added OSM api

backend/app/src/landmarks_manager.py

@@ -0,0 +1,52 @@
+from OSMPythonTools.api import Api
+from OSMPythonTools.overpass import Overpass
+from dataclasses import dataclass
+
+
+# Defines the landmark class (aka some place there is to visit)
+@dataclass
+class Landmark :
+    name : str
+    attractiveness : int
+    id : int
+
+
+# Converts a OSM id to a landmark
+def add_from_id(id: int, score: int) :
+
+    try :
+        s = 'way/' + str(id)           # prepare string for query
+        obj =  api.query(s)                             # object to add
+    except :
+        s = 'relation/' + str(id)           # prepare string for query
+        obj =  api.query(s)                             # object to add
+
+    return Landmark(obj.tag('name:fr'), score, id)      # create Landmark out of it
+
+
+# take a lsit of tuples (id, score) to generate a list of landmarks
+def generate_landmarks(ids_and_scores: list) :
+
+    L = []
+    for tup in ids_and_scores :
+        L.append(add_from_id(tup[0], tup[1]))
+
+    return L
+
+api = Api()
+
+
+l = (7515426, 70)
+t = (5013364, 100)
+n = (201611261, 99)
+a = (226413508, 50)
+m = (23762981, 30)
+
+
+ids_and_scores = [t, l, n, a, m]
+
+landmarks = generate_landmarks(ids_and_scores)
+
+
+for obj in landmarks :
+    print(obj)

backend/app/src/optimizer.py

@@ -1,15 +1,9 @@
 from scipy.optimize import linprog
 import numpy as np
 from scipy.linalg import block_diag
-from dataclasses import dataclass
 
 
-# Defines the landmark class (aka some place there is to visit)
-@dataclass
-class Landmark :
-    name : str
-    attractiveness : int
-    loc : tuple
+# landmarks = [Landmark_1, Landmark_2, ...]
 
 # Convert the solution of the optimization into the list of edges to follow. Order is taken into account
 def untangle(resx: list) :