#!/usr/bin/env python3
"""SAM3 常驻模型服务:启动时把 SAM3 video model 载入一次,之后跨视频复用。

服务的 op:
  - "masks": 进程内调 qwen_sam3_video_masks.main()(args.argv = 原 step2 CLI 参数列表)
  - "shutdown"

载入一次的模型:SAM3 video model(默认 ckpt 由 step2 传入;Qwen 标签在 masks 模式走 API,
无需常驻本地模型)。跑在 sam3 env:
  python masking/sam3_server.py --queue <dir>
"""
import argparse
import json
import os
import sys
from pathlib import Path

os.environ.setdefault("PYOPENGL_PLATFORM", "egl")

_QWEN_DIR = Path(__file__).resolve().parent
_OVOW_ROOT = _QWEN_DIR.parent
_ENGINE_DIR = Path(os.environ.get("ENGINE_DIR", str(_OVOW_ROOT / "engine")))
_SAM3_DIR = Path(os.environ.get("SAM3_DIR", str(_OVOW_ROOT / "third_party" / "sam3")))
_IPC_DIR = str(_ENGINE_DIR / "tools" / "model_server")
for p in (_IPC_DIR, str(_QWEN_DIR)):
    if p not in sys.path:
        sys.path.insert(0, p)

import ipc  # noqa: E402  (tools/model_server 在 path 上)

_DEFAULT_SAM3_CKPT = os.environ.get("SAM3_CKPT", str(_SAM3_DIR / "checkpoints" / "sam3" / "sam3.pt"))


def _warm_models():
    """一次性加载 SAM3 video model(常驻)。"""
    print("[sam3-server] 加载 SAM3 video model(常驻)...", flush=True)
    import qwen_sam3_video_masks as qsvm  # noqa: F401
    qsvm.load_once(os.environ.get("SAM3_CKPT") or _DEFAULT_SAM3_CKPT)
    print("[sam3-server] SAM3 ckpt 载入完成。", flush=True)


def _handle_masks(args):
    """进程内单视频 mask 生成:复用常驻 SAM3,init_state 按视频新建。"""
    import qwen_sam3_video_masks as qsvm
    argv = args["argv"]
    old = sys.argv
    try:
        sys.argv = ["qwen_sam3_video_masks.py"] + list(argv)
        qsvm.main()
    finally:
        sys.argv = old
    return {"video": args.get("video_name", "")}


def _as_bool_mask(mask):
    import numpy as np

    arr = np.asarray(mask)
    arr = np.squeeze(arr)
    if arr.ndim != 2:
        raise RuntimeError(f"Unexpected SAM3 mask shape: {arr.shape}")
    return arr > 0


def _normalize_points(raw_points, width: int, height: int):
    points = []
    labels = []
    for item in raw_points:
        if isinstance(item, dict):
            x = float(item.get("x", item.get("px", 0.0)))
            y = float(item.get("y", item.get("py", 0.0)))
            label = int(item.get("label", 1))
        else:
            x = float(item[0])
            y = float(item[1])
            label = int(item[2]) if len(item) > 2 else 1
        if x > 1.0 or y > 1.0:
            x = x / max(1, width)
            y = y / max(1, height)
        x = min(1.0, max(0.0, x))
        y = min(1.0, max(0.0, y))
        points.append([x, y])
        labels.append(1 if label else 0)
    if not any(labels):
        raise RuntimeError("manual SAM3 points need at least one positive point")
    return points, labels


def _prime_empty_cached_outputs(inference_state, num_frames: int):
    """Point prompts are tracker refinements; SAM3 expects a per-frame VG cache."""
    cached = inference_state.setdefault("cached_frame_outputs", {})
    for frame_idx in range(max(0, int(num_frames))):
        cached.setdefault(frame_idx, {})


def _collect_obj_masks(masks_by_frame: dict, frame_idx: int, out: dict | None, obj_id: int):
    if out is None:
        return
    for oid, mask in zip(out.get("out_obj_ids", []), out.get("out_binary_masks", [])):
        if int(oid) != obj_id:
            continue
        arr = _as_bool_mask(mask)
        if int(arr.sum()) > 0:
            masks_by_frame[int(frame_idx)] = arr


def _save_sparse_masks(obj_dir: Path, masks_by_frame: dict) -> None:
    obj_dir.mkdir(parents=True, exist_ok=True)
    from PIL import Image  # type: ignore

    for fid, mask in sorted(masks_by_frame.items()):
        out = (_as_bool_mask(mask).astype("uint8") * 255)
        Image.fromarray(out).save(obj_dir / f"frame_{int(fid):06d}.png")


def _click_boxes_from_points(points, point_labels, specs):
    positives = [p for p, label in zip(points, point_labels) if int(label)]
    if not positives:
        raise RuntimeError("manual box prompt needs at least one positive point")
    x, y = positives[-1]
    boxes = []
    for spec in specs or []:
        if isinstance(spec, dict):
            bw = float(spec.get("w", spec.get("width", 0.55)))
            bh = float(spec.get("h", spec.get("height", 0.45)))
        else:
            bw = float(spec[0])
            bh = float(spec[1] if len(spec) > 1 else spec[0])
        bw = min(0.98, max(0.03, bw))
        bh = min(0.98, max(0.03, bh))
        x0 = min(max(float(x) - bw * 0.5, 0.0), 1.0 - bw)
        y0 = min(max(float(y) - bh * 0.5, 0.0), 1.0 - bh)
        boxes.append([x0, y0, bw, bh])
    if not boxes:
        raise RuntimeError("manual box prompt has no valid click box sizes")
    return boxes


def _mask_has_point(mask, points, point_labels, radius: int = 4) -> bool:
    positives = [p for p, label in zip(points, point_labels) if int(label)]
    if not positives:
        return False
    h, w = mask.shape[:2]
    for x_norm, y_norm in positives:
        x = int(round(float(x_norm) * max(1, w - 1)))
        y = int(round(float(y_norm) * max(1, h - 1)))
        x0, x1 = max(0, x - radius), min(w, x + radius + 1)
        y0, y1 = max(0, y - radius), min(h, y + radius + 1)
        if bool(mask[y0:y1, x0:x1].any()):
            return True
    return False


def _select_prompt_object(out: dict | None, points, point_labels):
    if out is None:
        return None, None, 0
    best = None
    for oid, mask in zip(out.get("out_obj_ids", []), out.get("out_binary_masks", [])):
        arr = _as_bool_mask(mask)
        area = int(arr.sum())
        if area <= 0:
            continue
        has_point = _mask_has_point(arr, points, point_labels)
        score = (1 if has_point else 0, area)
        if best is None or score > best[0]:
            best = (score, int(oid), arr, area)
    if best is None:
        return None, None, 0
    return best[1], best[2], best[3]


def _next_object_name(mask_root: Path, label: str) -> str:
    safe = label
    idx = 1
    while (mask_root / f"{safe}_{idx:02d}").exists():
        idx += 1
    return f"{safe}_{idx:02d}"


def _append_unique(items, value):
    if value not in items:
        items.append(value)
    return items


def _labels_per_keyframe_dict(payload: dict) -> dict:
    out = {}
    for key, values in (payload.get("labels_per_keyframe") or {}).items():
        try:
            frame = int(key)
        except (TypeError, ValueError):
            continue
        out[frame] = [str(v) for v in (values or [])]
    return out


def _as_bool(value, default: bool = True) -> bool:
    if value is None:
        return default
    if isinstance(value, bool):
        return value
    text = str(value).strip().lower()
    if text in {"1", "true", "yes", "on"}:
        return True
    if text in {"0", "false", "no", "off"}:
        return False
    return default


def _commit_object_metadata(
    *, output_root, video_path, video_name, num_frames, fps, width, height,
    mask_root, obj_dir, obj_name, label, category, frame_idx, points, point_labels,
    object_summary, args,
):
    """Upsert one manual object into labels_json / mask_summary / pipeline_config.

    Shared by the single-object and batch manual handlers so both emit identical
    metadata (upsert-by-name, same schema the review UI + run_recon consume)."""
    import qwen_sam3_video_masks as qsvm
    meta_dir = output_root / "metadata"
    labels_json = meta_dir / f"{video_name}_labels.json"
    label_norm = qsvm._simple_normalize(label)
    if labels_json.is_file():
        labels_payload = json.loads(labels_json.read_text(encoding="utf-8"))
    else:
        qsvm._write_labels_json(
            labels_json, video_path, num_frames, fps, width, height, [], [], {},
        )
        labels_payload = json.loads(labels_json.read_text(encoding="utf-8"))
    keyframes = [int(x) for x in (labels_payload.get("keyframes") or [])]
    _append_unique(keyframes, int(frame_idx))
    labels = [str(x) for x in (labels_payload.get("labels") or [])]
    _append_unique(labels, label)
    labels_per_keyframe = _labels_per_keyframe_dict(labels_payload)
    labels_per_keyframe.setdefault(int(frame_idx), [])
    _append_unique(labels_per_keyframe[int(frame_idx)], label)
    label_categories = dict(labels_payload.get("label_categories") or {})
    label_categories[label_norm] = category
    labels_payload.update({
        "video": str(video_path),
        "video_name": video_name,
        "num_frames": int(num_frames),
        "fps": float(fps),
        "width": int(width),
        "height": int(height),
        "keyframes": sorted(keyframes),
        "labels": labels,
        "labels_per_keyframe": {str(k): v for k, v in sorted(labels_per_keyframe.items())},
        "label_categories": label_categories,
    })
    labels_payload["label_category_source"] = "manual_click"
    manual_points = list(labels_payload.get("manual_points") or [])
    manual_points.append({
        "object": obj_name,
        "label": label,
        "points": points,
        "point_labels": point_labels,
        "frame_idx": frame_idx,
    })
    labels_payload["manual_points"] = manual_points
    labels_json.write_text(json.dumps(labels_payload, indent=2), encoding="utf-8")

    objects_summary = [object_summary]
    summary_path = meta_dir / f"{video_name}_mask_summary.json"
    meta_dir.mkdir(parents=True, exist_ok=True)
    if summary_path.is_file():
        summary = json.loads(summary_path.read_text(encoding="utf-8"))
    else:
        summary = {
            "video": str(video_path),
            "video_name": video_name,
            "num_frames": int(num_frames),
            "fps": float(fps),
            "width": int(width),
            "height": int(height),
            "keyframes": [],
            "labels": [],
            "labels_per_keyframe": {},
            "moving_labels": [],
            "objects": [],
            "mask_root": str(mask_root),
            "labels_json": str(labels_json),
            "qwen_label_category_mode": "manual_click",
            "label_category_source": "manual_click",
            "label_categories": {},
            "label_category_reasons": {},
            "label_visual_descriptions": {},
            "label_prompt_aliases": {},
        }
    summary.update({
        "video": str(video_path),
        "video_name": video_name,
        "num_frames": int(num_frames),
        "fps": float(fps),
        "width": int(width),
        "height": int(height),
        "mask_root": str(mask_root),
        "labels_json": str(labels_json),
        "qwen_label_category_mode": "manual_click",
        "label_category_source": "manual_click",
    })
    keyframes = [int(x) for x in (summary.get("keyframes") or [])]
    _append_unique(keyframes, int(frame_idx))
    labels = [str(x) for x in (summary.get("labels") or [])]
    _append_unique(labels, label)
    labels_per_keyframe = _labels_per_keyframe_dict(summary)
    labels_per_keyframe.setdefault(int(frame_idx), [])
    _append_unique(labels_per_keyframe[int(frame_idx)], label)
    moving_labels = [str(x) for x in (summary.get("moving_labels") or [])]
    if category in {"rigid", "deform"}:
        _append_unique(moving_labels, label)
    else:
        moving_labels = [x for x in moving_labels if x != label]
    label_categories = dict(summary.get("label_categories") or {})
    label_categories[label_norm] = category
    existing_objects = [
        obj for obj in (summary.get("objects") or [])
        if str(obj.get("name", "")) != obj_name
    ]
    summary["keyframes"] = sorted(keyframes)
    summary["labels"] = labels
    summary["labels_per_keyframe"] = {str(k): v for k, v in sorted(labels_per_keyframe.items())}
    summary["moving_labels"] = moving_labels
    summary["label_categories"] = label_categories
    summary["objects"] = existing_objects + objects_summary
    summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")

    recon_root = output_root / "recon_ready" / video_name
    recon_root.mkdir(parents=True, exist_ok=True)
    batch_config = args.get("batch_config") or str(_ENGINE_DIR / "config" / "batch_config_example_legacy.json")
    cfg_path = recon_root / "pipeline_config.json"
    if cfg_path.is_file():
        pipeline_cfg = json.loads(cfg_path.read_text(encoding="utf-8"))
    else:
        pipeline_cfg = {
            "batch_config": str(Path(batch_config).resolve()),
            "video_path": str(video_path),
            "output_root": str(output_root),
            "mask_glob": "frame_*.png",
            "tracker_mask_mode": "static",
            "objects": [],
        }
    pipeline_cfg.update({
        "batch_config": str(Path(pipeline_cfg.get("batch_config") or batch_config).resolve()),
        "video_path": str(video_path),
        "output_root": str(output_root),
        "mask_glob": "frame_*.png",
        "tracker_mask_mode": "static",
    })
    existing_cfg_objects = [
        obj for obj in (pipeline_cfg.get("objects") or [])
        if str(obj.get("name", "")) != obj_name
    ]
    existing_cfg_objects.append({
        "name": obj_name,
        "masks_dir": str(obj_dir),
        "category": category,
    })
    pipeline_cfg["objects"] = existing_cfg_objects
    cfg_path.write_text(json.dumps(pipeline_cfg, indent=2), encoding="utf-8")

    return {
        "video": video_name,
        "object": obj_name,
        "category": category,
        "frames_with_mask": int(object_summary.get("frames_with_mask", 0)),
        "summary": str(summary_path),
        "pipeline_config": str(cfg_path),
    }


def _handle_manual_points_batch(args):
    """Segment several manually-clicked objects in ONE video pass.

    A single click only masks one frame; every object still needs its mask propagated
    across the whole video (Motion324 wants every frame; the scene animation gates each
    object's visibility on masked frames). Running one ``manual_points`` request per object
    re-encodes all video frames each time (~50s each) because SAM3 evicts per-frame features
    as it propagates (sliding window). Here we point-prompt every object into ONE
    ``inference_state`` (distinct obj_ids) and propagate once per prompt-frame group, so each
    frame's image features are computed once and shared by every object's tracker at that
    frame → ~N× faster. Instance identity is preserved: each obj_id yields its own binary
    mask written to its own ``masks/<video>/<obj>`` dir; masks are never merged."""
    import numpy as np  # noqa: F401
    import qwen_sam3_video_masks as qsvm

    video_path = Path(args["video_path"]).resolve()
    output_root = Path(args["output_root"]).resolve()
    objects_in = list(args.get("objects") or [])
    if not objects_in:
        raise RuntimeError("manual_points_batch needs a non-empty 'objects' list")
    min_obj_area = int(args.get("min_obj_area", 200))
    num_frames, fps, width, height = qsvm._video_info(video_path)
    video_name = video_path.stem
    mask_root = output_root / "masks" / video_name

    sam3_model, _ = qsvm.load_once(args.get("sam3_ckpt") or os.environ.get("SAM3_CKPT") or _DEFAULT_SAM3_CKPT)
    inference_state = sam3_model.init_state(resource_path=str(video_path))

    specs = []
    for i, obj in enumerate(objects_in, start=1):
        label = str(obj.get("label") or "object").strip() or "object"
        category = str(obj.get("category") or "rigid").strip().lower()
        if category not in {"static", "rigid", "deform"}:
            raise RuntimeError(f"invalid manual category: {category}")
        frame_idx = min(max(0, int(obj.get("frame_idx", 0))), max(0, num_frames - 1))
        pts, plabels = _normalize_points(obj.get("points") or [], width, height)
        name = str(obj.get("object_name") or "").strip() or _next_object_name(mask_root, qsvm._safe_id(label))
        specs.append({
            "obj_id": i, "label": label, "category": category, "frame_idx": frame_idx,
            "points": pts, "point_labels": plabels, "object_name": name,
            "masks_by_frame": {},
        })

    by_id = {s["obj_id"]: s for s in specs}

    frames = sorted({s["frame_idx"] for s in specs})
    for gi, gframe in enumerate(frames):
        group = [s for s in specs if s["frame_idx"] == gframe]
        if gi > 0:
            sam3_model.reset_state(inference_state)
        _prime_empty_cached_outputs(inference_state, num_frames)
        for s in group:
            _, prompt_out = sam3_model.add_prompt(
                inference_state,
                frame_idx=gframe,
                points=s["points"],
                point_labels=s["point_labels"],
                obj_id=s["obj_id"],
            )
            _collect_obj_masks(s["masks_by_frame"], gframe, prompt_out, s["obj_id"])
        group_ids = {s["obj_id"] for s in group}
        directions = [False] + ([True] if gframe > 0 else [])
        for reverse in directions:
            for fid, out in sam3_model.propagate_in_video(
                inference_state,
                start_frame_idx=gframe,
                max_frame_num_to_track=num_frames,
                reverse=reverse,
            ):
                if out is None:
                    continue
                for oid in group_ids:
                    _collect_obj_masks(by_id[oid]["masks_by_frame"], fid, out, oid)

    results = []
    for s in specs:
        masks_by_frame = s["masks_by_frame"]
        if not masks_by_frame:
            results.append({"object": s["object_name"], "error": "no mask; refine click points"})
            continue
        max_area = max(int(m.sum()) for m in masks_by_frame.values())
        if max_area < min_obj_area:
            results.append({"object": s["object_name"],
                            "error": f"mask too small: {max_area} < {min_obj_area}"})
            continue
        obj_dir = mask_root / s["object_name"]
        qsvm._save_masks(obj_dir, num_frames, masks_by_frame)
        is_static, med_iou, med_motion, valid_frames, occluded_frames, area_med = qsvm._classify_static(
            masks_by_frame,
            static_iou_thresh=float(args.get("static_iou_thresh", 0.6)),
            static_motion_px=float(args.get("static_motion_px", 10.0)),
            occlusion_area_ratio=float(args.get("occlusion_area_ratio", 0.6)),
            min_valid_frames=int(args.get("min_valid_frames", 10)),
            high_iou_thresh=float(args.get("high_iou_static", 0.8)),
            high_iou_motion_mult=float(args.get("high_iou_motion_mult", 3.0)),
        )
        category = s["category"]
        object_summary = {
            "name": s["object_name"],
            "label": s["label"],
            "sam3_obj_id": s["obj_id"],
            "frames_with_mask": len(masks_by_frame),
            "max_area": int(max_area),
            "median_area": float(area_med),
            "valid_frames": int(valid_frames),
            "occluded_frames": int(occluded_frames),
            "median_iou": float(med_iou),
            "median_motion_px": float(med_motion),
            "is_static": category == "static" or bool(is_static and category == "static"),
            "category": category,
            "category_source": "manual_click",
            "force_static": category == "static",
            "force_dynamic": category in {"rigid", "deform"},
            "prompt_frame": int(s["frame_idx"]),
            "prompt_mode": "points",
            "prompt_box_xywh": None,
            "manual_points": s["points"],
            "manual_point_labels": s["point_labels"],
        }
        res = _commit_object_metadata(
            output_root=output_root, video_path=video_path, video_name=video_name,
            num_frames=num_frames, fps=fps, width=width, height=height,
            mask_root=mask_root, obj_dir=obj_dir, obj_name=s["object_name"],
            label=s["label"], category=category, frame_idx=s["frame_idx"],
            points=s["points"], point_labels=s["point_labels"],
            object_summary=object_summary, args=args,
        )
        results.append(res)

    return {"video": video_name, "num_frames": int(num_frames), "objects": results}


def _handle_manual_points(args):
    """Point-prompt SAM3 masks for Gradio manual mode; writes OVOW mask summary/config."""
    import numpy as np
    import qwen_sam3_video_masks as qsvm

    video_path = Path(args["video_path"]).resolve()
    output_root = Path(args["output_root"]).resolve()
    label = str(args.get("label") or "object").strip() or "object"
    category = str(args.get("category") or "rigid").strip().lower()
    if category not in {"static", "rigid", "deform"}:
        raise RuntimeError(f"invalid manual category: {category}")
    commit = _as_bool(args.get("commit"), True)

    num_frames, fps, width, height = qsvm._video_info(video_path)
    frame_idx = int(args.get("frame_idx", 0))
    frame_idx = min(max(0, frame_idx), max(0, num_frames - 1))
    min_obj_area = int(args.get("min_obj_area", 200))
    points, point_labels = _normalize_points(args.get("points") or [], width, height)

    sam3_model, _ = qsvm.load_once(args.get("sam3_ckpt") or os.environ.get("SAM3_CKPT") or _DEFAULT_SAM3_CKPT)
    inference_state = None
    requested_mode = str(args.get("prompt_mode") or "points").strip().lower()
    prompt_mode = "box" if requested_mode in {"box", "click_box", "visual_box"} else "points"
    masks_by_frame = {}
    prompt_box = None
    obj_id = int(args.get("obj_id", 1))
    if prompt_mode == "box":
        prompt_out = None
        for prompt_box in _click_boxes_from_points(
            points,
            point_labels,
            args.get("click_boxes") or [[0.55, 0.45], [0.75, 0.60]],
        ):
            inference_state = sam3_model.init_state(resource_path=str(video_path))
            prompt_frame, prompt_out = sam3_model.add_prompt(
                inference_state,
                frame_idx=frame_idx,
                boxes_xywh=[prompt_box],
                box_labels=[1],
            )
            selected_obj_id, selected_mask, selected_area = _select_prompt_object(
                prompt_out,
                points,
                point_labels,
            )
            if selected_obj_id is None:
                continue
            obj_id = int(selected_obj_id)
            masks_by_frame = {int(prompt_frame): selected_mask}
            if int(selected_area) >= min_obj_area:
                break
    else:
        inference_state = sam3_model.init_state(resource_path=str(video_path))
        _prime_empty_cached_outputs(inference_state, num_frames)
        prompt_frame, prompt_out = sam3_model.add_prompt(
            inference_state,
            frame_idx=frame_idx,
            points=points,
            point_labels=point_labels,
            obj_id=obj_id,
        )
        _collect_obj_masks(masks_by_frame, prompt_frame, prompt_out, obj_id)

    if commit:
        directions = [False]
        if frame_idx > 0:
            directions.append(True)
        for reverse in directions:
            for fid, out in sam3_model.propagate_in_video(
                inference_state,
                start_frame_idx=frame_idx,
                max_frame_num_to_track=num_frames,
                reverse=reverse,
            ):
                if out is None:
                    continue
                _collect_obj_masks(masks_by_frame, fid, out, obj_id)

    if not masks_by_frame:
        raise RuntimeError("manual SAM3 produced no mask; add/refine click points")
    max_area = max(int(m.sum()) for m in masks_by_frame.values())
    if max_area < min_obj_area:
        raise RuntimeError(
            f"manual SAM3 mask too small: max_area={max_area}, min_obj_area={min_obj_area}"
        )

    video_name = video_path.stem
    mask_root = output_root / "masks" / video_name
    pending_root = output_root / "_pending" / video_name
    safe_label = qsvm._safe_id(label)
    obj_name = str(args.get("object_name") or "").strip() or _next_object_name(mask_root, safe_label)
    obj_dir = (mask_root if commit else pending_root) / obj_name
    if commit:
        qsvm._save_masks(obj_dir, num_frames, masks_by_frame)
    else:
        _save_sparse_masks(obj_dir, masks_by_frame)

    is_static, med_iou, med_motion, valid_frames, occluded_frames, area_med = qsvm._classify_static(
        masks_by_frame,
        static_iou_thresh=float(args.get("static_iou_thresh", 0.6)),
        static_motion_px=float(args.get("static_motion_px", 10.0)),
        occlusion_area_ratio=float(args.get("occlusion_area_ratio", 0.6)),
        min_valid_frames=int(args.get("min_valid_frames", 10)),
        high_iou_thresh=float(args.get("high_iou_static", 0.8)),
        high_iou_motion_mult=float(args.get("high_iou_motion_mult", 3.0)),
    )
    object_summary = {
        "name": obj_name,
        "label": label,
        "sam3_obj_id": obj_id,
        "frames_with_mask": len(masks_by_frame),
        "max_area": int(max_area),
        "median_area": float(area_med),
        "valid_frames": int(valid_frames),
        "occluded_frames": int(occluded_frames),
        "median_iou": float(med_iou),
        "median_motion_px": float(med_motion),
        "is_static": category == "static" or bool(is_static and category == "static"),
        "category": category,
        "category_source": "manual_click",
        "force_static": category == "static",
        "force_dynamic": category in {"rigid", "deform"},
        "prompt_frame": int(frame_idx),
        "prompt_mode": prompt_mode,
        "prompt_box_xywh": prompt_box,
        "manual_points": points,
        "manual_point_labels": point_labels,
    }

    if not commit:
        return {
            "video": video_name,
            "object": obj_name,
            "label": label,
            "category": category,
            "frames_with_mask": len(masks_by_frame),
            "max_area": int(max_area),
            "masks_dir": str(obj_dir),
            "prompt_frame": int(frame_idx),
            "num_frames": int(num_frames),
            "fps": float(fps),
            "width": int(width),
            "height": int(height),
            "object_summary": object_summary,
        }

    return _commit_object_metadata(
        output_root=output_root,
        video_path=video_path,
        video_name=video_name,
        num_frames=num_frames,
        fps=fps,
        width=width,
        height=height,
        mask_root=mask_root,
        obj_dir=obj_dir,
        obj_name=obj_name,
        label=label,
        category=category,
        frame_idx=frame_idx,
        points=points,
        point_labels=point_labels,
        object_summary=object_summary,
        args=args,
    )


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--queue", required=True, help="请求队列目录")
    args = ap.parse_args()
    handlers = {
        "masks": _handle_masks,
        "manual_points": _handle_manual_points,
        "manual_points_batch": _handle_manual_points_batch,
    }
    ipc.server_loop(args.queue, handlers, on_start=_warm_models)


if __name__ == "__main__":
    main()