Added refiner (for minor landmarks)

backend/src/landmarks_manager.py

@@ -120,15 +120,18 @@ def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
     Returns:
     List[Landmark]: A list of unique Landmark objects based on their names.
     """
+    
     L_clean = []
     names = []
+    coords = []
 
     for landmark in L :
-        if landmark.name in names : 
+        if landmark.name in names and landmark.location in coords: 
             continue     
         else :
             names.append(landmark.name)
             L_clean.append(landmark)
+            coords.append(tuple((round(landmark.location[0], 3), round(landmark.location[0], 3))))
     
     return L_clean
     

backend/src/optimizer.py

@@ -308,6 +308,7 @@ def add_time_to_reach(order: List[Landmark], landmarks: List[Landmark])->List[La
         elem = landmarks[order[j]]
         if elem != prev :
             elem.time_to_reach = get_distance(elem.location, prev.location, detour, speed)[1]
+            elem.must_do = True
         L.append(elem)
         prev = elem   
         j += 1

backend/src/refiner.py

@@ -0,0 +1,51 @@
+from shapely import buffer, LineString, Point, Polygon
+from typing import List
+from math import pi
+
+from structs.landmarks import Landmark
+
+
+def create_corridor(landmarks: List[Landmark], width: float) :
+
+  corrected_width = (180*width)/(6371000*pi)
+  
+  path = create_linestring(landmarks)
+  obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
+
+  return obj
+
+
+def create_linestring(landmarks: List[Landmark])->List[Point] :
+
+  points = []
+
+  for landmark in landmarks :
+    points.append(Point(landmark.location))
+
+  return LineString(points)
+
+
+"""def is_in_area_ring(area: Polygon, coordinates) -> bool :
+
+  point = Point(coordinates)
+  if area.contains(point) :
+    t = area.interiors[0]
+    return not t.contains(point)
+"""
+
+
+def is_in_area(area: Polygon, coordinates) -> bool :
+
+  point = Point(coordinates)
+  return point.within(area)
+
+def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
+
+  second_order_landmarks = []
+  area = create_corridor(visited_landmarks, width)
+
+  for landmark in all_landmarks :
+    if is_in_area(area, landmark.location) and landmark not in visited_landmarks:
+      second_order_landmarks.append(landmark)
+
+  return second_order_landmarks

backend/src/tester.py

@@ -5,13 +5,14 @@ from landmarks_manager import generate_landmarks
 from fastapi.encoders import jsonable_encoder
 
 from optimizer import solve_optimization
+from refiner import get_minor_landmarks
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
 
 
 # Helper function to create a .txt file with results
-def write_data(L: List[Landmark]): 
+def write_data(L: List[Landmark], file_name: str): 
 
     data = pd.DataFrame()
     i = 0
@@ -20,7 +21,7 @@ def write_data(L: List[Landmark]):
         data[i] = jsonable_encoder(landmark)
         i += 1
 
-    data.to_json('landmarks.txt', indent = 2, force_ascii=False)
+    data.to_json(file_name, indent = 2, force_ascii=False)
 
 def test3(city_country: str) -> List[Landmark]:
 
@@ -59,6 +60,9 @@ def test3(city_country: str) -> List[Landmark]:
     visiting_list = solve_optimization(test, max_walking_time*60, True)
 
 
+    
+
+
 def test4(coordinates: tuple[float, float]) -> List[Landmark]:
 
 
@@ -79,8 +83,7 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     city_country = None
 
     landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
-    write_data(landmarks)
+    #write_data(landmarks, "landmarks.txt")
 
 
     start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
@@ -91,12 +94,24 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     test.insert(0, start)
     test.append(finish)
 
-    max_walking_time = 4 # hours
+    max_walking_time = 4    # hours
+    detour = 30             # minutes
 
-    visiting_list = solve_optimization(test, max_walking_time*60, True)
+    visited_list = solve_optimization(test, max_walking_time*60, True)
+    #visited_list = [Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=0), Landmark(name='Palais du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8614768, 2.3351677), osm_type='relation', osm_id=3262297, attractiveness=32, must_do=False, n_tags=32, time_to_reach=85), Landmark(name='Musée du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8611474, 2.3358637), osm_type='relation', osm_id=7515426, attractiveness=34, must_do=False, n_tags=33, time_to_reach=1), Landmark(name='Bourse de Commerce — Pinault Collection', type=LandmarkType(landmark_type='sightseeing'), location=(48.8628167, 2.3428183), osm_type='way', osm_id=19856722, attractiveness=32, must_do=False, n_tags=32, time_to_reach=12), Landmark(name='Centre Georges Pompidou', type=LandmarkType(landmark_type='sightseeing'), location=(48.8605235, 2.3524395), osm_type='way', osm_id=55503397, attractiveness=43, must_do=False, n_tags=43, time_to_reach=15), Landmark(name='Tour Saint-Jacques', type=LandmarkType(landmark_type='sightseeing'), location=(48.8579983, 2.3489178), osm_type='way', osm_id=20326709, attractiveness=33, must_do=False, n_tags=31, time_to_reach=8), Landmark(name='Hôtel de Ville', type=LandmarkType(landmark_type='sightseeing'), location=(48.8564265, 2.352527), osm_type='relation', osm_id=284089, attractiveness=34, must_do=False, n_tags=32, time_to_reach=7), Landmark(name='Cathédrale Notre-Dame de Paris', type=LandmarkType(landmark_type='sightseeing'), location=(48.8529372, 2.3498701), osm_type='way', osm_id=201611261, attractiveness=55, must_do=False, n_tags=54, time_to_reach=9), Landmark(name='Sainte-Chapelle', type=LandmarkType(landmark_type='sightseeing'), location=(48.8553966, 2.3450136), osm_type='relation', osm_id=3344870, attractiveness=57, must_do=False, n_tags=54, time_to_reach=10), Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=90)] 
 
 
-    return visiting_list
+    minor_landmarks = get_minor_landmarks(landmarks, visited_list, 200)
+    #write_data(minor_landmarks, 'minor_landmarks.txt')
+    print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+
+    fuller_list = minor_landmarks + visited_list
+
+    new_visit = solve_optimization(fuller_list, max_walking_time*60+detour, True)
+
+    
+    return visited_list
 
 
 test4(tuple((48.8795156, 2.3660204)))