ykcheungab
0a042c7fbf
field_gain/1 用坐标增益图对无旋向量场做 L1 场加权，以连续 30 帧加权中位数替代旧 raw 零点，实时入口暂不支持。目标测试 102 项与 lint_gate 通过；契约见 orisys-sdk/docs/field-gain-model.zh.md。
458 lines
19 KiB
Python
458 lines
19 KiB
Python
"""Schema 3 lifecycle and readout tests without camera or hardware."""
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from types import SimpleNamespace
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import numpy as np
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import pytest
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from orisys.calibration import CalibrationError
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from orisys.force_readout import ForceReadout
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from orisys.sensor import Sensor
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class RecordingModel:
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schema_version = 3
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channels = {"fn": None}
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input_profile = "fn_delta_autocal_uv/1"
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def __init__(self):
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self.calls = []
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self.resets = []
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self.coordinate_map = np.array([[[0., 0.], [1., 0.]], [[0., 1.], [1., 1.]]])
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self.coordinate_valid_mask = np.array([[True, False], [True, True]])
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def reset(self, reference_id):
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self.resets.append(reference_id)
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def validate_for_context(self, context):
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if context != {"signature": "actual"}:
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raise CalibrationError("pipeline mismatch")
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def predict(self, **kwargs):
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self.calls.append(kwargs)
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features = kwargs["features"]
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status = "ok"
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if not features["zero_valid"]:
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status = "zero_unavailable"
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elif not features["contact_found"]:
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status = "no_contact"
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elif not features["mapping_valid"]:
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status = "coordinate_unmapped"
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value = features["delta_raw"] * .1 if status == "ok" else None
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return SimpleNamespace(values_N={"fn": value}, status={"fn": status})
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def make_sensor(*, median=1):
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sensor = Sensor.__new__(Sensor)
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sensor.calibration = RecordingModel()
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sensor._config = {"filters": {"force_median_window": median}}
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sensor._force_readout = ForceReadout(sensor._config["filters"])
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sensor._force_raw = (10., 20., 30.)
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sensor._model_readouts = {}
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sensor._calibration_context = None
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sensor._reset_calibration_session()
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sensor.bind_calibration_context({"signature": "actual"})
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return sensor
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def test_finger_model_swap_validates_actual_runtime_and_invalidates_zero(monkeypatch, tmp_path):
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import orisys.calibration_context as context_mod
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from orisys.finger.runtime import FingerRuntime
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sensor = make_sensor()
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sensor.device_id = None
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sensor.mirror = False
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sensor.set_calibration_reference({"fn": 10}, reference_id="old-zero")
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runtime = FingerRuntime.__new__(FingerRuntime)
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runtime.tactile = SimpleNamespace(raw_sensor=sensor)
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mesh = tmp_path / "mesh.npz"
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mesh.write_bytes(b"actual geometry")
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runtime.geometry = SimpleNamespace(config={}, export_paths=SimpleNamespace(mesh=mesh),
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source_scale=.42, out_height=2, out_width=2)
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runtime.domain = object()
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runtime.frame_processor = SimpleNamespace(flip_source_vertical=True)
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monkeypatch.setattr(context_mod, "domain_fingerprint", lambda domain: "domain")
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contexts = []
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def context(config, filters, **kwargs):
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contexts.append(kwargs)
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return {"signature": "actual"}
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monkeypatch.setattr(context_mod, "build_context", context)
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selected = RecordingModel()
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selected.pipeline = {"coordinates": {"frame": "autocal_flat"}}
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runtime.set_calibration(selected)
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assert sensor.calibration is selected
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assert sensor.calibration_status == {"fn": "zero_unavailable"}
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assert contexts[0]["geometry_sha256"] == context_mod.sha256_file(mesh)
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assert contexts[0]["output_domain_sha256"] == "domain"
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wrong_shape = RecordingModel()
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wrong_shape.pipeline = selected.pipeline
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wrong_shape.coordinate_map = np.zeros((1, 1, 2))
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with pytest.raises(ValueError, match="shape"):
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runtime.set_calibration(wrong_shape)
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assert sensor.calibration is selected
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def predict(sensor, frame, fn, *, contact=True, mapped=True):
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return sensor.apply_calibration_frame(frame_id=frame, timestamp_s=frame / 30.,
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raw=(fn, 20., 30.), uv=(0., 0.),
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contact_found=contact, mapping_valid=mapped)
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def test_once_per_frame_and_explicit_zero_subtracted_once():
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sensor = make_sensor()
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sensor.set_calibration_reference({"fn": 10.}, reference_id="zero-a")
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first = predict(sensor, 1, 25.)
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assert first.values_N["fn"] == pytest.approx(1.5)
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assert predict(sensor, 1, 99.) is first
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assert len(sensor.calibration.calls) == 1
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assert sensor.force_units == ("N", "raw", "raw")
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assert (sensor.fnormal, sensor.fshearx, sensor.fsheary) == (1.5, 20., 30.)
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assert sensor._force_raw_unfiltered == (25., 20., 30.)
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def test_unknown_zero_and_invalid_contact_never_become_zero_newtons():
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sensor = make_sensor()
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assert predict(sensor, 1, 20.).status["fn"] == "zero_unavailable"
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assert np.isnan(sensor.fnormal)
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sensor.set_calibration_reference({"fn": 10.}, reference_id="zero-b")
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assert predict(sensor, 2, 20., contact=False).status["fn"] == "no_contact"
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assert np.isnan(sensor.fnormal)
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assert predict(sensor, 3, 20., mapped=False).status["fn"] == "coordinate_unmapped"
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assert np.isnan(sensor.fnormal)
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def test_failed_frame_does_not_enter_newton_median_history():
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sensor = make_sensor(median=3)
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sensor.set_calibration_reference({"fn": 0.}, reference_id="zero")
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predict(sensor, 1, 10.)
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predict(sensor, 2, 1000., contact=False)
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predict(sensor, 3, 20.)
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predict(sensor, 4, 30.)
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assert sensor.fnormal == pytest.approx(2.)
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assert sensor._force_unfiltered[0] == 3.
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def test_new_reference_clears_cached_prediction_and_filter():
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sensor = make_sensor(median=3)
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sensor.set_calibration_reference({"fn": 0.}, reference_id="first")
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predict(sensor, 1, 10.)
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sensor.set_calibration_reference({"fn": 20.}, reference_id="second")
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assert sensor.calibration_result is None
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assert np.isnan(sensor.fnormal)
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predict(sensor, 1, 30.)
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assert sensor.fnormal == pytest.approx(1.)
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assert sensor.calibration.resets[-1] == "second"
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def test_requires_actual_pipeline_context():
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sensor = make_sensor()
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with pytest.raises(CalibrationError, match="pipeline mismatch"):
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sensor.bind_calibration_context({"signature": "different"})
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assert sensor._calibration_context == {"signature": "actual"}
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sensor._calibration_context = None
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with pytest.raises(CalibrationError, match="actual calibration context"):
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predict(sensor, 1, 10.)
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def test_contact_map_never_reuses_previous_coordinate():
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sensor = make_sensor()
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sensor.centroid_found = True
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sensor.centroid = (0, 1)
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assert sensor._calibration_contact_uv() == (0., 1.)
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sensor.centroid = (1, 0)
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assert sensor._calibration_contact_uv() is None
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sensor.centroid = (10, 10)
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assert sensor._calibration_contact_uv() is None
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sensor.centroid_found = False
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sensor.centroid = (0, 1)
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assert sensor._calibration_contact_uv() is None
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def test_hhd_infers_after_current_contact_only_once_and_clears_old_contact():
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sensor = make_sensor()
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sensor.set_calibration_reference({"fn": 10.}, reference_id="zero")
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field = np.zeros((2, 2, 2), dtype=np.float32)
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sensor._Sensor__hhd = SimpleNamespace(
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get_force=lambda *args, **kwargs: (None, (20., 0., 0., 0.)),
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get_normal=lambda: field, get_shear=lambda: field,
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)
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contact = {"centroid": (0, 1), "centroid_found": True,
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"divergence_map": np.zeros((2, 2))}
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sensor._Sensor__contact3d = SimpleNamespace(compute_foe=lambda *args, **kwargs: contact)
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sensor.cuda = sensor.verbose = False
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sensor._hhd_downsample = 1
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sensor._hhd_grid_shape = sensor._hhd_ref_shape = (2, 2)
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sensor.valid_mask = sensor.valid_mask_hhd = None
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sensor._model_frame_id = 0
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sensor.compute_hhd(field)
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assert sensor.calibration.calls[-1]["features"]["v"] == 1.
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assert sensor.fnormal == pytest.approx(1.)
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sensor.compute_contact()
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assert len(sensor.calibration.calls) == 1
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contact["centroid_found"] = False
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sensor.compute_hhd(field)
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assert len(sensor.calibration.calls) == 2
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assert sensor.calibration_status == {"fn": "no_contact"}
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assert sensor.calibration.calls[-1]["features"]["u"] is None
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assert np.isnan(sensor.fnormal)
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def test_real_model_adapter_uses_same_lifecycle_as_stateful_model():
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from orisys.calibration import parse
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data = {
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"schema_version": 3, "unit": "N",
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"identity": {"sensor_type": "finger", "sn": "test"},
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"pipeline": {"stitch_resolution": [2, 2], "dis_mode": 4, "hhd_downsample": 1,
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"filters": {"measurement": {"flow_ema_alpha": 1.}},
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"feature_signature_sha256": "a" * 64},
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"model": {"type": "global_gain/1", "input_profile": "force_delta/1",
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"output_frame": "sdk_force/1", "outputs": ["fn"],
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"params": {"gains_N_per_raw": {"fn": .1}, "valid_force_range_N": [0., 3.]}},
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}
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sensor = make_sensor()
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sensor.calibration = parse(data)
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sensor.bind_calibration_context(data["pipeline"])
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sensor.set_calibration_reference({"fn": 10.}, reference_id="reference")
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result = sensor.apply_calibration_frame(frame_id=1, raw=(20., 30., 40.))
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assert result.values_N == {"fn": 1.}
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assert sensor.force_units == ("N", "raw", "raw")
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assert sensor.calibration_status == {"fn": "ok"}
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result = sensor.apply_calibration_frame(frame_id=2, raw=(20., 30., 40.), contact_found=False)
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assert result.status == {"fn": "no_contact"}
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def test_reversed_frame_requires_a_new_zero_reference():
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sensor = make_sensor()
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sensor.set_calibration_reference({"fn": 10.}, reference_id="reference")
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predict(sensor, 10, 20.)
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assert predict(sensor, 2, 20.).status == {"fn": "zero_unavailable"}
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assert sensor.calibration.resets[-1] is None
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def test_replacement_is_atomic_when_pipeline_cannot_be_validated():
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sensor = make_sensor()
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sensor.device_id = None
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original = sensor.calibration
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replacement = RecordingModel()
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with pytest.raises(CalibrationError, match="pipeline mismatch"):
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sensor.set_calibration(replacement, context={"signature": "different"})
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assert sensor.calibration is original
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sensor._calibration_context = None
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with pytest.raises(CalibrationError, match="actual pipeline context"):
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sensor.set_calibration(replacement)
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assert sensor.calibration is original
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sensor.set_calibration(replacement, context={"signature": "actual"})
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assert sensor.calibration is replacement
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assert sensor._calibration_reference == {}
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@pytest.mark.parametrize("use_legacy", [False, True])
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def test_schema3_swap_cannot_relabel_cached_newtons_as_raw_or_legacy(use_legacy):
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from orisys.calibration import parse
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sensor = make_sensor()
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sensor.device_id = None
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sensor.set_calibration_reference({"fn": 10.}, reference_id="reference")
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predict(sensor, 1, 25.)
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assert sensor.fnormal == 1.5
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legacy = parse({"schema_version": 2, "unit": "N", "channels": {
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"normal": {"method": "piecewise_linear", "points": [[0., 0.], [100., 2.]],
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"extrapolate": True},
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}}) if use_legacy else None
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sensor.set_calibration(legacy)
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expected = .5 if use_legacy else 25.
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assert sensor.fnormal == pytest.approx(expected)
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assert sensor.force_triplets()["unfiltered"]["newton"][0] == pytest.approx(expected)
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assert sensor.force_triplets()["filtered"]["raw"][0] == 25.
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assert sensor.calibration_status == {}
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def test_reset_invalidates_contact_cache_and_coordinate():
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sensor = make_sensor()
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sensor._Sensor__tracker = SimpleNamespace(reset=lambda: None)
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sensor.img = None
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sensor.compute_contact_valid = sensor.centroid_found = True
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sensor.centroid = (1, 1)
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sensor._model_contact_result = {"centroid": (1, 1)}
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sensor.compute_deformation_valid = True
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sensor.reset()
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assert sensor.compute_contact_valid is False
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assert sensor.compute_deformation_valid is False
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assert sensor.centroid_found is False
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assert sensor._model_contact_result is None
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assert sensor._calibration_contact_uv() is None
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with pytest.raises(CalibrationError, match="current deformation"):
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sensor.compute_contact()
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def test_schema3_rejects_measurement_filter_change_before_mutation():
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sensor = make_sensor()
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sensor.compute_deformation_valid = False
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with pytest.raises(CalibrationError, match="measurement identity"):
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sensor.configure(flow_ema_alpha=.5)
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assert sensor._force_readout.flow_ema_alpha == 1.
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assert sensor._calibration_context == {"signature": "actual"}
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def test_readout_configuration_applies_before_and_after_first_model_frame():
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sensor = make_sensor()
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sensor.compute_deformation_valid = False
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sensor.configure(force_median_window=3, lowpass_sample_rate_hz=20.)
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sensor.set_calibration_reference({"fn": 0.}, reference_id="reference")
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predict(sensor, 1, 10.)
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readout = sensor._model_readouts["fn"]
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assert readout.force_median_window == 3
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assert readout.lowpass_sample_rate_hz == 20.
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predict(sensor, 2, 20.)
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predict(sensor, 3, 30.)
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assert sensor.fnormal == 2.
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sensor.configure(force_median_window=1)
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assert readout.force_median_window == 1
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predict(sensor, 4, 40.)
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assert sensor.fnormal == 4.
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def test_generic_global_fallback_binds_actual_mirror_identity():
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from orisys.calibration import parse
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from orisys.calibration_context import build_context
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sensor = make_sensor()
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sensor.device_id = None
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sensor.mirror = True
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sensor._config.update(camera={"resolution_stitch": [2, 2]}, tracker={"dis_mode": 1})
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context = build_context(sensor._config, sensor.filters_state(), preprocessing={"mirror": True})
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data = {"schema_version": 3, "unit": "N", "identity": {"sensor_type": "finger", "sn": "test"},
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"pipeline": context,
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"model": {"type": "global_gain/1", "input_profile": "force_delta/1",
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"output_frame": "sdk_force/1", "outputs": ["fn"],
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"params": {"gains_N_per_raw": {"fn": .1}, "valid_force_range_N": [0., 3.]}}}
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sensor._calibration_context = None
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sensor.set_calibration(parse(data))
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assert sensor._calibration_context == context
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sensor.mirror = False
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sensor._calibration_context = None
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with pytest.raises(CalibrationError, match="feature_signature"):
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sensor.set_calibration(parse(data))
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@pytest.mark.parametrize("reference", [{"fn": float("nan")}, {"other": 0.}])
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def test_bad_zero_reference_is_rejected_without_reset(reference):
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sensor = make_sensor()
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sensor.set_calibration_reference({"fn": 10.}, reference_id="valid")
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with pytest.raises(ValueError):
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sensor.set_calibration_reference(reference, reference_id="bad")
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assert sensor._calibration_reference == {"fn": 10.}
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class FieldRecordingModel:
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schema_version = 3
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channels = {"fn": "N"}
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input_profile = "hhd_normal_l1_field/1"
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def __init__(self):
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self.calls = []
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self.resets = []
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self.begin_calls = []
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self.coordinate_map = np.zeros((2, 2, 2), dtype=np.float32)
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self.coordinate_valid_mask = np.ones((2, 2), dtype=bool)
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def reset(self, reference_id):
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self.resets.append(reference_id)
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def begin_reference(self, reference_id):
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self.begin_calls.append(reference_id)
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def validate_for_context(self, context):
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if context != {"signature": "actual"}:
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raise CalibrationError("pipeline mismatch")
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def predict(self, **kwargs):
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self.calls.append(kwargs)
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features = kwargs["features"]
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if "field" not in features:
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return SimpleNamespace(values_N={"fn": None}, status={"fn": "invalid_input"},
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diagnostics={})
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return SimpleNamespace(values_N={"fn": 1.0}, status={"fn": "ok"}, diagnostics={})
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def test_hhd_field_model_infers_from_same_frame_field_without_contact():
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sensor = make_sensor()
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sensor.calibration = FieldRecordingModel()
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sensor._calibration_context = {"signature": "actual"}
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sensor.cuda = sensor.verbose = False
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sensor.valid_mask = sensor.valid_mask_hhd = None
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sensor._hhd_downsample = 1
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sensor._hhd_grid_shape = sensor._hhd_ref_shape = (2, 2)
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sensor._model_frame_id = 0
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field = np.zeros((2, 2, 2), dtype=np.float32)
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calls = []
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def get_field(valid_mask=None):
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calls.append(valid_mask)
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return {"d": field, "valid_mask": np.ones((2, 2), dtype=bool), "area_scale": 1.0,
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"grid_shape": (2, 2), "reference_shape": (2, 2)}
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sensor._Sensor__hhd = SimpleNamespace(
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get_force=lambda *args, **kwargs: (None, (10., 0., 0., 0.)),
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get_normal=lambda: field, get_shear=lambda: field, get_field=get_field,
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)
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sensor.compute_hhd(np.zeros((2, 2, 2), dtype=np.float32))
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features = sensor.calibration.calls[-1]["features"]
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assert features["field"]["grid_shape"] == (2, 2)
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assert features["output_valid_mask"].shape == (2, 2)
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assert "contact_found" not in features
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assert sensor.calibration_status == {"fn": "ok"}
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assert sensor.fnormal == pytest.approx(1.0)
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assert calls == [None]
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def test_weighted_unloaded_reference_is_declared_not_a_scalar_zero():
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sensor = make_sensor()
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sensor.calibration = FieldRecordingModel()
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sensor._calibration_context = {"signature": "actual"}
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sensor.declare_unloaded_reference(reference_id="video-unloaded-100")
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assert sensor.calibration.begin_calls == ["video-unloaded-100"]
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assert sensor._calibration_reference_id == "video-unloaded-100"
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with pytest.raises(CalibrationError, match="scalar zero"):
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sensor.set_calibration_reference({"fn": 0.}, reference_id="bad")
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with pytest.raises(CalibrationError, match="hhd_normal_l1_field"):
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make_sensor().declare_unloaded_reference(reference_id="scalar-model")
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def test_finger_runtime_binds_field_identity_and_forwards_the_window(monkeypatch, tmp_path):
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import orisys.calibration_context as context_mod
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from orisys.field_calibration import FIELD_IDENTITY
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from orisys.finger.runtime import FingerRuntime
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sensor = make_sensor()
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sensor.calibration = FieldRecordingModel()
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sensor._calibration_context = {"signature": "actual"}
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sensor.device_id = None
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sensor.mirror = False
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runtime = FingerRuntime.__new__(FingerRuntime)
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runtime.tactile = SimpleNamespace(raw_sensor=sensor)
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mesh = tmp_path / "mesh.npz"
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mesh.write_bytes(b"actual geometry")
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runtime.geometry = SimpleNamespace(config={}, export_paths=SimpleNamespace(mesh=mesh),
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source_scale=.42, out_height=2, out_width=2)
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runtime.domain = object()
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runtime.frame_processor = SimpleNamespace(flip_source_vertical=True)
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monkeypatch.setattr(context_mod, "domain_fingerprint", lambda domain: "domain")
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contexts = []
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def context(config, filters, **kwargs):
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contexts.append(kwargs)
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return {"signature": "actual"}
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monkeypatch.setattr(context_mod, "build_context", context)
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selected = FieldRecordingModel()
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selected.pipeline = {"coordinates": {"frame": "autocal_flat"}}
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runtime.set_calibration(selected)
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assert contexts[0]["field"] == FIELD_IDENTITY
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runtime.declare_unloaded_reference("runtime-window")
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assert sensor.calibration.begin_calls == ["runtime-window"]
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