yolov26_3d/tools/pdcl_inference/run_latest_two_aeb_rawids.py

from __future__ import annotations

import argparse
import glob
import json
import subprocess
import sys
from pathlib import Path
from typing import Any

FILE = Path(__file__).resolve()
ROOT = FILE.parents[2]
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))

from tools.pdcl_inference.run_batch_two_roi_infer import (
    DEFAULT_OUTPUT_ROOT,
    parse_rawid_tasks,
)


def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Select the latest N raw_ids for each scenario from an AEB manifest and invoke run_batch_two_roi_infer.py."
    )
    parser.add_argument(
        "--aeb-json",
        type=str,
        default=str(FILE.parent / "aeb*.json"),
        help="AEB manifest JSON path or glob pattern",
    )
    parser.add_argument(
        "--output-root",
        type=str,
        default=str(DEFAULT_OUTPUT_ROOT / "latest_two_rawids_per_scenario"),
        help="Output root forwarded to run_batch_two_roi_infer.py",
    )
    parser.add_argument(
        "--num-rawids-per-scenario",
        type=int,
        default=2,
        help="Keep the latest N raw_ids for each scenario",
    )
    parser.add_argument(
        "batch_args",
        nargs=argparse.REMAINDER,
        help="Extra args forwarded to run_batch_two_roi_infer.py; prefix them with '--'",
    )
    return parser.parse_args(argv)


def resolve_aeb_json(path_or_glob: str) -> Path:
    candidate = Path(path_or_glob)
    if candidate.is_file():
        return candidate.resolve()

    matches = sorted(Path(path) for path in glob.glob(path_or_glob))
    if not matches:
        raise FileNotFoundError(f"No AEB manifest matched: {path_or_glob}")
    return max(matches, key=lambda path: path.stat().st_mtime).resolve()


def load_manifest(path: Path) -> dict[str, Any]:
    with path.open("r", encoding="utf-8") as file:
        payload = json.load(file)
    if not isinstance(payload, dict):
        raise ValueError(f"{path} 顶层必须是 JSON object，实际: {type(payload).__name__}")
    return payload


def filter_manifest_by_rawids(payload: dict[str, Any], selected_rawids: set[str]) -> dict[str, Any]:
    scenarios = payload.get("scenarios", payload)
    if not isinstance(scenarios, dict):
        raise ValueError("AEB manifest 的 scenarios 字段必须是 dict")

    filtered_scenarios = {}
    for scenario_key, records in scenarios.items():
        if not isinstance(records, list):
            continue
        filtered_records = []
        for record in records:
            if not isinstance(record, dict):
                continue
            raw_id = str(record.get("rawid", "")).strip()
            if raw_id and raw_id in selected_rawids:
                filtered_records.append(record)
        if filtered_records:
            filtered_scenarios[scenario_key] = filtered_records

    summary = {
        "total_scenarios": len(filtered_scenarios),
        "total_rawids": sum(len(records) for records in filtered_scenarios.values()),
        "scenario_total_rawids": {
            scenario_key: len(records)
            for scenario_key, records in filtered_scenarios.items()
        },
    }
    return {
        "summary": summary,
        "scenarios": filtered_scenarios,
    }


def normalize_batch_args(batch_args: list[str]) -> list[str]:
    if batch_args and batch_args[0] == "--":
        return batch_args[1:]
    return batch_args


def select_latest_rawids_per_scenario(tasks, num_rawids_per_scenario: int):
    selected_tasks = []
    grouped_tasks = {}
    for task in tasks:
        grouped_tasks.setdefault(task.scenario_key, []).append(task)

    for scenario_key in sorted(grouped_tasks):
        scenario_tasks = sorted(
            grouped_tasks[scenario_key],
            key=lambda task: (task.cve_data or "", task.raw_id),
            reverse=True,
        )[:num_rawids_per_scenario]
        selected_tasks.extend(scenario_tasks)

    return selected_tasks


def main(argv: list[str] | None = None) -> None:
    args = parse_args(argv)
    batch_args = normalize_batch_args(args.batch_args)

    manifest_path = resolve_aeb_json(args.aeb_json)
    tasks = parse_rawid_tasks(str(manifest_path))
    if not tasks:
        raise ValueError(f"{manifest_path} 中没有可用于推理的 raw_id + clips 记录")

    if args.num_rawids_per_scenario <= 0:
        raise ValueError("--num-rawids-per-scenario 必须大于 0")

    selected_tasks = select_latest_rawids_per_scenario(
        tasks,
        num_rawids_per_scenario=args.num_rawids_per_scenario,
    )
    selected_rawids = {task.raw_id for task in selected_tasks}
    if not selected_rawids:
        raise ValueError("没有选出任何 raw_id")

    output_root = Path(args.output_root).resolve()
    status_dir = output_root / "_status"
    status_dir.mkdir(parents=True, exist_ok=True)

    selected_manifest = filter_manifest_by_rawids(load_manifest(manifest_path), selected_rawids)
    selected_manifest_path = status_dir / "latest_two_rawids_per_scenario.json"
    with selected_manifest_path.open("w", encoding="utf-8") as file:
        json.dump(selected_manifest, file, indent=2, ensure_ascii=False)

    print(f"Selected manifest: {manifest_path}")
    print(f"Selected latest {args.num_rawids_per_scenario} raw_ids for each scenario:")
    for task in sorted(selected_tasks, key=lambda task: (task.scenario_key, task.cve_data or "", task.raw_id)):
        print(
            f"  - scenario={task.scenario_key} raw_id={task.raw_id} "
            f"cve={task.cve_data or 'n/a'} clips={len(task.clips)}"
        )
    print(f"Filtered manifest saved to: {selected_manifest_path}")

    batch_script = FILE.parent / "run_batch_two_roi_infer.py"
    cmd = [
        sys.executable,
        str(batch_script),
        "--rawid-json",
        str(selected_manifest_path),
        "--output-root",
        str(output_root),
        *batch_args,
    ]
    print("Running:")
    print(" ".join(cmd))
    subprocess.run(cmd, check=True)


if __name__ == "__main__":
    main()