feat(trainer): phase-B body-type classifier — trainer job on the collector host + the classifier stage on the booth

apps/trainer (parking-trainer): inspect / train / evaluate / publish. Reads the wash
collector's SQLite + crops read-only off its volume; time split (validation = newest
slice); thin classes dropped; damped class weights; `features` mode (frozen ImageNet
backbone, on-disk feature cache, seconds to retrain) and `finetune` mode (light
augmentation). CPU-only torch from PyTorch's wheel index. ONNX export checked against
the torch model; NO model file below the validation floor (exit 3, report still written);
exit 2 = not enough labels. `evaluate` scores a shipped model on labels reviewed after
training + the unlabelled pile; `publish` PUTs a version folder to a Gitea generic package.
Light core deps; the `train` extra is heavy — CI syncs without it, torch tests skip.

apps/vision: BodyTypeClassifier (bodytype.onnx + sidecar = the preprocessing contract:
crop margin, input size, RGB 0-255, normalisation inside the graph) and
RefinedVehicleDetector over YOLOX — refines only `car` or a class the classifier trained
on, min-confidence, `detector_class` on the result; path set but no file = phase B off
without an error; a broken file is a health detail. models/bodytype.version (tracked,
empty) pins the published version the Dockerfile fetches at build (BuildKit secret;
a pin that cannot be fetched fails the build). Verified: a trainer model gives identical
probabilities inside the vision service; both images built and smoke-tested.

Delivery: parking-trainer image in build-images.yml, the `trainer` compose profile on the
collector stack (CPU, read-only data, TRAINER_OUT), commented TRAINER_OUT/PUBLISH_TOKEN in
the wash-collector stack, .dockerignore for both Python contexts, trainer deps synced in CI.

Wiki: bodytype-classifier-training rewritten as built (+ one fleet model not per site,
secrets/access, where the crops live), opencv-anpr-service §Phase B, vision-review-outbox,
vision-service-packaging, fleet-deployment-komodo, index, log.

Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU

apps/trainer/tests/conftest.py

@@ -0,0 +1,102 @@
+"""A synthetic collector volume: the collector's `items` table (same DDL as apps/collector
+src/db.ts) + JPEG crops. Classes are told apart by COLOUR so even a random-init backbone's
+features separate them — the tests check the plumbing (split, floor, export, sidecar),
+not accuracy on real cars."""
+
+from __future__ import annotations
+
+import sqlite3
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+DDL = """
+CREATE TABLE IF NOT EXISTS items (
+  id TEXT PRIMARY KEY, booth TEXT NOT NULL, kind TEXT NOT NULL DEFAULT 'wash',
+  order_ref TEXT NOT NULL, at TEXT NOT NULL, operator_ref TEXT NOT NULL DEFAULT '',
+  operator_category_id TEXT NOT NULL DEFAULT '', operator_category_name TEXT NOT NULL DEFAULT '',
+  operator_classes TEXT NOT NULL DEFAULT '[]', service TEXT NOT NULL, vision_class TEXT NOT NULL,
+  vision_confidence REAL NOT NULL, vision_category_id TEXT, downgraded INTEGER NOT NULL DEFAULT 0,
+  image_width INTEGER NOT NULL, image_height INTEGER NOT NULL, plate_blurred INTEGER NOT NULL,
+  image_path TEXT NOT NULL, received_at TEXT NOT NULL, review_label TEXT, reviewed_at TEXT, reviewer TEXT
+);
+"""
+
+COLOURS = {"sedan": (200, 40, 40), "suv": (40, 200, 40), "van": (40, 40, 200), "truck": (200, 200, 40)}
+
+
+def write_jpeg(path: Path, colour: tuple[int, int, int], rng: np.random.Generator) -> None:
+    import cv2
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    h, w = int(rng.integers(120, 200)), int(rng.integers(160, 260))
+    img = np.empty((h, w, 3), np.uint8)
+    img[:] = colour[::-1]  # BGR
+    noise = rng.integers(-20, 20, size=img.shape, dtype=np.int16)
+    img = np.clip(img.astype(np.int16) + noise, 0, 255).astype(np.uint8)
+    cv2.imwrite(str(path), img, [cv2.IMWRITE_JPEG_QUALITY, 85])
+
+
+@pytest.fixture
+def collector_dir(tmp_path: Path) -> Path:
+    """40 labelled crops per class for sedan/suv/van, 5 for truck (below the minimum), a few
+    unusable, a few pending, one labelled row whose file is missing."""
+    rng = np.random.default_rng(1)
+    con = sqlite3.connect(tmp_path / "collector.sqlite")
+    con.executescript(DDL)
+    t0 = datetime(2026, 9, 1, tzinfo=timezone.utc)
+    n = 0
+
+    def add(label: str | None, reviewed: bool, kind: str = "wash", missing: bool = False) -> None:
+        nonlocal n
+        n += 1
+        item = f"item-{n:04d}"
+        rel = f"crops/booth-2/{item}.jpg"
+        colour = COLOURS.get(label or "sedan", (128, 128, 128))
+        if not missing:
+            write_jpeg(tmp_path / rel, colour, rng)
+        at = (t0 + timedelta(minutes=10 * n)).isoformat().replace("+00:00", "Z")
+        reviewed_at = (
+            (t0 + timedelta(days=1, minutes=n)).isoformat().replace("+00:00", "Z") if reviewed else None
+        )
+        con.execute(
+            "INSERT INTO items (id, booth, kind, order_ref, at, service, vision_class, vision_confidence, "
+            "image_width, image_height, plate_blurred, image_path, received_at, review_label, "
+            "reviewed_at, reviewer) "
+            "VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)",
+            (
+                item,
+                "booth-2",
+                kind,
+                "o",
+                at,
+                "wash",
+                "car" if label != "truck" else "truck",
+                0.9,
+                200,
+                150,
+                1,
+                rel,
+                at,
+                label if reviewed else None,
+                reviewed_at,
+                "reviewer" if reviewed else None,
+            ),
+        )
+
+    # Interleaved in time so every class exists on both sides of the time split.
+    for i in range(40):
+        for label in ("sedan", "suv", "van"):
+            add(label, True)
+        if i % 8 == 0:
+            add("truck", True)
+    add("unusable", True)
+    add("unusable", True)
+    add("sedan", True, missing=True)
+    for _ in range(6):
+        add(None, False, kind="entry")
+    con.commit()
+    con.close()
+    return tmp_path