| """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 |
| |
| |
| 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 |
|
|
| pillow_heif.register_heif_opener() |
| ALLOWED_FORMATS.add("HEIF") |
| return True |
| except Exception: |
| 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: |
| 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: |
| 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() |
| 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: |
| 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 |
|
|
