#!/usr/bin/env python3 """Build MVEB IUST subset from a precomputed train/test split JSON.""" 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 person with the following text." QUERY_TEXT = "Re-identify the person in the given image." CANDIDATE_INSTRUCTION = "Represent the person with the following text." CANDIDATE_TEXT = "Re-identify the person in the given image." def _pid_from_name(rel_path: str) -> str: name = Path(rel_path).name if "_" not in name: raise ValueError(f"Invalid IUST filename: {rel_path}") return name.split("_", 1)[0] def _filter_valid(paths: List[str]) -> List[str]: uniq = sorted(set(paths)) valid = [] for p in uniq: pid = _pid_from_name(p) if pid == "-1": continue valid.append(p) return valid def _build_train_rows(train_paths: List[str]) -> Tuple[List[dict], List[dict]]: candidate_paths = _filter_valid([p for p in train_paths if p.startswith("bounding_box_train/")]) inst_to_ids: Dict[str, List[str]] = {} candidate_rows: List[dict] = [] for idx, rel_path in enumerate(candidate_paths): pid = _pid_from_name(rel_path) inst_to_ids.setdefault(pid, []).append(rel_path) candidate_rows.append( { "id": rel_path, "instance_id": pid, "image_path": rel_path, "instruction": CANDIDATE_INSTRUCTION, "text": CANDIDATE_TEXT, } ) query_rows: List[dict] = [] for row in candidate_rows: pos_ids = [x for x in inst_to_ids[row["instance_id"]] if x != row["id"]] if not pos_ids: continue query_rows.append( { "id": row["id"], "instance_id": row["instance_id"], "image_path": row["image_path"], "instruction": QUERY_INSTRUCTION, "text": QUERY_TEXT, "pos_ids": pos_ids, } ) return query_rows, candidate_rows def _build_test_rows(test_paths: List[str]) -> Tuple[List[dict], List[dict]]: candidate_paths = _filter_valid([p for p in test_paths if p.startswith("bounding_box_test/")]) query_paths = _filter_valid([p for p in test_paths if p.startswith("query/")]) if not candidate_paths or not query_paths: raise ValueError("test split must contain both 'bounding_box_test/' and 'query/' images") inst_to_candidate_ids: Dict[str, List[str]] = {} candidate_rows: List[dict] = [] for idx, rel_path in enumerate(candidate_paths): row_id = str(idx) pid = _pid_from_name(rel_path) inst_to_candidate_ids.setdefault(pid, []).append(row_id) candidate_rows.append( { "id": row_id, "instance_id": pid, "image_path": rel_path, "instruction": CANDIDATE_INSTRUCTION, "text": CANDIDATE_TEXT, } ) query_rows: List[dict] = [] base = len(candidate_rows) for q_idx, rel_path in enumerate(query_paths): pid = _pid_from_name(rel_path) pos_ids = inst_to_candidate_ids.get(pid, []) if not pos_ids: continue query_rows.append( { "id": str(base + q_idx), "instance_id": pid, "image_path": rel_path, "instruction": QUERY_INSTRUCTION, "text": QUERY_TEXT, "pos_ids": list(pos_ids), } ) return query_rows, candidate_rows def _process_one_split( split_name: str, rel_paths: List[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, "IUST") if overwrite and out_dir.exists(): shutil.rmtree(out_dir) out_dir.mkdir(parents=True, exist_ok=True) if split_name == "train": query_rows, candidate_rows = _build_train_rows(rel_paths) elif split_name == "test": query_rows, candidate_rows = _build_test_rows(rel_paths) else: raise ValueError(f"Unsupported split: {split_name}") 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="IUST", 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 IUST 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( "--image-root", type=Path, default=root_dir / "source" / "IUSTPersonReID", help="Root directory containing bounding_box_train/bounding_box_test/query.", ) 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.image_root.exists(): raise FileNotFoundError(f"image root not found: {args.image_root}") 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"], 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"], args.image_root, args.output_root, args.overwrite, args.media_rows_per_shard, args.row_group_size, args.num_workers, ) if __name__ == "__main__": main()