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/pyproject.toml

@@ -0,0 +1,73 @@
+[project]
+name = "parking-trainer"
+version = "0.0.0"
+description = "Phase-B body-type classifier trainer: reviewer labels + crops off the wash collector's volume → an ONNX classifier the vision image bakes in."
+requires-python = ">=3.10,<4.0"
+# Core deps are LIGHT on purpose (same rule as the vision service): `inspect`, `evaluate`
+# and the data/report code run with only these, so `uv sync` and the test suite work
+# in CI without the PyTorch stack. Training itself needs the `train` extra.
+# See wiki/decisions/bodytype-classifier-training.md.
+dependencies = [
+    "numpy>=1.26",
+    # OpenCV does the decode + resize on BOTH sides (trainer and vision service): same
+    # library, same interpolation, same pixels — the preprocessing contract (preprocess.py).
+    "opencv-python-headless>=4.10",
+    "onnxruntime>=1.19",
+]
+
+[project.scripts]
+parking-trainer = "trainer.cli:main"
+
+[project.optional-dependencies]
+# The training stack. CPU-only PyTorch (the reviewer's host has no usable GPU — the
+# decision is recorded in the wiki page above): resolved from PyTorch's CPU wheel index,
+# ~200 MB instead of the ~5 GB CUDA build. Install with: uv sync --extra train
+#   torch / torchvision are BSD-3; the ImageNet backbone weights ship under the same
+#   licence (the licence rule applies to weights as much as code).
+train = [
+    "torch>=2.4",
+    "torchvision>=0.19",
+    "onnx>=1.16",
+    "onnxscript>=0.3",  # the torch.export-based ONNX exporter (MIT)
+]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.8",
+    "pytest>=8.3",
+    "mypy>=1.13",
+]
+
+[tool.uv]
+# Pick the CPU wheels for torch/torchvision from PyTorch's own index; everything else
+# from PyPI. `explicit = true` keeps the index from shadowing PyPI for other packages.
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.uv.sources]
+torch = [{ index = "pytorch-cpu" }]
+torchvision = [{ index = "pytorch-cpu" }]
+
+[tool.ruff]
+line-length = 110
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "B", "UP"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[tool.mypy]
+python_version = "3.12"
+strict = true
+ignore_missing_imports = true
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["trainer"]