processing/common/convert_videos.py

#!/usr/bin/env python3
"""Convert prompt JSONL into sensor CSVs, cropped videos and OmniTS metadata.

Supported video domains include CoVLA, AgiBot, CUHK-X, Future Factories and
HoloAssist. Intermediate outputs may use local paths; package_release.py
copies referenced assets and writes relative paths for distribution.
Video stream copying preserves the original pipeline's keyframe behavior.
"""

from __future__ import annotations

import argparse
import hashlib
import json
import re
import shutil
import subprocess
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path

import pandas as pd

from probe import probe_ts_shape, probe_video_meta

TS_BLOCK_RE = re.compile(r"<timeseries>(.*?)</timeseries>", re.DOTALL)
DATA_LINE_RE = re.compile(r"^[\d.]+:\s*(.+)$")
TEMPORAL_FIELD_KEYS = ("frames", "segment", "start_frame", "end_frame", "start", "end")


def safe_filename_component(value: str) -> str:
    safe_value = re.sub(r"[^A-Za-z0-9_.-]+", "_", value).strip("_")
    return safe_value or "unknown"


def source_cache_key(value: str) -> str:
    return hashlib.sha1(value.encode("utf-8")).hexdigest()[:10]


def stable_video_id(entry: dict, dataset_name: str) -> str:
    """Use existing video_id when present; otherwise create a stable trace id."""
    existing = entry.get("video_id")
    if existing:
        return safe_filename_component(str(existing))

    identity = {
        "dataset": dataset_name,
        "task_id": entry.get("task_id"),
        "video_path": entry.get("video_path"),
        "frames": entry.get("frames"),
        "segment": entry.get("segment"),
        "start_frame": entry.get("start_frame"),
        "end_frame": entry.get("end_frame"),
        "start": entry.get("start"),
        "end": entry.get("end"),
    }
    digest = source_cache_key(json.dumps(identity, sort_keys=True, ensure_ascii=False))
    task_id = safe_filename_component(str(entry.get("task_id", "no_task_id")))
    dataset_part = safe_filename_component(dataset_name)
    return f"{dataset_part}_{task_id}_{digest}"


def has_temporal_bounds(entry: dict) -> bool:
    """Return True when a row has the temporal fields used by video preprocessing."""
    if entry.get("frames") is not None or entry.get("segment") is not None:
        return True
    if entry.get("start_frame") is not None and entry.get("end_frame") is not None:
        return True
    return entry.get("start") is not None and entry.get("end") is not None


def has_frame_bounds(entry: dict) -> bool:
    """Frame bounds take priority over time bounds, as in preprocess_video_segments."""
    if isinstance(entry.get("frames"), dict):
        return True
    return entry.get("start_frame") is not None and entry.get("end_frame") is not None


def extract_frame_bounds(entry: dict) -> tuple[int, int]:
    """Extract [start_frame, end_frame] from ``frames`` or top-level fields."""
    frames = entry.get("frames")
    if isinstance(frames, dict):
        start = int(frames.get("start_frame", entry.get("start_frame", 0)))
        end = int(frames.get("end_frame", entry.get("end_frame", 0)))
    else:
        start = int(entry.get("start_frame", 0))
        end = int(entry.get("end_frame", 0))
    if end < start:
        raise ValueError(f"invalid frame range: start={start}, end={end}")
    return start, end


def extract_time_bounds(entry: dict) -> tuple[float, float]:
    """Extract [start, end] seconds from ``segment`` or top-level fields."""
    segment = entry.get("segment")
    if isinstance(segment, dict):
        start = segment.get("start")
        end = segment.get("end")
        if start is not None and end is not None:
            start = float(start)
            end = float(end)
            if end < start:
                raise ValueError(f"invalid time range: start={start}, end={end}")
            return start, end

    start = entry.get("start")
    end = entry.get("end")
    if start is not None and end is not None:
        start = float(start)
        end = float(end)
        if end < start:
            raise ValueError(f"invalid time range: start={start}, end={end}")
        return start, end

    raise KeyError("no valid time bounds found")


def _video_meta_dict(meta) -> dict | None:
    """Shape the 5-tuple from probe_video_meta into the jsonl dict form."""
    if meta is None:
        return None
    w, h, sfps, nf, dur = meta
    return {
        "width": int(w), "height": int(h),
        "source_fps": float(sfps),
        "nb_frames": int(nf),
        "duration": float(dur),
    }


def parse_timeseries_data_line(line: str) -> dict[str, float] | None:
    """Parse one prompt timeseries row into numeric channel values.

    Supported prompt row shapes:
      * ``12.345: x=1, y=2`` used by CoVLA/Holo/AgiBot/CUHK-X
      * ``2023-...Z, x=1, y=2`` used by Future Factories
    """
    stripped = line.strip()
    if not stripped:
        return None

    m = DATA_LINE_RE.match(stripped)
    if m:
        kv_part = m.group(1)
    elif "," in stripped:
        timestamp, kv_part = stripped.split(",", 1)
        if not re.fullmatch(r"\d{4}-\d{2}-\d{2}(?:[T ].+)?", timestamp.strip()):
            return None
    else:
        return None

    if "..." in kv_part:
        return None

    row: dict[str, float] = {}
    for pair in kv_part.split(","):
        if "=" not in pair:
            continue
        k, v = pair.split("=", 1)
        k, v = k.strip(), v.strip()
        if not k:
            continue
        if v in ("null", "None", "nan", "NaN", ""):
            row[k] = float("nan")
        else:
            row[k] = float(v)
    return row or None


def parse_timeseries_block(text: str) -> pd.DataFrame:
    """Extract numeric rows from a <timeseries>...</timeseries> block into a DataFrame.

    Each data line looks like: "25.5: vEgo=6.517, aEgo=0.197, ..., lead_a=null".
    Returns a DataFrame with one column per variable; "null" or empty becomes NaN.
    """
    match = TS_BLOCK_RE.search(text)
    if not match:
        raise ValueError("no <timeseries> block found")
    body = match.group(1)

    records: list[dict[str, float]] = []
    for line in body.splitlines():
        row = parse_timeseries_data_line(line)
        if row:
            records.append(row)

    if not records:
        raise ValueError("<timeseries> block had no data rows")

    return pd.DataFrame.from_records(records)


def probe_video_fps(video_path: Path) -> float:
    """Use ffprobe to read the source video's avg_frame_rate as a float."""
    result = subprocess.run(
        [
            "ffprobe", "-v", "error", "-select_streams", "v:0",
            "-show_entries", "stream=avg_frame_rate",
            "-of", "default=nw=1:nk=1",
            str(video_path),
        ],
        capture_output=True, text=True, check=True,
    )
    num, den = result.stdout.strip().split("/")
    num_f, den_f = float(num), float(den)
    if den_f == 0:
        return 0.0
    return num_f / den_f


def probe_video_frame_count(video_path: Path) -> int:
    """Use ffprobe to estimate total frames for whole-clip conversion."""
    meta = probe_video_meta(str(video_path))
    if meta is None:
        return 0
    _width, _height, source_fps, nb_frames, duration = meta
    if nb_frames > 0:
        return int(nb_frames)
    if source_fps > 0 and duration > 0:
        return max(1, int(duration * source_fps))
    return 0


def get_video_fps(src_video: Path, fps_cache: dict[Path, float]) -> float:
    """Read and cache source fps for frame/time conversion."""
    if src_video not in fps_cache:
        fps_cache[src_video] = probe_video_fps(src_video)
    fps = fps_cache[src_video]
    if fps <= 0:
        raise RuntimeError(f"could not probe fps for {src_video}")
    return fps


def time_to_frame_bounds(start_time: float, end_time: float, fps: float) -> tuple[int, int]:
    """Convert seconds to inclusive frame bounds, matching preprocess_video_segments."""
    if fps <= 0:
        raise RuntimeError(f"cannot convert time bounds with invalid fps={fps}")
    start_frame = max(0, int(start_time * fps))
    end_frame = max(start_frame, int(end_time * fps))
    return start_frame, end_frame


def resolve_frame_bounds(
    entry: dict,
    src_video: Path,
    fps_cache: dict[Path, float],
) -> tuple[int, int]:
    """Resolve supported temporal fields into inclusive frame bounds.

    ``preprocess_video_segments.py`` accepts both frame fields and time fields.
    This converter keeps the same precedence: explicit frame bounds are used
    directly; otherwise ``segment`` / ``start`` / ``end`` are converted through
    the source video's fps.
    """
    if has_frame_bounds(entry):
        return extract_frame_bounds(entry)

    if has_temporal_bounds(entry):
        start_sec, end_sec = extract_time_bounds(entry)
        fps = get_video_fps(src_video, fps_cache)
        return time_to_frame_bounds(start_sec, end_sec, fps)

    total_frames = probe_video_frame_count(src_video)
    if total_frames <= 0:
        raise KeyError(
            "no temporal bounds found and failed to probe whole-video frame count; "
            "expected frames, segment, start_frame/end_frame, or start/end"
        )
    return 0, total_frames - 1


def frame_bounds_to_seconds(
    start_frame: int,
    end_frame: int,
    src_video: Path,
    fps_cache: dict[Path, float],
) -> tuple[float, float]:
    """Convert inclusive frame bounds to ffmpeg -ss / -t seconds."""
    fps = get_video_fps(src_video, fps_cache)
    start_sec = start_frame / fps
    duration_sec = max(1e-3, (end_frame - start_frame + 1) / fps)
    return start_sec, duration_sec


def slice_video(src: Path, dst: Path, start_sec: float, duration_sec: float) -> None:
    """Copy a segment with -c copy (stream copy, near-instant). Overwrites dst.

    Caveat: `-ss` before `-i` with `-c copy` seeks to the nearest keyframe, so
    clip start may be offset by up to the GOP length (typically ≤ 1s). For
    video-QA training on multi-second segments this is acceptable.
    """
    dst.parent.mkdir(parents=True, exist_ok=True)
    if shutil.which("ffmpeg") is None:
        raise RuntimeError("ffmpeg not found on PATH")
    cmd = [
        "ffmpeg", "-y", "-loglevel", "error",
        "-ss", f"{start_sec:.3f}",
        "-i", str(src),
        "-t", f"{duration_sec:.3f}",
        "-an",
        "-c", "copy",
        "-avoid_negative_ts", "make_zero",
        str(dst),
    ]
    subprocess.run(cmd, check=True, capture_output=True)


def convert_entry(
    entry: dict,
    csv_dir: Path,
    clip_dir: Path,
    fps_cache: dict[Path, float],
    prompt_only: bool = False,
    dataset_name: str = "dataset",
    copy_full_video: bool = True,
) -> dict:
    """Convert one CoVLA row into the unified OmniTS row.

    Returns: ``{video_id, video_path, timeseries_path, prompt, answer}`` where
    ``prompt`` retains the inline ``<timeseries>...</timeseries>`` body.

    ``prompt_only=True`` skips TS-CSV writing and video slicing. The derived
    paths must already exist on disk. Use this to refresh prompts after the
    source prompt text changes without re-running the expensive work.
    """
    csv_dir = Path(csv_dir).resolve()
    clip_dir = Path(clip_dir).resolve()
    if not prompt_only:
        csv_dir.mkdir(parents=True, exist_ok=True)
        clip_dir.mkdir(parents=True, exist_ok=True)

    video_id = stable_video_id(entry, dataset_name)
    src_video = Path(entry["video_path"]).expanduser().resolve()
    start_frame, end_frame = resolve_frame_bounds(
        entry=entry,
        src_video=src_video,
        fps_cache=fps_cache,
    )

    # 1. Time-series CSV. In prompt-only mode we read the shape from the
    # existing file; in fresh mode we use the in-memory DataFrame's shape
    # directly (no extra IO).
    csv_name = f"{video_id}_{start_frame}_{end_frame}.csv"
    csv_path = csv_dir / csv_name
    if prompt_only:
        if not csv_path.exists():
            raise FileNotFoundError(f"prompt-only mode: missing csv {csv_path}")
        ts_shape = probe_ts_shape(str(csv_path))
    else:
        df = parse_timeseries_block(entry["prompt"])
        df.to_csv(csv_path, index=False)
        ts_shape = (int(df.shape[0]), int(df.shape[1]))

    # 2. Video clip (ffmpeg-sliced). Reuse if it already exists.
    clip_name = f"{video_id}_{start_frame}_{end_frame}.mp4"
    clip_path = clip_dir / clip_name
    if prompt_only:
        if not (clip_path.exists() and clip_path.stat().st_size > 0):
            raise FileNotFoundError(f"prompt-only mode: missing clip {clip_path}")
    elif not (clip_path.exists() and clip_path.stat().st_size > 0):
        if copy_full_video and not has_temporal_bounds(entry):
            clip_path.parent.mkdir(parents=True, exist_ok=True)
            shutil.copy2(src_video, clip_path)
        else:
            start_sec, duration_sec = frame_bounds_to_seconds(
                start_frame=start_frame,
                end_frame=end_frame,
                src_video=src_video,
                fps_cache=fps_cache,
            )
            slice_video(src_video, clip_path, start_sec, duration_sec)

    # 2b. Probe the sliced clip for the meta that `compute_exact_lengths`
    # needs — same whether we just wrote it or are reusing an existing clip.
    video_meta = _video_meta_dict(probe_video_meta(str(clip_path)))

    # 3. Unified row — prompt retains inline TS body; baseline uses it as-is,
    # omnits strips it in the collator via strip_ts_block(). video_meta /
    # ts_shape are optional hints consumed by the length estimator.
    row: dict = {
        "video_id": video_id,
        "video_path": str(clip_path),
        "timeseries_path": str(csv_path),
        "prompt": entry["prompt"],
        "answer": entry["ground_truth"],
    }
    if video_meta is not None:
        row["video_meta"] = video_meta
    if ts_shape is not None:
        row["ts_shape"] = [int(ts_shape[0]), int(ts_shape[1])]
    return row


def _worker_convert(args: tuple[dict, str, str, bool, str, bool]) -> tuple[int, dict]:
    """Worker for ProcessPoolExecutor: convert a single entry.

    Returns (line_no, row). fps_cache is per-worker (no sharing).
    """
    entry, csv_dir, clip_dir, prompt_only, dataset_name, copy_full_video = args
    line_no = entry["_line_no"]
    _entry = {k: v for k, v in entry.items() if k != "_line_no"}
    row = convert_entry(
        _entry, csv_dir=Path(csv_dir), clip_dir=Path(clip_dir),
        fps_cache={}, prompt_only=prompt_only, dataset_name=dataset_name,
        copy_full_video=copy_full_video,
    )
    return line_no, row


def _annotate_only(jsonl_path: Path, num_workers: int, force: bool) -> None:
    """Backfill video_meta / ts_shape into an existing unified OmniTS jsonl.

    Use when clips + csvs already exist on disk and you only need the meta
    fields added. Rewrites `jsonl_path` in place via .tmp + rename. Skips
    rows that already have both fields unless --force is passed."""
    with jsonl_path.open() as f:
        rows = [json.loads(l) for l in f if l.strip()]
    print(f"[ANNOTATE] loaded {len(rows)} rows from {jsonl_path}")

    v_todo = [
        i for i, r in enumerate(rows)
        if force or not isinstance(r.get("video_meta"), dict)
    ]
    t_todo = [
        i for i, r in enumerate(rows)
        if r.get("timeseries_path") and
           (force or not isinstance(r.get("ts_shape"), (list, tuple)))
    ]
    print(f"[ANNOTATE] to probe: {len(v_todo)} videos, {len(t_todo)} TS csvs "
          f"(workers={num_workers})")

    if v_todo:
        done = 0
        with ProcessPoolExecutor(max_workers=num_workers) as pool:
            futs = {pool.submit(probe_video_meta, rows[i]["video_path"]): i for i in v_todo}
            for fut in as_completed(futs):
                i = futs[fut]
                vm = _video_meta_dict(fut.result())
                if vm is not None:
                    rows[i]["video_meta"] = vm
                done += 1
                if done % 1000 == 0:
                    print(f"[ANNOTATE]   video {done}/{len(v_todo)}", flush=True)

    if t_todo:
        done = 0
        with ProcessPoolExecutor(max_workers=num_workers) as pool:
            futs = {pool.submit(probe_ts_shape, rows[i]["timeseries_path"]): i for i in t_todo}
            for fut in as_completed(futs):
                i = futs[fut]
                shape = fut.result()
                if shape is not None:
                    rows[i]["ts_shape"] = [int(shape[0]), int(shape[1])]
                done += 1
                if done % 2000 == 0:
                    print(f"[ANNOTATE]   ts {done}/{len(t_todo)}", flush=True)

    tmp = jsonl_path.with_suffix(jsonl_path.suffix + ".tmp")
    with tmp.open("w") as f:
        for r in rows:
            f.write(json.dumps(r, ensure_ascii=False) + "\n")
    tmp.replace(jsonl_path)

    n_vm = sum(1 for r in rows if isinstance(r.get("video_meta"), dict))
    n_ts = sum(1 for r in rows if isinstance(r.get("ts_shape"), (list, tuple)))
    print(f"[ANNOTATE] wrote {jsonl_path}: {n_vm}/{len(rows)} video_meta, "
          f"{n_ts}/{len(rows)} ts_shape")


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "--annotate-only", action="store_true",
        help="Skip slicing/parsing. Probe existing clips+csvs and write "
             "video_meta / ts_shape back into --jsonl in place.",
    )
    ap.add_argument("--jsonl", type=Path,
                   help="[annotate-only mode] path to unified omnits jsonl to update in place.")
    ap.add_argument("--force", action="store_true",
                   help="[annotate-only mode] re-probe rows that already have meta.")
    ap.add_argument("--in-jsonl", type=Path)
    ap.add_argument("--out-jsonl", type=Path)
    ap.add_argument("--csv-dir", type=Path)
    ap.add_argument("--clip-dir", type=Path)
    ap.add_argument(
        "--dataset-name",
        default="dataset",
        help="Prefix used when rows do not already contain video_id.",
    )
    ap.add_argument("--limit", type=int, default=None, help="First N rows only (smoke).")
    ap.add_argument(
        "--num-workers",
        type=int,
        default=1,
        help="Parallel ffmpeg / probe workers. 1 = sequential.",
    )
    ap.add_argument(
        "--prompt-only",
        action="store_true",
        help="Refresh prompts only: reuse existing TS CSVs and video clips, "
             "skip ffmpeg slicing and TS parsing. Fails if derived files are missing.",
    )
    ap.add_argument(
        "--slice-full-video",
        action="store_true",
        help="When a row has no temporal bounds, still rewrite the whole video with ffmpeg. "
             "Default is to copy the already-segmented video into --clip-dir.",
    )
    ap.add_argument(
        "--skip-errors",
        action="store_true",
        help="Skip rows that fail conversion and report their line numbers instead of stopping.",
    )
    ap.add_argument("--source-root", type=Path, help="Base directory for relative input video paths.")
    ap.add_argument("--path-map", action="append", default=[], metavar="OLD=NEW",
                    help="Remap a legacy path prefix; repeat for multiple roots.")
    args = ap.parse_args()
    path_maps = []
    for item in args.path_map:
        if "=" not in item:
            ap.error("--path-map must have the form OLD=NEW")
        path_maps.append(tuple(item.split("=", 1)))

    if args.annotate_only:
        if args.jsonl is None:
            ap.error("--annotate-only requires --jsonl")
        _annotate_only(args.jsonl, args.num_workers, args.force)
        return

    missing = [n for n in ("in_jsonl", "out_jsonl", "csv_dir", "clip_dir")
               if getattr(args, n) is None]
    if missing:
        ap.error(f"missing required args for conversion: {missing}")

    args.out_jsonl.parent.mkdir(parents=True, exist_ok=True)
    entries: list[dict] = []
    with args.in_jsonl.open() as fin:
        for line_no, line in enumerate(fin, 1):
            if args.limit is not None and line_no > args.limit:
                break
            entry = json.loads(line)
            raw = entry["video_path"]
            for old, new in path_maps:
                if raw == old or raw.startswith(old.rstrip("/") + "/"):
                    raw = new.rstrip("/") + raw[len(old.rstrip("/")):]
                    break
            source_video = Path(raw).expanduser()
            if not source_video.is_absolute():
                source_video = (args.source_root or args.in_jsonl.resolve().parent) / source_video
            entry["video_path"] = str(source_video.resolve())
            entry["_line_no"] = line_no
            entries.append(entry)

    fout = args.out_jsonl.open("w")
    n = 0
    skipped: list[tuple[int, str]] = []

    if args.num_workers <= 1:
        fps_cache: dict[Path, float] = {}
        try:
            for entry in entries:
                line_no = entry.pop("_line_no")
                try:
                    row = convert_entry(
                        entry, csv_dir=args.csv_dir, clip_dir=args.clip_dir,
                        fps_cache=fps_cache, prompt_only=args.prompt_only,
                        dataset_name=args.dataset_name,
                        copy_full_video=not args.slice_full_video,
                    )
                except (ValueError, KeyError, RuntimeError,
                        subprocess.CalledProcessError) as exc:
                    if args.skip_errors:
                        skipped.append((line_no, str(exc)))
                        continue
                    raise RuntimeError(f"line {line_no}: {exc}") from exc
                fout.write(json.dumps(row, ensure_ascii=False) + "\n")
                n += 1
        finally:
            fout.close()
    else:
        # Parallel: write rows as they complete (no order guarantee — shuffle-safe).
        work = [
            (
                e,
                str(args.csv_dir),
                str(args.clip_dir),
                args.prompt_only,
                args.dataset_name,
                not args.slice_full_video,
            )
            for e in entries
        ]
        try:
            with ProcessPoolExecutor(max_workers=args.num_workers) as pool:
                for future in as_completed(
                    [pool.submit(_worker_convert, w) for w in work]
                ):
                    try:
                        _line_no, row = future.result()
                    except Exception as exc:
                        if args.skip_errors:
                            skipped.append((-1, str(exc)))
                            continue
                        raise
                    fout.write(json.dumps(row, ensure_ascii=False) + "\n")
                    fout.flush()
                    n += 1
                    if n % 200 == 0:
                        print(f"  converted {n}/{len(entries)}", flush=True)
        finally:
            fout.close()

    print(f"converted {n} entries → {args.out_jsonl}")
    if skipped:
        print(f"skipped {len(skipped)} entries")
        for line_no, reason in skipped[:20]:
            location = f"line {line_no}" if line_no > 0 else "worker"
            print(f"  {location}: {reason}")
        if len(skipped) > 20:
            print(f"  ... {len(skipped) - 20} more")


if __name__ == "__main__":
    main()