feat: trim graph to fit image bbox

src/deep_neurographs/densegraph.py

@@ -10,11 +10,11 @@
 """
 
 import os
-import networkx as nx
-import numpy as np
 from random import sample
 from time import time
 
+import networkx as nx
+
 from deep_neurographs import swc_utils, utils
 
 
@@ -75,7 +75,7 @@ def init_graph(self, paths):
             graph, _ = swc_utils.to_graph(swc_dict, set_attrs=True)
             graph = add_swc_id(graph, swc_id)
             self.graph = nx.disjoint_union(self.graph, graph)
-            
+
             # Report progress
             if i > cnt * chunk_size:
                 cnt, t1 = report_progress(
@@ -111,12 +111,13 @@ def component_to_swc(self, path, component):
 
         swc_utils.write(path, entry_list)
 
-    
+
 def add_swc_id(graph, swc_id):
     for i in graph.nodes:
         graph.nodes[i]["swc_id"] = swc_id
     return graph
 
+
 def report_progress(current, total, chunk_size, cnt, t0, t1):
     eta = get_eta(current, total, chunk_size, t1)
     runtime = get_runtime(current, total, chunk_size, t0, t1)

src/deep_neurographs/graph_utils.py

@@ -32,6 +32,7 @@
 
 def get_irreducibles(
     swc_dict,
+    bbox=None,
     prune_connectors=False,
     prune_spurious=True,
     connector_length=8,
@@ -71,6 +72,7 @@ def get_irreducibles(
     # Build dense graph
     swc_dict["idx"] = dict(zip(swc_dict["id"], range(len(swc_dict["id"]))))
     graph, _ = swc_utils.to_graph(swc_dict, set_attrs=True)
+    graph = trim_branches(graph, bbox)
     graph, connector_centroids = prune_branches(
         graph,
         prune_connectors=prune_connectors,
@@ -91,6 +93,17 @@ def get_irreducibles(
     return irreducibles, connector_centroids
 
 
+def trim_branches(graph, bbox):
+    if bbox:
+        delete_nodes = set()
+        for i in graph.nodes:
+            xyz = utils.to_img(graph.nodes[i]["xyz"])
+            if not utils.is_contained(bbox, xyz):
+                delete_nodes.add(i)
+        graph.remove_nodes_from(delete_nodes)
+    return graph
+
+
 def prune_branches(
     graph,
     prune_connectors=False,

src/deep_neurographs/intake.py

@@ -281,6 +281,7 @@ def build_neurograph(
         print("# connected components:", utils.reformat_number(n_components))
     irreducibles, n_nodes, n_edges = get_irreducibles(
         swc_dicts,
+        bbox=img_bbox,
         progress_bar=progress_bar,
         prune_connectors=prune_connectors,
         prune_spurious=prune_spurious,
@@ -315,6 +316,7 @@ def build_neurograph(
 
 def get_irreducibles(
     swc_dicts,
+    bbox=None,
     progress_bar=True,
     prune_connectors=PRUNE_CONNECTORS,
     prune_spurious=PRUNE_SPURIOUS,
@@ -333,6 +335,7 @@ def get_irreducibles(
             processes[i] = executor.submit(
                 gutils.get_irreducibles,
                 swc_dict,
+                bbox,
                 prune_connectors,
                 prune_spurious,
                 connector_length,

src/deep_neurographs/machine_learning/inference.py

@@ -23,10 +23,8 @@ def predict(dataset, model, model_type):
         for batch in dataloader:
             with torch.no_grad():
                 x_i = batch["inputs"]
-                y_i = batch["targets"]
                 y_pred_i = sigmoid(model(x_i))
             y_pred.extend(np.array(y_pred_i).tolist())
-            #print((np.sum((np.array(y_pred_i) > 0.5) == (np.array(y_i) > 0))) / len(y_i))
     else:
         y_pred = model.predict_proba(dataset["inputs"])[:, 1]
     return np.array(y_pred)

src/deep_neurographs/machine_learning/ml_utils.py

@@ -16,6 +16,7 @@
 from deep_neurographs import feature_extraction as extracter
 from deep_neurographs.machine_learning.datasets import (
     ImgProposalDataset,
+    MultiModalDataset,
     ProposalDataset,
 )
 from deep_neurographs.machine_learning.models import (

src/deep_neurographs/utils.py

@@ -336,7 +336,7 @@ def read_tensorstore(arr, xyz, shape, from_center=True):
 def get_chunk(arr, xyz, shape, from_center=True):
     start, end = get_start_end(xyz, shape, from_center=from_center)
     return deepcopy(
-        arr[start[0] : end[0], start[1] : end[1], start[2] : end[2]]
+        arr[start[0]: end[0], start[1]: end[1], start[2]: end[2]]
     )