feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

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

platform/as_platform/data/batch.py

@@ -0,0 +1,161 @@
+"""批次元数据 batch.meta.yaml 与目录结构推断。"""
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+META_FILENAME = "batch.meta.yaml"
+SCHEMA = "huaxu-batch-v1"
+IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".JPG", ".JPEG", ".PNG"}
+
+
+def count_images(dir_path: Path) -> int:
+    if not dir_path.is_dir():
+        return 0
+    n = 0
+    for p in dir_path.rglob("*"):
+        if p.is_file() and p.suffix in IMG_EXTS:
+            n += 1
+    return n
+
+
+def count_label_files(dir_path: Path) -> int:
+    if not dir_path.is_dir():
+        return 0
+    n = 0
+    for p in dir_path.rglob("*"):
+        if p.is_file() and p.suffix.lower() in (".txt", ".xml"):
+            n += 1
+    return n
+
+
+def dms_has_images(batch_dir: Path) -> bool:
+    for sub in ("images", "images/train"):
+        if count_images(batch_dir / sub) > 0:
+            return True
+    return False
+
+
+def dms_has_labels(batch_dir: Path) -> bool:
+    for sub in ("labels", "labels/train"):
+        d = batch_dir / sub
+        if d.is_dir() and count_label_files(d) > 0:
+            return True
+    return False
+
+
+def infer_dms_stage(batch_dir: Path) -> str:
+    has_img = dms_has_images(batch_dir)
+    has_lab = dms_has_labels(batch_dir)
+    if has_img and has_lab:
+        return "returned"
+    if has_img:
+        return "raw_pool"
+    return "raw_pool"
+
+
+def infer_lane_stage(path: Path) -> str:
+    if (path / "list" / "train_gt.txt").is_file():
+        return "ingested"
+    if (path / "train_val_gt.txt").is_file():
+        return "returned"
+    if any(path.glob("**/train_val_gt.txt")):
+        return "returned"
+    if count_images(path) > 0:
+        return "raw_pool"
+    return "raw_pool"
+
+
+def read_meta(batch_dir: Path) -> dict[str, Any] | None:
+    p = batch_dir / META_FILENAME
+    if not p.is_file():
+        return None
+    try:
+        data = yaml.safe_load(p.read_text(encoding="utf-8"))
+        return data if isinstance(data, dict) else None
+    except Exception:
+        return None
+
+
+def write_meta(batch_dir: Path, data: dict[str, Any]) -> Path:
+    batch_dir.mkdir(parents=True, exist_ok=True)
+    data.setdefault("schema", SCHEMA)
+    p = batch_dir / META_FILENAME
+    p.write_text(
+        yaml.dump(data, allow_unicode=True, sort_keys=False, default_flow_style=False),
+        encoding="utf-8",
+    )
+    return p
+
+
+def enrich_batch(
+    batch_dir: Path,
+    *,
+    project: str,
+    task: str | None = None,
+    pack: str | None = None,
+    batch: str,
+    location: str,
+) -> dict[str, Any]:
+    meta = read_meta(batch_dir) or {}
+    if project == "dms":
+        stage = meta.get("stage") or infer_dms_stage(batch_dir)
+        img_n = meta.get("counts", {}).get("images")
+        lab_n = meta.get("counts", {}).get("labels")
+        if img_n is None:
+            img_n = count_images(batch_dir / "images") + count_images(batch_dir / "images" / "train")
+        if lab_n is None:
+            lab_n = count_label_files(batch_dir / "labels") + count_label_files(batch_dir / "labels" / "train")
+        fmt = meta.get("format", "yolo")
+    else:
+        stage = meta.get("stage") or infer_lane_stage(batch_dir)
+        img_n = meta.get("counts", {}).get("images") or count_images(batch_dir)
+        lab_n = meta.get("counts", {}).get("labels")
+        fmt = meta.get("format", "ufld_archive")
+        if (batch_dir / "list" / "train_gt.txt").is_file():
+            try:
+                lab_n = sum(1 for _ in (batch_dir / "list" / "train_gt.txt").open(encoding="utf-8"))
+            except OSError:
+                lab_n = lab_n or 0
+
+    return {
+        "project": project,
+        "task": task or meta.get("task"),
+        "pack": pack or meta.get("pack"),
+        "batch": batch,
+        "stage": stage,
+        "location": location,
+        "path": str(batch_dir.resolve()),
+        "engineer": meta.get("engineer"),
+        "format": fmt,
+        "counts": {"images": img_n, "labels": lab_n},
+        "has_meta": bool(meta),
+        "next_cli": suggest_cli(project, task, pack, batch, stage, location),
+    }
+
+
+def suggest_cli(
+    project: str,
+    task: str | None,
+    pack: str | None,
+    batch: str,
+    stage: str,
+    location: str,
+) -> str:
+    if project == "dms":
+        p = pack or "dms_v2"
+        t = task or "<task>"
+        if location == "inbox" and stage == "returned":
+            return f"python as.py build dms {t} --pack {p} --batch {batch}"
+        if location == "sources" and stage == "returned":
+            return f"python as.py build dms {t} --pack {p} --all-sources"
+        if stage == "raw_pool":
+            return f"# 送标完成后放入 labels，或: python as.py register-batch dms {t} --batch {batch} --stage returned"
+        return f"python as.py build dms {t} --pack {p}"
+    if stage == "returned":
+        return "python as.py add lane --src <archive> --engineer <name> --date YYYYMMDD"
+    if stage == "ingested":
+        return f"python as.py enable lane {pack or '<pack>'} && python as.py build lane"
+    return "python as.py add lane --src <path> --engineer <name> --date YYYYMMDD"