remove server side clustering, increase frequency, cleanup tf

serverless/alpr_clusters/src/alpr_clusters.py

@@ -37,6 +37,7 @@ WHITELISTED_TAGS = [
     "manufacturer",
     "direction",
     "brand",
+    "camera:direction",
 ]
 
 def get_all_nodes():
@@ -54,50 +55,6 @@ def get_all_nodes():
     response.raise_for_status()
     return response.json()["elements"]
 
-
-def get_clusters(nodes: list[Any]):
-    # Request data from Overpass API
-    print("Requesting data from Overpass API.")
-
-    print("Data received. Parsing nodes...")
-
-    # Parse nodes and extract lat/lon for clustering
-    coordinates = []
-    node_ids = []
-    for element in nodes:
-        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 (50 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}
-
-    output = {"clusters": list(clusters.values())}
-    print("Clustering complete.")
-    return output
-
-
 def segment_regions(nodes: Any, tile_size_degrees: int) -> dict[Any]:
     print("Segmenting regions...")
     tile_dict = defaultdict(list)
@@ -118,22 +75,12 @@ def segment_regions(nodes: Any, tile_size_degrees: int) -> dict[Any]:
 
 def lambda_handler(event, context):
     nodes = get_all_nodes()
-    alpr_clusters = get_clusters(nodes)
     regions_dict = segment_regions(nodes=nodes, tile_size_degrees=TILE_SIZE_DEGREES)
 
     print("Uploading data to S3...")
 
     s3 = boto3.client("s3")
-    bucket = "deflock-clusters"
     bucket_new = "cdn.deflock.me"
-    key = "alpr_clusters.json"
-
-    s3.put_object(
-        Bucket=bucket,
-        Key=key,
-        Body=json.dumps(alpr_clusters),
-        ContentType="application/json",
-    )
 
     # TODO: handle outdated index files when their referenced files are deleted
     epoch = int(time.time())
@@ -174,7 +121,7 @@ def lambda_handler(event, context):
 
     return {
         "statusCode": 200,
-        "body": "Successfully clustered.",
+        "body": "Successfully cached OSM nodes",
     }