b7e0f07e6e
- 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>
114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Live WiFi sensing monitor — collects RSSI from Windows WiFi and classifies
|
|
presence/motion in real-time using the ADR-013 commodity sensing pipeline.
|
|
|
|
Usage:
|
|
python v1/tests/integration/live_sense_monitor.py
|
|
|
|
Walk around the room (especially between laptop and router) to trigger detection.
|
|
Press Ctrl+C to stop.
|
|
"""
|
|
import sys
|
|
import time
|
|
|
|
from v1.src.sensing.rssi_collector import WindowsWifiCollector
|
|
from v1.src.sensing.feature_extractor import RssiFeatureExtractor
|
|
from v1.src.sensing.classifier import PresenceClassifier
|
|
|
|
SAMPLE_RATE = 2.0 # Hz (netsh is slow, 2 Hz is practical max)
|
|
WINDOW_SEC = 15.0 # Analysis window
|
|
REPORT_INTERVAL = 3.0 # Print classification every N seconds
|
|
|
|
|
|
def main():
|
|
collector = WindowsWifiCollector(interface="Wi-Fi", sample_rate_hz=SAMPLE_RATE)
|
|
extractor = RssiFeatureExtractor(window_seconds=WINDOW_SEC)
|
|
classifier = PresenceClassifier(
|
|
presence_variance_threshold=0.3, # Lower threshold for netsh quantization
|
|
motion_energy_threshold=0.05,
|
|
)
|
|
|
|
print("=" * 65)
|
|
print(" WiFi-DensePose Live Sensing Monitor (ADR-013)")
|
|
print(" Pipeline: WindowsWifiCollector -> Extractor -> Classifier")
|
|
print("=" * 65)
|
|
print(f" Sample rate: {SAMPLE_RATE} Hz")
|
|
print(f" Window: {WINDOW_SEC}s")
|
|
print(f" Report every: {REPORT_INTERVAL}s")
|
|
print()
|
|
print(" Collecting baseline... walk around after 15s to test detection.")
|
|
print(" Press Ctrl+C to stop.")
|
|
print("-" * 65)
|
|
|
|
collector.start()
|
|
|
|
try:
|
|
last_report = 0.0
|
|
while True:
|
|
time.sleep(0.5)
|
|
now = time.time()
|
|
if now - last_report < REPORT_INTERVAL:
|
|
continue
|
|
last_report = now
|
|
|
|
samples = collector.get_samples()
|
|
n = len(samples)
|
|
if n < 4:
|
|
print(f" [{time.strftime('%H:%M:%S')}] Buffering... ({n} samples)")
|
|
continue
|
|
|
|
rssi_vals = [s.rssi_dbm for s in samples]
|
|
features = extractor.extract(samples)
|
|
result = classifier.classify(features)
|
|
|
|
# Motion bar visualization
|
|
bar_len = min(40, max(0, int(features.variance * 20)))
|
|
bar = "#" * bar_len + "." * (40 - bar_len)
|
|
|
|
level_icon = {
|
|
"absent": " ",
|
|
"present_still": "🧍",
|
|
"active": "🏃",
|
|
}.get(result.motion_level.value, "??")
|
|
|
|
print(
|
|
f" [{time.strftime('%H:%M:%S')}] "
|
|
f"RSSI: {features.mean:6.1f} dBm | "
|
|
f"var: {features.variance:6.3f} | "
|
|
f"motion_e: {features.motion_band_power:7.4f} | "
|
|
f"breath_e: {features.breathing_band_power:7.4f} | "
|
|
f"{result.motion_level.value:14s} {level_icon} "
|
|
f"({result.confidence:.0%})"
|
|
)
|
|
print(f" [{bar}] n={n} rssi=[{min(rssi_vals):.0f}..{max(rssi_vals):.0f}]")
|
|
|
|
except KeyboardInterrupt:
|
|
print()
|
|
print("-" * 65)
|
|
print(" Stopped. Final sample count:", len(collector.get_samples()))
|
|
|
|
# Print summary
|
|
samples = collector.get_samples()
|
|
if len(samples) >= 4:
|
|
features = extractor.extract(samples)
|
|
result = classifier.classify(features)
|
|
rssi_vals = [s.rssi_dbm for s in samples]
|
|
print()
|
|
print(" SUMMARY")
|
|
print(f" Duration: {samples[-1].timestamp - samples[0].timestamp:.1f}s")
|
|
print(f" Total samples: {len(samples)}")
|
|
print(f" RSSI range: {min(rssi_vals):.1f} to {max(rssi_vals):.1f} dBm")
|
|
print(f" RSSI variance: {features.variance:.4f}")
|
|
print(f" Motion energy: {features.motion_band_power:.4f}")
|
|
print(f" Breath energy: {features.breathing_band_power:.4f}")
|
|
print(f" Change points: {features.n_change_points}")
|
|
print(f" Final verdict: {result.motion_level.value} ({result.confidence:.0%})")
|
|
print("=" * 65)
|
|
finally:
|
|
collector.stop()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|