"""Validation engine. Checks run cheapest-first so most bad files fail early: 1. file size < min_file_bytes -> discard too_small_file 2. integrity open -> verify() -> reopen -> load() -> discard corrupt 3. animated multi-frame GIF/WEBP/APNG -> flag animated (first frame used) 4. resolution short/long side below minimum -> discard low_resolution 5. aspect (hard) > hard_aspect : 1 -> discard extreme_aspect_hard 6. aspect (soft) > flag_aspect : 1 -> flag extreme_aspect (goes to review) `verify()` alone does not detect truncated JPEGs, so the file is reopened and fully decoded with `load()` (verify() invalidates the image object, hence the reopen). The soft aspect limit is a *flag*, not a discard: RAM modules and edge-finger strips are naturally ~4.5-5:1 and must survive. """ from __future__ import annotations import logging from concurrent.futures import ThreadPoolExecutor, as_completed from dataclasses import dataclass, field from pathlib import Path from PIL import Image, ImageFile from tqdm import tqdm from .manifest import Manifest log = logging.getLogger(__name__) ImageFile.LOAD_TRUNCATED_IMAGES = False # never silently accept truncated files # Pillow's own bomb check works through process-global warnings (not thread-safe), so it is # disabled and replaced by an explicit header-size check in validate_image(). Image.MAX_IMAGE_PIXELS = None ALLOWED_FORMATS = {"JPEG", "MPO", "PNG", "WEBP", "GIF", "BMP", "TIFF"} def register_heif() -> bool: """Enable iPhone HEIC/HEIF photos if the optional `pillow-heif` package is installed.""" try: import pillow_heif # type: ignore pillow_heif.register_heif_opener() ALLOWED_FORMATS.add("HEIF") return True except Exception: # pragma: no cover - optional dependency return False register_heif() @dataclass class ValidationResult: ok: bool reason: str | None = None flags: list[str] = field(default_factory=list) width: int | None = None height: int | None = None fmt: str | None = None aspect: float | None = None DEFAULT_AI_KEYWORDS = { "midjourney", "stable diffusion", "dall-e", "dalle", "comfyui", "novelai", "c2pa", "firefly", "bing image creator", "flux.1", "fooocus", "automatic1111", "ai_generated", "generated by" } def check_ai_metadata(img: Image.Image, custom_keywords: list[str] | set[str] | None = None) -> bool: """Return True if image metadata contains AI generator signatures.""" keywords = set(custom_keywords) if custom_keywords else DEFAULT_AI_KEYWORDS info = getattr(img, "info", {}) if not isinstance(info, dict): return False raw_str = " ".join(f"{k} {v}" for k, v in info.items()).lower() return any(kw.lower() in raw_str for kw in keywords) def validate_image(path: str | Path, vcfg: dict) -> ValidationResult: path = Path(path) try: size = path.stat().st_size except OSError: return ValidationResult(False, "missing_file") if size < int(vcfg["min_file_bytes"]): return ValidationResult(False, "too_small_file") max_pixels = int(vcfg.get("max_pixels", 120_000_000)) flags: list[str] = [] try: with Image.open(path) as img: # lazy: reads the header only if img.width * img.height > max_pixels: return ValidationResult(False, "decompression_bomb", width=img.width, height=img.height) img.verify() with Image.open(path) as img: # reopen: verify() leaves the object unusable fmt = img.format if fmt not in ALLOWED_FORMATS: return ValidationResult(False, "unsupported_format", fmt=fmt) animated = bool(getattr(img, "is_animated", False)) img.load() # full decode -> catches truncation that verify() misses width, height = img.size if check_ai_metadata(img, vcfg.get("ai_keywords")): return ValidationResult(False, "ai_generated_metadata", width=width, height=height, fmt=fmt) except Exception: # OSError, SyntaxError, ValueError, struct errors, ... return ValidationResult(False, "corrupt") if animated: flags.append("animated") short, long_ = min(width, height), max(width, height) if short < int(vcfg["min_short_side"]) or long_ < int(vcfg["min_long_side"]): return ValidationResult(False, "low_resolution", flags, width, height, fmt) aspect = long_ / short if short else float("inf") if aspect > float(vcfg["hard_aspect"]): return ValidationResult(False, "extreme_aspect_hard", flags, width, height, fmt, aspect) if aspect > float(vcfg["flag_aspect"]): flags.append("extreme_aspect") return ValidationResult(True, None, flags, width, height, fmt, round(aspect, 4)) def run(manifest: Manifest, cfg: dict, data_dir: Path, force: bool = False) -> dict[str, int]: """Validate images. If force=True, re-checks previously validated/normalized images for AI metadata.""" vcfg = cfg["validate"] statuses = ["downloaded", "validated", "normalized", "kept"] if force else ["downloaded"] todo = manifest.images(statuses=statuses) stats: dict[str, int] = {"validated": 0} if not todo: log.info("validate: nothing to do") return stats def work(img: dict) -> tuple[dict, ValidationResult]: return img, validate_image(data_dir / img["orig_path"], vcfg) workers = int(vcfg.get("workers", 4)) with ThreadPoolExecutor(max_workers=workers) as ex: futures = [ex.submit(work, img) for img in todo] with manifest.transaction(): for fut in tqdm(as_completed(futures), total=len(futures), desc="validate", unit="img"): img, res = fut.result() if res.ok: flags = sorted(set(img["flags"]) | set(res.flags)) manifest.update_image( img["image_id"], status="validated", width=res.width, height=res.height, aspect_ratio=res.aspect, orig_format=(res.fmt or "").lower(), flags=flags) stats["validated"] += 1 else: manifest.update_image( img["image_id"], status="discarded", discard_reason=res.reason, width=res.width, height=res.height, aspect_ratio=res.aspect, flags=sorted(set(img["flags"]) | set(res.flags))) stats[res.reason or "discarded"] = stats.get(res.reason or "discarded", 0) + 1 log.info("validate: %s", stats) return stats