ilaandClaude Opus 4.8
04422d9ca0
A: PoseAdapter.set_confidence_threshold is applied on start, so the
settings model-confidence field actually affects inference.
B: config source.mode ('stream'|'replay') is explicit; app no longer
guesses the source type from the URL prefix.
C: FallStateMachine takes a session_id and from_config generates a
unique one per run, so event ids never collide across restarts
(no screenshot overwrite or duplicate JSONL identity in a day).
51 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
import hashlib
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import numpy as np
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import pytest
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from v1.pose import ModelValidationError, PoseAdapter
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class _FakeKeypoints:
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def __init__(self, data):
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self.data = data
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class _FakeBoxes:
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def __init__(self):
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self.xyxy = np.array([[10.0, 20.0, 50.0, 90.0], [1.0, 2.0, 3.0, 4.0]])
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self.conf = np.array([0.9, 0.8])
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self.cls = np.array([0, 1])
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class _FakeResult:
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names = {0: "person", 1: "chair"}
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def __init__(self):
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person = [[float(index), float(index + 1), 0.9] for index in range(17)]
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chair = [[float(index), float(index + 1), 0.8] for index in range(17)]
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self.boxes = _FakeBoxes()
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self.keypoints = _FakeKeypoints(np.array([person, chair]))
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def test_pose_adapter_rejects_hash_mismatch_before_loading_model(tmp_path):
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model_path = tmp_path / "pose.pt"
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model_path.write_bytes(b"not a real model")
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with pytest.raises(ModelValidationError, match="SHA-256"):
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PoseAdapter(model_path, expected_sha256="0" * 64)
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def test_from_results_extracts_person_box_confidence_and_seventeen_keypoints():
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poses = PoseAdapter.from_results(_FakeResult())
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assert len(poses) == 1
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assert poses[0].box_xyxy == (10.0, 20.0, 50.0, 90.0)
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assert poses[0].box_confidence == 0.9
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assert len(poses[0].keypoints) == 17
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assert poses[0].keypoints[5].confidence == 0.9
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class _RecordingPoseModel:
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task = "pose"
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names = {0: "person"}
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class model:
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kpt_shape = (17, 3)
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def __init__(self):
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self.calls = []
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def __call__(self, image, conf, verbose):
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self.calls.append(conf)
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class _Empty:
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names = {0: "person"}
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class boxes:
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xyxy = []
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conf = []
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cls = []
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class keypoints:
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data = []
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return _Empty()
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def test_set_confidence_threshold_is_forwarded_to_inference(tmp_path):
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weights = tmp_path / "pose.pt"
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weights.write_bytes(b"fake-weights")
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expected_sha256 = hashlib.sha256(weights.read_bytes()).hexdigest()
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model = _RecordingPoseModel()
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adapter = PoseAdapter(
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weights, expected_sha256, confidence_threshold=0.25, model_factory=lambda _p: model
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)
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adapter.set_confidence_threshold(0.6)
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adapter.infer(np.zeros((10, 10, 3), dtype=np.uint8))
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assert adapter.confidence_threshold == 0.6
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assert model.calls == [0.6]
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def test_set_confidence_threshold_rejects_out_of_range(tmp_path):
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weights = tmp_path / "pose.pt"
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weights.write_bytes(b"fake-weights")
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expected_sha256 = hashlib.sha256(weights.read_bytes()).hexdigest()
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adapter = PoseAdapter(
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weights, expected_sha256, model_factory=lambda _p: _RecordingPoseModel()
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)
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with pytest.raises(ModelValidationError):
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adapter.set_confidence_threshold(1.5)
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def test_pose_adapter_rejects_non_pose_model_after_hash_validation(tmp_path):
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model_path = tmp_path / "model.pt"
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model_path.write_bytes(b"model bytes")
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expected_sha256 = hashlib.sha256(model_path.read_bytes()).hexdigest()
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class _WrongTaskModel:
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task = "detect"
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names = {0: "person"}
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class model:
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kpt_shape = (17, 3)
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with pytest.raises(ModelValidationError, match="task"):
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PoseAdapter(
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model_path,
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expected_sha256=expected_sha256,
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model_factory=lambda _path: _WrongTaskModel(),
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)
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