simplified the bbox creation

backend/src/main.py

@@ -47,7 +47,7 @@ def new_trip(preferences: Preferences,
         raise HTTPException(status_code=406,
                             detail="Start coordinates not provided")
     if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
-        raise HTTPException(status_code=423,
+        raise HTTPException(status_code=422,
                             detail="Start coordinates not in range")
     if end is None:
         end = start

backend/src/sandbox/get_streets.py

@@ -6,8 +6,11 @@ from sklearn.cluster import DBSCAN
 from sklearn.decomposition import PCA
 import matplotlib.pyplot as plt
 from pydantic import BaseModel
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+from math import sin, cos, sqrt, atan2, radians
 
 
+EARTH_RADIUS_KM = 6373
 
 class ShoppingLocation(BaseModel):
     type: Literal['street', 'area']
@@ -15,10 +18,34 @@ class ShoppingLocation(BaseModel):
     centroid: tuple
     start: Optional[list] = None
     end: Optional[list] = None
-    end: Optional[tuple] = None
 
 
 
+# Output to frontend
+class Landmark(BaseModel) :
+    # Properties of the landmark
+    name : str
+    type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
+    location : tuple
+    osm_type : str
+    osm_id : int
+    attractiveness : int
+    n_tags : int
+    image_url : Optional[str] = None
+    website_url : Optional[str] = None
+    description : Optional[str] = None                          # TODO future
+    duration : Optional[int] = 0
+    name_en : Optional[str] = None
+
+    # Additional properties depending on specific tour
+    must_do : Optional[bool] = False
+    must_avoid : Optional[bool] = False
+    is_secondary : Optional[bool] = False
+
+    time_to_reach_next : Optional[int] = 0
+    next_uuid : Optional[str] = None
+
+
 def extract_points(filestr: str) :
     """
     Extract points from geojson file.
@@ -44,6 +71,37 @@ def extract_points(filestr: str) :
     return np.array(points)
 
 
+def get_distance(p1: tuple[float, float], p2: tuple[float, float]) -> int:
+    """
+    Calculate the time in minutes to travel from one location to another.
+
+    Args:
+        p1 (Tuple[float, float]): Coordinates of the starting location.
+        p2 (Tuple[float, float]): Coordinates of the destination.
+
+        Returns:
+        int: Time to travel from p1 to p2 in minutes.
+    """
+
+
+    if p1 == p2:
+        return 0
+    else:
+        # Compute the distance in km along the surface of the Earth
+        # (assume spherical Earth)
+        # this is the haversine formula, stolen from stackoverflow
+        # in order to not use any external libraries
+        lat1, lon1 = radians(p1[0]), radians(p1[1])
+        lat2, lon2 = radians(p2[0]), radians(p2[1])
+
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+
+        return EARTH_RADIUS_KM * c
+
 def filter_clusters(cluster_points, cluster_labels):
     """
     Remove clusters of less importance.
@@ -113,16 +171,16 @@ def fit_lines(points, labels):
         if np.linalg.norm(t) <= 0.0045 :
             loc = ShoppingLocation(
                 type='area',
-                centroid=tuple(centroid),
+                centroid=tuple((centroid[1], centroid[0])),
-                importance = len(cluster_points)
+                importance = len(cluster_points),
             )
         else :
             loc = ShoppingLocation(
                 type='street',
                 centroid=tuple(centroid),
+                importance = len(cluster_points),
                 start=start_point,
-                end=end_point,
+                end=end_point
-                importance = len(cluster_points)
             )
 
         locations.append(loc)
@@ -137,16 +195,73 @@ def fit_lines(points, labels):
 
 
 
+def create_landmark(shopping_location: ShoppingLocation):
+
+    # Define the bounding box for a given radius around the coordinates
+    lat, lon = shopping_location.centroid
+    bbox = ("around:500", str(lat), str(lon))
+    
+    overpass = Overpass()
+
+    # Query neighborhoods and shopping malls
+    query = overpassQueryBuilder(
+        bbox = bbox,
+        elementType = ['node'],
+        selector = ['"place"="suburb"'],
+        includeCenter = True,
+        out = 'body'
+    )
+
+    try:
+        result = overpass.query(query)
+        print(f'query OK with {len(result.elements())} elements')
+    except Exception as e:
+        raise Exception("query unsuccessful")
+
+    min_dist = float('inf')
+    new_name = 'Shopping Area'
+    new_name_en = None
+    osm_id = 0
+    osm_type = 'node'
+
+    for elem in result.elements():
+        location = (elem.centerLat(), elem.centerLon())
+        print(f"Distance : {get_distance(shopping_location.centroid, location)}")
+        if get_distance(shopping_location.centroid, location) < min_dist :
+            new_name = elem.tag('name')
+            osm_type = elem.type()              # Add type: 'way' or 'relation'
+            osm_id = elem.id()                  # Add OSM id 
+
+            print("closer thing found")
+
+            # add english name if it exists
+            try :
+                new_name_en = elem.tag('name:en')
+            except:
+                pass 
+
+    return Landmark(
+        name=new_name,
+        type='shopping',
+        location=shopping_location.centroid,              # TODO: use the fact the we can also recognize streets.
+        attractiveness=shopping_location.importance,
+        n_tags=0,
+        osm_id=osm_id,
+        osm_type=osm_type,
+        name_en=new_name_en
+    )
+
+
 # Extract points
-points = extract_points('strasbourg_data.json')
+points = extract_points('lyon_data.json')
 
-# Create a figure with 1 row and 3 columns for side-by-side plots
+######## Create a figure with 1 row and 3 columns for side-by-side plots
 fig, axes = plt.subplots(1, 3, figsize=(15, 5))
-
+# Plot Raw data points
-# Plot 0: Raw data points
 axes[0].set_title('Raw Data')
 axes[0].scatter(points[:, 0], points[:, 1], color='blue', s=20)
 
+
 # Apply DBSCAN to find clusters. Choose different settings for different cities.
 if len(points) > 400 :
     dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
@@ -160,40 +275,41 @@ clustered_points = points[labels != -1]
 clustered_labels = labels[labels != -1]
 noise_points = points[labels == -1] 
 
-# Plot n°1: DBSCAN Clustering Results
+######## Plot n°1: DBSCAN Clustering Results
 axes[1].set_title('DBSCAN Clusters')
 axes[1].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=20)
 axes[1].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=7, label='Noise')
 
 clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels)
 
-
-
 # Fit lines
 corners, locations = fit_lines(clustered_points, clustered_labels)
 (xmin, xmax, ymin, ymax) = corners
 
 
-# Plot clustered points in normal size and noise points separately
+######## Plot clustered points in normal size and noise points separately
 axes[2].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=30)
-# axes[2].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=10, label='Noise')
 
-# Step 4: Plot the detected lines in the final plot
+
+# Create a list of Landmarks for the shopping things
+shopping_landmarks = []
+for loc in locations :
+    landmark = create_landmark(loc)
+    shopping_landmarks.append(landmark)
+    print(f"{landmark.name} is a shopping area with a score of {landmark.attractiveness}")
+
+
+####### Plot the detected lines in the final plot #######
 for loc in locations:
-    
     if loc.type == 'street' :
         line_x = loc.start
         line_y = loc.end
         axes[2].plot(line_x, line_y, color='lime', linewidth=3)
     else :
         axes[2].scatter(loc.centroid[0], loc.centroid[1], color='None', edgecolors='lime', s=200, linewidth=3)
-    # print(8)
 
 axes[2].set_title('PCA Fitted Lines on Clusters')
 
-# print(all_x)
-
-# Adjust the axis limit for previous plots
 axes[0].set_xlim(xmin-0.01, xmax+0.01)
 axes[0].set_ylim(ymin-0.01, ymax+0.01)
 
@@ -203,8 +319,5 @@ axes[1].set_ylim(ymin-0.01, ymax+0.01)
 axes[2].set_xlim(xmin-0.01, xmax+0.01)
 axes[2].set_ylim(ymin-0.01, ymax+0.01)
 
-# Adjust layout for better spacing
+# plt.tight_layout()
-plt.tight_layout()
+# plt.show()
-
-# Show the plots
-plt.show()

backend/src/structs/shopping_location.py

@@ -0,0 +1,23 @@
+from typing import Literal, Optional
+from pydantic import BaseModel
+
+
+class ShoppingLocation(BaseModel):
+    """"
+    A classe representing an interesting area for shopping.
+    
+    It can represent either a general area or a specifc route with start and end point.
+    The importance represents the number of shops found in this cluster.
+    
+    Attributes:
+        type :       either a 'street' or 'area' (representing a denser field of shops).
+        importance : size of the cluster (number of points).
+        centroid :   center of the cluster.
+        start :      if the type is a street it goes from here...
+        end :        ...to here
+    """
+    type: Literal['street', 'area']
+    importance: int
+    centroid: tuple
+    start: Optional[list] = None
+    end: Optional[list] = None

backend/src/utils/get_time_separation.py

@@ -48,3 +48,35 @@ def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
     walk_time = walk_distance / AVERAGE_WALKING_SPEED * 60
 
     return round(walk_time)
+
+
+def get_distance(p1: tuple[float, float], p2: tuple[float, float]) -> int:
+    """
+    Calculate the time in minutes to travel from one location to another.
+
+    Args:
+        p1 (Tuple[float, float]): Coordinates of the starting location.
+        p2 (Tuple[float, float]): Coordinates of the destination.
+
+        Returns:
+        int: Time to travel from p1 to p2 in minutes.
+    """
+
+
+    if p1 == p2:
+        return 0
+    else:
+        # Compute the distance in km along the surface of the Earth
+        # (assume spherical Earth)
+        # this is the haversine formula, stolen from stackoverflow
+        # in order to not use any external libraries
+        lat1, lon1 = radians(p1[0]), radians(p1[1])
+        lat2, lon2 = radians(p2[0]), radians(p2[1])
+
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+        c = 2 * atan2(sqrt(a), sqrt(1 - a))
+
+        return EARTH_RADIUS_KM * c

backend/src/utils/landmarks_manager.py

@@ -79,7 +79,8 @@ class LandmarkManager:
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
-        bbox = self.create_bbox(center_coordinates, reachable_bbox_side)
+        # Create a bbox using the around
+        bbox = tuple((f"around:{reachable_bbox_side/2}", str(center_coordinates[0]), str(center_coordinates[1])))
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
@@ -97,7 +98,7 @@ class LandmarkManager:
             score_function = lambda score: score * 10 * preferences.shopping.score / 5
             current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
             # set time for all shopping activites :
-            for landmark in current_landmarks : landmark.duration = 45
+            for landmark in current_landmarks : landmark.duration = 30
             all_landmarks.update(current_landmarks)
 
 
@@ -151,36 +152,24 @@ class LandmarkManager:
             return 0
 
 
-    def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
+    # def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
-        """
+    #     """
-        Create a bounding box around the given coordinates.
+    #     Create a bounding box around the given coordinates.
 
-        Args:
+    #     Args:
-            coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
+    #         coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
-            reachable_bbox_side (int): The side length of the bounding box in meters.
+    #         reachable_bbox_side (int): The side length of the bounding box in meters.
 
-        Returns:
+    #     Returns:
-            tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+    #         tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
-                                                defining the bounding box.
+    #                                             defining the bounding box.
-        """
+    #     """
 
-        lat = coordinates[0]
+    #     # Half the side length in m (since it's a square bbox)
-        lon = coordinates[1]
+    #     half_side_length_m = reachable_bbox_side / 2
 
-        # Half the side length in km (since it's a square bbox)
+    #     return tuple((f"around:{half_side_length_m}", str(coordinates[0]), str(coordinates[1])))
-        half_side_length_km = reachable_bbox_side / 2 / 1000
 
-        # Convert distance to degrees
-        lat_diff = half_side_length_km / 111  # 1 degree latitude is approximately 111 km
-        lon_diff = half_side_length_km / (111 * math.cos(math.radians(lat)))  # Adjust for longitude based on latitude
-
-        # Calculate bbox
-        min_lat = lat - lat_diff
-        max_lat = lat + lat_diff
-        min_lon = lon - lon_diff
-        max_lon = lon + lon_diff
-
-        return min_lat, min_lon, max_lat, max_lon
 
 
     def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
@@ -212,7 +201,9 @@ class LandmarkManager:
         for sel in dict_to_selector_list(amenity_selector):
             self.logger.debug(f"Current selector: {sel}")
 
-            query_conditions = ['count_tags()>5']
+            # query_conditions = ['count_tags()>5']
+            # if landmarktype == 'shopping' :       # use this later for shopping clusters
+            #     element_types = ['node']
             element_types = ['way', 'relation']
 
             if 'viewpoint' in sel :