101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
import types
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from aarecommon.models.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(daq, "_AareDAQ__set_state", lambda state: calls["set_state"].append(state))
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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, "_handle_operation_error", 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, "_handle_operation_error", 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(try_set_busy=lambda timeout=360: None, state_busy=False)
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setattr(daq, "_AareDAQ__cfg", cfg)
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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={
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"running": False,
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"samples": [{"angle": 45}],
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"height_fit": {},
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"area_fit": {},
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},
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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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