backend/src/parameters/amenity_selectors.yaml

@@ -60,15 +60,18 @@ sightseeing:
     - cantilever
     - abandoned
   building:
-    - church
-    - chapel
-    - mosque
-    - synagogue
-    - ruins
-    - temple
-    # - government
     - cathedral
-    - castle
+
+# unused sightseeing/buildings:
+    # - church
+    # - chapel
+    # - mosque
+    # - synagogue
+    # - ruins
+    # - temple
+    # - government
+    # - cathedral
+    # - castle
     # - museum
 
 museums:

backend/src/parameters/landmark_parameters.yaml

@@ -4,9 +4,9 @@ church_coeff: 0.65
 nature_coeff: 1.35
 overall_coeff: 10
 tag_exponent: 1.15
-image_bonus: 10
+image_bonus: 1.1
 viewpoint_bonus: 5
-wikipedia_bonus: 4
+wikipedia_bonus: 1.1
 name_bonus: 3
 N_important: 40
 pay_bonus: -1

backend/src/sandbox/overpass_test.py

@@ -0,0 +1,288 @@
+from typing import Literal, List, Optional
+from pydantic import BaseModel
+import urllib.request
+import urllib.parse
+import json
+import yaml
+from pathlib import Path
+import xml.etree.ElementTree as ET
+
+
+
+OSM_ENDPOINT = 'http://overpass-api.de/api/'
+LOCATION_PREFIX = Path('src')
+PARAMETERS_DIR = LOCATION_PREFIX / 'parameters'
+AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
+
+
+ElementTypes = List[Literal['way', 'node', 'relation']]
+
+
+
+# Output to frontend
+class Landmark(BaseModel) :
+    """
+    A class representing a landmark or point of interest (POI) in the context of a trip.
+
+    The Landmark class is used to model visitable locations, such as tourist attractions,
+    natural sites, shopping locations, and start/end points in travel itineraries. It
+    holds information about the landmark's attributes and supports comparisons and
+    calculations, such as distance between landmarks.
+
+    Attributes:
+        name (str): The name of the landmark.
+        type (Literal): The type of the landmark, which can be one of ['sightseeing', 'nature', 
+            'shopping', 'start', 'finish'].
+        location (tuple): A tuple representing the (latitude, longitude) of the landmark.
+        osm_type (str): The OpenStreetMap (OSM) type of the landmark.
+        osm_id (int): The OpenStreetMap (OSM) ID of the landmark.
+        attractiveness (int): A score representing the attractiveness of the landmark.
+        n_tags (int): The number of tags associated with the landmark.
+        image_url (Optional[str]): A URL to an image of the landmark.
+        website_url (Optional[str]): A URL to the landmark's official website.
+        description (Optional[str]): A text description of the landmark.
+        duration (Optional[int]): The estimated time to visit the landmark (in minutes).
+        name_en (Optional[str]): The English name of the landmark.
+        uuid (UUID): A unique identifier for the landmark, generated by default using uuid4.
+        must_do (Optional[bool]): Whether the landmark is a "must-do" attraction.
+        must_avoid (Optional[bool]): Whether the landmark should be avoided.
+        is_secondary (Optional[bool]): Whether the landmark is secondary or less important.
+        time_to_reach_next (Optional[int]): Estimated time (in minutes) to reach the next landmark.
+        next_uuid (Optional[UUID]): UUID of the next landmark in sequence (if applicable).
+    """
+
+    # 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
+    wiki_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
+    
+    # More properties to define the score
+    is_viewpoint : Optional[bool] = False
+    is_cathedral : Optional[bool] = False
+    is_place_of_worship : Optional[bool] = False
+
+
+def OverpassQueryBuilder(area: tuple, element_types: ElementTypes, selector: str, 
+                         conditions=[], out='center'):
+
+    if not isinstance(conditions, list) :
+        conditions = [conditions]
+
+    query = '('
+    search_area = f"({', '.join(map(str, area))})"
+    if conditions :
+        conditions = '(if: ' + ' && '.join(conditions) + ')'
+    else :
+        conditions = ''
+
+    for elem in element_types :
+        query += elem + '[' + selector + ']' + conditions + search_area + ';'
+
+    query += ');' + f'out {out};'
+
+    return query
+
+def dict_to_selector_list(d: dict) -> list:
+    """
+    Convert a dictionary of key-value pairs to a list of Overpass query strings.
+
+    Args:
+        d (dict): A dictionary of key-value pairs representing the selector.
+
+    Returns:
+        list: A list of strings representing the Overpass query selectors.
+    """
+    return_list = []
+    for key, value in d.items():
+        if isinstance(value, list):
+            val = '|'.join(value)
+            return_list.append(f'{key}~"^({val})$"')
+        elif isinstance(value, str) and len(value) == 0:
+            return_list.append(f'{key}')
+        else:
+            return_list.append(f'{key}={value}')
+    return return_list
+
+
+def send_overpass_query(query: str) -> dict:
+    """
+    Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
+
+    Args:
+        query (str): The Overpass QL query to be sent to the Overpass API.
+
+    Returns:
+        dict: The parsed JSON response from the Overpass API, or None if the request fails.
+    """
+
+    # Define the Overpass API endpoint
+    overpass_url = "https://overpass-api.de/api/interpreter"
+
+    # Prepare the data to be sent as POST request, encoded as bytes
+    data = urllib.parse.urlencode({'data': query}).encode('utf-8')
+
+    # Create a custom header with a User-Agent
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (compatible; OverpassQuery/1.0; +http://example.com)',
+    }
+
+    try:
+        # Create a Request object with the specified URL, data, and headers
+        request = urllib.request.Request(overpass_url, data=data, headers=headers)
+        
+        # Send the request and read the response
+        with urllib.request.urlopen(request) as response:
+            # Read and decode the response
+            response_data = response.read().decode('utf-8')
+            return ET.fromstring(response_data)
+
+    except urllib.error.URLError as e:
+        print(f"Error connecting to Overpass API: {e}")
+        return None
+    except json.JSONDecodeError:
+        print("Error decoding the JSON response from Overpass API.")
+        return None
+
+
+
+def parse_result(root: ET.Element, elem_type) -> List[Landmark]:
+
+    landmarks = []
+    if root is None :
+        return landmarks
+
+    for osm_type in ['node', 'way', 'relation'] :
+        for elem in root.findall(osm_type):
+
+            # Extract basic info from the landmark.
+            name = elem.find("tag[@k='name']").get('v') if elem.find("tag[@k='name']") is not None else None
+            center = elem.find('center')
+            tags = elem.findall('tag')
+
+            # Extract the center latitude and longitude if available.
+            if name is not None and center is not None:
+                lat = center.get('lat')
+                lon = center.get('lon')
+                coords = tuple((lat, lon))
+            else :
+                continue
+            
+            # Convert this to Landmark object
+            landmark = Landmark(name=name,
+                                type=elem_type,
+                                location=coords,
+                                osm_id=elem.get('id'), 
+                                osm_type=osm_type,
+                                attractiveness=0,
+                                n_tags=len(tags))
+
+            # Browse through tags to add information to landmark.
+            for tag in tags:
+                key = tag.get('k')
+                value = tag.get('v')
+
+                # Skip this landmark if not suitable.
+                if key == 'building:part' and value == 'yes' :
+                    break
+                if 'disused:' in key :
+                    break
+                if 'boundary:' in key :
+                    break
+                if 'shop' in key and elem_type != 'shopping' :
+                    break
+                # if value == 'apartments' :
+                #     break
+                
+                # Fill in the other attributes.
+                if key == 'image' :
+                    landmark.image_url = value
+                if key == 'website' :
+                    landmark.website_url = value
+                if key == 'place_of_worship' :
+                    landmark.is_place_of_worship = True
+                if key == 'wikipedia' :
+                    landmark.wiki_url = value
+                if key == 'name:en' :
+                    landmark.name_en = value
+                if 'building:' in key or 'pay' in key :
+                    landmark.n_tags -= 1
+                
+                # Set the duration.
+                if value in ['museum', 'aquarium', 'planetarium'] :
+                    landmark.duration = 60
+                elif value == 'viewpoint' :
+                    landmark.is_viewpoint = True
+                    landmark.duration = 10
+                elif value == 'cathedral' :
+                    landmark.is_place_of_worship = False
+                    landmark.duration = 10
+                else :
+                    landmark.duration = 5
+
+            else: 
+                set_score(landmark, elem_type)
+                landmarks.append(landmark)
+            continue
+
+    return landmarks
+
+
+def set_score(landmark: Landmark, landmarktype: str) :
+
+        score = landmark.n_tags**1.15
+        if landmark.wiki_url :
+            score *= 1.1
+        if landmark.image_url :
+            score *= 1.1
+        if landmark.website_url :
+            score *= 1.1
+        if landmark.is_place_of_worship :
+            score *= 0.65
+        if landmark.is_viewpoint :
+            # print(f"{landmark.name}:  n_tags={landmark.n_tags} and score={score*3*1.35*10}")
+            score *= 3
+        if landmarktype == 'nature' :
+            score *= 1.35
+
+        landmark.attractiveness = int(score * 10)
+
+
+with AMENITY_SELECTORS_PATH.open('r') as f:
+    amenity_selectors = yaml.safe_load(f)
+    amenity_selector = amenity_selectors['nature']
+bbox = tuple(('around:1714', 45.7576485, 4.8330241))
+
+landmarks = []
+for sel in dict_to_selector_list(amenity_selector):
+
+    query = OverpassQueryBuilder(area=bbox,
+                                 element_types=['way', 'relation'],
+                                 selector=sel,
+                                #  conditions='count_tags()>5',
+                                 out='center')
+    print(query + '\n')
+
+    root = send_overpass_query(query)
+
+    landmarks += parse_result(root, 'nature')
+
+
+print(len(landmarks))

backend/src/structs/landmark.py

@@ -45,8 +45,11 @@ class Landmark(BaseModel) :
     osm_id : int
     attractiveness : int
     n_tags : int
+
+    # Optional properties to gather more information.
     image_url : Optional[str] = None
     website_url : Optional[str] = None
+    wiki_url : Optional[str] = None
     description : Optional[str] = None                          # TODO future
     duration : Optional[int] = 0
     name_en : Optional[str] = None
@@ -62,6 +65,10 @@ class Landmark(BaseModel) :
     time_to_reach_next : Optional[int] = 0
     next_uuid : Optional[UUID] = None
 
+    # More properties to define the score
+    is_viewpoint : Optional[bool] = False
+    is_place_of_worship : Optional[bool] = False
+
     def __str__(self) -> str:
         """
         String representation of the Landmark object.

backend/src/structs/linked_landmarks.py

@@ -1,7 +1,7 @@
 """Linked and ordered list of Landmarks that represents the visiting order."""
 
 from .landmark import Landmark
-from ..utils.get_time_separation import get_time
+from ..utils.get_time_distance import get_time
 
 class LinkedLandmarks:
     """

backend/src/tests/test_main.py

@@ -11,7 +11,7 @@ def client():
     """Client used to call the app."""
     return TestClient(app)
 
-
+'''
 def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     """
     Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
@@ -54,7 +54,7 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     assert len(landmarks) > 2           # check that there is something to visit
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     # assert 2==3
-
+'''
 
 def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     """
@@ -67,6 +67,7 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     start_time = time.time()  # Start timer
     duration_minutes = 120
 
+
     response = client.post(
         "/trip/new",
         json={
@@ -96,7 +97,7 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     # assert 2 == 3
 
-
+'''
 def test_cologne(client, request) :   # pylint: disable=redefined-outer-name
     """
     Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
@@ -335,7 +336,7 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     assert response.status_code == 200  # check for successful planning
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
-
+'''
 
 # def test_new_trip_single_prefs(client):
 #     response = client.post(

backend/src/utils/cluster_manager.py

@@ -9,7 +9,7 @@ from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
 from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
 from ..structs.landmark import Landmark
-from ..utils.get_time_separation import get_distance
+from .get_time_distance import get_distance
 from ..constants import OSM_CACHE_DIR
 
 

backend/src/utils/get_time_distance.py

@@ -1,4 +1,4 @@
-"""Computes the distance (in meters) or the walking time (in minutes) between two coordinates."""
+"""Contains various helper functions to help with distance or score computations."""
 from math import sin, cos, sqrt, atan2, radians
 import yaml
 

backend/src/utils/landmarks_manager.py

@@ -1,13 +1,12 @@
 """Module used to import data from OSM and arrange them in categories."""
 import logging
 import yaml
-from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
-from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
 
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
+from .overpass import OverpassQueryBuilder, send_overpass_query, parse_result
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
@@ -54,8 +53,8 @@ class LandmarkManager:
             self.walking_speed = parameters['average_walking_speed']
             self.detour_factor = parameters['detour_factor']
 
-        self.overpass = Overpass()
+        # self.overpass = Overpass()
-        CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+        # CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
 
         self.logger.info('LandmakManager successfully initialized.')
 
@@ -78,13 +77,13 @@ class LandmarkManager:
         - A list of the most important landmarks based on the user's preferences.
         """
         self.logger.debug('Starting to fetch landmarks...')
-        max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
+        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)
 
         # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
         all_landmarks = set()
 
-        # Create a bbox using the around technique
+        # Create a bbox using the around technique, tuple of strings
         bbox = tuple((f"around:{min(2000, reachable_bbox_side/2)}", str(center_coordinates[0]), str(center_coordinates[1])))
 
         # list for sightseeing
@@ -134,7 +133,21 @@ class LandmarkManager:
 
         return all_landmarks, landmarks_constrained
 
+    def set_score(self, landmark: Landmark, landmarktype: str, preference_level: int) :
 
+        score = landmark.n_tags**self.tag_exponent
+        if landmark.wiki_url :
+            score *= self.wikipedia_bonus
+        if landmark.image_url :
+            score *= self.image_bonus
+        if landmark.website_url :
+            score *= self.wikipedia_bonus
+        if landmark.is_place_of_worship :
+            score *= self.church_coeff
+
+        landmark.attractiveness = int(score * preference_level)
+
+    '''
     def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
         """
         Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
@@ -170,15 +183,11 @@ class LandmarkManager:
                 query_conditions = []
                 element_types.append('node')
 
-            query = overpassQueryBuilder(
+            query = OverpassQueryBuilder(
-                bbox = bbox,
+                area = bbox,
-                elementType = element_types,
+                element_types = element_types,
-                # selector can in principle be a list already,
-                # but it generates the intersection of the queries
-                # we want the union
                 selector = sel,
                 conditions = query_conditions,        # except for nature....
-                includeCenter = True,
                 out = 'center'
                 )
             self.logger.debug(f"Query: {query}")
@@ -295,7 +304,63 @@ class LandmarkManager:
         self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
 
         return return_list
+    '''
 
+    def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
+        """
+        Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
+
+        Args:
+            bbox (tuple[float, float, float, float]): The bounding box coordinates (around:radius, center_lat, center_lon).
+            amenity_selector (dict): The Overpass API query selector for the desired landmark type. 
+            landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
+            score_function (callable): The function to compute the score of the landmark based on its attributes.
+
+        Returns:
+            list[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
+
+        Notes:
+            - Landmarks are fetched using Overpass API queries.
+            - Selectors are translated from the dictionary to the Overpass query format. (e.g., 'amenity'='place_of_worship')
+            - Landmarks are filtered based on various conditions including tags and type.
+            - Scores are assigned to landmarks based on their attributes and surrounding elements.
+        """
+        return_list = []
+
+        if landmarktype == 'nature' : query_conditions = []
+        else : query_conditions = ['count_tags()>5']
+
+        # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
+        # we need to split the selectors into separate queries and merge the results
+        for sel in dict_to_selector_list(amenity_selector):
+            # self.logger.debug(f"Current selector: {sel}")
+
+            element_types = ['way', 'relation']
+
+            if 'viewpoint' in sel :
+                query_conditions = []
+                element_types.append('node')
+
+            query = OverpassQueryBuilder(
+                area = bbox,
+                element_types = element_types,
+                selector = sel,
+                conditions = query_conditions,        # except for nature....
+                out = 'center'
+                )
+            self.logger.debug(f"Query: {query}")
+
+            try:
+                result = send_overpass_query(query)
+            except Exception as e:
+                self.logger.error(f"Error fetching landmarks: {e}")
+                continue
+                
+            return_list = parse_result(result, landmarktype)
+
+        self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
+
+        return return_list
 
 def dict_to_selector_list(d: dict) -> list:
     """

backend/src/utils/optimizer.py

@@ -6,7 +6,7 @@ import numpy as np
 import pulp as pl
 
 from ..structs.landmark import Landmark
-from .get_time_separation import get_time
+from .get_time_distance import get_time
 from ..constants import OPTIMIZER_PARAMETERS_PATH
 
 

backend/src/utils/overpass.py

@@ -0,0 +1,199 @@
+from typing import Literal, List
+import urllib
+import json
+import xml.etree.ElementTree as ET
+
+from ..structs.landmark import Landmark
+
+ElementTypes = List[Literal['way', 'node', 'relation']]
+
+
+
+def OverpassQueryBuilder(area: tuple, element_types: ElementTypes, selector: str, 
+                         conditions=[], out='center'):
+    """
+    Constructs a query string for the Overpass API to retrieve OpenStreetMap (OSM) data.
+
+    Args:
+        area (tuple): A tuple representing the geographical search area, typically in the format 
+                      (radius, latitude, longitude). The first element is a string like "around:2000" 
+                      specifying the search radius, and the second and third elements represent 
+                      the latitude and longitude as floats or strings.
+        element_types (list[str]): A list of OSM element types to search for. Must be one or more of 
+                                   'Way', 'Node', or 'Relation'.
+        selector (str): The key or tag to filter the OSM elements (e.g., 'amenity', 'highway', etc.).
+        conditions (list, optional): A list of conditions to apply as additional filters for the 
+                                     selected OSM elements. The conditions should be written in 
+                                     the Overpass QL format, and they are combined with '&&' if 
+                                     multiple are provided. Defaults to an empty list.
+        out (str, optional): Specifies the output type, such as 'center', 'body', or 'tags'. 
+                             Defaults to 'center'.
+
+    Returns:
+        str: The constructed Overpass QL query string.
+
+    Notes:
+        - If no conditions are provided, the query will just use the `selector` to filter the OSM 
+          elements without additional constraints.
+        - The search area must always formatted as "(radius, lat, lon)".
+    """
+    if not isinstance(conditions, list) :
+        conditions = [conditions]
+
+    query = '('
+    search_area = f"({', '.join(map(str, area))})"
+
+    if conditions :
+        conditions = '(if: ' + ' && '.join(conditions) + ')'
+    else :
+        conditions = ''
+
+    for elem in element_types :
+        query += elem + '[' + selector + ']' + conditions + search_area + ';'
+
+    query += ');' + f'out {out};'
+
+    return query
+
+
+def send_overpass_query(query: str) -> dict:
+    """
+    Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
+
+    Args:
+        query (str): The Overpass QL query to be sent to the Overpass API.
+
+    Returns:
+        dict: The parsed JSON response from the Overpass API, or None if the request fails.
+    """
+
+    # Define the Overpass API endpoint
+    overpass_url = "https://overpass-api.de/api/interpreter"
+
+    # Prepare the data to be sent as POST request, encoded as bytes
+    data = urllib.parse.urlencode({'data': query}).encode('utf-8')
+
+    # Create a custom header with a User-Agent
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (compatible; OverpassQuery/1.0; +http://example.com)',
+    }
+
+    try:
+        # Create a Request object with the specified URL, data, and headers
+        request = urllib.request.Request(overpass_url, data=data, headers=headers)
+        
+        # Send the request and read the response
+        with urllib.request.urlopen(request) as response:
+            # Read and decode the response
+            response_data = response.read().decode('utf-8')
+            return ET.fromstring(response_data)
+
+    except urllib.error.URLError as e:
+        print(f"Error connecting to Overpass API: {e}")
+        return None
+    except json.JSONDecodeError:
+        print("Error decoding the JSON response from Overpass API.")
+        return None
+
+
+def parse_result(root: ET.Element, elem_type) -> List[Landmark]:
+
+    landmarks = []
+    if root is None :
+        return landmarks
+
+    for osm_type in ['node', 'way', 'relation'] :
+        for elem in root.findall(osm_type):
+
+            # Extract basic info from the landmark.
+            name = elem.find("tag[@k='name']").get('v') if elem.find("tag[@k='name']") is not None else None
+            center = elem.find('center')
+            tags = elem.findall('tag')
+
+            # Extract the center latitude and longitude if available.
+            if name is not None and center is not None:
+                lat = float(center.get('lat'))
+                lon = float(center.get('lon'))
+                coords = tuple((lat, lon))
+            else :
+                continue
+            
+            # Convert this to Landmark object
+            landmark = Landmark(name=name,
+                                type=elem_type,
+                                location=coords,
+                                osm_id=elem.get('id'), 
+                                osm_type=osm_type,
+                                attractiveness=0,
+                                n_tags=len(tags))
+
+            # Browse through tags to add information to landmark.
+            for tag in tags:
+                key = tag.get('k')
+                value = tag.get('v')
+
+                # Skip this landmark if not suitable.
+                if key == 'building:part' and value == 'yes' :
+                    break
+                if 'disused:' in key :
+                    break
+                if 'boundary:' in key :
+                    break
+                if 'shop' in key and elem_type != 'shopping' :
+                    break
+                # if value == 'apartments' :
+                #     break
+                
+                # Fill in the other attributes.
+                if key == 'image' :
+                    landmark.image_url = value
+                if key == 'website' :
+                    landmark.website_url = value
+                if key == 'place_of_worship' :
+                    landmark.is_place_of_worship = True
+                if key == 'wikipedia' :
+                    landmark.wiki_url = value
+                if key == 'name:en' :
+                    landmark.name_en = value
+                if 'building:' in key or 'pay' in key :
+                    landmark.n_tags -= 1
+                
+                # Set the duration.
+                if value in ['museum', 'aquarium', 'planetarium'] :
+                    landmark.duration = 60
+                elif value == 'viewpoint' :
+                    landmark.is_viewpoint = True
+                    landmark.duration = 10
+                elif value == 'cathedral' :
+                    landmark.is_place_of_worship = False
+                    landmark.duration = 10
+                else :
+                    landmark.duration = 5
+
+            else: 
+                set_score(landmark, elem_type)
+                landmarks.append(landmark)
+            continue
+
+    return landmarks
+
+
+
+def set_score(landmark: Landmark, landmarktype: str) :
+
+        score = landmark.n_tags**1.15
+        if landmark.wiki_url :
+            score *= 1.1
+        if landmark.image_url :
+            score *= 1.1
+        if landmark.website_url :
+            score *= 1.1
+        if landmark.is_place_of_worship :
+            score *= 0.65
+        if landmark.is_viewpoint :
+            # print(f"{landmark.name}:  n_tags={landmark.n_tags} and score={score*3*1.35*10}")
+            score *= 3
+        if landmarktype == 'nature' :
+            score *= 1.35
+
+        landmark.attractiveness = int(score * 10)

backend/src/utils/refiner.py

@@ -6,7 +6,7 @@ from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
 
 
 from ..structs.landmark import Landmark
-from . import take_most_important, get_time_separation
+from . import get_time_distance, take_most_important
 from .optimizer import Optimizer
 from ..constants import OPTIMIZER_PARAMETERS_PATH
 
@@ -195,7 +195,7 @@ class Refiner :
 
         # Step 4: Use nearest neighbor heuristic to visit all landmarks
         while unvisited_landmarks:
-            nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time_separation.get_time(current_landmark.location, lm.location))
+            nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time_distance.get_time(current_landmark.location, lm.location))
             path.append(nearest_landmark)
             coordinates.append(nearest_landmark.location)
             current_landmark = nearest_landmark