File size: 4,243 Bytes
706821a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | """Which world frame a pose array is in — read it, and check it.
The 2026-05-19 motherboard session redefined the OptiTrack origin: its raw
poses sit (0.23, 0, 0.175) m from every other date. The published poses have
the correction baked in, so all 32 episodes share one frame — but that fact
used to live in one free-text sentence inside `calibration.json`, which a
machine cannot read, which states a difference without a direction, and which
nothing can check.
Each episode's parquet now carries the declaration, and a FINGERPRINT: the
median pixel the gel centre projects to in each camera, computed from the
poses that ship. Recompute it from whatever poses you hold and compare.
dec = read_world_frame("episode_002.parquet")
err = verify_world_frame(my_poses, "left", "motherboard", dec)
assert err < 6.0
Measured discriminating power on 2026-05-19/episode_002:
missing world offset 222.9 px
y/z axes swapped 227.2 px
metres read as mm 1741-2782 px
1 degree of yaw 1.7-3.2 px
against a calibration rmse of 4.75 mm — about 3 px at this depth. So it
catches every frame error this rig can plausibly suffer, including ones nobody
has thought of, which a check for one known offset cannot.
"""
from __future__ import annotations
import json
import numpy as np
VIEWS = ("left", "middle", "right")
def read_world_frame(parquet_path):
"""The declaration embedded in an episode's parquet, or None."""
import pyarrow.parquet as pq
md = pq.read_schema(str(parquet_path)).metadata or {}
raw = md.get(b"twm.world_frame")
return json.loads(raw.decode()) if raw is not None else None
def projection_fingerprint(pose7, gel_center_mm, cams) -> dict:
"""Median projected gel-centre pixel per camera.
Median, not mean: a handful of tracking dropouts move a mean by tens of
pixels and leave a median untouched, and a signature that drifts with the
noise cannot be compared against a stored one.
"""
from .calibration import project_gel_to_pixel
p = np.asarray(pose7, float)
ok = np.isfinite(p).all(1) & (np.linalg.norm(p[:, 3:], axis=1) > 0.5)
p = p[ok]
if len(p) < 10:
raise ValueError(f"only {len(p)} valid poses — cannot fingerprint")
out = {}
for v in VIEWS:
if v not in cams:
continue
uv = [project_gel_to_pixel(q, gel_center_mm, cams[v]) for q in p]
uv = np.asarray([x for x in uv if x is not None], float)
if len(uv) < 10:
continue
out[v] = [float(np.median(uv[:, 0])), float(np.median(uv[:, 1]))]
return out
def verify_world_frame(pose7, side: str, task_root, declaration) -> float:
"""Worst per-camera pixel distance from the declared fingerprint.
WORST, not mean: a frame error along one camera's optical axis is
invisible to that camera and obvious to the others, so averaging would
dilute exactly the evidence that matters.
`task_root` is the directory holding `calibration/` — the same argument
`load_calibration` takes.
"""
from .calibration import load_calibration
if not declaration or "fingerprint" not in declaration:
raise ValueError("declaration has no fingerprint; this episode "
"predates the world-frame metadata")
stored = declaration["fingerprint"].get(side)
if not isinstance(stored, dict):
raise ValueError(
f"no fingerprint for side {side!r}. Pass the whole declaration; "
f"this function selects the side. (The sides and the cameras "
f"share the names left/right, so selecting by hand is easy to "
f"get wrong — an earlier version of this check did, and returned "
f"0.0 for every input as a result.)")
cal = load_calibration(task_root)
got = projection_fingerprint(pose7, cal[f"gel_{side}"], cal["cams"])
common = [v for v in VIEWS if v in got and v in stored]
if not common:
raise ValueError("no camera in common between the fingerprint and "
"this calibration")
return max(float(np.hypot(got[v][0] - stored[v][0],
got[v][1] - stored[v][1])) for v in common)
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