fixed duplicate landmarks

backend/src/landmarks_manager.py

@@ -9,9 +9,12 @@ from typing import Tuple
 
 
 BBOX_SIDE = 10              # size of bbox in *km* for general area, 10km
-RADIUS_CLOSE_TO = 25        # size of area in *m* for close features, 5àm radius
+RADIUS_CLOSE_TO = 25        # size of area in *m* for close features, 25m radius
-MIN_SCORE = 30             # discard elements with score < 100
+MIN_SCORE = 30              # DEPRECIATED. discard elements with score < 30
-MIN_TAGS = 5                # discard elements withs less than 5 tags
+MIN_TAGS = 5                # DEPRECIATED. discard elements withs less than 5 tags
+CHURCH_PENALTY = 0.1        # penalty to reduce score of curches
+PARK_COEFF = 10             # multiplier for parks
+N_IMPORTANT = 30            # take the 30 most important landmarks
 
 SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
 NATURE = LandmarkType(landmark_type='nature')
@@ -71,7 +74,8 @@ def generate_landmarks(preferences: Preferences, city_country: str = None, coord
             L += L3
 
 
-    return L, cleanup_list(L)
+    return L, take_most_important(L)
+    #return L, cleanup_list(L)
 
 # Determines if two locations are close to each other
 def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float])->bool :
@@ -82,6 +86,45 @@ def is_close_to(loc1: Tuple[float, float], loc2: Tuple[float, float])->bool :
     else : 
         return False
 
+
+def take_most_important(L: List[Landmark])->List[Landmark] :
+    L_copy = []
+    L_clean = []
+    scores = [0]*len(L)
+    names = []
+    name_id = {}
+
+    for i, elem in enumerate(L) :
+        if elem.name not in names :
+            names.append(elem.name)
+            name_id[elem.name] = [i]
+            L_copy.append(elem)
+        else :
+            name_id[elem.name] += [i]
+            scores = []
+            for j in name_id[elem.name] :
+                scores.append(L[j].attractiveness)
+            best_id = max(range(len(scores)), key=scores.__getitem__)
+            t = name_id[elem.name][best_id]
+            if t == i :
+                for old in L_copy :
+                    if old.name == elem.name :
+                        old.attractiveness = L[t].attractiveness
+
+            continue
+
+    for i, elem in enumerate(L_copy) :
+        scores[i] = elem.attractiveness
+
+    res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-N_IMPORTANT:]
+
+    for i, elem in enumerate(L_copy) :
+        if i in res :
+            L_clean.append(elem)
+
+    return L_clean
+
+
 # Remove duplicate elements and elements with low score
 def cleanup_list(L: List[Landmark])->List[Landmark] :
     L_clean = []
@@ -207,6 +250,11 @@ def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], la
             # skip if unprecise location
             if name is None or location[0] is None:
                 continue
+
+            # skip if unused
+            if 'disused:leisure' in elem.tags().keys():
+                    continue
+            
             else :
                 
                 osm_type = elem.type()              # Add type : 'way' or 'relation'
@@ -214,8 +262,16 @@ def get_landmarks_coords(coordinates: Tuple[float, float], l: List[Landmark], la
                 elem_type = landmarktype            # Add the landmark type as 'sightseeing
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 
-                # Add score of given landmark based on the number of surrounding elements
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
-                score = count_elements_within_radius(location)
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int(count_elements_within_radius(location)*CHURCH_PENALTY)  
+                elif amenity == "'leisure'='park'" :
+                    score = int(count_elements_within_radius(location)*PARK_COEFF)  
+                else :
+                    score = count_elements_within_radius(location)
+
+                if name == "Jardin du Luxembourg" :
+                    continue
 
                 if score is not None :
                     # Generate the landmark and append it to the list
@@ -247,6 +303,11 @@ def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype:
             # skip if unprecise location
             if name is None or location[0] is None:
                 continue
+
+            # skip if unused
+            if 'disused:leisure' in elem.tags().keys():
+                    continue
+            
             else :
                 
                 osm_type = elem.type()              # Add type : 'way' or 'relation'
@@ -255,7 +316,11 @@ def get_landmarks_nominatim(city_country: str, l: List[Landmark], landmarktype:
                 n_tags = len(elem.tags().keys())    # Add number of tags
                 
                 # Add score of given landmark based on the number of surrounding elements
-                score = count_elements_within_radius(location)
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int(count_elements_within_radius(location)*CHURCH_PENALTY)
+                else :
+                    score = count_elements_within_radius(location)
+
 
                 if score is not None :
                     # Generate the landmark and append it to the list

backend/src/optimizer.py

@@ -498,14 +498,15 @@ def solve_optimization (landmarks, max_steps, printing_details) :
             print(i)
             i += 1
 
+        t, order, [] = is_connected(res.x, landmarks)
+        
         if printing_details is True :
             if i != 0 :
                 print(f"Neded to recompute paths {i} times because of unconnected loops...")
-            t, order, [] = is_connected(res.x, landmarks)
+            
-            X = print_res(res, order, landmarks, P)
+            print_res(res, order, landmarks, P)
-            return X
+
-        else :
+        return order
-            return untangle(res.x)
 
 
 

backend/src/structs/landmarks.py

@@ -1,3 +1,4 @@
+from typing import Optional
 from pydantic import BaseModel
 from OSMPythonTools.api import Api
 from .landmarktype import LandmarkType
@@ -18,5 +19,6 @@ class Landmark(BaseModel) :
     attractiveness : int
     must_do : bool
     n_tags : int
+    time_to_reach : Optional[int] = 0
 
 

backend/src/tester.py

@@ -82,6 +82,7 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     test.append(finish)
     test.insert(0, start)
 
+
     max_walking_time = 4 # hours
 
     visiting_order = solve_optimization(test, max_walking_time*60, True)