""" format_detector.py ================== Auto-detects: - File format (parquet, tar/webdataset, zip, arrow, jsonl, image folder) - Image field (image, img, jpeg, png, pixel_values, …) - Caption field (caption, text, prompt, label, description, …) Used by ingest_universal.py — import and call detect(). """ import os import io import json import tarfile import zipfile import struct from pathlib import Path from typing import Optional from PIL import Image # ── Known field names ───────────────────────────────────────────────────────── IMAGE_FIELDS = ["image", "img", "jpeg", "png", "gif", "webp", "pixel_values", "image_bytes", "image_data", "bytes", "data", "photo", "thumbnail"] CAPTION_FIELDS = ["caption", "text", "prompt", "label", "description", "title", "alt", "alt_text", "sentence", "query", "blip_caption", "llava_caption", "cogvlm_caption", "tag", "tags", "keyword", "keywords"] # ── Format signatures ───────────────────────────────────────────────────────── MAGIC = { b"\x50\x4b\x03\x04": "zip", b"\x1f\x8b": "tar.gz", b"PAR1": "parquet", b"\x41\x52\x52\x4f": "arrow", # ARROW1 } def sniff_bytes(path: str) -> Optional[str]: with open(path, "rb") as f: header = f.read(8) for magic, fmt in MAGIC.items(): if header[:len(magic)] == magic: return fmt # Bare tar (no gzip) if tarfile.is_tarfile(path): return "tar" return None # ── Per-format sample readers ───────────────────────────────────────────────── def _sample_parquet(path: str) -> dict: import pyarrow.parquet as pq tbl = pq.read_table(path, memory_map=True).slice(0, 1) row = {col: tbl.column(col)[0].as_py() for col in tbl.schema.names} return row def _sample_arrow(path: str) -> dict: import pyarrow as pa with pa.memory_map(path, "r") as src: reader = pa.ipc.open_file(src) batch = reader.get_batch(0) row = {col: batch.column(col)[0].as_py() for col in batch.schema.names} return row def _sample_tar(path: str) -> dict: """ WebDataset tars group files by key: 000000.jpg 000000.txt 000000.json … We read enough members to reconstruct one sample dict. """ sample = {} current_key = None with tarfile.open(path, "r:*") as tf: for member in tf: if member.isdir(): continue stem, ext = os.path.splitext(os.path.basename(member.name)) ext = ext.lstrip(".").lower() if current_key is None: current_key = stem if stem != current_key: break # moved to next sample fobj = tf.extractfile(member) if fobj is None: continue raw = fobj.read() if ext in ("jpg", "jpeg", "png", "gif", "webp", "bmp"): sample["__image_bytes__"] = raw sample["image"] = Image.open(io.BytesIO(raw)) elif ext in ("txt",): sample["caption"] = raw.decode("utf-8", errors="replace").strip() elif ext in ("json",): try: d = json.loads(raw) sample.update(d) except Exception: pass else: sample[ext] = raw return sample def _sample_zip(path: str) -> dict: sample = {} with zipfile.ZipFile(path) as zf: names = zf.namelist() # Find first image file for name in names: ext = Path(name).suffix.lower().lstrip(".") if ext in ("jpg", "jpeg", "png", "gif", "webp"): raw = zf.read(name) sample["image"] = Image.open(io.BytesIO(raw)) # Look for sibling caption file stem = Path(name).stem for cap_ext in ("txt", "caption"): cap_name = f"{stem}.{cap_ext}" if cap_name in names: sample["caption"] = zf.read(cap_name).decode("utf-8", errors="replace").strip() # Also check for a metadata JSON for json_name in (f"{stem}.json", "metadata.json", "captions.json"): if json_name in names: try: d = json.loads(zf.read(json_name)) sample.update(d) except Exception: pass break return sample def _sample_jsonl(path: str) -> dict: with open(path) as f: line = f.readline() return json.loads(line) def _sample_image_folder(path: str) -> dict: """path is a directory; find first image + sibling txt.""" for root, _, files in os.walk(path): for fname in sorted(files): ext = Path(fname).suffix.lower().lstrip(".") if ext in ("jpg", "jpeg", "png", "gif", "webp"): img_path = os.path.join(root, fname) sample = {"image": Image.open(img_path)} txt = os.path.join(root, Path(fname).stem + ".txt") if os.path.exists(txt): sample["caption"] = open(txt).read().strip() return sample return {} # ── Field detection ─────────────────────────────────────────────────────────── def _is_image_value(v) -> bool: if isinstance(v, Image.Image): return True if isinstance(v, bytes): try: Image.open(io.BytesIO(v)) return True except Exception: return False if isinstance(v, dict) and "bytes" in v: return _is_image_value(v["bytes"]) return False def detect_fields(sample: dict) -> tuple[Optional[str], Optional[str]]: """Return (image_field, caption_field) from a sample dict.""" image_field = None caption_field = None # Priority: known names first, then any field whose value looks like an image for name in IMAGE_FIELDS: if name in sample and _is_image_value(sample[name]): image_field = name break if image_field is None: for k, v in sample.items(): if _is_image_value(v): image_field = k break for name in CAPTION_FIELDS: if name in sample and isinstance(sample[name], str): caption_field = name break if caption_field is None: for k, v in sample.items(): if k != image_field and isinstance(v, str) and len(v) > 1: caption_field = k break return image_field, caption_field # ── Main entry ──────────────────────────────────────────────────────────────── class DatasetInfo: def __init__(self, fmt, image_field, caption_field, sample): self.fmt = fmt # "parquet" | "tar" | "zip" | "arrow" | "jsonl" | "folder" | "hf_streaming" self.image_field = image_field self.caption_field = caption_field self.sample = sample def __repr__(self): return (f"DatasetInfo(fmt={self.fmt!r}, " f"image_field={self.image_field!r}, " f"caption_field={self.caption_field!r})") def detect(path: str) -> DatasetInfo: """ Detect format + fields for a single file or directory. Returns a DatasetInfo object. """ if os.path.isdir(path): fmt = "folder" sample = _sample_image_folder(path) else: ext = Path(path).suffix.lower() sniffed = sniff_bytes(path) fmt = sniffed or ext.lstrip(".") if fmt == "parquet": sample = _sample_parquet(path) elif fmt in ("tar", "tar.gz"): sample = _sample_tar(path) elif fmt == "zip": sample = _sample_zip(path) elif fmt == "arrow": sample = _sample_arrow(path) elif ext in (".jsonl", ".json"): fmt = "jsonl" sample = _sample_jsonl(path) else: # Unknown — try HF datasets as last resort fmt = "hf_streaming" sample = {} img_f, cap_f = detect_fields(sample) return DatasetInfo(fmt, img_f, cap_f, sample) if __name__ == "__main__": import sys if len(sys.argv) < 2: print("Usage: python format_detector.py ") sys.exit(1) info = detect(sys.argv[1]) print(info) print(f" Fields in sample: {list(info.sample.keys())}")