feat: Sensing-only UI mode with Gaussian splat visualization and Rust migration ADR
- Add Python WebSocket sensing server (ws_server.py) with ESP32 UDP CSI and Windows RSSI auto-detect collectors on port 8765 - Add Three.js Gaussian splat renderer with custom GLSL shaders for real-time WiFi signal field visualization (blue→green→red gradient) - Add SensingTab component with RSSI sparkline, feature meters, and motion classification badge - Add sensing.service.js WebSocket client with reconnect and simulation fallback - Implement sensing-only mode: suppress all DensePose API calls when FastAPI backend (port 8000) is not running, clean console output - ADR-019: Document sensing-only UI architecture and data flow - ADR-020: Migrate AI/model inference to Rust with RuVector ONNX Runtime, replacing ~2.7GB Python stack with ~50MB static binary - Add ruvnet/ruvector as upstream remote for RuVector crate ecosystem Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
113
v1/tests/integration/live_sense_monitor.py
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v1/tests/integration/live_sense_monitor.py
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#!/usr/bin/env python3
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"""
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Live WiFi sensing monitor — collects RSSI from Windows WiFi and classifies
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presence/motion in real-time using the ADR-013 commodity sensing pipeline.
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Usage:
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python v1/tests/integration/live_sense_monitor.py
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Walk around the room (especially between laptop and router) to trigger detection.
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Press Ctrl+C to stop.
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"""
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import sys
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import time
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from v1.src.sensing.rssi_collector import WindowsWifiCollector
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from v1.src.sensing.feature_extractor import RssiFeatureExtractor
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from v1.src.sensing.classifier import PresenceClassifier
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SAMPLE_RATE = 2.0 # Hz (netsh is slow, 2 Hz is practical max)
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WINDOW_SEC = 15.0 # Analysis window
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REPORT_INTERVAL = 3.0 # Print classification every N seconds
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def main():
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=SAMPLE_RATE)
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extractor = RssiFeatureExtractor(window_seconds=WINDOW_SEC)
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classifier = PresenceClassifier(
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presence_variance_threshold=0.3, # Lower threshold for netsh quantization
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motion_energy_threshold=0.05,
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)
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print("=" * 65)
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print(" WiFi-DensePose Live Sensing Monitor (ADR-013)")
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print(" Pipeline: WindowsWifiCollector -> Extractor -> Classifier")
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print("=" * 65)
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print(f" Sample rate: {SAMPLE_RATE} Hz")
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print(f" Window: {WINDOW_SEC}s")
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print(f" Report every: {REPORT_INTERVAL}s")
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print()
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print(" Collecting baseline... walk around after 15s to test detection.")
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print(" Press Ctrl+C to stop.")
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print("-" * 65)
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collector.start()
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try:
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last_report = 0.0
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while True:
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time.sleep(0.5)
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now = time.time()
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if now - last_report < REPORT_INTERVAL:
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continue
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last_report = now
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samples = collector.get_samples()
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n = len(samples)
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if n < 4:
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print(f" [{time.strftime('%H:%M:%S')}] Buffering... ({n} samples)")
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continue
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rssi_vals = [s.rssi_dbm for s in samples]
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features = extractor.extract(samples)
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result = classifier.classify(features)
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# Motion bar visualization
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bar_len = min(40, max(0, int(features.variance * 20)))
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bar = "#" * bar_len + "." * (40 - bar_len)
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level_icon = {
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"absent": " ",
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"present_still": "🧍",
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"active": "🏃",
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}.get(result.motion_level.value, "??")
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print(
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f" [{time.strftime('%H:%M:%S')}] "
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f"RSSI: {features.mean:6.1f} dBm | "
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f"var: {features.variance:6.3f} | "
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f"motion_e: {features.motion_band_power:7.4f} | "
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f"breath_e: {features.breathing_band_power:7.4f} | "
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f"{result.motion_level.value:14s} {level_icon} "
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f"({result.confidence:.0%})"
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)
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print(f" [{bar}] n={n} rssi=[{min(rssi_vals):.0f}..{max(rssi_vals):.0f}]")
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except KeyboardInterrupt:
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print()
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print("-" * 65)
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print(" Stopped. Final sample count:", len(collector.get_samples()))
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# Print summary
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samples = collector.get_samples()
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if len(samples) >= 4:
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features = extractor.extract(samples)
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result = classifier.classify(features)
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rssi_vals = [s.rssi_dbm for s in samples]
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print()
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print(" SUMMARY")
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print(f" Duration: {samples[-1].timestamp - samples[0].timestamp:.1f}s")
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print(f" Total samples: {len(samples)}")
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print(f" RSSI range: {min(rssi_vals):.1f} to {max(rssi_vals):.1f} dBm")
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print(f" RSSI variance: {features.variance:.4f}")
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print(f" Motion energy: {features.motion_band_power:.4f}")
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print(f" Breath energy: {features.breathing_band_power:.4f}")
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print(f" Change points: {features.n_change_points}")
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print(f" Final verdict: {result.motion_level.value} ({result.confidence:.0%})")
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print("=" * 65)
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finally:
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collector.stop()
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if __name__ == "__main__":
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main()
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156
v1/tests/integration/test_windows_live_sensing.py
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v1/tests/integration/test_windows_live_sensing.py
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#!/usr/bin/env python3
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"""
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Live integration test: WindowsWifiCollector → FeatureExtractor → Classifier.
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Runs the full ADR-013 commodity sensing pipeline against a real Windows WiFi
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interface using ``netsh wlan show interfaces`` as the RSSI source.
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Usage:
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python -m pytest v1/tests/integration/test_windows_live_sensing.py -v -o "addopts=" -s
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Requirements:
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- Windows with connected WiFi
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- scipy, numpy installed
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"""
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import platform
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import subprocess
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import sys
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import time
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import pytest
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# Skip the entire module on non-Windows or when WiFi is disconnected
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_IS_WINDOWS = platform.system() == "Windows"
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def _wifi_connected() -> bool:
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if not _IS_WINDOWS:
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return False
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try:
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r = subprocess.run(
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["netsh", "wlan", "show", "interfaces"],
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capture_output=True, text=True, timeout=5,
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)
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return "connected" in r.stdout.lower() and "disconnected" not in r.stdout.lower().split("state")[1][:30]
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except Exception:
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return False
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pytestmark = pytest.mark.skipif(
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not (_IS_WINDOWS and _wifi_connected()),
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reason="Requires Windows with connected WiFi",
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)
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from v1.src.sensing.rssi_collector import WindowsWifiCollector, WifiSample
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from v1.src.sensing.feature_extractor import RssiFeatureExtractor, RssiFeatures
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from v1.src.sensing.classifier import PresenceClassifier, MotionLevel, SensingResult
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from v1.src.sensing.backend import CommodityBackend, Capability
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class TestWindowsWifiCollectorLive:
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"""Live tests against real Windows WiFi hardware."""
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def test_collect_once_returns_valid_sample(self):
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=1.0)
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sample = collector.collect_once()
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assert isinstance(sample, WifiSample)
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assert -100 <= sample.rssi_dbm <= 0, f"RSSI {sample.rssi_dbm} out of range"
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assert sample.noise_dbm <= 0
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assert 0.0 <= sample.link_quality <= 1.0
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assert sample.interface == "Wi-Fi"
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print(f"\n Single sample: RSSI={sample.rssi_dbm} dBm, "
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f"quality={sample.link_quality:.0%}, ts={sample.timestamp:.3f}")
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def test_collect_multiple_samples_over_time(self):
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0)
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collector.start()
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time.sleep(6) # Collect ~12 samples at 2 Hz
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collector.stop()
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samples = collector.get_samples()
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assert len(samples) >= 5, f"Expected >= 5 samples, got {len(samples)}"
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rssi_values = [s.rssi_dbm for s in samples]
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print(f"\n Collected {len(samples)} samples over ~6s")
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print(f" RSSI range: {min(rssi_values):.1f} to {max(rssi_values):.1f} dBm")
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print(f" RSSI values: {[f'{v:.1f}' for v in rssi_values]}")
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# All RSSI values should be in valid range
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for s in samples:
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assert -100 <= s.rssi_dbm <= 0
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def test_rssi_varies_between_samples(self):
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"""RSSI should show at least slight natural variation."""
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0)
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collector.start()
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time.sleep(8) # Collect ~16 samples
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collector.stop()
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samples = collector.get_samples()
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rssi_values = [s.rssi_dbm for s in samples]
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# With real hardware, we expect some variation (even if small)
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# But netsh may quantize RSSI so identical values are possible
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unique_count = len(set(rssi_values))
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print(f"\n {len(rssi_values)} samples, {unique_count} unique RSSI values")
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print(f" Values: {rssi_values}")
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class TestFullPipelineLive:
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"""End-to-end: WindowsWifiCollector → Extractor → Classifier."""
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def test_full_pipeline_produces_sensing_result(self):
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0)
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extractor = RssiFeatureExtractor(window_seconds=10.0)
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classifier = PresenceClassifier()
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collector.start()
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time.sleep(10) # Collect ~20 samples
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collector.stop()
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samples = collector.get_samples()
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assert len(samples) >= 5, f"Need >= 5 samples, got {len(samples)}"
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features = extractor.extract(samples)
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assert isinstance(features, RssiFeatures)
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assert features.n_samples >= 5
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print(f"\n Features from {features.n_samples} samples:")
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print(f" mean={features.mean:.2f} dBm")
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print(f" variance={features.variance:.4f}")
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print(f" std={features.std:.4f}")
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print(f" range={features.range:.2f}")
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print(f" dominant_freq={features.dominant_freq_hz:.3f} Hz")
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print(f" breathing_band={features.breathing_band_power:.4f}")
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print(f" motion_band={features.motion_band_power:.4f}")
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print(f" spectral_power={features.total_spectral_power:.4f}")
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print(f" change_points={features.n_change_points}")
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result = classifier.classify(features)
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assert isinstance(result, SensingResult)
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assert isinstance(result.motion_level, MotionLevel)
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assert 0.0 <= result.confidence <= 1.0
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print(f"\n Classification:")
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print(f" motion_level={result.motion_level.value}")
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print(f" presence={result.presence_detected}")
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print(f" confidence={result.confidence:.2%}")
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print(f" details: {result.details}")
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def test_commodity_backend_with_windows_collector(self):
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collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=2.0)
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backend = CommodityBackend(collector=collector)
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assert backend.get_capabilities() == {Capability.PRESENCE, Capability.MOTION}
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backend.start()
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time.sleep(10)
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result = backend.get_result()
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backend.stop()
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assert isinstance(result, SensingResult)
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print(f"\n CommodityBackend result:")
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print(f" motion={result.motion_level.value}")
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print(f" presence={result.presence_detected}")
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print(f" confidence={result.confidence:.2%}")
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print(f" rssi_variance={result.rssi_variance:.4f}")
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print(f" motion_energy={result.motion_band_energy:.4f}")
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print(f" breathing_energy={result.breathing_band_energy:.4f}")
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