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Helldragon67
2025-01-23 11:28:41 +01:00
parent 78f1dcaab4
commit b9356dc4ee
8 changed files with 173 additions and 91 deletions
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26
backend/src/utils/cluster_manager.py
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@@ -5,11 +5,9 @@ from typing import Literal
import numpy as np
from sklearn.cluster import DBSCAN
from pydantic import BaseModel
# from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
# from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
from ..overpass.overpass import build_query, send_overpass_query
from ..overpass.caching_strategy import CachingStrategy
from ..overpass.overpass import build_query, send_query
from ..overpass.caching_strategy import CachingStrategy
from ..structs.landmark import Landmark
from .get_time_distance import get_distance
from ..constants import OSM_CACHE_DIR
@@ -81,8 +79,6 @@ class ClusterManager:
Args:
bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
"""
# self.overpass = Overpass()
# CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
CachingStrategy.use('XML', cache_dir=OSM_CACHE_DIR)
self.cluster_type = cluster_type
@@ -94,6 +90,8 @@ class ClusterManager:
osm_types = ['way']
sel = '"historic"~"^(monument|building|yes)$"'
out = 'ids center'
else :
raise NotImplementedError("Please choose only an available option for cluster detection")
# Initialize the points for cluster detection
query = build_query(
@@ -105,25 +103,25 @@ class ClusterManager:
self.logger.debug(f"Cluster query: {query}")
try:
result = send_overpass_query(query)
result = send_query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
if result is None :
self.logger.error(f"Error fetching {cluster_type} clusters, overpass query returned None.")
self.valid = False
else :
points = []
for osm_type in osm_types :
for elem in result.findall(osm_type):
center = elem.find('center')
if osm_type != 'node' :
center = elem.find('center')
lat = float(center.get('lat'))
lon = float(center.get('lon'))
points.append(tuple((lat, lon)))
else :
lat = float(elem.get('lat'))
lon = float(elem.get('lon'))
@@ -136,7 +134,7 @@ class ClusterManager:
if self.cluster_type == 'shopping' and len(self.all_points) > 200 :
dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree') # for large cities
elif self.cluster_type == 'sightseeing' :
dbscan = DBSCAN(eps=0.003, min_samples=10, algorithm='kd_tree') # for historic neighborhoods
dbscan = DBSCAN(eps=0.0025, min_samples=15, algorithm='kd_tree') # for historic neighborhoods
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
@@ -249,7 +247,7 @@ class ClusterManager:
)
try:
result = send_overpass_query(query)
result = send_query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
continue
@@ -270,7 +268,7 @@ class ClusterManager:
if osm_type != 'node' :
lat = float(center.get('lat'))
lon = float(center.get('lon'))
else :
lat = float(elem.get('lat'))
lon = float(elem.get('lon'))
@@ -290,7 +288,7 @@ class ClusterManager:
return Landmark(
name=new_name,
type=self.cluster_type,
location=cluster.centroid, # TODO: use the fact the we can also recognize streets.
location=cluster.centroid, # later: use the fact the we can also recognize streets.
attractiveness=cluster.importance,
n_tags=0,
osm_id=osm_id,