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/fit_cuboid_batch.py

@@ -0,0 +1,95 @@
+"""Batch-level cuboid 3D fit for quaternion_json."""
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
+
+
+def _task_id_for_image(image_path: Path, batch_dir: Path) -> str:
+    try:
+        rel = image_path.relative_to(batch_dir)
+        stem = rel.as_posix()
+    except ValueError:
+        stem = image_path.name
+    return hashlib.sha256(stem.encode()).hexdigest()[:16]
+
+
+def _load_ls_cuboid_points(batch_dir: Path, stem: str) -> list[list[float]]:
+    ann_dir = batch_dir / "labels" / "ls_annotations"
+    if not ann_dir.is_dir():
+        return []
+    for p in ann_dir.glob("*.json"):
+        try:
+            data = json.loads(p.read_text(encoding="utf-8"))
+        except (OSError, json.JSONDecodeError):
+            continue
+        img = str(data.get("image") or "")
+        if stem in img or p.stem:
+            regions = data.get("result") or []
+            pts_list = []
+            for r in regions:
+                if r.get("type") != "cuboid":
+                    continue
+                pts = list(r.get("points") or [])
+                if len(pts) >= 16:
+                    pts_list.append(pts[:16])
+            if pts_list:
+                return pts_list
+    return []
+
+
+def fit_batch(batch_dir: Path) -> dict[str, Any]:
+    from algorithms.adas_mono3d.fit_cuboid import fit_cuboid_detection
+
+    batch_dir = batch_dir.resolve()
+    qdir = batch_dir / "labels" / "quaternion_json"
+    if not qdir.is_dir():
+        raise ValueError(f"missing {qdir}")
+
+    updated = 0
+    fit_ok = 0
+    total = 0
+    for p in sorted(qdir.glob("*.json")):
+        data = json.loads(p.read_text(encoding="utf-8"))
+        K = data.get("K")
+        if not K:
+            continue
+        stem = data.get("image_stem") or p.stem
+        cuboid_pts_list = _load_ls_cuboid_points(batch_dir, stem)
+        new_dets = []
+        for i, det in enumerate(data.get("detections") or []):
+            det = dict(det)
+            if det.get("fit_ok"):
+                new_dets.append(det)
+                total += 1
+                fit_ok += 1
+                continue
+            class_name = str(det.get("class_name") or "car")
+            points = cuboid_pts_list[i] if i < len(cuboid_pts_list) else None
+            if not points:
+                box = det.get("box2d_xyxy") or []
+                if len(box) >= 4:
+                    x1, y1, x2, y2 = box[:4]
+                    points = [x1, y1, x2, y1, x1, y2, x2, y2, x1, y1, x2, y1, x1, y2, x2, y2]
+            if points:
+                fitted = fit_cuboid_detection(points, K, class_name)
+                det.update({k: v for k, v in fitted.items() if k != "box2d_xyxy" or "box2d_xyxy" not in det})
+            new_dets.append(det)
+            total += 1
+            if det.get("fit_ok"):
+                fit_ok += 1
+        data["detections"] = new_dets
+        data["num_detections"] = len(new_dets)
+        p.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
+        updated += 1
+
+    return {
+        "updated_files": updated,
+        "detections": total,
+        "fit_ok": fit_ok,
+        "fit_ok_ratio": fit_ok / max(total, 1),
+    }