csi_pipeline: rename WiFlow stub to heuristic_pose_from_amplitude, decouple UDP
Blocker 3 (PR #405 review): The "WiFlow inference" path was a stub that built a model from empty weight vectors and synthesised keypoints from amplitude energy. Presenting this as "WiFlow inference" was misleading. - Rename WiFlowModel to PoseModelMetadata (empty tag struct; we only care if the on-disk file exists) - Rename load_wiflow_model() -> detect_pose_model_metadata() and log "amplitude-energy heuristic enabled/disabled" (no "WiFlow" claim) - Rename estimate_pose() -> heuristic_pose_from_amplitude() with prominent `STUB:` doc comment saying this is NOT a trained model Blocker 4 (PR #405 review): The UDP receiver held the shared Arc<Mutex> across a synchronous process_frame() call, starving HTTP handlers. - Introduce a std::sync::mpsc channel between the UDP thread (which only parses + pushes) and a dedicated processor thread (which locks only briefly around a single process_frame). HTTP snapshots via get_pipeline_output no longer contend with the socket read loop. Also: - Move ADR-018 parser to parser.rs (see next commit); csi_pipeline re-exports - send_test_frames now uses parser::build_test_frame for synthetic frames - Log a one-line node stats summary every 500 frames (reads every public CsiFrame field on the runtime path) Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
parent
8505662af4
commit
4d5bdb1570
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@ -1,76 +1,22 @@
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//! Complete CSI processing pipeline — ADR-018 parser → WiFlow pose → vitals → tomography.
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#![allow(dead_code)]
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//! Complete CSI processing pipeline — ADR-018 parser → heuristic pose → vitals → tomography.
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//!
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//! Receives raw UDP frames from ESP32 nodes, extracts I/Q subcarrier data,
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//! runs the WiFlow pose model, detects motion, estimates vitals, and produces
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//! 3D occupancy + skeleton for fusion with camera depth.
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//! detects motion, estimates vitals, and produces 3D occupancy + skeleton
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//! for fusion with camera depth.
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//!
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//! **Note on pose**: the pose estimator here is an amplitude-energy
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//! heuristic — NOT a trained WiFlow model. See
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//! [`CsiPipelineState::heuristic_pose_from_amplitude`] for the exact shape.
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//! A real WiFlow integration requires loading and running the TCN weights,
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//! which this crate does not currently do.
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use std::collections::VecDeque;
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use std::net::UdpSocket;
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use std::sync::{Arc, Mutex};
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// ─── ADR-018 Binary Frame Parser ────────────────────────────────────────────
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const CSI_MAGIC_V6: u32 = 0xC511_0006;
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const CSI_MAGIC_V1: u32 = 0xC511_0001;
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const CSI_HEADER_SIZE: usize = 20;
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#[derive(Clone, Debug)]
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pub struct CsiFrame {
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pub node_id: u8,
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pub n_antennas: u8,
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pub n_subcarriers: u16,
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pub channel: u8,
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pub rssi: i8,
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pub noise_floor: i8,
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pub timestamp_us: u32,
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/// Raw I/Q data: [I0, Q0, I1, Q1, ...] for each subcarrier
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pub iq_data: Vec<i8>,
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/// Computed amplitude per subcarrier: sqrt(I^2 + Q^2)
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pub amplitudes: Vec<f32>,
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/// Computed phase per subcarrier: atan2(Q, I)
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pub phases: Vec<f32>,
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}
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/// Parse an ADR-018 binary CSI frame from UDP packet.
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pub fn parse_adr018(data: &[u8]) -> Option<CsiFrame> {
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if data.len() < CSI_HEADER_SIZE { return None; }
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let magic = u32::from_le_bytes([data[0], data[1], data[2], data[3]]);
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if magic != CSI_MAGIC_V6 && magic != CSI_MAGIC_V1 { return None; }
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let node_id = data[4];
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let n_antennas = data[5].max(1);
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let n_subcarriers = u16::from_le_bytes([data[6], data[7]]);
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let channel = data[8];
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let rssi = data[9] as i8;
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let noise_floor = data[10] as i8;
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let timestamp_us = u32::from_le_bytes([data[16], data[17], data[18], data[19]]);
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let iq_len = (n_subcarriers as usize) * 2 * (n_antennas as usize);
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if data.len() < CSI_HEADER_SIZE + iq_len { return None; }
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let iq_data: Vec<i8> = data[CSI_HEADER_SIZE..CSI_HEADER_SIZE + iq_len]
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.iter().map(|&b| b as i8).collect();
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// Compute amplitude and phase per subcarrier (first antenna)
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let mut amplitudes = Vec::with_capacity(n_subcarriers as usize);
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let mut phases = Vec::with_capacity(n_subcarriers as usize);
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for i in 0..n_subcarriers as usize {
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let idx = i * 2;
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if idx + 1 < iq_data.len() {
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let ii = iq_data[idx] as f32;
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let qq = iq_data[idx + 1] as f32;
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amplitudes.push((ii * ii + qq * qq).sqrt());
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phases.push(qq.atan2(ii));
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}
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}
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Some(CsiFrame {
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node_id, n_antennas, n_subcarriers, channel, rssi, noise_floor,
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timestamp_us, iq_data, amplitudes, phases,
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})
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}
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// ADR-018 parser moved to src/parser.rs. Re-export here so downstream code
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// (and the reviewer's referenced public API) keeps working unchanged.
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pub use crate::parser::{parse_adr018, CsiFrame};
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// ─── CSI Fingerprint Database ──────────────────────────────────────────────
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@ -83,7 +29,7 @@ pub struct CsiFingerprint {
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pub samples: u32,
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}
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// ─── CSI State — accumulates frames for WiFlow + vitals ─────────────────────
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// ─── CSI State — accumulates frames for heuristic pose + vitals ───────────
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#[derive(Clone, Debug)]
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pub struct Skeleton {
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@ -102,7 +48,7 @@ pub struct VitalSigns {
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pub struct CsiPipelineState {
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/// Per-node frame history (node_id → last N frames)
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pub node_frames: std::collections::HashMap<u8, VecDeque<CsiFrame>>,
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/// Latest skeleton from WiFlow
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/// Latest skeleton from the amplitude-energy heuristic (NOT ML-derived)
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pub skeleton: Option<Skeleton>,
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/// Latest vital signs
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pub vitals: VitalSigns,
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@ -119,21 +65,18 @@ pub struct CsiPipelineState {
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pub current_location: Option<(String, f32)>,
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/// Night mode — true when camera luminance is below threshold
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pub is_dark: bool,
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/// WiFlow model weights (loaded once)
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wiflow_weights: Option<WiFlowModel>,
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/// Metadata from the on-disk WiFlow JSON, if one is present. NOTE: the
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/// weights themselves are NOT loaded or executed in this crate — this
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/// flag merely enables the amplitude-energy heuristic pose code path.
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pose_model_present: Option<PoseModelMetadata>,
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}
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struct WiFlowModel {
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/// TCN weights from wiflow-v1.json (simplified inference)
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conv1_w: Vec<f32>,
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conv1_b: Vec<f32>,
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conv2_w: Vec<f32>,
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conv2_b: Vec<f32>,
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fc_w: Vec<f32>,
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fc_b: Vec<f32>,
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input_subcarriers: usize,
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time_steps: usize,
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}
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/// Placeholder tag indicating the `wiflow-v1.json` file is present on disk.
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/// This does NOT contain real TCN weights — the actual pose estimator in
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/// this crate is an amplitude-energy heuristic, not a neural network. The
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/// struct itself is empty; we only care whether it exists (`Option::Some`
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/// means "heuristic enabled").
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struct PoseModelMetadata;
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impl Default for CsiPipelineState {
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fn default() -> Self {
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@ -148,14 +91,19 @@ impl Default for CsiPipelineState {
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fingerprints: Vec::new(),
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current_location: None,
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is_dark: false,
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wiflow_weights: load_wiflow_model(),
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pose_model_present: detect_pose_model_metadata(),
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}
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}
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}
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// ─── WiFlow Model Loading ───────────────────────────────────────────────────
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// ─── Pose Model Metadata Probe ──────────────────────────────────────────────
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//
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// NOTE: This only reads the shape metadata from `wiflow-v1.json` on disk.
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// The weights are NOT loaded or evaluated. The actual pose used by this
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// crate is an amplitude-energy heuristic (see
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// `heuristic_pose_from_amplitude`), not WiFlow.
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fn load_wiflow_model() -> Option<WiFlowModel> {
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fn detect_pose_model_metadata() -> Option<PoseModelMetadata> {
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let paths = [
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"/tmp/ruview-firmware/wiflow-v1.json",
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"~/.local/share/ruview/wiflow-v1.json",
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@ -164,22 +112,17 @@ fn load_wiflow_model() -> Option<WiFlowModel> {
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let expanded = p.replace('~', &std::env::var("HOME").unwrap_or_default());
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if let Ok(data) = std::fs::read_to_string(&expanded) {
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if let Ok(model) = serde_json::from_str::<serde_json::Value>(&data) {
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if let Some(_weights_b64) = model.get("weightsBase64").and_then(|v| v.as_str()) {
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eprintln!(" WiFlow: loaded from {expanded} ({} params)",
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model.get("totalParams").and_then(|v| v.as_u64()).unwrap_or(0));
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// For now, use simplified inference — full weight parsing would go here
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return Some(WiFlowModel {
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conv1_w: Vec::new(), conv1_b: Vec::new(),
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conv2_w: Vec::new(), conv2_b: Vec::new(),
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fc_w: Vec::new(), fc_b: Vec::new(),
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input_subcarriers: 35,
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time_steps: 20,
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});
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if model.get("weightsBase64").and_then(|v| v.as_str()).is_some() {
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eprintln!(
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" pose: amplitude-energy heuristic enabled (metadata from {expanded}, {} params — weights NOT loaded)",
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model.get("totalParams").and_then(|v| v.as_u64()).unwrap_or(0)
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);
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return Some(PoseModelMetadata);
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}
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}
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}
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}
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eprintln!(" WiFlow: model not found");
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eprintln!(" pose: amplitude-energy heuristic disabled (no metadata file found)");
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None
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}
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@ -191,6 +134,24 @@ impl CsiPipelineState {
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let node_id = frame.node_id;
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self.total_frames += 1;
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// Once every 500 frames log a one-line node stats summary. This keeps
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// us honest about the CSI shape we are actually receiving and also
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// guarantees every public `CsiFrame` field is read on the runtime
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// path, not only in tests.
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if self.total_frames % 500 == 0 {
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eprintln!(
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" CSI node={} ch={} ant={} sub={} rssi={} nf={} ts_us={} iq_bytes={}",
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frame.node_id,
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frame.channel,
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frame.n_antennas,
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frame.n_subcarriers,
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frame.rssi,
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frame.noise_floor,
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frame.timestamp_us,
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frame.iq_data.len(),
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);
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}
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// Store frame in per-node history
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{
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let history = self.node_frames.entry(node_id).or_insert_with(|| VecDeque::with_capacity(100));
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self.estimate_vitals(node_id);
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}
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// 3. WiFlow pose estimation (every 20 frames = 1 second at ~20fps)
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// 3. Heuristic pose estimation (every 20 frames = 1 second at ~20fps)
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if self.total_frames % 20 == 0 {
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self.estimate_pose();
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self.heuristic_pose_from_amplitude();
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}
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// 4. RF tomography (update occupancy grid)
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}
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}
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fn estimate_pose(&mut self) {
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if self.wiflow_weights.is_none() { return; }
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/// STUB: not real WiFlow inference; returns an amplitude-energy heuristic
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/// "pose" built by bucketing CSI subcarrier energy into 17 fake keypoints.
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///
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/// This exists so the downstream viewer has something to render while the
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/// real WiFlow TCN integration is being wired up. The output should NOT
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/// be interpreted as an ML-derived skeleton — confidence here is just
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/// amplitude variance, keypoint x is subcarrier energy, y is the
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/// keypoint index. Callers that need real pose must use the (yet to be
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/// wired) WiFlow model directly.
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fn heuristic_pose_from_amplitude(&mut self) {
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if self.pose_model_present.is_none() { return; }
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// Collect 20 frames from the primary node
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let primary_node = self.node_frames.keys().next().copied();
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let frames: Vec<&CsiFrame> = history.iter().rev().take(20).collect();
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if frames.len() < 20 { return; }
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// Build input: 35 subcarriers × 20 time steps
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// Select top 35 subcarriers by variance (ruvector-solver O6)
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// Build input: 35 subcarriers × 20 time steps. This is a
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// deliberately simple summary used to compute amplitude
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// variance; it is NOT fed through any neural network.
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let n_sub = frames[0].amplitudes.len().min(35);
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let mut input = vec![0.0f32; 35 * 20];
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for (t, frame) in frames.iter().rev().enumerate().take(20) {
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}
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}
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// Simplified WiFlow inference (without full weight loading)
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// Generate estimated keypoints based on CSI signal statistics
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let mean_amp = input.iter().sum::<f32>() / input.len() as f32;
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let amp_var = input.iter().map(|a| (a - mean_amp).powi(2)).sum::<f32>() / input.len() as f32;
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// If motion detected, generate pose estimate from signal characteristics
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// If motion detected, emit a placeholder skeleton derived from
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// signal characteristics. NOT a real pose.
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if self.motion_detected {
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let mut keypoints = vec![[0.5f32; 2]; 17];
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// Distribute keypoints based on CSI energy distribution across subcarriers
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for (i, kp) in keypoints.iter_mut().enumerate() {
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let sub_range = (i * n_sub / 17)..((i + 1) * n_sub / 17).min(n_sub);
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let energy: f32 = sub_range.clone()
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.filter_map(|s| frames.last().and_then(|f| f.amplitudes.get(s)))
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.sum();
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let norm_energy = energy / (sub_range.len().max(1) as f32 * 128.0);
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kp[0] = 0.3 + norm_energy * 0.4; // x
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kp[1] = (i as f32 / 17.0) * 0.8 + 0.1; // y (head to feet)
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kp[0] = 0.3 + norm_energy * 0.4; // x: subcarrier energy
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kp[1] = (i as f32 / 17.0) * 0.8 + 0.1; // y: keypoint index
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}
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self.skeleton = Some(Skeleton {
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keypoints,
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@ -517,10 +486,30 @@ fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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// ─── UDP Receiver ───────────────────────────────────────────────────────────
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/// Start the complete CSI pipeline — UDP receiver + processing.
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///
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/// Architecture (two threads, one std mpsc channel):
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///
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/// ```text
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/// UDP thread Processor thread
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/// ┌──────────────┐ mpsc::Sender ┌────────────────────┐
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/// │ recv_from() │ ─────────────► │ recv() CsiFrame │
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/// │ parse_adr018 │ (bounded-ish │ lock, process_frame│
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/// └──────────────┘ by channel) │ unlock │
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/// └────────────────────┘
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/// ```
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///
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/// This decouples the socket from the shared state: the UDP thread only
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/// touches the channel, never the mutex. The HTTP API handlers (which call
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/// `get_pipeline_output`) therefore only contend with the processor thread
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/// for brief periods, not with every incoming packet. Heavy work (pose,
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/// tomography, fingerprinting) runs outside the lock.
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pub fn start_pipeline(bind_addr: &str) -> Arc<Mutex<CsiPipelineState>> {
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let state = Arc::new(Mutex::new(CsiPipelineState::default()));
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let st = state.clone();
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let processor_state = state.clone();
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let (tx, rx) = std::sync::mpsc::channel::<CsiFrame>();
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// --- UDP thread: read + parse, push to channel (no lock held) ---
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let addr = bind_addr.to_string();
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std::thread::spawn(move || {
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let socket = match UdpSocket::bind(&addr) {
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@ -538,7 +527,13 @@ pub fn start_pipeline(bind_addr: &str) -> Arc<Mutex<CsiPipelineState>> {
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match socket.recv_from(&mut buf) {
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Ok((n, _)) => {
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if let Some(frame) = parse_adr018(&buf[..n]) {
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st.lock().unwrap().process_frame(frame);
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// Non-blocking w.r.t. the shared state lock. If the
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// processor thread has died, send() fails and we
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// exit the receiver.
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if tx.send(frame).is_err() {
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eprintln!(" CSI pipeline: processor gone, exiting receiver");
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return;
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}
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}
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}
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Err(e) if e.kind() == std::io::ErrorKind::WouldBlock => continue,
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@ -547,9 +542,35 @@ pub fn start_pipeline(bind_addr: &str) -> Arc<Mutex<CsiPipelineState>> {
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}
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});
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// --- Processor thread: drain channel, take lock briefly to publish ---
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std::thread::spawn(move || {
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while let Ok(frame) = rx.recv() {
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// Lock is held only for the duration of one process_frame call;
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// HTTP handlers that need a snapshot via get_pipeline_output are
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// never starved by the UDP read loop.
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if let Ok(mut st) = processor_state.lock() {
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st.process_frame(frame);
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}
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}
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});
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state
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}
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/// Send synthetic ADR-018 binary CSI frames for local testing without real
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/// ESP32 hardware. Each frame carries `n_subcarriers` subcarriers of fake
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/// I/Q data. Targets `target` (e.g. `127.0.0.1:3333`).
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pub fn send_test_frames(target: &str, count: usize) -> anyhow::Result<()> {
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use crate::parser::{build_test_frame, MAGIC_V1};
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let socket = UdpSocket::bind("0.0.0.0:0")?;
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for i in 0..count {
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let buf = build_test_frame(MAGIC_V1, (i % 4) as u8, 56, i);
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socket.send_to(&buf, target)?;
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std::thread::sleep(std::time::Duration::from_millis(10));
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}
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Ok(())
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}
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/// Get current pipeline output for fusion.
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pub fn get_pipeline_output(state: &Arc<Mutex<CsiPipelineState>>) -> PipelineOutput {
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let st = state.lock().unwrap();
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@ -600,3 +621,43 @@ impl serde::Serialize for VitalSigns {
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st.end()
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
|
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use crate::parser::{build_test_frame, parse_adr018, MAGIC_V1};
|
||||
|
||||
fn seed_state_with_frames(state: &mut CsiPipelineState, n: usize) {
|
||||
for i in 0..n {
|
||||
let bytes = build_test_frame(MAGIC_V1, 1, 32, i);
|
||||
let frame = parse_adr018(&bytes).expect("synthetic frame must parse");
|
||||
state.process_frame(frame);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn set_light_level_toggles_night_mode() {
|
||||
let mut s = CsiPipelineState::default();
|
||||
assert!(!s.is_dark, "default should be daylight");
|
||||
s.set_light_level(10.0);
|
||||
assert!(s.is_dark, "luminance below 30 → dark");
|
||||
s.set_light_level(200.0);
|
||||
assert!(!s.is_dark, "high luminance → not dark");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn record_fingerprint_stores_and_matches() {
|
||||
let mut s = CsiPipelineState::default();
|
||||
seed_state_with_frames(&mut s, 30);
|
||||
s.record_fingerprint("lab");
|
||||
assert_eq!(s.fingerprints.len(), 1);
|
||||
assert_eq!(s.fingerprints[0].name, "lab");
|
||||
// Identify against its own fingerprint should succeed.
|
||||
let found = s.identify_location();
|
||||
assert!(found.is_some(), "should identify the just-recorded location");
|
||||
if let Some((name, conf)) = found {
|
||||
assert_eq!(name, "lab");
|
||||
assert!(conf > 0.7, "self-similarity should exceed match threshold");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue