ready for testing

backend/src/utils/cluster_manager.py

@@ -10,6 +10,8 @@ from ..overpass.overpass import Overpass, get_base_info
 from ..structs.landmark import Landmark
 from .get_time_distance import get_distance
 from ..constants import OSM_CACHE_DIR
+from .utils import create_bbox
+
 
 
 # silence the overpass logger
@@ -81,7 +83,6 @@ class ClusterManager:
         # Setup the caching in the Overpass class.
         self.overpass = Overpass(caching_strategy='XML', cache_dir=OSM_CACHE_DIR)
 
-
         self.cluster_type = cluster_type
         if cluster_type == 'shopping' :
             osm_types = ['node']
@@ -95,16 +96,13 @@ class ClusterManager:
             raise NotImplementedError("Please choose only an available option for cluster detection")
 
         # Initialize the points for cluster detection
-        query = self.overpass.build_query(
-            area = bbox,
+        try:
+            result = self.overpass.send_query(
+            bbox = bbox,
             osm_types = osm_types,
             selector = sel,
             out = out
         )
-        self.logger.debug(f"Cluster query: {query}")
-
-        try:
-            result = self.overpass.send_query(query)
         except Exception as e:
             self.logger.error(f"Error fetching landmarks: {e}")
 
@@ -218,9 +216,8 @@ class ClusterManager:
         """
 
         # Define the bounding box for a given radius around the coordinates
-        lat, lon = cluster.centroid
-        bbox = (1000, lat, lon)
-
+        bbox = create_bbox(cluster.centroid, 1000)
+        
         # Query neighborhoods and shopping malls
         selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
 
@@ -238,15 +235,12 @@ class ClusterManager:
         osm_types = ['node', 'way', 'relation']
 
         for sel in selectors :
-            query = self.overpass.build_query(
-                area = bbox,
-                osm_types = osm_types,
-                selector = sel,
-                out = 'ids center'
-            )
-
             try:
-                result = self.overpass.send_query(query)
+                result = self.overpass.send_query(bbox = bbox,
+                                                  osm_types = osm_types,
+                                                  selector = sel,
+                                                  out = 'ids center'
+                                                  )
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue

backend/src/utils/landmarks_manager.py

@@ -1,5 +1,6 @@
 """Module used to import data from OSM and arrange them in categories."""
 import logging
+import math as m
 import xml.etree.ElementTree as ET
 import yaml
 
@@ -9,12 +10,10 @@ from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
 from ..overpass.overpass import Overpass, get_base_info
+from .utils import create_bbox
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
-# silence the overpass logger
-logging.getLogger('Overpass').setLevel(level=logging.CRITICAL)
-
 
 class LandmarkManager:
     """
@@ -37,8 +36,7 @@ class LandmarkManager:
 
         with LANDMARK_PARAMETERS_PATH.open('r') as f:
             parameters = yaml.safe_load(f)
-            self.max_bbox_side = parameters['city_bbox_side']
-            self.radius_close_to = parameters['radius_close_to']
+            self.max_bbox_side = parameters['max_bbox_side']
             self.church_coeff = parameters['church_coeff']
             self.nature_coeff = parameters['nature_coeff']
             self.overall_coeff = parameters['overall_coeff']
@@ -80,13 +78,13 @@ class LandmarkManager:
         """
         self.logger.debug('Starting to fetch landmarks...')
         max_walk_dist = int((preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor)
-        reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
+        radius = min(max_walk_dist, int(self.max_bbox_side/2))
 
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
         # Create a bbox using the around technique, tuple of strings
-        bbox = tuple((min(2000, reachable_bbox_side/2), center_coordinates[0], center_coordinates[1]))
+        bbox = create_bbox(center_coordinates, radius)
 
         # list for sightseeing
         if preferences.sightseeing.score != 0:
@@ -193,17 +191,15 @@ class LandmarkManager:
                 query_conditions = []
                 osm_types.append('node')
 
-            query = self.overpass.build_query(
-                area = bbox,
+            # Send the overpass query
+            try:
+                result = self.overpass.send_query(
+                bbox = bbox,
                 osm_types = osm_types,
                 selector = sel,
                 conditions = query_conditions,        # except for nature....
                 out = 'center'
                 )
-            self.logger.debug(f"Query: {query}")
-
-            try:
-                result = self.overpass.send_query(query)
             except Exception as e:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue

backend/src/utils/utils.py

@@ -0,0 +1,27 @@
+"""Various helper functions"""
+import math as m
+
+def create_bbox(coords: tuple[float, float], radius: int):
+    """
+    Create a bounding box around the given coordinates.
+
+    Args:
+        coords (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+        radius (int): The half-side length of the bounding box in meters.
+
+    Returns:
+        tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+                                            defining the bounding box.
+    """
+    # Earth's radius in meters
+    R = 6378137
+    lat, lon = coords
+    d_lat = radius / R
+    d_lon = radius / (R * m.cos(m.pi * lat / 180))
+
+    lat_min = lat - d_lat * 180 / m.pi
+    lat_max = lat + d_lat * 180 / m.pi
+    lon_min = lon - d_lon * 180 / m.pi
+    lon_max = lon + d_lon * 180 / m.pi
+
+    return (lat_min, lon_min, lat_max, lon_max)