将场加权标定模型接入回放分析
回放按声明的 30 帧加权空载窗口取基线并逐帧保存外推质量，field 模型单独进缓存键，实时入口拒绝。 新增测试覆盖窗口、质量与拒绝路径。全量门禁的 scene3d 两项失败与 studio 访问冲突为既有问题， 已归因并另立 studio-gate-hang。
This commit is contained in:
ykcheungab
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"""Fake SDK surface for the S4 field-model tests (shared by two test modules).
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The real SDK field model is exercised by ``orisys-sdk/tests/test_field_calibration.py``;
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the app tests describe the *calls and data* the app owns. Keeping the fakes in one
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place means the pass test and the integration test cannot drift apart.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import math
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from pathlib import Path
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from types import SimpleNamespace
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import numpy as np
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from orisys_app_shared.acquisition import sdk_worker
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from orisys_app_shared.analysis import service as service_module
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from orisys_app_shared.analysis.model import Provenance
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from orisys_app_shared.record.playback import RecordingFrame
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FIELD_PROFILE = "hhd_normal_l1_field/1"
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# ------------------------------------------------------------------ calibration
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def write_model_file(tmp_path: Path, model_type: str) -> Path:
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"""A schema 3 calibration document the SDK accepts, with its coordinate map.
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``field_gain/1`` and ``spatial_gain/1`` differ only in the model block, so a test
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can compare two documents that really load through the SDK's parser.
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"""
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from orisys.field_calibration import BASELINE_POLICY, FIELD_IDENTITY, MAPPING_POLICY
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rows, cols = np.mgrid[0:4, 0:4]
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uv = np.stack((cols, rows), axis=-1).astype(np.float64)
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attachment = tmp_path / "map.npz"
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np.savez(
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attachment,
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uv_by_output_px=uv,
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valid_mask=np.ones((4, 4), dtype=bool),
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origin_xy=np.array([0, 0]),
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)
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digest = hashlib.sha256(attachment.read_bytes()).hexdigest()
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pipeline = {
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"stitch_resolution": [4, 4],
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"dis_mode": 1,
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"hhd_downsample": 1,
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"filters": {"measurement": {"flow_ema_alpha": 1.0}},
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"feature_signature_sha256": "a" * 64,
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"geometry_sha256": "b" * 64,
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"source_scale": 0.42,
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"coordinates": {
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"frame": "autocal_flat",
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"unit": "px",
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"origin_xy": [0, 0],
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"map_sha256": digest,
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},
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}
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params = {
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"points_uv": [[0, 0], [3, 0], [0, 3], [3, 3]],
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"gains_N_per_raw": [1.0, 1.0, 1.0, 1.0],
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"triangles": [[0, 1, 2], [1, 3, 2]],
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"valid_force_range_N": [0.0, 3.0],
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}
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if model_type == "field_gain/1":
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pipeline["field"] = dict(FIELD_IDENTITY)
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profile = FIELD_PROFILE
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params["mapping"] = dict(MAPPING_POLICY)
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params["baseline"] = dict(BASELINE_POLICY)
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else:
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profile = "fn_delta_autocal_uv/1"
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document = {
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"schema_version": 3,
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"unit": "N",
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"identity": {"sensor_type": "finger", "sn": "dd02-gl"},
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"pipeline": pipeline,
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"model": {
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"type": model_type,
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"input_profile": profile,
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"output_frame": "sdk_force/1",
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"outputs": ["fn"],
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"params": params,
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"artifacts": [
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{"role": "coordinate_map", "path": attachment.name, "sha256": digest}
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],
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},
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}
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path = tmp_path / ("field.json" if model_type == "field_gain/1" else "spatial.json")
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path.write_text(json.dumps(document), encoding="utf-8")
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return path
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# --------------------------------------------------------------------- recording
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def minimal_recording(root: Path, *, segments: int = 1) -> Path:
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"""The two text files and the segment bytes a reader needs, and nothing else."""
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recording = root / "recording"
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recording.mkdir(parents=True, exist_ok=True)
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(recording / "frames.csv").write_text(
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"frame_id,t_rel_s,acquired_at,segment,segment_frame,in_video,dropped\n"
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"1,0.0,,1,1,1,0\n2,0.033,,1,2,1,0\n",
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encoding="utf-8",
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)
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for number in range(1, segments + 1):
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(recording / f"raw_{number:04d}.mp4").write_bytes(b"")
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(recording / "session.json").write_text(
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json.dumps(
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{
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"session_id": 1,
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"started_at": "2026-01-01T00:00:00+0000",
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"ended_at": "",
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"device": {"sensor_type": "finger", "config_ref": ""},
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"stats": {"frames": 2, "video_frames": 2, "segments": segments},
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}
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),
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encoding="utf-8",
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)
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return recording
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def field_rows(count: int, *, segment: int = 1) -> list[RecordingFrame]:
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return [
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RecordingFrame(i + 1, i / 30.0, "", segment, i + 1, True)
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for i in range(count)
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]
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# ------------------------------------------------------------------------- SDK
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class FakeFieldModel:
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schema_version = 3
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input_profile = FIELD_PROFILE
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model_type = "field_gain/1"
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channels = {"fn": "N"}
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fingerprint = "abcdef0123456789" * 4
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def __init__(self) -> None:
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self.field_baseline_N: float | None = None
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class FakeFieldSensor:
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"""The slice of ``Sensor`` the pass touches, simulating the SDK's window."""
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def __init__(self, *, zero_frames: int = 30) -> None:
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self.info = SimpleNamespace(
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FNORMAL=0,
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FSHEARX=1,
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FSHEARY=2,
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FNORMAL_RAW=3,
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FSHEARX_RAW=4,
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FSHEARY_RAW=5,
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)
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self.calibration = FakeFieldModel()
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self.force_calibrated = True
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self.centroid_found = True # the field model must not gate on it
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self.flow = np.zeros((4, 4, 2), dtype=np.float32)
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self.valid_mask = None
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self.calibration_status = {"fn": "zero_unavailable"}
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self.calibration_result = None
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self.declared: list[str] = []
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self.set_reference_calls: list[tuple] = []
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self.pending = 0
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self.frame_index = 0
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self.fn_value = float("nan")
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self.zero_frames = zero_frames
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self._hhd_downsample = 2
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self._hhd_grid_shape = (2, 2)
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self._hhd_ref_shape = (4, 4)
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def declare_unloaded_reference(self, *, reference_id: str) -> None:
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self.declared.append(str(reference_id))
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self.pending = 0
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self.calibration.field_baseline_N = None
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def set_calibration_reference(self, raw, *, reference_id) -> None: # pragma: no cover
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self.set_reference_calls.append((raw, reference_id))
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def read_info(self, *keys):
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values = {
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0: self.fn_value,
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1: float("nan"),
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2: float("nan"),
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3: 100.0,
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4: 2.0,
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5: 3.0,
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}
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return tuple(values[key] for key in keys)
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def step(self):
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"""One frame of the SDK's field model: 30-frame window, then ok frames."""
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self.frame_index += 1
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diagnostics: dict = {}
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if self.declared:
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if self.pending < self.zero_frames:
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self.pending += 1
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diagnostics = {
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"pending_reference_frames": self.pending,
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"extrapolated_fraction": 0.4,
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"coverage_fraction": 0.9,
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"model_fingerprint": self.calibration.fingerprint,
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"frame_id": self.frame_index,
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}
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if self.pending == self.zero_frames:
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self.calibration.field_baseline_N = 0.25
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diagnostics["baseline_N"] = 0.25
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diagnostics["pending_reference_frames"] = 0
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diagnostics["extrapolated_fraction"] = 0.3
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self.calibration_status = {"fn": "zero_unavailable"}
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self.fn_value = float("nan")
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else:
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diagnostics = {
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"baseline_N": 0.25,
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"extrapolated_fraction": 0.2,
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"coverage_fraction": 0.97,
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"model_fingerprint": self.calibration.fingerprint,
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}
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self.calibration_status = {"fn": "ok"}
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self.fn_value = 1.5
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else:
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diagnostics = {"model_fingerprint": self.calibration.fingerprint}
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self.calibration_result = SimpleNamespace(
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diagnostics=diagnostics,
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status=dict(self.calibration_status),
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values_N={"fn": None if math.isnan(self.fn_value) else self.fn_value},
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)
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return None
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class FakeChain:
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def __init__(self, sensor, *, frames: int = 35) -> None:
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self.sensor = sensor
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self.frames = int(frames)
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self.index = 0
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self.open_seconds = 0.0
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self.debug_files: list[str] = []
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self.decode_position = 0
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def __enter__(self):
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return self
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def __exit__(self, *args):
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return False
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def read(self):
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if self.index >= self.frames:
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return False, None
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self.index += 1
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return True, np.zeros((4, 4), dtype=np.uint8)
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def feed(self, frame):
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return frame
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class FakeReader:
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def __init__(self, rows, *, segments: int = 1, base: Path) -> None:
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self._rows = list(rows)
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self._segments = [
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base / f"raw_{number:04d}.mp4" for number in range(1, segments + 1)
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]
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def session(self):
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return {
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"session_id": 1,
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"started_at": "2026-01-01T00:00:00+0000",
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"ended_at": "",
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"device": {"sensor_type": "finger", "config_ref": ""},
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"stats": {"frames": len(self._rows), "segments": len(self._segments)},
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}
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def info(self):
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return SimpleNamespace(frames=self._rows, video_frames=len(self._rows))
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def segments(self):
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return list(self._segments)
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def segment_path(self, number: int):
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index = int(number) - 1
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return self._segments[index] if 0 <= index < len(self._segments) else None
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def install_pass(monkeypatch, processor, chain: FakeChain, reader: FakeReader) -> None:
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"""Make the service walk the fake chain instead of opening the SDK."""
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monkeypatch.setattr(processor, "reader", lambda: reader)
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monkeypatch.setattr(processor, "_segment_offset", lambda *args: (0, True))
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monkeypatch.setattr(service_module, "_OpenChain", lambda *args: chain)
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monkeypatch.setattr(sdk_worker, "process_chain", lambda sensor: (sensor.step(), ""))
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def new_pass_state(processor, tmp_path: Path, *, count: int = 35):
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rows = field_rows(count)
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return service_module._PassState(
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Provenance(), [1], {1: list(rows)}, tmp_path
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)
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