ykcheungab
ee830d8505
把同帧测量、空载归零、模型推断与共用后处理收进一个 SDK 会话，Studio 显示与记录使用同一样本；旧 Sensor 读数接口保持兼容。以 force session 与受影响 SDK 套件、lint 与文档门禁验证，硬件与真实载荷精度未验证。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CpCfJF6SvonMKmPZhzKZHb
116 lines
4.6 KiB
Python
116 lines
4.6 KiB
Python
"""Measurement identities shared by SDK runtime and offline calibration exporters."""
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from __future__ import annotations
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import hashlib
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import json
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from copy import deepcopy
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from pathlib import Path
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import numpy as np
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from .calibration import filters_fingerprint
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def _feature_signature(identity) -> str:
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encoded = json.dumps(identity, sort_keys=True, separators=(",", ":"), allow_nan=False)
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return hashlib.sha256(encoded.encode("utf-8")).hexdigest()
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class CalibrationContext(dict):
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"""Actual pipeline fields plus the runtime inputs needed to compare HHD settings.
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JSON serialization contains only the actual fields. Retaining the signature
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inputs lets old files differ in downsample without ignoring other hashed settings.
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"""
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def __init__(self, fields, feature_identity):
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super().__init__(fields)
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self._feature_identity = deepcopy(feature_identity)
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def signature_for_downsample(self, downsample: int) -> str:
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"""Recompute the runtime identity with only the requested HHD resolution."""
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identity = deepcopy(self._feature_identity)
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identity["tracker"]["hhd_downsample"] = downsample
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return _feature_signature(identity)
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@property
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def actual_downsample(self) -> int:
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"""The HHD downsample this runtime context was actually built with."""
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raw = self.get("hhd_downsample", 1)
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try:
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return max(1, int(raw)) if raw is not None else 1
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except (TypeError, ValueError):
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return 1
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def sha256_file(path) -> str:
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return hashlib.sha256(Path(path).read_bytes()).hexdigest()
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def domain_fingerprint(domain) -> str:
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"""Bind a coordinate lookup to the actual unroll, not just its dimensions."""
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data = domain.to_dict() if hasattr(domain, "to_dict") else domain
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digest = hashlib.sha256()
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for key, dtype in (("map_x", "<f4"), ("map_y", "<f4"), ("valid_mask", "u1"), ("world_xyz", "<f4")):
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array = np.ascontiguousarray(data[key], dtype=dtype)
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digest.update(key.encode("ascii"))
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digest.update(str(array.shape).encode("ascii"))
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digest.update(array.tobytes())
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return digest.hexdigest()
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def build_context(config, filters=None, *, geometry_sha256=None, source_scale=None,
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coordinates=None, output_domain_sha256=None, processing_backend=None,
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preprocessing=None, field=None) -> dict:
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"""Build the closed v1 feature identity from effective values, excluding readout filters.
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``field`` carries the closed profile identity for field models (shape, dtype,
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unit, mask and lifecycle). It is omitted for scalar profiles so their
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signatures stay byte-identical to previously exported files.
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"""
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camera = config.get("camera", {})
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tracker = dict(config.get("tracker", {}))
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raw_downsample = tracker.get("hhd_downsample", 1)
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try:
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downsample = max(1, int(raw_downsample)) if raw_downsample is not None else 1
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except (TypeError, ValueError):
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downsample = 1
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tracker["hhd_downsample"] = downsample
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if source_scale is not None:
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source_scale = float(source_scale)
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measurement = filters_fingerprint(config.get("filters", {}) if filters is None else filters).get("measurement", {})
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# A missing EMA setting has the same effective meaning as the SDK default.
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measurement.setdefault("flow_ema_alpha", 1.0)
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result = {
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"stitch_resolution": camera.get("resolution_stitch"),
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"dis_mode": tracker.get("dis_mode"),
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"hhd_downsample": downsample,
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"filters": {"measurement": measurement},
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}
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if geometry_sha256 is not None:
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result["geometry_sha256"] = geometry_sha256
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if source_scale is not None:
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result["source_scale"] = float(source_scale)
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if coordinates is not None:
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result["coordinates"] = coordinates
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if output_domain_sha256 is not None:
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result["output_domain_sha256"] = output_domain_sha256
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effective = {
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"feature_version": "orisys_hhd_raw/1",
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"processing_backend": processing_backend or config.get("type", "compound_eye"),
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"camera": {key: camera.get(key) for key in ("resolution_raw", "resolution_stitch")},
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"tracker": tracker,
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"contact": config.get("contact", {}),
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"measurement": measurement,
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"geometry_sha256": geometry_sha256,
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"output_domain_sha256": output_domain_sha256,
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"source_scale": source_scale,
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"preprocessing": preprocessing or {},
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}
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if field is not None:
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result["field"] = deepcopy(field)
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effective["field"] = deepcopy(field)
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result["feature_signature_sha256"] = _feature_signature(effective)
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return CalibrationContext(result, effective)
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