Feat report summary (#160)

* doc: skeleton metrics and swc_loading

* feat: fix label misalignments

* doc: skeletongraph and dataloading

* refactor: improved doc

---------

Co-authored-by: anna-grim <[email protected]>

Author: anna-grim

demo/merged_segment_ids.txt

@@ -13,3 +13,82 @@ Average Results...
 Total Results...
   # Splits: 27
   # Merges: 0
+Average Results...
+  # Splits: 7.2780
+  # Merges: nan
+  % Split Edges: 1.1997
+  % Omit Edges: 0.9740
+  % Merged Edges: 0.0000
+  ERL: 187.0877
+  Normalized ERL: 0.4006
+  Edge Accuracy: 97.8253
+  Split Rate: 116.9354
+  Merge Rate: nan
+
+Total Results...
+  # Splits: 27.0
+  # Merges: 0.0
+
+Average Results...
+  # Splits: 7.2780
+  # Merges: nan
+  % Split Edges: 1.1997
+  % Omit Edges: 0.9740
+  % Merged Edges: 0.0000
+  ERL: 187.0877
+  Normalized ERL: 0.4006
+  Edge Accuracy: 97.8253
+  Split Rate: 116.9354
+  Merge Rate: nan
+
+Total Results...
+  # Splits: 27.0
+  # Merges: 0.0
+
+Average Results...
+  # Splits: 7.2780
+  # Merges: nan
+  % Split Edges: 1.1997
+  % Omit Edges: 0.9740
+  % Merged Edges: 0.0000
+  ERL: 187.0877
+  Normalized ERL: 0.4006
+  Edge Accuracy: 97.8253
+  Split Rate: 116.9354
+  Merge Rate: nan
+
+Total Results...
+  # Splits: 27.0
+  # Merges: 0.0
+
+Average Results...
+  # Splits: 7.2780
+  # Merges: nan
+  % Split Edges: 1.1997
+  % Omit Edges: 0.9740
+  % Merged Edges: 0.0000
+  ERL: 187.0877
+  Normalized ERL: 0.4006
+  Edge Accuracy: 97.8253
+  Split Rate: 116.9354
+  Merge Rate: nan
+
+Total Results...
+  # Splits: 27.0
+  # Merges: 0.0
+
+Average Results...
+  # Splits: 7.2780
+  # Merges: nan
+  % Split Edges: 1.1997
+  % Omit Edges: 0.9740
+  % Merged Edges: 0.0000
+  ERL: 187.0877
+  Normalized ERL: 0.4006
+  Edge Accuracy: 97.8253
+  Split Rate: 116.9354
+  Merge Rate: nan
+
+Total Results...
+  # Splits: 27.0
+  # Merges: 0.0

demo/results-overview.txt

@@ -1,12 +1,12 @@
 ,SWC Run Length,# Splits,# Merges,% Split Edges,% Omit Edges,% Merged Edges,ERL,Normalized ERL,Edge Accuracy,Split Rate,Merge Rate
 SNT_Data-002,84.0837616185713,0.0,,0.0,0.0,0.0,84.08,1.0,100.0,,
 SNT_Data-035,163.37074041968341,0.0,,0.0,0.0,0.0,163.37,1.0,100.0,,
-SNT_Data-037,214.98055903143316,1.0,,0.9803921568627451,0.9803921568627451,0.0,40.27,0.1873,98.04,62.82,
-SNT_Data-038,254.78664303157612,1.0,,0.8264462809917356,5.785123966942149,0.0,146.36,0.5744,93.39,224.52,
+SNT_Data-037,214.98055903143316,1.0,,0.9803921568627451,0.0,0.0,184.57,0.8585,99.02,211.98,
+SNT_Data-038,254.78664303157612,1.0,,0.8264462809917356,0.0,0.0,144.38,0.5667,99.17,251.79,
 SNT_Data-041,230.16780853555437,3.0,,2.830188679245283,0.0,0.0,45.66,0.1984,97.17,51.2,
 SNT_Data-043,105.95635789438495,1.0,,1.9230769230769231,1.9230769230769231,0.0,30.3,0.286,96.15,51.19,
 SNT_Data-051,106.56843799568803,0.0,,0.0,0.0,0.0,106.57,1.0,100.0,,
 SNT_Data-052,633.5729425811579,5.0,,1.3651877133105803,2.3890784982935154,0.0,293.17,0.4627,96.25,103.53,
-SNT_Data-053,1547.4466172913558,14.0,,1.3123359580052494,0.7874015748031497,0.0,217.71,0.1407,97.9,92.8,
+SNT_Data-053,1547.4466172913558,14.0,,1.3123359580052494,0.5249343832020997,0.0,212.3,0.1372,98.16,103.02,
 SNT_Data-062,227.24329460444093,2.0,,0.9009009009009009,4.504504504504505,0.0,116.9,0.5144,94.59,105.19,
 SNT_Data-074,80.17991450259146,0.0,,0.0,0.0,0.0,80.18,1.0,100.0,,

demo/results.csv

@@ -16,7 +16,6 @@ dynamic = ["version"]
 
 dependencies = [
     "boto3",
-    'cloud-volume[crackle,seg-codecs]',
     "google-cloud-storage",
     "networkx",
     "numpy",

demo/results.xls

@@ -93,7 +93,7 @@ def load_groundtruth(self, swc_pointer, label_mask):
 
     def load_fragments(self, swc_pointer, gt_graphs):
         """
-        Load fragment graphs (predicted skeletons).
+        Loads fragment graphs (predicted skeletons).
 
         Parameters
         ----------
@@ -129,7 +129,7 @@ def load_fragments(self, swc_pointer, gt_graphs):
     # --- Helpers ---
     def get_all_node_labels(self, graphs):
         """
-        Get the set of unique node labels across all given graphs.
+        Gets the set of unique node labels across all given graphs.
 
         Parameters
         ----------
@@ -138,7 +138,7 @@ def get_all_node_labels(self, graphs):
 
         Returns
         -------
-        labels : Set[int]
+        node_labels : Set[int]
             Unique node labels across all graphs.
         """
         node_labels = set()
@@ -149,7 +149,7 @@ def get_all_node_labels(self, graphs):
 
 class GraphLoader:
     """
-    A class that builds a graphs from SWC files.
+    A class that builds graphs from SWC files.
     """
 
     def __init__(
@@ -176,8 +176,8 @@ def __init__(
         label_mask : ImageReader, optional
             Predicted segmentation mask.
         selected_ids : Set[int], optional
-            Only SWC files with a name contained in this set are read.
-            Default is None.
+            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
             from physical to image coordinates using the given anisotropy.
@@ -210,7 +210,7 @@ def run(self, swc_pointer):
 
         Returns
         -------
-        dict
+        graphs : Dict[str, SkeletonGraph]
             Dictionary where the keys are unique identifiers (i.e. filenames
             of SWC files) and values are the corresponding SkeletonGraph.
         """
@@ -233,7 +233,7 @@ def _build_graphs_from_swcs(self, swc_pointer):
 
         Returns
         -------
-        dict
+        graphs : Dict[str, SkeletonGraph]
             Dictionary where the keys are unique identifiers (i.e. SWC
             filenames) and values are the corresponding SkeletonGraphs.
         """
@@ -243,11 +243,11 @@ def _build_graphs_from_swcs(self, swc_pointer):
             pbar = tqdm(total=len(swc_dicts), desc="Build Graphs")
 
         # Main
-        graph_dict = dict()
+        graphs = dict()
         if len(swc_dicts) > 10 ** 4:
             while len(swc_dicts) > 0:
                 swc_dict = swc_dicts.pop()
-                graph_dict.update(self.to_graph(swc_dict))
+                graphs.update(self.to_graph(swc_dict))
                 if self.verbose:
                     pbar.update(1)
         else:
@@ -260,10 +260,10 @@ def _build_graphs_from_swcs(self, swc_pointer):
 
                 # Store results
                 for process in as_completed(processes):
-                    graph_dict.update(process.result())
+                    graphs.update(process.result())
                     if self.verbose:
                         pbar.update(1)
-        return graph_dict
+        return graphs
 
     def to_graph(self, swc_dict):
         """
@@ -278,7 +278,7 @@ def to_graph(self, swc_dict):
 
         Returns
         -------
-        SkeletonGraph
+        Dict[str, SkeletonGraph]
             Graph built from an SWC file.
         """
         # Initialize graph
@@ -307,7 +307,7 @@ def _init_graph(self, swc_dict):
     
         Returns
         -------
-        SkeletonGraph
+        graph : SkeletonGraph
             An initialized LabeledGraph or FragmentGraph instance with voxel
             data, filename, and node count set.
         """
@@ -399,7 +399,7 @@ def get_patch_labels(self, graph, nodes):
 
         Returns
         -------
-        dict
+        node_to_label : Dict[int, int]
             Dictionary that maps node IDs to their respective labels.
         """
         bbox = graph.get_bbox(nodes)
@@ -578,7 +578,7 @@ def build_labels_graph(self, connections_path):
 
         Returns
         -------
-        LabeledGraph
+        labels_graph : networkx.Graph
             Graph with nodes that represent labels and edges are based on the
             connections read from the "connections_path".
         """
@@ -627,14 +627,14 @@ def get_class(self, label):
 
         Returns
         -------
-        List[int] or Set[int]
+        List[int]
             Labels corresponding to the class.
         """
         return self.inverse_mapping[label] if self.use_mapping() else [label]
 
     def use_mapping(self):
         """
-        Check whether mappings have been initialized.
+        Checks whether mappings have been initialized.
 
         Returns
         -------

pyproject.toml

@@ -398,7 +398,7 @@ def get_bbox(self, nodes):
 
         Returns
         -------
-        dict
+        Dict[str, Tuple[int]
             Dictionary containing the bounding box coordinates:
             - "min": minimum voxel coordinates along each axis.
             - "max": maximum voxel coordinates along each axis.
@@ -435,7 +435,7 @@ def get_radius(self):
 
 class FragmentGraph(SkeletonGraph):
     """
-    Subclass of SkeletonGraph for skeletons derived from the segmentation.
+    Subclass of SkeletonGraph for skeletons obtained from a segmentation.
     """
 
     def __init__(