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SEEING HEAT. FINDING THE INVISIBLE.
FLIR/SEEK integration • Anomaly detection AI • Presence identification • Environmental mapping
Heat reveals truth. Every object, every being, every electronic device leaves a thermal signature. thermal-eye integrates with your thermal imaging equipment to detect anomalies that shouldn't exist—heat signatures without sources, cold spots that defy physics, and patterns that suggest hidden presence.
"In the infrared spectrum, ghosts have nowhere to hide."
| Device | Connection | Resolution | Features |
|---|---|---|---|
| FLIR One Pro | USB/Lightning | 160×120 | MSX enhancement, 70mK sensitivity |
| FLIR E8-XT | USB | 320×240 | Extended temp range, WiFi |
| FLIR Lepton 3.5 | SPI/I2C | 160×120 | Embedded integration, radiometric |
| Seek Thermal CompactPRO | USB | 320×240 | Wide FOV, adjustable focus |
| Seek Reveal PRO | USB | 320×240 | Built-in LED, long range |
| InfiRay P2 Pro | USB-C | 256×192 | Macro lens, high sensitivity |
| Custom sensors | GPIO/SPI | Variable | DIY integration support |
from thermal_eye import ThermalScanner, AnomalyDetector
# Initialize with your device
scanner = ThermalScanner(device="flir_one_pro")
detector = AnomalyDetector(sensitivity="high")
# Real-time scanning with ML anomaly detection
async for frame in scanner.stream():
analysis = detector.analyze(frame)
for anomaly in analysis.anomalies:
print(f"▸ {anomaly.type} at {anomaly.location}")
print(f" Temperature: {anomaly.temp}°C")
print(f" Strangeness: {anomaly.strangeness}%")
print(f" Explanation: {anomaly.explanation}")Identify heat signatures that indicate hidden presence:
- Human-shaped heat patterns in "empty" rooms
- Residual heat from recent occupation
- Hidden electronics (surveillance devices)
- Animal presence detection
from thermal_eye import PresenceDetector
presence = PresenceDetector()
async for detection in presence.monitor():
if detection.human_probable:
print(f"⚠️ Human presence detected")
print(f" Location: {detection.grid_location}")
print(f" Confidence: {detection.confidence}%")
print(f" Motion: {detection.motion_vector}")Build 3D thermal maps of spaces over time:
from thermal_eye import ThermalMapper
mapper = ThermalMapper()
# Capture thermal panorama
await mapper.capture_panorama()
# Generate 3D thermal model
model = mapper.build_model()
model.export("room_thermal.obj")
# Find anomalous cold/hot spots
anomalies = model.find_anomalies()
for spot in anomalies:
print(f"Anomaly: {spot.type} at {spot.coords}")Track thermal changes over time to detect patterns:
from thermal_eye import TemporalAnalyzer
analyzer = TemporalAnalyzer()
# Record thermal timelapse
await analyzer.record(duration_hours=24)
# Analyze patterns
patterns = analyzer.find_patterns()
for pattern in patterns:
print(f"Pattern: {pattern.description}")
print(f"Recurrence: {pattern.period}")
print(f"Correlation: {pattern.correlation}")◈ THERMAL-EYE v2.0 › LIVE SCAN
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
DEVICE: FLIR One Pro
RESOLUTION: 160×120 @ 9 FPS
SENSITIVITY: 70mK
ANOMALIES DETECTED
▸ COLD SPOT [A1]
Grid: 47,82 • Size: 23px² • Temp: 12.4°C
Ambient: 21.3°C • Delta: -8.9°C
Classification: UNEXPLAINED
Physics violation: Localized cold without source
Strangeness ████████░░ 84%
▸ HEAT RESIDUE [A2]
Grid: 112,45 • Size: 187px² • Temp: 28.7°C
Pattern: Humanoid silhouette
Decay rate: Slower than expected
Last occupied: ~47 minutes ago
Strangeness ███████░░░ 71%
▸ PHANTOM HEAT [A3]
Grid: 89,67 • Size: 12px² • Temp: 34.2°C
No visible source • Stationary
Duration: 12 minutes continuous
Strangeness █████████░ 91%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SCAN ACTIVE • ANOMALIES: 3 • HIGH STRANGENESS: 2
Correlate thermal anomalies with EMF readings:
from thermal_eye import ThermalScanner
from spectral import EMFScanner
from baudrillard.correlation import MultiModalAnalyzer
thermal = ThermalScanner()
emf = EMFScanner()
analyzer = MultiModalAnalyzer()
# Synchronous multi-modal scan
async for t_frame, e_frame in zip(thermal.stream(), emf.stream()):
correlations = analyzer.correlate(t_frame, e_frame)
if correlations.significant:
print(f"Correlated anomaly detected!")
print(f"Thermal: {correlations.thermal}")
print(f"EMF: {correlations.emf}")
print(f"Correlation: {correlations.coefficient}")Automatically log thermal anomalies:
from thermal_eye import ThermalScanner
from cool_memories import ImmutableLog
log = ImmutableLog()
scanner = ThermalScanner()
async for frame in scanner.stream():
for anomaly in frame.anomalies:
if anomaly.strangeness > 70:
await log.record(
event_type="thermal_anomaly",
data=anomaly.to_dict(),
attachment=frame.raw_data,
timestamp=frame.timestamp
)Native Tauri app with:
- Real-time thermal video feed
- Anomaly highlighting and tracking
- Historical analysis dashboard
- Multi-camera support
- Recording and export
◈ THERMAL-EYE DESKTOP
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
LIVE FEED ANALYSIS
╔═══════════════╗ Anomalies: 3
║ ▓▓▓░░░▒▒▓▓▓ ║ Strangeness: 74%
║ ▓▒▒░░░░░▒▓▓ ║ Recording: 00:14:37
║ ▓▒▒▒░A1░░▒▓ ║
║ ▓▓▒▒░░░▒▒▓▓ ║ TEMPERATURE
║ ▓▓▓▓▒▒▒▓▓▓▓ ║ Min: 14.2°C
╚═══════════════╝ Max: 31.7°C
Ambient: 21.4°C
▸ Recording ▸ Screenshot ▸ Export
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
FLIR One Pro • Connected • 9 FPS
Thermal scanning on the go with phone-compatible devices:
- FLIR One (iOS/Android)
- Seek Thermal (iOS/Android)
- InfiRay (Android)
Features:
- Live anomaly detection
- GPS-tagged recordings
- Cloud sync with desktop
- Share findings to community
# Core library
pip install baudrillard-thermal-eye
# Desktop app
cd apps/thermal-eye-desktop
npm install && npm run tauri build
# Device drivers
sudo apt install libusb-1.0-0-dev
pip install pyflir pyseekit"Heat is the signature of existence. The absence of heat where heat should be is the signature of something else."
BAUDRILLARD SUITE