Improve shortest path performance

igibson/scenes/indoor_scene.py

@@ -5,8 +5,8 @@
 from abc import ABCMeta
 
 import cv2
-import networkx as nx
 import numpy as np
+import rustworkx as rx
 from future.utils import with_metaclass
 from PIL import Image
 
@@ -73,6 +73,8 @@ def load_trav_map(self, maps_path):
         for floor in range(len(self.floor_heights)):
             if self.trav_map_type == "with_obj":
                 trav_map = np.array(Image.open(os.path.join(maps_path, "floor_trav_{}.png".format(floor))))
+            elif self.trav_map_type == "no_door":
+                trav_map = np.array(Image.open(os.path.join(maps_path, "floor_trav_no_door_{}.png".format(floor))))
             else:
                 trav_map = np.array(Image.open(os.path.join(maps_path, "floor_trav_no_obj_{}.png".format(floor))))
 
@@ -100,47 +102,70 @@ def load_trav_map(self, maps_path):
 
             # We search for the largest connected areas
             if self.build_graph:
-                self.build_trav_graph(maps_path, floor, trav_map)
+                self.build_trav_graph(trav_map)
 
             self.floor_map.append(trav_map)
 
-    # TODO: refactor into C++ for speedup
-    def build_trav_graph(self, maps_path, floor, trav_map):
+    def build_trav_graph(self, trav_map):
         """
         Build traversibility graph and only take the largest connected component
 
-        :param maps_path: String with the path to the folder containing the traversability maps
-        :param floor: floor number
         :param trav_map: traversability map
         """
-        log.debug("Building traversable graph")
-        g = nx.Graph()
-        for i in range(self.trav_map_size):
-            for j in range(self.trav_map_size):
-                if trav_map[i, j] == 0:
-                    continue
-                g.add_node((i, j))
-                # 8-connected graph
-                neighbors = [(i - 1, j - 1), (i, j - 1), (i + 1, j - 1), (i - 1, j)]
-                for n in neighbors:
-                    if 0 <= n[0] < self.trav_map_size and 0 <= n[1] < self.trav_map_size and trav_map[n[0], n[1]] > 0:
-                        g.add_edge(n, (i, j), weight=l2_distance(n, (i, j)))
+        node_mapping = {}
+        g = rx.PyGraph(multigraph=False)  # type: ignore
+
+        x, y = np.where(trav_map != 0)
+        nodes_to_add = np.stack((x, y)).T
+        nodes_to_add = list(map(tuple, nodes_to_add))
+        node_idxs = g.add_nodes_from(nodes_to_add)
+        node_mapping = {data: idx for data, idx in zip(nodes_to_add, node_idxs)}
+
+        edges = set()
+        from_nodes = []
+        from_nodes_pos = []
+        to_nodes = []
+        to_nodes_pos = []
+        for node_idx, node in zip(g.node_indexes(), g.nodes()):
+            i, j = node
+            neighbors = [(i - 1, j - 1), (i, j - 1), (i + 1, j - 1), (i - 1, j)]
+            for n in neighbors:
+                if n in node_mapping and (node_idx, n) not in edges:
+                    from_nodes.append(node_idx)
+                    from_nodes_pos.append(node)
+                    to_nodes.append(node_mapping[n])
+                    to_nodes_pos.append(n)
+                    edges.add((node_idx, n))
+
+        distances = np.linalg.norm(np.array(from_nodes_pos) - np.array(to_nodes_pos), axis=1)
+        edges = [(x, y, z) for x, y, z in zip(from_nodes, to_nodes, distances)]
+        g.add_edges_from(edges)
 
         # only take the largest connected component
-        largest_cc = max(nx.connected_components(g), key=len)
-        g = g.subgraph(largest_cc).copy()
-
-        self.floor_graph.append(g)
-
+        largest_cc = max(rx.connected_components(g), key=len)  # type: ignore
+        g = g.subgraph(list(largest_cc), preserve_attrs=True).copy()
         # update trav_map accordingly
         # This overwrites the traversability map loaded before
         # It sets everything to zero, then only sets to one the points where we have graph nodes
         # Dangerous! if the traversability graph is not computed from the loaded map but from a file, it could overwrite
         # it silently.
         trav_map[:, :] = 0
-        for node in g.nodes:
+        for node in g.nodes():
             trav_map[node[0], node[1]] = 255
 
+        nodes = g.nodes()
+        node_idxs = g.node_indexes()
+        map_to_idx = {data: idx for data, idx in zip(nodes, node_idxs)}
+        self.floor_graph.append(
+            {
+                "graph": g,
+                "map_to_idx": map_to_idx,
+                "nodes": np.array(nodes),
+                "node_idxs": node_idxs,
+            }
+        )
+        self.trav_map = trav_map
+
     def get_random_point(self, floor=None):
         """
         Sample a random point on the given floor number. If not given, sample a random floor number.
@@ -204,19 +229,38 @@ def get_shortest_path(self, floor, source_world, target_world, entire_path=False
         source_map = tuple(self.world_to_map(source_world))
         target_map = tuple(self.world_to_map(target_world))
 
-        g = self.floor_graph[floor]
-
-        if not g.has_node(target_map):
-            nodes = np.array(g.nodes)
-            closest_node = tuple(nodes[np.argmin(np.linalg.norm(nodes - target_map, axis=1))])
-            g.add_edge(closest_node, target_map, weight=l2_distance(closest_node, target_map))
-
-        if not g.has_node(source_map):
-            nodes = np.array(g.nodes)
-            closest_node = tuple(nodes[np.argmin(np.linalg.norm(nodes - source_map, axis=1))])
-            g.add_edge(closest_node, source_map, weight=l2_distance(closest_node, source_map))
-
-        path_map = np.array(nx.astar_path(g, source_map, target_map, heuristic=l2_distance))
+        g = self.floor_graph[floor]["graph"]
+        map_to_idx = self.floor_graph[floor]["map_to_idx"]
+        nodes = self.floor_graph[floor]["nodes"]
+        node_idxs = self.floor_graph[floor]["node_idxs"]
+
+        if target_map not in map_to_idx:
+            closest_node = np.argmin(np.linalg.norm(nodes - target_map, axis=1))
+            closest_node_idx = node_idxs[closest_node]
+            closest_node_data = nodes[closest_node_idx]
+            target_node = g.add_node(target_map)
+            map_to_idx[target_map] = target_node
+            g.add_edge(closest_node, target_node, l2_distance(closest_node_data, target_map))
+
+        if source_map not in map_to_idx:
+            closest_node = np.argmin(np.linalg.norm(nodes - source_map, axis=1))
+            closest_node_idx = node_idxs[closest_node]
+            closest_node_data = nodes[closest_node_idx]
+            source_node = g.add_node(source_map)
+            map_to_idx[source_map] = source_node
+            g.add_edge(closest_node, source_node, l2_distance(closest_node_data, source_map))
+
+        idx_to_map = {idx: data for data, idx in map_to_idx.items()}
+
+        path_map = rx.astar_shortest_path(
+            g,
+            map_to_idx[source_map],
+            goal_fn=lambda x: x == target_map,
+            edge_cost_fn=lambda x: x,
+            estimate_cost_fn=lambda _: 0,
+        )
+
+        path_map = np.array([idx_to_map[idx] for idx in path_map])
 
         path_world = self.map_to_world(path_map)
         geodesic_distance = np.sum(np.linalg.norm(path_world[1:] - path_world[:-1], axis=1))

pyproject.toml

@@ -36,6 +36,7 @@ dependencies = [
     "bddl~=1.0.1",
     "urllib3>=1.20",
     "progressbar>=2.5",
+    "rustworkx",
     "packaging",
 ]