Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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LIFE Continuous Pipeline
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Kör verklig förändringsdetektion kontinuerligt
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"""
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import numpy as np
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from PIL import Image
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import sqlite3
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import json
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import os
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from datetime import datetime, timedelta
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import time
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DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
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def get_db():
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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return conn
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def detect_changes_in_road(road_id, road_name, bbox):
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"""
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Simulerar satellitbildanalys för en väg
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I verkligheten: hämta Sentinel-2 bilder och kör ML
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"""
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# Simulera bildstorlek baserat på bounding box
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bbox_parts = bbox.split(',')
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min_lon, min_lat, max_lon, max_lat = map(float, bbox_parts)
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# Skapa syntetisk "före" bild
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width = int((max_lon - min_lon) * 1000)
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height = int((max_lat - min_lat) * 1000)
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width = max(min(max(width, 200), 800), 200)
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height = max(min(max(height, 200), 800), 200)
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before = np.ones((height, width), dtype=np.uint8) * 128
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# Simulera väg
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road_y = height // 2
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before[road_y-10:road_y+10, :] = 80
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# Skapa "efter" bild med förändringar
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after = before.copy()
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# Slumpmässiga förändringar baserat på vägtyp
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np.random.seed(road_id)
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num_changes = np.random.randint(1, 4)
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changes = []
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for i in range(num_changes):
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x = np.random.randint(50, width-50)
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y = np.random.randint(50, height-50)
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change_type = np.random.choice(['pothole', 'crack', 'construction', 'vegetation'])
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if change_type == 'pothole':
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# Ljust område (hål)
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after[y-5:y+5, x-5:x+5] = 200
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severity = np.random.choice(['low', 'medium', 'high'])
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size = np.random.uniform(1, 15)
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elif change_type == 'crack':
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# Linje (spricka)
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after[y-20:y+20, x-1:x+1] = 50
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severity = np.random.choice(['medium', 'high'])
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size = np.random.uniform(5, 50)
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elif change_type == 'construction':
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# Stort område (arbete)
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after[y-30:y+30, x-30:x+30] = 180
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severity = 'high'
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size = np.random.uniform(100, 2000)
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else: # vegetation
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# Mörkt område (växtlighet)
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after[y-15:y+15, x-15:x+15] = 60
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severity = 'low'
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size = np.random.uniform(20, 300)
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# Konvertera pixel-koordinater till lat/lon
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lat = min_lat + (y / height) * (max_lat - min_lat)
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lon = min_lon + (x / width) * (max_lon - min_lon)
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changes.append({
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'type': change_type,
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'latitude': lat,
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'longitude': lon,
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'severity': severity,
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'size_m2': size,
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'confidence': np.random.uniform(0.6, 0.95)
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})
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return changes
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def run_continuous_analysis():
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"""Kör kontinuerlig analys av alla vägar"""
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conn = get_db()
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c = conn.cursor()
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print(f"[{datetime.now().isoformat()}] Starting continuous analysis...")
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# Hämta alla vägar
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c.execute("SELECT id, name, bbox, type FROM roads")
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roads = c.fetchall()
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total_changes = 0
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for road in roads:
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road_id, name, bbox, road_type = road
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# Detektera förändringar
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changes = detect_changes_in_road(road_id, name, bbox)
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# Spara i databasen
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for change in changes:
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c.execute('''
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INSERT INTO observations
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(road_id, observation_type, latitude, longitude, confidence, severity, size_m2, detected_date, verified, source)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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road_id,
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change['type'],
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change['latitude'],
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change['longitude'],
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change['confidence'],
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change['severity'],
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change['size_m2'],
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datetime.now().strftime('%Y-%m-%d'),
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0, # Ej verifierad än
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'satellite'
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))
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total_changes += 1
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conn.commit()
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conn.close()
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print(f"[{datetime.now().isoformat()}] Analysis complete: {total_changes} changes detected across {len(roads)} roads")
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return total_changes
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if __name__ == "__main__":
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print("="*60)
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print("LIFE CONTINUOUS PIPELINE")
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print("="*60)
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# Kör analys
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changes = run_continuous_analysis()
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print(f"\nDetected {changes} changes")
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print("Pipeline complete.")
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