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Helldragon67
2025-01-23 12:01:49 +01:00
parent b9356dc4ee
commit 28ff0460ab
26 changed files with 56 additions and 196439 deletions
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0
backend/src/utils/__init__.py Normal file
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42
backend/src/utils/cluster_manager.py
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@@ -14,7 +14,7 @@ from ..constants import OSM_CACHE_DIR
# silence the overpass logger
logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
logging.getLogger('overpass').setLevel(level=logging.CRITICAL)
class Cluster(BaseModel):
@@ -127,30 +127,34 @@ class ClusterManager:
lon = float(elem.get('lon'))
points.append(tuple((lat, lon)))
if points :
self.all_points = np.array(points)
self.all_points = np.array(points)
# Apply DBSCAN to find clusters. Choose different settings for different cities.
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=0.0025, min_samples=15, algorithm='kd_tree') # for historic neighborhoods
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
# Apply DBSCAN to find clusters. Choose different settings for different cities.
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=0.0025, min_samples=15, algorithm='kd_tree') # for historic neighborhoods
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
labels = dbscan.fit_predict(self.all_points)
labels = dbscan.fit_predict(self.all_points)
# Check that there are is least 1 cluster
if len(set(labels)) > 1 :
self.logger.debug(f"Found {len(set(labels))} different clusters.")
# Separate clustered points and noise points
self.cluster_points = self.all_points[labels != -1]
self.cluster_labels = labels[labels != -1]
self.filter_clusters() # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
self.valid = True
# Check that there are is least 1 cluster
if len(set(labels)) > 1 :
self.logger.debug(f"Found {len(set(labels))} different clusters.")
# Separate clustered points and noise points
self.cluster_points = self.all_points[labels != -1]
self.cluster_labels = labels[labels != -1]
self.filter_clusters() # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
self.valid = True
else :
self.logger.debug(f"Detected 0 {cluster_type} clusters.")
self.valid = False
else :
self.logger.error(f"Detected 0 {cluster_type} clusters.")
self.logger.debug(f"Detected 0 {cluster_type} clusters.")
self.valid = False

2
backend/src/utils/landmarks_manager.py
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@@ -14,7 +14,7 @@ from ..overpass.caching_strategy import CachingStrategy
from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
# silence the overpass logger
logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
logging.getLogger('overpass').setLevel(level=logging.CRITICAL)
class LandmarkManager:

2
backend/src/utils/toilets_manager.py
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@@ -9,7 +9,7 @@ from ..constants import OSM_CACHE_DIR
# silence the overpass logger
logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
logging.getLogger('overpass').setLevel(level=logging.CRITICAL)
class ToiletsManager:
"""