Extract ruview-swarm → ruvnet/ruv-drone and world crates → ruvnet/worldgraph (submodules)

.github/workflows/ruview-swarm-ci.yml

@@ -36,7 +36,7 @@ jobs:
         features:
           - { label: 'default',          flags: '--no-default-features' }
           - { label: 'train',            flags: '--features train' }
-          - { label: 'ruflo+itar',       flags: '--features ruflo,itar-unrestricted' }
+          - { label: 'ruflo',            flags: '--features ruflo' }
           - { label: 'full+train',       flags: '--features full,train' }
     steps:
       - uses: actions/checkout@v4

.gitmodules

@@ -21,3 +21,11 @@
 [submodule "vendor/rufield"]
 	path = vendor/rufield
 	url = https://github.com/ruvnet/rufield
+[submodule "v2/crates/ruview-swarm"]
+	path = v2/crates/ruview-swarm
+	url = https://github.com/ruvnet/ruv-drone.git
+	branch = main
+[submodule "v2/crates/worldgraph"]
+	path = v2/crates/worldgraph
+	url = https://github.com/ruvnet/worldgraph.git
+	branch = main

examples/through-wall/README.md

@@ -0,0 +1,135 @@
+# WiFlow Browser Trainer (`wiflow_browser.html`)
+
+A **single self-contained HTML page** that does the entire camera-supervised
+WiFi-pose loop **in your browser, in your laptop camera's coordinate frame**, as
+a **4-stage gated flow** with a progress stepper (each stage unlocks the next):
+
+0. **CALIBRATE** *(ADR-151 empty-room baseline)* — you step OUT of the space; the
+   page captures ~10 s of the quiescent CSI and computes a per-feature running
+   **mean + std (Welford)** over the 410-d vector. Every CSI vector afterwards is
+   expressed as **deviation from baseline**
+   (`x_norm = (x − base_mean) / (base_std + ε)`), so a body's perturbation stands
+   out from the static channel. Persisted to IndexedDB. *Can't capture without it.*
+1. **CAPTURE** — MediaPipe Pose runs on your laptop camera → 17 COCO keypoints
+   (the *label*), paired with the **baseline-normalized** 410-d ESP32 CSI vector
+   (the *input*). A **guided, balanced routine** cycles big on-screen prompts
+   (stand / turn / walk / arms / crouch / sit / reach) with a countdown, and a
+   **per-pose coverage meter** so you build a balanced dataset, not 2 000 frames
+   of standing.
+2. **TRAIN** — a TensorFlow.js MLP learns `CSI → pose` in-browser. Honest
+   held-out PCK@0.10 / PCK@0.05 / MPJPE, plus a **mean-pose baseline** the model
+   must beat (the project's whole ethos — no baseline-beating signal, it says so).
+   *Can't train with <200 samples.*
+3. **INFER** — the trained model drives a skeleton **from WiFi CSI only**
+   (baseline-normalized → standardized → model), drawn over the **same** camera
+   frame it trained in — so the inferred skeleton **aligns** with the camera
+   image. That alignment is the entire point of doing this in-browser instead of
+   with a separate Python camera. *Can't infer without a model.*
+
+## Why in-browser
+
+The Python pipeline (`wiflow_capture.py` → `wiflow_train.py` → `wiflow_infer.py`)
+proved the signal is real (held-out PCK@0.10 ≈ 59.5% vs a 50% mean-pose baseline
+= +9.4 pp). But it trained in a *different* camera's frame, so the inferred
+skeleton never lined up with the laptop camera. Doing capture + train + infer all
+in the browser with the **same** camera makes the training frame and the
+inference frame identical → the skeleton aligns.
+
+## Compute backends (WebGPU / WASM / WebGL)
+
+Training and inference run on TensorFlow.js. The page selects the backend at
+startup, preferring the fastest available:
+
+- **WebGPU** (Chrome / Edge, secure context — `localhost` qualifies) — GPU compute.
+- **WASM-SIMD** fallback (`tfjs-backend-wasm`, SIMD enabled, `.wasm` from the CDN).
+- **WebGL** last-resort fallback (ships inside tfjs core).
+
+The **active backend is shown as a badge in the header** (`compute: WebGPU` /
+`WASM-SIMD` / `WebGL`) so it's honest about what's actually running. The model
+code is backend-agnostic — tf.js abstracts the device.
+
+## Honesty (baked in)
+
+- The **CAPTURE** skeleton (blue) is the camera = ground truth, labeled as such.
+- The **INFER** skeleton (green) is **CSI-only**, labeled, and **coarse** — the
+  real measured held-out PCK is shown, not a marketing number.
+- The **mean-pose baseline** is always computed and shown in TRAIN; the verdict
+  states plainly whether the model **beats** it (real signal) or **does not**
+  (no usable signal). This guards against the project's retracted 92.9% that
+  failed exactly this check.
+- Status banner is strict and mutually exclusive:
+  **LIVE** (real `source: "esp32"`) / **SIMULATED — not real** (any other source)
+  / **NO-CSI-SERVER**. The page never invents frames.
+
+## How to run
+
+### 1. Start the real sensing-server (provides the CSI WebSocket on :8765)
+
+```bash
+cd v2
+cargo build -p wifi-densepose-sensing-server
+./target/debug/sensing-server.exe --ws-port 8765 --udp-port 5005
+```
+
+A real ESP32-S3 must be provisioned and streaming for `source` to read `esp32`
+(see `CLAUDE.local.md` for the firmware build/provision steps). The page expects
+the verified live endpoint **`ws://localhost:8765/ws/sensing`** with
+`source:"esp32"`, nodes `[9, 13]`, `features.*`, `node_features[].features.*`,
+and `signal_field.values` (400 floats).
+
+### 2. Serve this page over localhost (camera + WebGPU need a localhost/secure origin)
+
+Any static localhost server works. For example:
+
+```bash
+python -m http.server 8099
+# then open: http://localhost:8099/examples/through-wall/wiflow_browser.html
+```
+
+(8099 is just the static file server — 8765 is a separate process, the CSI
+WebSocket.) Allow camera access when the browser prompts.
+
+Point at a CSI server on another host with `?ws=`:
+
+```
+http://localhost:8099/examples/through-wall/wiflow_browser.html?ws=ws://192.168.1.20:8765/ws/sensing
+```
+
+### 3. Use it
+
+1. **CAPTURE** tab → *enable laptop camera* → *start recording*. Follow the guided
+   routine (stand / turn / walk / arms / crouch / sit). A pair is stored only when
+   a confident pose AND a fresh live `esp32` CSI frame coexist. Aim for a few
+   thousand samples. Samples persist in IndexedDB across refreshes.
+2. **TRAIN** tab → *train model*. Watch the live loss curve, held-out PCK, and the
+   baseline verdict. The model saves to IndexedDB.
+3. **INFER** tab → the green skeleton is now driven by WiFi CSI only, aligned over
+   your camera. Toggle *hide camera* to see the CSI-only skeleton on black.
+
+## The 410-d CSI vector (matches the Python pipeline exactly)
+
+```
+[ mean_rssi, variance, motion_band_power, breathing_band_power ]   # 4  (features.*)
++ for node 9 then node 13: [ mean_rssi, variance, motion_band_power ]  # 6 (node_features[].features.*)
++ signal_field.values, padded / truncated to 400                  # 400
+= 410-d
+```
+
+Verified against a real live frame: the in-browser `csiVector()` produces the
+identical 410 vector as `wiflow_capture.py`'s `csi_vector()` (node 9 first, then
+node 13; field zero-padded).
+
+## Libraries (CDN only, no bundler)
+
+| Library | CDN |
+|---|---|
+| TensorFlow.js core | `@tensorflow/tfjs@4.22.0/dist/tf.min.js` |
+| TF.js WebGPU backend | `@tensorflow/tfjs-backend-webgpu@4.22.0/dist/tf-backend-webgpu.min.js` |
+| TF.js WASM backend | `@tensorflow/tfjs-backend-wasm@4.22.0/dist/tf-backend-wasm.min.js` |
+| MediaPipe Pose 0.5 (legacy solutions) | `@mediapipe/pose@0.5/pose.js` |
+
+## Scope / honesty caveats
+
+Same person, same room, same session. **Not** validated cross-day, cross-room, or
+through-wall. The inferred pose is coarse (PCK@0.05 is typically weak). If the
+model does not beat the mean-pose baseline, the page says so — that is a feature.