self._find_mask_for(img) y = self._mask_is_defect(mask) yield str(img), {"image": str(img), "label": y, "path": str(img)} else: for img, y in cache: yield str(img), {"image": str(img), "label": y, "path": str(img)} return