DAQ: updated face_detection added operations/face_detection fodler including service and models, new tests of face_detection
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import types
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import pytest
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from aare.common.coordinate import Coordinate, SmargonCoordinate
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from aare.common.models import BoundingBoxModel, MLBoxModel, MLBoxType, ZoomModeEnum
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from aare.daq.operations.face_detection.models import (
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FaceDetectionContext,
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FaceDetectionResult,
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)
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from aare.daq.operations.face_detection.service import FaceDetectionService
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class DummyLogger:
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def info(self, *args, **kwargs):
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pass
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def debug(self, *args, **kwargs):
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pass
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def error(self, *args, **kwargs):
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pass
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def log(self, *args, **kwargs):
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pass
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class DummyGeometry:
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def __init__(self):
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self.beam_location_pxl = Coordinate(x=100.0, y=100.0, z=0.0)
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def picture_to_smargon(self, coord: Coordinate) -> Coordinate:
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return Coordinate(x=coord.x / 100.0, y=coord.y / 100.0, z=0.0)
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def _box(cls: MLBoxType, x1: float, y1: float, x2: float, y2: float) -> MLBoxModel:
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return MLBoxModel(
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cls=cls,
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box=BoundingBoxModel(
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top_x=x1,
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top_y=y1,
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bottom_x=x2,
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bottom_y=y2,
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),
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conf=0.95,
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)
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@pytest.fixture
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def context():
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progress_events = []
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devs = types.SimpleNamespace(
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lamp_light=0.0,
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zoom=200.0,
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aerotech_omega=0.0,
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smargon_pos=None,
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set_zoom=lambda zoom_value, wait=True: None,
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smargon_wait=lambda timeout=60: None,
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)
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cfg = types.SimpleNamespace(zoom_mode=ZoomModeEnum.User)
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ctx = FaceDetectionContext(
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cfg=cfg,
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devs=devs,
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mlbox=types.SimpleNamespace(
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predict=lambda **kwargs: types.SimpleNamespace(
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box=None,
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target_point=None,
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focus=None,
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)
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),
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sample_geometry_provider=lambda: DummyGeometry(),
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emit_progress=lambda payload: progress_events.append(payload),
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)
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ctx._progress_events = progress_events
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return ctx
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def test_service_returns_empty_payload_when_no_boxes(monkeypatch, context):
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service = FaceDetectionService(context=context, logger=DummyLogger())
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monkeypatch.setattr(
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context.mlbox,
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"predict",
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lambda **kwargs: types.SimpleNamespace(
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box=None,
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target_point=None,
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focus=None,
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),
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)
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result = service.run(steps=2, step_size=30)
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assert isinstance(result, FaceDetectionResult)
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assert result.success is True
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assert result.payload["running"] is False
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assert result.payload["samples"] == []
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assert result.payload["height_fit"] == {}
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assert result.payload["area_fit"] == {}
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assert context.cfg.zoom_mode == ZoomModeEnum.User
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assert len(context._progress_events) >= 1
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assert context._progress_events[-1]["running"] is False
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def test_service_prefers_face_boxes_when_ratio_is_high(monkeypatch, context):
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service = FaceDetectionService(context=context, logger=DummyLogger())
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predictions = iter(
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[
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_FACE, 10, 20, 30, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_FACE, 12, 20, 32, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_ALL, 14, 20, 34, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_FACE, 16, 20, 36, 40), target_point=None, focus=None),
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]
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)
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monkeypatch.setattr(context.mlbox, "predict", lambda **kwargs: next(predictions))
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monkeypatch.setattr(
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"aare.daq.operations.face_detection.service.fd.get_flat_face",
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lambda boxes, start_angle, end_angle, use_area: (
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45 if use_area else 50,
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{"A": 1.0, "B": 2.0, "phi_rad": 0.1, "C": 3.0},
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),
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)
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monkeypatch.setattr(
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"aare.daq.operations.face_detection.service.fd.choose_best_fit",
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lambda fit_results: (47, {"A": 1.0}, "Area"),
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)
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monkeypatch.setattr(
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"aare.daq.operations.face_detection.service.fd.get_samples_out",
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lambda boxes: [{"angle": angle, "box": box} for angle, box in sorted(boxes.items())],
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)
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result = service.run(steps=3, step_size=30, face_min_ratio=0.5)
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assert result.success is True
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assert result.payload["running"] is False
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assert result.payload["height_fit"]["best_angle_deg"] == 50
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assert result.payload["area_fit"]["best_angle_deg"] == 45
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assert context.devs.aerotech_omega == 47
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assert [sample["angle"] for sample in result.payload["samples"]] == [0, 30, 90]
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assert context._progress_events[-1]["running"] is False
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def test_service_falls_back_to_loop_all_when_face_ratio_is_low(monkeypatch, context):
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service = FaceDetectionService(context=context, logger=DummyLogger())
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predictions = iter(
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[
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_FACE, 10, 20, 30, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_ALL, 11, 20, 31, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_ALL, 12, 20, 32, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_ALL, 13, 20, 33, 40), target_point=None, focus=None),
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types.SimpleNamespace(box=_box(MLBoxType.LOOP_ALL, 14, 20, 34, 40), target_point=None, focus=None),
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]
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)
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monkeypatch.setattr(context.mlbox, "predict", lambda **kwargs: next(predictions))
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captured = {}
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def fake_get_flat_face(boxes, start_angle, end_angle, use_area):
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captured["boxes_used"] = dict(boxes)
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return 60, {"A": 1.0, "B": 2.0, "phi_rad": 0.2, "C": 4.0}
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monkeypatch.setattr("aare.daq.operations.face_detection.service.fd.get_flat_face", fake_get_flat_face)
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monkeypatch.setattr(
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"aare.daq.operations.face_detection.service.fd.choose_best_fit",
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lambda fit_results: (60, {"A": 1.0}, "Height"),
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)
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monkeypatch.setattr(
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"aare.daq.operations.face_detection.service.fd.get_samples_out",
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lambda boxes: [{"angle": angle} for angle in sorted(boxes.keys())],
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)
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result = service.run(steps=4, step_size=30, face_min_ratio=0.5)
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assert result.success is True
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assert len(captured["boxes_used"]) == 4
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assert sorted(captured["boxes_used"].keys()) == [30, 60, 90, 120]
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assert context.devs.aerotech_omega == 60
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assert context._progress_events[-1]["running"] is False
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def test_service_applies_centre_correction_when_target_is_far_from_beam(monkeypatch, context):
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service = FaceDetectionService(context=context, logger=DummyLogger())
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model = _box(MLBoxType.LOOP_FACE, 40, 160, 80, 200)
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service._centre_correction(model, tolerance=0.2)
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assert isinstance(context.devs.smargon_pos, SmargonCoordinate)
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assert context.devs.smargon_pos.sh_mm.x == pytest.approx(1.0)
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assert context.devs.smargon_pos.sh_mm.y == pytest.approx(1.8)
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def test_service_returns_failed_result_when_prediction_raises(monkeypatch, context):
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service = FaceDetectionService(context=context, logger=DummyLogger())
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monkeypatch.setattr(
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context.mlbox,
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"predict",
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lambda **kwargs: (_ for _ in ()).throw(RuntimeError("prediction failed")),
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)
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result = service.run(steps=1, step_size=30)
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assert result.success is False
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assert isinstance(result.error, RuntimeError)
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assert result.comment == "Face detection sequence failed"
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assert result.payload["running"] is False
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assert context.cfg.zoom_mode == ZoomModeEnum.User
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assert context._progress_events[-1]["running"] is False
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@@ -0,0 +1,105 @@
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import types
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from aare.common.models import DAQOperation
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from aare.daq.operations.face_detection.models import FaceDetectionResult
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def test_execute_face_detection_reports_failure(monkeypatch):
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from aare.daq.daq import AareDAQ
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daq = object.__new__(AareDAQ)
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calls = {"set_state": [], "handle_error": []}
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monkeypatch.setattr(
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daq,
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"_AareDAQ__set_state",
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lambda state: calls["set_state"].append(state),
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)
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monkeypatch.setattr(
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daq,
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"_create_face_detection_service",
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lambda: types.SimpleNamespace(
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run=lambda **kwargs: FaceDetectionResult(
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success=False,
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payload={"running": False, "samples": [], "height_fit": {}, "area_fit": {}},
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error=RuntimeError("fd failed"),
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comment="face detection sequence failed",
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)
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),
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)
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monkeypatch.setattr(
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daq,
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"_handle_operation_error",
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lambda **kwargs: calls["handle_error"].append(kwargs),
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)
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monkeypatch.setattr(
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type(daq),
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"sample",
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property(lambda self: types.SimpleNamespace(sample_name="sample-1", db_id=1)),
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)
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result = daq._execute_face_detection(steps=7, step_size=30, report_error=True)
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assert result.success is False
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assert len(calls["set_state"]) == 1
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assert len(calls["handle_error"]) == 1
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assert calls["handle_error"][0]["operation"] == DAQOperation.FACE_CENTERING
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def test_execute_face_detection_can_skip_error_reporting(monkeypatch):
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from aare.daq.daq import AareDAQ
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daq = object.__new__(AareDAQ)
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handle_error_calls = []
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monkeypatch.setattr(daq, "_AareDAQ__set_state", lambda state: None)
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monkeypatch.setattr(
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daq,
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"_create_face_detection_service",
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lambda: types.SimpleNamespace(
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run=lambda **kwargs: FaceDetectionResult(
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success=False,
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payload={"running": False, "samples": [], "height_fit": {}, "area_fit": {}},
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error=RuntimeError("fd failed"),
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comment="face detection sequence failed",
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)
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),
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)
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monkeypatch.setattr(
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daq,
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"_handle_operation_error",
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lambda **kwargs: handle_error_calls.append(kwargs),
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)
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result = daq._execute_face_detection(report_error=False)
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assert result.success is False
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assert handle_error_calls == []
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def test_public_face_detection_uses_execute_face_detection(monkeypatch):
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from aare.daq.daq import AareDAQ
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daq = object.__new__(AareDAQ)
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cfg = types.SimpleNamespace(
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try_set_busy=lambda timeout=360: None,
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state_busy=False,
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)
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setattr(daq, "_AareDAQ__cfg", cfg)
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monkeypatch.setattr(
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daq,
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"_execute_face_detection",
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lambda **kwargs: FaceDetectionResult(
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success=True,
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payload={"running": False, "samples": [{"angle": 45}], "height_fit": {}, "area_fit": {}},
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),
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)
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result = daq.face_detection(steps=7, step_size=30)
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assert result["running"] is False
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assert result["samples"] == [{"angle": 45}]
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assert cfg.state_busy is False
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