major refactor

Author: anna-grim

demo/demo.py

@@ -8,34 +8,24 @@
 
 """
 
-from segmentation_skeleton_metrics.skeleton_metric import SkeletonMetric
+from segmentation_skeleton_metrics.evaluate import evaluate
 from segmentation_skeleton_metrics.utils.img_util import TiffReader
 
 
-def evaluate():
+def main():
     """
     Evaluates the accuracy of a predicted segmentation by comparing it to a
     set of ground truth skeletons, then reports and saves various performance
     metrics.
-
-    Parameters
-    ----------
-    None
-
-    Returns
-    -------
-    None
-
     """
     # Initializations
     pred_labels = TiffReader(pred_labels_path, swap_axes=False)
-    skeleton_metric = SkeletonMetric(
+    skeleton_metric = evaluate(
         groundtruth_pointer,
         pred_labels,
+        output_dir,
         fragments_pointer=fragments_pointer,
-        output_dir=output_dir,
     )
-    skeleton_metric.run()
 
 
 if __name__ == "__main__":
@@ -46,4 +36,4 @@ def evaluate():
     groundtruth_pointer = "./data/target_swcs.zip"
 
     # Run
-    evaluate()
+    main()

demo/results-overview.txt

@@ -1,8 +1,8 @@
 Average Results...
-  # Splits: 7.9680
+  # Splits: 7.2780
   # Merges: 0.0000
-  Split Rate: 114.6940
-  Merge Rate: 0.0000
+  Split Rate: 124.3079
+  Merge Rate: nan
   % Split Edges: 1.1996
   % Omit Edges: 4.5086
   % Merged Edges: 0.0000
@@ -11,20 +11,5 @@ Average Results...
   Normalized ERL: 0.3949
 
 Total Results...
-  # Splits: 31
-  # Merges: 0
-Average Results...
-  # Splits: 7.9680
-  # Merges: 0.0000
-  Split Rate: 114.6940
-  Merge Rate: 0.0000
-  % Split Edges: 1.1996
-  % Omit Edges: 4.5086
-  % Merged Edges: 0.0000
-  Edge Accuracy: 95.4914
-  ERL: 180.8849
-  Normalized ERL: 0.3949
-
-Total Results...
-  # Splits: 31
+  # Splits: 27
   # Merges: 0

demo/results.csv

@@ -1,12 +1,12 @@
-,# Splits,# Merges,Split Rate,Merge Rate,% Split Edges,% Omit Edges,% Merged Edges,Edge Accuracy,ERL,Normalized ERL,GT Run Length
-SNT_Data-002,0,,84.08376161857129,,0.0,0.0,0.0,100.0,84.08,1.0,84.08
-SNT_Data-035,0,,163.37074041968347,,0.0,0.0,0.0,100.0,163.37,1.0,163.37
-SNT_Data-037,1,,211.9805590314331,,0.98,1.96,0.0,98.04,184.57,0.8585,214.98
-SNT_Data-038,1,,251.78664303157595,,0.83,1.65,0.0,98.35,144.38,0.5667,254.79
-SNT_Data-041,4,,55.46881994891758,,2.83,7.55,0.0,92.45,52.62,0.2286,230.17
-SNT_Data-043,2,,46.354813254114404,,1.92,9.62,0.0,90.38,35.32,0.3334,105.96
-SNT_Data-051,0,,106.56843799568803,,0.0,0.0,0.0,100.0,106.57,1.0,106.57
-SNT_Data-052,6,,99.17738848187402,,1.37,6.48,0.0,93.52,265.1,0.4184,633.57
-SNT_Data-053,15,,98.7153068880868,,1.31,4.46,0.0,95.54,207.79,0.1343,1547.45
-SNT_Data-062,2,,105.1897956496423,,0.9,8.11,0.0,91.89,116.9,0.5144,227.24
-SNT_Data-074,0,,80.17991450259146,,0.0,0.0,0.0,100.0,80.18,1.0,80.18
+# Splits,# Merges,% Split Edges,% Omit Edges,% Merged Edges,ERL,Normalized ERL,Edge Accuracy,Split Rate,Merge Rate
+0.0,,0.0,0.0,0.0,84.08,1.0,100.0,,
+0.0,,0.0,0.0,0.0,163.37,1.0,100.0,,
+1.0,,0.9803921568627451,0.9803921568627451,0.0,40.27,0.1873,98.04,62.82,
+1.0,,0.8264462809917356,5.785123966942149,0.0,146.36,0.5744,93.39,224.52,
+3.0,,2.830188679245283,0.0,0.0,45.66,0.1984,97.17,51.2,
+1.0,,1.9230769230769231,1.9230769230769231,0.0,30.3,0.286,96.15,51.19,
+0.0,,0.0,0.0,0.0,106.57,1.0,100.0,,
+5.0,,1.3651877133105803,2.3890784982935154,0.0,293.17,0.4627,96.25,103.53,
+14.0,,1.3123359580052494,0.7874015748031497,0.0,217.71,0.1407,97.9,92.8,
+2.0,,0.9009009009009009,4.504504504504505,0.0,116.9,0.5144,94.59,105.19,
+0.0,,0.0,0.0,0.0,80.18,1.0,100.0,,

pyproject.toml

@@ -25,7 +25,6 @@ dependencies = [
     "scikit-image",
     "tensorstore",
     "tifffile",
-    "xlwt",
     "zarr",
 ]
 

src/segmentation_skeleton_metrics/data_handling/graph_loading.py

@@ -17,8 +17,11 @@
 import networkx as nx
 import numpy as np
 
-from segmentation_skeleton_metrics.data_handling.skeleton_graph import SkeletonGraph
-from segmentation_skeleton_metrics.utils import swc_util, util
+from segmentation_skeleton_metrics.data_handling import swc_loading
+from segmentation_skeleton_metrics.data_handling.skeleton_graph import (
+    FragmentGraph, LabeledGraph
+)
+from segmentation_skeleton_metrics.utils import util
 
 
 class DataLoader:
@@ -93,6 +96,7 @@ def load_fragments(self, swc_pointer, gt_graphs):
         graph_loader = GraphLoader(
             anisotropy=self.anisotropy,
             is_groundtruth=False,
+            label_handler=self.label_handler,
             selected_ids=selected_ids,
             use_anisotropy=self.use_anisotropy,
         )
@@ -112,10 +116,10 @@ def get_all_node_labels(self, graphs):
         labels : Set[int]
             Unique node labels across all graphs.
         """
-        labels = set()
+        node_labels = set()
         for graph in graphs.values():
-            labels |= self.label_handler.get_node_labels(graph)
-        return labels
+            node_labels |= self.label_handler.get_node_labels(graph)
+        return node_labels
 
 
 class GraphLoader:
@@ -146,7 +150,7 @@ def __init__(
         label_mask : ImageReader, optional
             Predicted segmentation mask.
         selected_ids : Set[int], optional
-            Only SWC files with an swc_id contained in this set are read.
+            Only SWC files with a name contained in this set are read.
             Default is None.
         use_anisotropy : bool, optional
             Indication of whether coordinates in SWC files should be converted
@@ -161,7 +165,7 @@ def __init__(
 
         # Reader
         anisotropy = anisotropy if use_anisotropy else (1.0, 1.0, 1.0)
-        self.swc_reader = swc_util.Reader(
+        self.swc_reader = swc_loading.Reader(
             anisotropy, selected_ids=selected_ids
         )
 
@@ -181,11 +185,11 @@ def run(self, swc_pointer):
             Dictionary where the keys are unique identifiers (i.e. filenames
             of SWC files) and values are the corresponding SkeletonGraph.
         """
-        graph_dict = self._build_graphs_from_swcs(swc_pointer)
+        graphs = self._build_graphs_from_swcs(swc_pointer)
         if self.label_mask:
-            for key in graph_dict:
-                self._label_graph(graph_dict[key])
-        return graph_dict
+            for name in graphs:
+                self._label_graph(graphs[name])
+        return graphs
 
     # --- Build Graphs ---
     def _build_graphs_from_swcs(self, swc_pointer):
@@ -246,25 +250,40 @@ def to_graph(self, swc_dict):
             Graph built from an SWC file.
         """
         # Initialize graph
-        graph = SkeletonGraph(
-            anisotropy=self.anisotropy, is_groundtruth=self.is_groundtruth
-        )
-        graph.init_voxels(swc_dict["voxel"])
-        graph.set_filename(swc_dict["swc_id"] + ".swc")
-        graph.set_nodes(len(swc_dict["id"]))
+        graph = self._init_graph(swc_dict)
 
-        # Build graph
+        # Build graph structure
         id_lookup = dict()
         for i, id_i in enumerate(swc_dict["id"]):
             id_lookup[id_i] = i
             if swc_dict["pid"][i] != -1:
                 parent = id_lookup[swc_dict["pid"][i]]
                 graph.add_edge(i, parent)
                 graph.run_length += graph.dist(i, parent)
+        graph.prune_branches()
+        return {graph.name: graph}
+
+    def _init_graph(self, swc_dict):
+        # Instantiate graph
+        if self.is_groundtruth:
+            graph = LabeledGraph(
+                anisotropy=self.anisotropy, name=swc_dict["swc_name"]
+            )
+        else:
+            segment_id = util.get_segment_id(swc_dict["swc_name"])
+            label = self.label_handler.get(segment_id)
+            graph = FragmentGraph(
+                anisotropy=self.anisotropy,
+                name=swc_dict["swc_name"],
+                label=label,
+                segment_id=segment_id
+            )
 
-        # Set graph-level attributes
-        graph.graph["n_initial_edges"] = graph.number_of_edges()
-        return {swc_dict["swc_id"]: graph}
+        # Set class attributes
+        graph.init_voxels(swc_dict["voxel"])
+        graph.set_filename(swc_dict["swc_name"] + ".swc")
+        graph.set_nodes(len(swc_dict["id"]))
+        return graph
 
     # --- Label Graphs ---
     def _label_graph(self, graph):
@@ -311,11 +330,11 @@ def _label_graph(self, graph):
             )
 
             # Store results
-            graph.init_labels()
+            graph.init_node_labels()
             for thread in as_completed(threads):
                 node_to_label = thread.result()
                 for i, label in node_to_label.items():
-                    graph.labels[i] = label
+                    graph.node_labels[i] = label
 
     def get_patch_labels(self, graph, nodes):
         """
@@ -366,6 +385,9 @@ def to_local_voxels(self, graph, i, offset):
         offset = np.array(offset)
         return tuple(voxel - offset)
 
+    def fix_label_misalignments(self, graph):
+        pass
+
 
 class LabelHandler:
     """
@@ -527,7 +549,7 @@ def get_node_labels(self, graph):
         labels : Set[int]
             Labels corresponding to nodes in the graph identified by "key".
         """
-        labels = graph.get_labels()
+        labels = graph.get_node_labels()
         if self.use_mapping():
             labels = set().union(*(self.inverse_mapping[l] for l in labels))
         return labels