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"""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()
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