Train Active

lib/objects/map.py

@@ -29,11 +29,11 @@ class Map(object):
 
     @property
     def width(self):
-        return self.shape[0]
+        return self.shape[-2]
 
     @property
     def height(self):
-        return self.shape[1]
+        return self.shape[-1]
 
     @property
     def as_graph(self):
@@ -43,6 +43,10 @@ class Map(object):
     def as_array(self):
         return self.map_array
 
+    @property
+    def as_2d_array(self):
+        return self.map_array[1:]
+
     def __init__(self, name='', array_like_map_representation=None):
         if array_like_map_representation is not None:
             if array_like_map_representation.ndim == 2:
@@ -72,22 +76,26 @@ class Map(object):
                     # Differentiate between 8 and 4 neighbors
                     if not full_neighbors and n >= 2:
                         break
-
                     # ToDO: make this explicite and less ugly
                     query_node = idx[:1] + (idx[1] + ydif,) + (idx[2] + xdif,)
                     if graph.has_node(query_node):
                         graph.add_edge(idx, query_node, weight=weight)
-
         return graph
 
     @classmethod
-    def from_image(cls, imagepath: Path):
+    def from_image(cls, imagepath: Path, embedding_size=None):
         with Image.open(imagepath) as image:
             # Turn the image to single Channel Greyscale
             if image.mode != 'L':
                 image = image.convert('L')
             map_array = np.expand_dims(np.array(image), axis=0)
-            return cls(name=imagepath.name, array_like_map_representation=map_array)
+        if embedding_size:
+            assert isinstance(embedding_size, tuple), f'embedding_size was of type: {type(embedding_size)}'
+            embedding = np.zeros(embedding_size)
+            embedding[:map_array.shape[0], :map_array.shape[1], :map_array.shape[2]] = map_array
+            map_array = embedding
+
+        return cls(name=imagepath.name, array_like_map_representation=map_array)
 
     def simple_trajectory_between(self, start, dest):
         vertices = list(nx.shortest_path(self._G, start, dest))
@@ -105,36 +113,46 @@ class Map(object):
         return Trajectory(coords)
 
     def get_random_trajectory(self):
-        start = self.get_valid_position()
-        dest = self.get_valid_position()
-        return self.simple_trajectory_between(start, dest)
+        simple_trajectory = None
+        while simple_trajectory is None:
+            try:
+                start = self.get_valid_position()
+                dest = self.get_valid_position()
+                simple_trajectory = self.simple_trajectory_between(start, dest)
+            except nx.exception.NetworkXNoPath:
+                pass
+        return simple_trajectory
 
     def generate_alternative(self, trajectory, mode='one_patching'):
         start, dest = trajectory.endpoints
-        if mode == 'one_patching':
-            patch = self.get_valid_position()
-            alternative = self.get_trajectory_from_vertices(start, patch, dest)
-        else:
-            raise RuntimeError(f'mode checking went wrong...')
-
+        alternative = None
+        while alternative is None:
+            try:
+                if mode == 'one_patching':
+                    patch = self.get_valid_position()
+                    alternative = self.get_trajectory_from_vertices(start, patch, dest)
+                else:
+                    raise RuntimeError(f'mode checking went wrong...')
+            except nx.exception.NetworkXNoPath:
+                pass
         return alternative
 
     def are_homotopic(self, trajectory, other_trajectory):
         if not all(isinstance(x, Trajectory) for x in [trajectory, other_trajectory]):
             raise TypeError
-        polyline = trajectory.vertices.copy()
-        polyline.extend(reversed(other_trajectory.vertices))
+        polyline = trajectory.xy_vertices
+        polyline.extend(reversed(other_trajectory.xy_vertices))
 
         img = Image.new('L', (self.height, self.width), 0)
         draw = ImageDraw.Draw(img)
         draw.polygon(polyline, outline=255, fill=255)
 
-        a = (np.array(img) * np.where(self.map_array == self.white, 0, 1)).sum()
+        a = (np.asarray(img) * np.where(self.as_2d_array == self.white, 0, 1)).sum()
 
-        if a >= 1:
-            return False
+        if a:
+            return False  # Non-Homotoph
         else:
-            return True
+            return True  # Homotoph
 
     def draw(self):
         fig, ax = plt.gcf(), plt.gca()