refactor terraform modules, add clustering lambda (#4)

serverless/alpr_clusters/deploy.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+
+ECR_REPO_URL=912821578123.dkr.ecr.us-east-1.amazonaws.com/alpr_clusters-lambda
+
+set -e
+
+# check if AWS role is assumed
+if ! aws sts get-caller-identity &> /dev/null; then
+  echo "Error: AWS role is not assumed. Please assume the necessary role and try again."
+  exit 1
+fi
+
+cd src
+
+# build Docker image
+docker build -t alpr_clusters .
+
+# tag docker image with ECR repo
+docker tag alpr_clusters:latest $ECR_REPO_URL:latest
+
+# login to ECR
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $ECR_REPO_URL
+
+# push Docker image to ECR
+docker push $ECR_REPO_URL:latest
+
+# update lambda function
+export AWS_PAGER=""
+aws lambda update-function-code --function-name alpr_clusters --image-uri $ECR_REPO_URL:latest
+
+echo "Deployed!"

serverless/alpr_clusters/src/alpr_clusters.py

@@ -0,0 +1,78 @@
+import boto3
+import requests
+import json
+from sklearn.cluster import DBSCAN
+import numpy as np
+
+def get_clusters():
+    # Set up the Overpass API query
+    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
+        }
+
+    output = {"clusters": list(clusters.values())}
+    print("Clustering complete.")
+    return output
+
+
+def lambda_handler(event, context):
+  alpr_clusters = get_clusters()
+
+  s3 = boto3.client('s3')
+  bucket = 'deflock-clusters'
+  key = 'alpr_clusters.json'
+
+  s3.put_object(
+    Bucket=bucket,
+    Key=key,
+    Body=json.dumps(alpr_clusters),
+    ContentType='application/json'
+  )
+
+  return {
+    'statusCode': 200,
+    'body': 'Successfully fetched ALPR counts.',
+  }

serverless/alpr_clusters/src/requirements.txt

@@ -0,0 +1,4 @@
+boto3
+requests
+scikit-learn
+numpy