#!/usr/bin/env python3 """Build MVEB MS-Celeb-1M subset from train/test split JSON + identity metadata.""" from __future__ import annotations import argparse import json import shutil import sys from pathlib import Path from typing import Dict, List, Tuple _SCRIPTS_ROOT = Path(__file__).resolve().parent.parent if str(_SCRIPTS_ROOT) not in sys.path: sys.path.insert(0, str(_SCRIPTS_ROOT)) from pack_media_parquet import pack_dataset_with_media, resolve_split_output_dir QUERY_INSTRUCTION = "Represent the face with the following text." QUERY_TEXT = "Retrieve all images with the same facial identity." CANDIDATE_INSTRUCTION = "Represent the face with the following text." CANDIDATE_TEXT = "Retrieve all images with the same facial identity." def _load_path_to_instance_id(metadata_json: Path) -> Dict[str, str]: with metadata_json.open("r", encoding="utf-8") as f: annotations = json.load(f) if not isinstance(annotations, dict): raise ValueError(f"metadata must be a dict: {metadata_json}") path_to_inst: Dict[str, str] = {} for inst_id, image_names in annotations.items(): if not isinstance(image_names, list): raise ValueError(f"metadata[{inst_id!r}] must be a list of image paths") inst = str(inst_id) for image_name in image_names: rel = str(image_name).replace("\\", "/") if rel in path_to_inst and path_to_inst[rel] != inst: raise ValueError( f"Image path {rel!r} maps to multiple instance ids: " f"{path_to_inst[rel]!r} and {inst!r}" ) path_to_inst[rel] = inst return path_to_inst def _make_annotations( rel_paths: List[str], path_to_inst: Dict[str, str], ) -> Tuple[List[dict], List[dict]]: candidate_rows: List[dict] = [] inst_to_ids: Dict[str, List[str]] = {} missing: List[str] = [] for idx, rel_path in enumerate(sorted(set(rel_paths))): rel = rel_path.replace("\\", "/") inst_id = path_to_inst.get(rel) if inst_id is None: missing.append(rel) continue candidate_rows.append( { "id": rel, "instance_id": inst_id, "image_path": rel, "instruction": CANDIDATE_INSTRUCTION, "text": CANDIDATE_TEXT, } ) inst_to_ids.setdefault(inst_id, []).append(rel) if missing: preview = ", ".join(missing[:5]) raise KeyError( f"{len(missing)} split image(s) not found in metadata.json " f"(e.g. {preview})" ) query_rows: List[dict] = [] for row in candidate_rows: target_ids = [x for x in inst_to_ids[row["instance_id"]] if x != row["id"]] if not target_ids: continue query_rows.append( { "id": row["id"], "instance_id": row["instance_id"], "image_path": row["image_path"], "instruction": QUERY_INSTRUCTION, "text": QUERY_TEXT, "target_ids": target_ids, } ) return query_rows, candidate_rows def _process_one_split( split_name: str, rel_paths: List[str], path_to_inst: Dict[str, str], image_root: Path, output_root: Path, overwrite: bool, media_rows_per_shard: int, row_group_size: int, num_workers: int, ) -> None: out_dir = resolve_split_output_dir(output_root, split_name, "MS-Celeb-1M") if overwrite and out_dir.exists(): shutil.rmtree(out_dir) out_dir.mkdir(parents=True, exist_ok=True) query_rows, candidate_rows = _make_annotations(rel_paths, path_to_inst) stats = pack_dataset_with_media( query_annotations=query_rows, candidate_annotations=candidate_rows, image_dir=str(image_root), output_dir=str(out_dir), media_rows_per_shard=media_rows_per_shard, row_group_size=row_group_size, num_workers=num_workers, dataset_name="MS-Celeb-1M", data_split=split_name, write_subset_readme=True, show_progress=True, ) print( f"[{split_name}] done: media={stats['num_media']}, " f"query={stats['num_query']}, candidate={stats['num_candidate']}, " f"shards={stats['num_shards']} -> {out_dir}" ) def main() -> None: script_dir = Path(__file__).resolve().parent root_dir = script_dir.parent.parent.parent parser = argparse.ArgumentParser(description="Process MS1M split JSON to MVEB parquet format.") parser.add_argument( "--split-json", type=Path, default=script_dir / "train_test_split.json", help="JSON containing train/test relative image paths.", ) parser.add_argument( "--metadata-json", type=Path, default=script_dir / "metadata.json", help="Identity metadata: instance_id -> list of relative image paths.", ) parser.add_argument( "--image-root", type=Path, default=root_dir / "source" / "ms1m", help="Root directory containing extracted shard folders 0000..0099.", ) parser.add_argument( "--output-root", type=Path, default=root_dir, help="Output MVEB root directory (contains train/ and test/).", ) parser.add_argument("--overwrite", action="store_true", help="Delete existing output split dir before writing.") parser.add_argument("--media-rows-per-shard", type=int, default=5000) parser.add_argument("--row-group-size", type=int, default=100) parser.add_argument("--num-workers", type=int, default=1) args = parser.parse_args() if not args.split_json.exists(): raise FileNotFoundError(f"split json not found: {args.split_json}") if not args.metadata_json.exists(): raise FileNotFoundError(f"metadata json not found: {args.metadata_json}") if not args.image_root.exists(): raise FileNotFoundError(f"image root not found: {args.image_root}") print(f"==> Load metadata: {args.metadata_json}") path_to_inst = _load_path_to_instance_id(args.metadata_json) print(f" images indexed: {len(path_to_inst)}") with args.split_json.open("r", encoding="utf-8") as f: split_data = json.load(f) for key in ("train", "test"): if key not in split_data or not isinstance(split_data[key], list): raise ValueError(f"split json must contain key {key!r} with a list value") _process_one_split( "train", split_data["train"], path_to_inst, args.image_root, args.output_root, args.overwrite, args.media_rows_per_shard, args.row_group_size, args.num_workers, ) _process_one_split( "test", split_data["test"], path_to_inst, args.image_root, args.output_root, args.overwrite, args.media_rows_per_shard, args.row_group_size, args.num_workers, ) if __name__ == "__main__": main()