feat: Unified Ingest SDK for DMS/ADAS promote, cuboid export and 3D fit

Replace subprocess build with promote_batch SDK, add ADAS cuboid export/fit/validate pipeline, stage normalization, and offline unit tests wired into smoke_labeling_api.

Co-authored-by: Cursor <cursoragent@cursor.com>

platform/as_platform/labeling/class_map.py

@@ -0,0 +1,74 @@
+"""ADAS class_id 映射（BK2/MOON 单源）。"""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from as_platform.config import WORKSPACE
+
+_ADAS_REGISTRY = WORKSPACE / "datasets" / "adas" / "adas.registry.yaml"
+_LABELING_REGISTRY = WORKSPACE / "datasets" / "labeling.registry.yaml"
+
+
+def load_adas_class_names() -> list[str]:
+    if _ADAS_REGISTRY.is_file():
+        reg = yaml.safe_load(_ADAS_REGISTRY.read_text(encoding="utf-8")) or {}
+        names = (reg.get("classes") or {}).get("names")
+        if names:
+            return [str(n) for n in names]
+    if _LABELING_REGISTRY.is_file():
+        reg = yaml.safe_load(_LABELING_REGISTRY.read_text(encoding="utf-8")) or {}
+        labels = (reg.get("profiles") or {}).get("cuboid_7cls", {}).get("cvat_labels")
+        if labels:
+            return [str(n) for n in labels]
+    from as_platform.labeling.format_converter import CUBOID_7CLS_NAMES
+
+    return list(CUBOID_7CLS_NAMES)
+
+
+def class_name_to_id(name: str, class_map: dict[str, int] | None = None) -> int | None:
+    cmap = class_map or {n: i for i, n in enumerate(load_adas_class_names())}
+    if name in cmap:
+        return cmap[name]
+    low = name.lower()
+    for k, v in cmap.items():
+        if k.lower() == low:
+            return v
+    return None
+
+
+def build_class_map(names: list[str] | None = None) -> dict[str, int]:
+    return {str(n): idx for idx, n in enumerate(names or load_adas_class_names())}
+
+
+def remap_class_id(old_names: list[str], new_names: list[str], class_id: int) -> int:
+    if class_id < 0 or class_id >= len(old_names):
+        return class_id
+    label = old_names[class_id]
+    new_id = build_class_map(new_names).get(label)
+    if new_id is None:
+        for k, v in build_class_map(new_names).items():
+            if k.lower() == label.lower():
+                return v
+    return new_id if new_id is not None else class_id
+
+
+def normalize_detection_class(det: dict[str, Any], class_map: dict[str, int] | None = None) -> dict[str, Any]:
+    cmap = class_map or build_class_map()
+    name = str(det.get("class_name") or "")
+    cid = det.get("class_id")
+    if name:
+        mapped = class_name_to_id(name, cmap)
+        if mapped is not None:
+            det = dict(det)
+            det["class_id"] = mapped
+            det["class_name"] = name
+    elif cid is not None:
+        names = list(cmap.keys())
+        idx = int(cid)
+        if 0 <= idx < len(names):
+            det = dict(det)
+            det["class_name"] = names[idx]
+    return det