mirror of
https://github.com/ruvnet/RuView
synced 2026-08-07 20:01:43 +00:00
17471e93ff
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1) PerceptAlign-motivated geometry capture at enrollment: per-node optional records (position, antenna orientation, inter-node distances, acquisition method) — recorded when known, never required. Event-sourced via EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly (fixture-proven, and geometry-free banks serialize byte-shape-identical to the old schema); threaded through MultiNodeMixture as data only — the learned geometry embeddings and algorithmic fusion use are §2.1.2, deliberately deferred until the ADR-151 P6 LoRA heads exist. Geometry recorded from now on means banks captured today remain usable for layout-conditioned training later — you can't retroactively add geometry to data you didn't record. 8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop extension (2-node geometry, one tape-measured + one unknown, surviving the bank JSON round-trip the runtime loads from). 50/50 calibration (both feature configs) + 23 CLI tests green. Co-Authored-By: RuFlo <ruv@ruv.net> * feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3) Defends the camera-supervised pipeline against PerceptAlign's "coordinate overfitting": MediaPipe keypoints were emitted in raw camera coordinates with no shared frame and no transceiver-geometry metadata — the exact label shape that memorizes deployment layout and collapses cross-layout. - scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV two-checkerboard calibration → versioned bundle JSON (intrinsics, camera→room extrinsics, checkerboard spec, transceiver geometry, sha256 calibration_id). Intrinsics resolve from file > cache > multi-view computation > loud-warning 2-view fallback. - collect-ground-truth.py --calibration <bundle>: every sample gains keypoints_room (unit bearing rays from the camera center in the room frame — documented projective alignment; raw image coords preserved so training chooses), camera_origin_room, calibration_id, and the transceiver geometry stamp. Without the flag, output is byte-identical to before (tested) + a one-line ADR-152 warning. Design finding (recorded for ADR-152): a single planar checkerboard's corner grid is centrosymmetric — the reversed corner ordering fits a ghost camera pose with IDENTICAL reprojection error, so per-board flip disambiguation is mathematically ill-posed. solve_two_board_extrinsics solves the joint wall+floor set over all 4 flip combinations, where the minimum is unique — an independent reason the TWO-checkerboard method is required, beyond what PerceptAlign states. 15 headless pytest tests green (synthetic corners: extrinsics recovery incl. ghost resolution, bundle round-trip + hash stability, ray transforms w/ distortion + cross-resolution, no-calibration byte identity). Co-Authored-By: RuFlo <ruv@ruv.net> * feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2) Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> * feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model - calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived SpecialistBank::geometry_embedding() accessor; 59 tests - train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict no-truncate/no-NaN policy; proptest properties - train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20 WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula pinned to 2,225,042; 15/17-keypoint support; 239 crate tests - hardware: ieee80211bf forward-compatibility protocol model (ADR-153): SpecProfile gates, SensingCapabilities negotiation, required ConsentMode, session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge; full acceptance checklist covered; 156+4 tests - deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f - docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added Workspace: 162 test suites green (--no-default-features); Python proof PASS. Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults (unserialized env-var mutation) — untouched, follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(train): repair tch-backend bit-rot — gated path compiles and tests run again Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from (+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() / divide_scalar_mode(floor) — the latter fixed a silent true-division bug in heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar overflow panic); petgraph EdgeRef import; missing EvalMetrics and verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11 Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged. Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof seed race under parallel threads — documented, deliberate follow-ups. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248 mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore] integration test loads it strictly and asserts forward-pass agreement: max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes the tch name layout authoritative. Zero architecture discrepancies found. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs Concurrent validation workflow (2 review lanes + adversarial verification, 13 agents): 5 confirmed findings, 3 refuted. Fixes: - wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) — silently halves activations in from-scratch training; loaded checkpoints unaffected, parity re-verified after the change) - wiflow_std: document the conv-init divergences vs the reference's effective kaiming_normal(fan_out) re-init (from-scratch dynamics only) - ieee80211bf: ThresholdParams deserialization validates via try_from so the <=100 invariant holds for untrusted payloads (+ rejection test) Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s), MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s — nothing suspicious. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(user-guide): corrected camera-supervised collection tutorial Step 0 CSI-rate check + session-length math (window yield = frames/20 — the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and confidence guidance; torso-normalized PCK + temporal-split + pred-variance eval protocol (lessons from the 92.9% retraction); scale presets re-keyed to realistic window counts. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63% vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops QDQ strictly worse (int8 activations through attention glue). Entropy calibration verified bit-identical to MinMax on this data. Deployment: ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB). Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for empty-room baselines (no glob collisions, detach-safe). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows (temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords). Honesty gates held: pred std 0.0113 (non-constant model) but mean-baseline dominance means no citable CSI->pose capability from this data. ADR-152 open question 1 answered partially; definitive answer needs multi-subject/position data. Two new aligner findings: heterogeneous csi_shape with silent zero-padding (~20%), and extractCsiMatrix's transposed shape label (frame-major data, [nSc, nFrames] label) — fixes pending. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference Compact WiFlow-STD variants on the same data/split/protocol: half (843,834 params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs 96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published architecture is over-parameterized for its own benchmark. quarter (338k) 96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge candidate. In-domain caveats recorded; cross-domain untested. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152) WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified) wiflow_std: min_feature_width (default 15) replaces the keypoints->stride coupling — for_keypoints(17) now provably builds the trained [2,2,2,2] graph and pools 15->17, matching the validated Python protocol (pinned by tests); param_count() total on invalid configs; random_mask returns Result and rejects non-finite/out-of-range ratios; trainer checkpoints switched to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11). ieee80211bf: SBP proxy now re-triggers instances and relays reports via Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via their existing path); missed_instances reset on success = consecutive semantics; SessionTable gains a guarded SBP entry point + unknown-id drop counter; initiator-role sessions reject inbound setup/SBP requests (RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp outside Idle return InvalidStateForCommand; SBP validation unified through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping. events.rs split out to honor the 500-line cap. calibration/cli: enrollment geometry now actually reaches trained banks — both production call sites attach .with_geometry; --geometry flag on train-room and POST /enroll/geometry + train-body geometry on calibrate-serve give production a recording surface; geometry-free banks log the ADR-152 §2.1.2 note. benchmarks: corruption masks committed as ground truth (unregenerable after in-place cleaning; verified bit-identical regeneration from the pristine copy) + generate_corruption_masks.py producer; _bench_common.py dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20 re-verified equal to the last digit); remote scripts get the mmap patch; tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer executes (and overwrote) the export — artifact restored byte-exact. Workspace: 2,963 tests passed, 0 failed; Python proof PASS. Co-Authored-By: claude-flow <ruv@ruv.net> * ci: build workspace tests without debuginfo — runner disk exhaustion The combined 38-crate debug target exceeds the GitHub runner's disk ('final link failed: No space left on device'); the same tree measured 151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0 shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs. Co-Authored-By: claude-flow <ruv@ruv.net>
341 lines
12 KiB
Rust
341 lines
12 KiB
Rust
//! Enrollment protocol — per-anchor capture with an adaptive quality gate
|
||
//! (ADR-151 Stage 2).
|
||
//!
|
||
//! Bad anchors poison small calibrated models far more than large ones, so an
|
||
//! anchor is only *accepted* when its captured statistics match what the anchor
|
||
//! is supposed to teach: a person present (or absent for `empty`), and the
|
||
//! expected stillness/motion. Failed anchors are re-prompted, not silently kept.
|
||
//!
|
||
//! Quality is measured against the ADR-135 empty-room baseline via
|
||
//! [`wifi_densepose_signal::BaselineCalibration::deviation`], whose
|
||
//! `CalibrationDeviationScore` gives a per-frame amplitude z-score (presence
|
||
//! strength).
|
||
//!
|
||
//! **Motion is NOT taken from the score's `motion_flagged`** (ADR-152 finding,
|
||
//! "z-band squeeze"): that flag fires on `amplitude_z_median > 2.0` — deviation
|
||
//! from the *empty* baseline — which conflates presence strength with motion. A
|
||
//! strongly-reflecting person standing perfectly still (z > 2 on every frame)
|
||
//! would be rejected as "too much motion". Instead the recorder derives motion
|
||
//! from the frame-to-frame *change* in the deviation series (|Δz| and |Δφ|),
|
||
//! which is presence-independent: a still strong reflector has high z but a
|
||
//! flat z-series; a moving person has a jittery one.
|
||
|
||
use wifi_densepose_core::types::CsiFrame;
|
||
use wifi_densepose_signal::{BaselineCalibration, CalibrationDeviationScore};
|
||
|
||
use crate::anchor::{Anchor, AnchorLabel, AnchorQuality};
|
||
|
||
/// Thresholds for accepting an anchor.
|
||
#[derive(Debug, Clone, Copy)]
|
||
pub struct AnchorQualityGate {
|
||
/// Minimum mean amplitude z-score to consider a person present.
|
||
pub min_presence_z: f32,
|
||
/// For `empty`: maximum mean z-score to consider the room truly empty.
|
||
pub empty_max_z: f32,
|
||
/// For "still" anchors: maximum motion-flag rate tolerated.
|
||
pub max_still_motion: f32,
|
||
/// For the "move" anchor: minimum motion-flag rate required.
|
||
pub min_move_motion: f32,
|
||
/// Minimum frames required to evaluate an anchor.
|
||
pub min_frames: u32,
|
||
}
|
||
|
||
impl Default for AnchorQualityGate {
|
||
fn default() -> Self {
|
||
Self {
|
||
min_presence_z: 1.5,
|
||
empty_max_z: 1.0,
|
||
max_still_motion: 0.6,
|
||
min_move_motion: 0.3,
|
||
min_frames: 60,
|
||
}
|
||
}
|
||
}
|
||
|
||
impl AnchorQualityGate {
|
||
/// Evaluate accumulated stats for `label`, returning the quality verdict
|
||
/// and (on rejection) a human-readable reason.
|
||
pub fn evaluate(
|
||
&self,
|
||
label: AnchorLabel,
|
||
presence_z: f32,
|
||
motion_rate: f32,
|
||
frames: u32,
|
||
) -> (AnchorQuality, Option<String>) {
|
||
let mut reason: Option<String> = None;
|
||
|
||
if frames < self.min_frames {
|
||
reason = Some(format!(
|
||
"only {frames} frames (need ≥{}); is the ESP32 streaming?",
|
||
self.min_frames
|
||
));
|
||
} else if label.expects_presence() {
|
||
if presence_z < self.min_presence_z {
|
||
reason = Some(format!(
|
||
"no person detected (presence_z {presence_z:.2} < {:.2}) — move closer / face the sensor",
|
||
self.min_presence_z
|
||
));
|
||
} else if label.expects_still() && motion_rate > self.max_still_motion {
|
||
reason = Some(format!(
|
||
"too much motion ({:.0}% > {:.0}%) for a still anchor — hold still",
|
||
motion_rate * 100.0,
|
||
self.max_still_motion * 100.0
|
||
));
|
||
} else if !label.expects_still() && motion_rate < self.min_move_motion {
|
||
reason = Some(format!(
|
||
"not enough motion ({:.0}% < {:.0}%) — move a bit more",
|
||
motion_rate * 100.0,
|
||
self.min_move_motion * 100.0
|
||
));
|
||
}
|
||
} else {
|
||
// `empty` anchor: the room must actually be empty.
|
||
if presence_z > self.empty_max_z {
|
||
reason = Some(format!(
|
||
"room not empty (presence_z {presence_z:.2} > {:.2}) — clear the room",
|
||
self.empty_max_z
|
||
));
|
||
}
|
||
}
|
||
|
||
let quality = AnchorQuality {
|
||
presence_z,
|
||
motion_rate,
|
||
frames,
|
||
accepted: reason.is_none(),
|
||
};
|
||
(quality, reason)
|
||
}
|
||
}
|
||
|
||
/// Frame-to-frame amplitude-z change above which a frame counts as motion.
|
||
///
|
||
/// Presence-independent by construction: a still person shifts the z *level*
|
||
/// but not its frame-to-frame delta (only noise-scale jitter survives), while
|
||
/// body movement modulates the reflected paths every frame. Sized well above
|
||
/// the delta the baseline's own noise floor produces (≲0.3σ) and well below
|
||
/// the delta even small limb movements produce (≳1σ). See ADR-152.
|
||
pub const Z_DELTA_MOTION: f32 = 0.5;
|
||
|
||
/// Frame-to-frame phase-drift change above which a frame counts as motion.
|
||
/// Same constant family as the absolute π/6 drift bound in
|
||
/// `CalibrationDeviationScore`, applied to the delta (static body phase shift
|
||
/// cancels out).
|
||
pub const PHASE_DELTA_MOTION: f32 = std::f32::consts::PI / 6.0;
|
||
|
||
/// Accumulates per-frame deviation statistics for a single anchor capture.
|
||
pub struct AnchorRecorder {
|
||
label: AnchorLabel,
|
||
z_sum: f64,
|
||
motion_count: u32,
|
||
frames: u32,
|
||
/// Previous frame's (amplitude_z_median, phase_drift_median) for the
|
||
/// delta-based motion measure (ADR-152 z-band-squeeze fix).
|
||
prev: Option<(f32, f32)>,
|
||
}
|
||
|
||
impl AnchorRecorder {
|
||
/// Start recording the given anchor.
|
||
pub fn new(label: AnchorLabel) -> Self {
|
||
Self {
|
||
label,
|
||
z_sum: 0.0,
|
||
motion_count: 0,
|
||
frames: 0,
|
||
prev: None,
|
||
}
|
||
}
|
||
|
||
/// The anchor being recorded.
|
||
pub fn label(&self) -> AnchorLabel {
|
||
self.label
|
||
}
|
||
|
||
/// Frames recorded so far.
|
||
pub fn frames(&self) -> u32 {
|
||
self.frames
|
||
}
|
||
|
||
/// Record a pre-computed deviation score (caller runs `baseline.deviation`).
|
||
///
|
||
/// Motion is derived from the frame-to-frame change of the deviation
|
||
/// series, NOT from `score.motion_flagged` — the flag conflates presence
|
||
/// strength with motion (z-band squeeze, see module docs / ADR-152). The
|
||
/// first frame of a capture is never motion (no predecessor).
|
||
pub fn record_score(&mut self, score: &CalibrationDeviationScore) {
|
||
let z = score.amplitude_z_median;
|
||
let phase = score.phase_drift_median;
|
||
if let Some((pz, pp)) = self.prev {
|
||
if (z - pz).abs() > Z_DELTA_MOTION || (phase - pp).abs() > PHASE_DELTA_MOTION {
|
||
self.motion_count += 1;
|
||
}
|
||
}
|
||
self.prev = Some((z, phase));
|
||
self.z_sum += z as f64;
|
||
self.frames += 1;
|
||
}
|
||
|
||
/// Convenience: record a CSI frame directly against a baseline.
|
||
/// Frames that fail baseline geometry checks are skipped (not counted).
|
||
pub fn record_frame(&mut self, baseline: &BaselineCalibration, frame: &CsiFrame) {
|
||
if let Ok(score) = baseline.deviation(frame) {
|
||
self.record_score(&score);
|
||
}
|
||
}
|
||
|
||
/// Mean presence z-score over the capture.
|
||
pub fn presence_z(&self) -> f32 {
|
||
if self.frames == 0 {
|
||
0.0
|
||
} else {
|
||
(self.z_sum / self.frames as f64) as f32
|
||
}
|
||
}
|
||
|
||
/// Fraction of frames flagged as motion.
|
||
pub fn motion_rate(&self) -> f32 {
|
||
if self.frames == 0 {
|
||
0.0
|
||
} else {
|
||
self.motion_count as f32 / self.frames as f32
|
||
}
|
||
}
|
||
|
||
/// Evaluate the capture against the gate and produce an `Anchor` (accepted
|
||
/// or not) plus a rejection reason.
|
||
pub fn finalize(&self, gate: &AnchorQualityGate, at_unix_s: i64) -> (Anchor, Option<String>) {
|
||
let (quality, reason) = gate.evaluate(
|
||
self.label,
|
||
self.presence_z(),
|
||
self.motion_rate(),
|
||
self.frames,
|
||
);
|
||
(
|
||
Anchor {
|
||
label: self.label,
|
||
captured_at_unix_s: at_unix_s,
|
||
quality,
|
||
},
|
||
reason,
|
||
)
|
||
}
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
/// Build a score the way `BaselineCalibration::deviation` actually would:
|
||
/// `motion_flagged` is DERIVED from z (z > 2.0 ⇒ flagged), never free.
|
||
/// The old tests mocked `(z=3.0, motion=false)` — a combination the real
|
||
/// producer can never emit, which is exactly how the z-band squeeze hid.
|
||
fn score(z: f32) -> CalibrationDeviationScore {
|
||
CalibrationDeviationScore {
|
||
amplitude_z_median: z,
|
||
amplitude_z_max: z + 1.0,
|
||
phase_drift_median: 0.05,
|
||
motion_flagged: z > 2.0,
|
||
}
|
||
}
|
||
|
||
/// Record a z-series and finalize against the default gate.
|
||
fn run_series(label: AnchorLabel, zs: &[f32]) -> (Anchor, Option<String>) {
|
||
let mut r = AnchorRecorder::new(label);
|
||
for &z in zs {
|
||
r.record_score(&score(z));
|
||
}
|
||
r.finalize(&AnchorQualityGate::default(), 100)
|
||
}
|
||
|
||
/// Constant z (a perfectly still capture at the given presence strength).
|
||
fn run_still(label: AnchorLabel, z: f32, n: usize) -> (Anchor, Option<String>) {
|
||
run_series(label, &vec![z; n])
|
||
}
|
||
|
||
/// Alternating z (every frame's |Δz| exceeds Z_DELTA_MOTION ⇒ all motion).
|
||
fn run_jittery(label: AnchorLabel, z: f32, n: usize) -> (Anchor, Option<String>) {
|
||
let zs: Vec<f32> = (0..n)
|
||
.map(|i| {
|
||
if i % 2 == 0 {
|
||
z
|
||
} else {
|
||
z + 2.0 * Z_DELTA_MOTION
|
||
}
|
||
})
|
||
.collect();
|
||
run_series(label, &zs)
|
||
}
|
||
|
||
/// ADR-152 z-band-squeeze regression: a STRONGLY-reflecting still person
|
||
/// (z = 3.0, so every frame is motion_flagged by the baseline heuristic)
|
||
/// must still pass a still anchor — presence strength is not motion.
|
||
#[test]
|
||
fn still_anchor_with_strong_still_person_accepts() {
|
||
let (a, reason) = run_still(AnchorLabel::StandStill, 3.0, 400);
|
||
assert!(a.quality.accepted, "z-band squeeze is back: {reason:?}");
|
||
assert!(reason.is_none());
|
||
assert!(
|
||
a.quality.motion_rate < 0.05,
|
||
"flat z-series must read still"
|
||
);
|
||
}
|
||
|
||
#[test]
|
||
fn still_anchor_rejects_when_no_presence() {
|
||
let (a, reason) = run_still(AnchorLabel::Sit, 0.4, 400);
|
||
assert!(!a.quality.accepted);
|
||
assert!(reason.unwrap().contains("no person"));
|
||
}
|
||
|
||
#[test]
|
||
fn still_anchor_rejects_on_motion() {
|
||
let (a, reason) = run_jittery(AnchorLabel::LieDown, 3.0, 400);
|
||
assert!(!a.quality.accepted);
|
||
assert!(reason.unwrap().contains("motion"));
|
||
}
|
||
|
||
#[test]
|
||
fn move_anchor_requires_motion() {
|
||
let (still, r1) = run_still(AnchorLabel::SmallMove, 3.0, 400);
|
||
assert!(!still.quality.accepted);
|
||
assert!(r1.unwrap().contains("not enough motion"));
|
||
let (moving, r2) = run_jittery(AnchorLabel::SmallMove, 3.0, 400);
|
||
assert!(moving.quality.accepted, "reason: {r2:?}");
|
||
}
|
||
|
||
#[test]
|
||
fn phase_delta_also_counts_as_motion() {
|
||
// Constant z but a phase-drift series that swings past PHASE_DELTA_MOTION
|
||
// every frame — motion must be detected from the phase channel alone.
|
||
let mut r = AnchorRecorder::new(AnchorLabel::LieDown);
|
||
for i in 0..400 {
|
||
let mut s = score(1.8);
|
||
s.phase_drift_median = if i % 2 == 0 {
|
||
0.0
|
||
} else {
|
||
PHASE_DELTA_MOTION * 1.5
|
||
};
|
||
r.record_score(&s);
|
||
}
|
||
let (a, reason) = r.finalize(&AnchorQualityGate::default(), 100);
|
||
assert!(!a.quality.accepted);
|
||
assert!(reason.unwrap().contains("motion"));
|
||
}
|
||
|
||
#[test]
|
||
fn empty_anchor_rejects_when_occupied() {
|
||
let (occupied, reason) = run_still(AnchorLabel::Empty, 3.0, 400);
|
||
assert!(!occupied.quality.accepted);
|
||
assert!(reason.unwrap().contains("not empty"));
|
||
let (empty, _) = run_still(AnchorLabel::Empty, 0.3, 400);
|
||
assert!(empty.quality.accepted);
|
||
}
|
||
|
||
#[test]
|
||
fn too_few_frames_rejected() {
|
||
let (a, reason) = run_still(AnchorLabel::Sit, 3.0, 10);
|
||
assert!(!a.quality.accepted);
|
||
assert!(reason.unwrap().contains("frames"));
|
||
}
|
||
}
|