feat: added additional skeleton features

src/deep_neurographs/feature_extraction.py

@@ -177,21 +177,35 @@ def generate_mutable_skel_features(neurograph):
     for edge in neurograph.mutable_edges:
         i, j = tuple(edge)
         radius_i, radius_j = get_radii(neurograph, edge)
-        dot1, dot2, dot3 = get_directionals(neurograph, edge, 8)
-        ddot1, ddot2, ddot3 = get_directionals(neurograph, edge, 16)
+        avg_radius_i, avg_radius_j = get_avg_radii(neurograph, edge)
+        avg_len_i, avg_len_j = get_avg_branch_len(neurograph, edge)
+        dot4_1, dot4_2, dot4_3 = get_directionals(neurograph, edge, 4)
+        dot8_1, dot8_2, dot8_3 = get_directionals(neurograph, edge, 8)
+        dot16_1, dot16_2, dot16_3 = get_directionals(neurograph, edge, 16)
+        dot32_1, dot32_2, dot_32_3 = get_directionals(neurograph, edge, 32)
         features[edge] = np.concatenate(
             (
                 neurograph.compute_length(edge),
                 neurograph.immutable_degree(i),
                 neurograph.immutable_degree(j),
                 radius_i,
                 radius_j,
-                dot1,
-                dot2,
-                dot3,
-                ddot1,
-                ddot2,
-                ddot3,
+                #avg_radius_i,
+                #avg_radius_j,
+                #avg_len_i,
+                #avg_len_j,
+                #dot4_1,
+                #dot4_2,
+                #dot4_3,
+                dot8_1,
+                dot8_2,
+                dot8_3,
+                dot16_1,
+                dot16_2,
+                dot16_3,
+                dot32_1,
+                dot32_2,
+                dot32_3,
             ),
             axis=None,
         )
@@ -218,6 +232,21 @@ def get_directionals(neurograph, edge, window):
     return inner_product_1, inner_product_2, inner_product_3
 
 
+def get_avg_radii(neurograph, edge):
+    i, j = tuple(edge)
+    radii_i = neurograph.get_branches(i, key="radius")
+    radii_j = neurograph.get_branches(j, key="radius")
+    return get_avg_radii(radii_i), get_avg_radii(radii_j)
+
+
+def get_avg_radius(radii_list):
+    avg = 0
+    for radii in radii_list:
+        end = min(16, len(radii) - 1)
+        avg += np.mean(radii[0:end]) / len(radii_list)
+    return avg
+
+
 def get_radii(neurograph, edge):
     i, j = tuple(edge)
     radius_i = neurograph.nodes[i]["radius"]

src/deep_neurographs/neurograph.py

@@ -308,10 +308,10 @@ def run_optimization(self):
             proposal = [self.to_world(xyz_1), self.to_world(xyz_2)]
             self.edges[edge]["xyz"] = np.vstack(proposal)
 
-    def get_branch(self, xyz_or_node):
+    def get_branch(self, xyz_or_node, key="xyz"):
         if type(xyz_or_node) == int:
             nb = self.get_immutable_nbs(xyz_or_node)[0]
-            return self.orient_edge((xyz_or_node, nb), xyz_or_node)
+            return self.orient_edge((xyz_or_node, nb), xyz_or_node, key=key)
         else:
             edge = self.xyz_to_edge[tuple(xyz_or_node)]
             branch = deepcopy(self.edges[edge]["xyz"])
@@ -320,18 +320,17 @@ def get_branch(self, xyz_or_node):
             else:
                 return branch
 
-    def get_branches(self, i):
+    def get_branches(self, i, key="xyz"):
         branches = []
-        for j in self.neighbors(i):
-            if frozenset((i, j)) in self.immutable_edges:
-                branches.append(self.orient_edge((i, j), i))
+        for j in self.get_immutable_nbs(i):
+            branches.append(self.orient_edge((i, j), i, key=key))
         return branches
 
-    def orient_edge(self, edge, i):
+    def orient_edge(self, edge, i, key="xyz"):
         if (self.edges[edge]["xyz"][0, :] == self.nodes[i]["xyz"]).all():
-            return self.edges[edge]["xyz"]
+            return self.edges[edge][key]
         else:
-            return np.flip(self.edges[edge]["xyz"], axis=0)
+            return np.flip(self.edges[edge][key], axis=0)
 
     # --- Ground Truth Generation ---
     def init_targets(self, target_neurograph):