fix(macos-rssi-bridge): make `make start` show data flow + figure actually move
Two debuggability fixes: * `make start` now passes `--verbose` to both the bridge and the presence injector so you can see `[bridge] seq=… max_var=…` and `[presence] aps=… weight=… motion=… posted=…` lines while running. Previously start was silent and you couldn't tell whether data was flowing or where the pipeline was stuck. * `presence_to_pose.py` sway amplitudes bumped ~3x (e.g. lateral sway 6 cm → 20 cm at full motion, knee bend 5 cm → 18 cm, head turn 4 cm → 12 cm). The old values were tuned for a close-up viewer; at the Observatory's orbit camera distance (~5–10 m) they were imperceptible, so a real-motion figure looked frozen even though the data was live. Still seeded purely by RSSI variance — same intent as before, just visible. Co-authored-by: Cursor <cursoragent@cursor.com>
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griffin
parent
672be201f4
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1d2fc51790
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@ -73,12 +73,12 @@ start: build build-server
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echo "[start] sensing-server pid=$$SERVER_PID (source=esp32) — waiting 3s for UDP bind…"; \
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sleep 3; \
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./$(BRIDGE) --helper ./$(HELPER) --target-host $(TARGET_HOST) \
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--target-port $(TARGET_PORT) --interval $(INTERVAL) & \
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--target-port $(TARGET_PORT) --interval $(INTERVAL) --verbose & \
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BRIDGE_PID=$$!; \
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echo "[start] bridge pid=$$BRIDGE_PID — dashboard: $(BRIDGE_DASHBOARD) — waiting 2s for /aps…"; \
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sleep 2; \
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python3 presence_to_pose.py --bridge-url http://127.0.0.1:9090/aps \
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--server-url $(SENSING_UI_URL)/api/v1/pose/external & \
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--server-url $(SENSING_UI_URL)/api/v1/pose/external --verbose & \
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PRESENCE_PID=$$!; \
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echo "[start] presence injector pid=$$PRESENCE_PID — feeding /api/v1/pose/external @ 10 Hz"; \
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( sleep 2; open $(SENSING_UI_URL) 2>/dev/null; open $(BRIDGE_DASHBOARD) 2>/dev/null ) & \
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@ -1,23 +1,23 @@
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#!/usr/bin/env python3
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"""
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presence_to_pose.py — RSSI tomography centroid → sensing-server pose injector.
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presence_to_pose.py — RSSI motion-energy → sensing-server presence injector.
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Polls the macos-rssi-bridge HTTP endpoint at /aps for live per-AP state,
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computes the multistatic tomography centroid the same way dashboard.html
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does (Gaussian perturbation strips along each AP↔laptop link, weighted by
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per-AP RSSI variance), and POSTs the result as a single PersonDetection
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to the sensing-server's /api/v1/pose/external endpoint.
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The sensing-server bypasses its heuristic skeleton when external pose is
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fresh (within EXTERNAL_POSE_TTL = 500ms), so the 3D Observatory renders
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one honest figure standing where the heatmap localizes you instead of
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five hallucinated skeletons.
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Polls the macos-rssi-bridge HTTP endpoint at /aps for live per-AP RSSI
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variance, and POSTs a single PersonDetection to the sensing-server's
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/api/v1/pose/external endpoint so the 3D Observatory renders one honest
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figure (instead of the heuristic five-skeleton fallback).
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Honest about what's real:
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- Position: derived from real RSSI variance + Fresnel-zone geometry.
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- Pose: STATIC standing skeleton (no joint estimation from RSSI; that
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needs CSI hardware).
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- Count: capped at 1 (single sensor, single observed environment).
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- Position: PINNED at the laptop (origin). With one macOS RSSI receiver
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and unknown AP locations, RSSI variance can only detect that *something*
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moved in the environment — it cannot tell us where. Earlier versions
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fabricated a centroid from hash-positioned APs; that produced wildly
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wrong jumps and is gone.
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- Confidence + motion: derived from total per-AP RSSI variance. Higher
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variance → higher confidence and a more animated standing pose.
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- Pose: STATIC standing skeleton. No joint estimation from RSSI; that
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needs CSI hardware.
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- Count: capped at 1.
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"""
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from __future__ import annotations
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@ -29,35 +29,26 @@ import urllib.error
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import urllib.request
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from typing import Any
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# Coordinate frame: observatory uses meters with the laptop near origin.
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# Standing keypoints in poseStanding() (figure-pool's pose-system.js) sit
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# at y ≈ 0..1.72; we put the figure at the room's floor level (y=0) and
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# let the pose-system raise the joints. Width of the playable area is
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# roughly ±4m.
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ROOM_HALF_X = 3.5
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ROOM_HALF_Z = 3.5
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# 17-point COCO standing-pose offsets, anchored at the figure's foot
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# centroid (px, 0, pz). We don't actually need to send these — the
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# observatory regenerates keypoints from `pose='standing'` + `position`.
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# We include a sane skeleton anyway so the basic /ui/index.html viewer
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# also renders something coherent.
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def standing_keypoints(
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px: float, pz: float, conf: float, motion: float, t: float
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) -> list[dict[str, Any]]:
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# In meters above floor. Mirrors PoseSystem.poseStanding() in
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# ui/observatory/js/pose-system.js so both viewers agree.
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#
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# `motion` ∈ [0..1] modulates a subtle weight-shift sway so the figure
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# looks alive when there's real RSSI motion. We're explicit that this
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# is *not* derived pose — it's animation seeded by total motion energy
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# so a frozen-looking figure doesn't suggest a frozen sensor.
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sway_x = math.sin(t * 1.4) * 0.06 * motion
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sway_z = math.cos(t * 0.9) * 0.04 * motion
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head_turn = math.sin(t * 0.5) * 0.04 * (0.3 + motion)
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shoulder_dip = math.sin(t * 1.4) * 0.025 * motion
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knee_bend_l = max(0.0, math.sin(t * 1.4)) * 0.05 * motion
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knee_bend_r = max(0.0, -math.sin(t * 1.4)) * 0.05 * motion
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"""17-point COCO standing skeleton anchored at (px, 0, pz).
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Mirrors PoseSystem.poseStanding() in ui/observatory/js/pose-system.js
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so the basic /ui/index.html viewer also renders something coherent.
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`motion` ∈ [0..1] modulates a weight-shift sway so the figure looks
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alive when there's real RSSI activity — explicitly *not* derived
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pose, just animation seeded by motion energy so a frozen-looking
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figure doesn't suggest a frozen sensor. Amplitudes are tuned for
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visibility at the Observatory's orbit camera distance (~5–10 m).
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"""
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sway_x = math.sin(t * 1.4) * 0.20 * motion
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sway_z = math.cos(t * 0.9) * 0.14 * motion
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head_turn = math.sin(t * 0.5) * 0.12 * (0.3 + motion)
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shoulder_dip = math.sin(t * 1.4) * 0.08 * motion
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knee_bend_l = max(0.0, math.sin(t * 1.4)) * 0.18 * motion
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knee_bend_r = max(0.0, -math.sin(t * 1.4)) * 0.18 * motion
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layout = [
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("nose", (sway_x + head_turn, 1.72, sway_z)),
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@ -84,52 +75,9 @@ def standing_keypoints(
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]
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def stable_hash_angle(s: str) -> float:
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"""FNV-1a → angle in [0, 2π). Same as dashboard.html so AP layouts agree."""
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h = 0x811c9dc5
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for c in s.encode("utf-8"):
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h ^= c
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h = (h * 0x01000193) & 0xFFFFFFFF
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return (h / 0x100000000) * math.tau
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def rssi_to_radius(rssi: float, max_r: float) -> float:
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"""Linear in dB. -30 dBm → close, -100 dBm → max_r away."""
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t = max(0.0, min(1.0, (-rssi - 30.0) / 70.0))
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return 0.4 + t * (max_r - 0.4) # 0.4m floor so the figure isn't on top of the laptop
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def compute_centroid(aps: list[dict[str, Any]]) -> tuple[float, float, float]:
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"""Variance-weighted centroid in (x_m, z_m) plus a 'localization weight'."""
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if not aps:
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return 0.0, 0.0, 0.0
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# 1. Place each AP in 2D using the same scheme as dashboard.html.
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placed = []
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for ap in aps:
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angle = stable_hash_angle(ap["id"])
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radius = rssi_to_radius(ap["rssi_dbm"], min(ROOM_HALF_X, ROOM_HALF_Z))
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ax = math.cos(angle) * radius
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az = math.sin(angle) * radius
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placed.append((ax, az, ap.get("variance", 0.0)))
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# 2. Centroid: along each AP↔laptop line, the most informative point is
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# the midpoint (Fresnel zone widest there). Weight each midpoint by
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# variance.
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sum_w = 0.0
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sum_x = 0.0
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sum_z = 0.0
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for ax, az, var in placed:
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if var <= 0.01:
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continue
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# Midpoint between (0,0) (laptop) and (ax, az) (AP).
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mx, mz = ax * 0.5, az * 0.5
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sum_w += var
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sum_x += var * mx
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sum_z += var * mz
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if sum_w < 1e-6:
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# No motion — figure parks at the laptop.
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return 0.0, 0.0, 0.0
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return sum_x / sum_w, sum_z / sum_w, sum_w
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def total_variance(aps: list[dict[str, Any]]) -> float:
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"""Sum of per-AP RSSI variance — the only honest motion signal we have."""
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return sum(max(0.0, ap.get("variance", 0.0)) for ap in aps)
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def main() -> int:
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@ -140,15 +88,10 @@ def main() -> int:
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help="sensing-server pose ingestion endpoint")
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ap.add_argument("--rate-hz", type=float, default=10.0,
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help="how often to POST a pose (Hz)")
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ap.add_argument("--smooth", type=float, default=0.6,
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help="EMA factor for centroid smoothing (0..1, higher = snappier)")
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ap.add_argument("--amplify", type=float, default=4.0,
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help="multiplier on the centroid coordinates so small RSSI shifts produce visible figure motion in the room")
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ap.add_argument("--verbose", action="store_true")
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args = ap.parse_args()
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period = 1.0 / args.rate_hz
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sx, sz = 0.0, 0.0
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last_log = 0.0
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n_posted = 0
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n_fail = 0
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@ -169,38 +112,24 @@ def main() -> int:
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continue
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aps = snap.get("aps", [])
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cx, cz, weight = compute_centroid(aps)
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# Amplify the centroid so small RSSI shifts (a few cm of raw
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# variance-weighted midpoint motion) produce visible motion in
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# the room view. Without amplification the centroid only spans
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# ~0.5m which is invisible at 7m room scale.
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cx *= args.amplify
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cz *= args.amplify
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# Clamp to room half-extents so the figure never walks through walls.
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cx = max(-ROOM_HALF_X + 0.5, min(ROOM_HALF_X - 0.5, cx))
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cz = max(-ROOM_HALF_Z + 0.5, min(ROOM_HALF_Z - 0.5, cz))
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# EMA smoothing — sensing-server runs at 10 Hz tick, so we want
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# the figure to glide rather than jitter on every micro-fluctuation.
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sx = sx * (1 - args.smooth) + cx * args.smooth
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sz = sz * (1 - args.smooth) + cz * args.smooth
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weight = total_variance(aps)
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# Confidence ~ how much variance is present. Caps at ~1.0 once
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# there's a few dB² of cumulative motion energy across APs.
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conf = max(0.3, min(1.0, 0.3 + weight / 10.0))
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# Normalize motion intensity from cumulative variance.
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motion = max(0.0, min(1.0, weight / 5.0))
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t = time.time()
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# Position is pinned at the laptop. RSSI variance from one receiver
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# cannot localize — it can only flag that something moved.
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px, pz = 0.0, 0.0
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person = {
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"id": 1,
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"confidence": conf,
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"keypoints": standing_keypoints(sx, sz, conf, motion, t),
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"bbox": {"x": sx - 0.4, "y": 0.0, "width": 0.8, "height": 1.85},
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"zone": "rssi_localized",
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"keypoints": standing_keypoints(px, pz, conf, motion, t),
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"bbox": {"x": px - 0.4, "y": 0.0, "width": 0.8, "height": 1.85},
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"zone": "rssi_presence",
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}
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body = json.dumps([person]).encode("utf-8")
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@ -222,8 +151,8 @@ def main() -> int:
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now = time.time()
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if args.verbose and now - last_log > 1.0:
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print(
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f"[presence] aps={len(aps):>2} centroid=({sx:+.2f}, {sz:+.2f})m "
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f"weight={weight:>5.2f} conf={conf:.2f} posted={n_posted} fail={n_fail}",
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f"[presence] aps={len(aps):>2} weight={weight:>5.2f} "
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f"conf={conf:.2f} motion={motion:.2f} posted={n_posted} fail={n_fail}",
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flush=True,
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)
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last_log = now
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