primitive clustering, cache nominatim

clustering/cluster.py

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+import requests
+import json
+from sklearn.cluster import DBSCAN
+from geopy.distance import great_circle
+from geopy.point import Point
+import numpy as np
+
+# Set up the Overpass API query
+# TODO: remove the bbox
+query = """
+[out:json];
+node["man_made"="surveillance"]["surveillance:type"="ALPR"];
+out body;
+"""
+
+# Request data from Overpass API
+print("Requesting data from Overpass API...")
+url = "http://overpass-api.de/api/interpreter"
+response = requests.get(url, params={'data': query}, headers={'User-Agent': 'DeFlock/1.0'})
+data = response.json()
+print("Data received. Parsing nodes...")
+
+# Parse nodes and extract lat/lon for clustering
+coordinates = []
+node_ids = []
+for element in data['elements']:
+    if element['type'] == 'node':
+        coordinates.append([element['lat'], element['lon']])
+        node_ids.append(element['id'])
+
+# Convert coordinates to NumPy array for DBSCAN
+coordinates = np.array(coordinates)
+
+# Define the clustering radius (10 miles in meters)
+radius_miles = 50
+radius_km = radius_miles * 1.60934  # 1 mile = 1.60934 km
+radius_in_radians = radius_km / 6371.0  # Earth's radius in km
+
+# Perform DBSCAN clustering
+db = DBSCAN(eps=radius_in_radians, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coordinates))
+labels = db.labels_
+
+# Prepare clusters and calculate centroids
+clusters = {}
+for label in set(labels):
+    cluster_points = coordinates[labels == label]
+    centroid = np.mean(cluster_points, axis=0)
+    first_node_id = node_ids[labels.tolist().index(label)]
+    
+    # Store in clusters dict with centroid and first node ID
+    clusters[label] = {
+        "lat": centroid[0],
+        "lon": centroid[1],
+        "id": first_node_id
+    }
+
+# Save clusters to JSON
+output = {"clusters": list(clusters.values())}
+with open("alpr_clusters.json", "w") as outfile:
+    json.dump(output, outfile, indent=2)
+
+print("Clustering complete. Results saved to alpr_clusters.json.")