format

sleap/nn/tracking.py

@@ -74,7 +74,6 @@ def from_instance(
         shift_score: float = 0.0,
         with_skeleton: bool = False,
     ):
-
         points_array = new_points_array
         if points_array is None:
             points_array = ref_instance.points_array
@@ -517,6 +516,7 @@ def get_candidates(
 @attr.s(auto_attribs=True)
 class BaseTracker(abc.ABC):
     """Abstract base class for tracker."""
+
     verbosity: str
     report_rate: float
 
@@ -547,10 +547,10 @@ def run_step(self, lf: LabeledFrame) -> LabeledFrame:
         )
 
     def run_tracker(
-        self, 
-        frames: List[LabeledFrame], 
-        *, 
-        verbosity: Optional[str] = None, 
+        self,
+        frames: List[LabeledFrame],
+        *,
+        verbosity: Optional[str] = None,
         final_pass: bool = True,
     ) -> List[LabeledFrame]:
         """Run the tracker on a set of labeled frames.
@@ -745,7 +745,6 @@ class Tracker(BaseTracker):
     )
     report_rate: float = 2.0
 
-
     @property
     def is_valid(self):
         return self.similarity_function is not None
@@ -813,7 +812,6 @@ def track(
         if t is None:
             if self.has_max_tracking:
                 if len(self.track_matching_queue_dict) > 0:
-
                     # Default to last timestep + 1 if available.
                     # Here we find the track that has the most instances.
                     track_with_max_instances = max(
@@ -829,7 +827,6 @@ def track(
                     t = 0
             else:
                 if len(self.track_matching_queue) > 0:
-
                     # Default to last timestep + 1 if available.
                     t = self.track_matching_queue[-1].t + 1
 
@@ -844,7 +841,6 @@ def track(
 
         # Process untracked instances.
         if untracked_instances:
-
             if self.pre_cull_function:
                 self.pre_cull_function(untracked_instances)
 
@@ -934,7 +930,6 @@ def spawn_for_untracked_instances(
     ) -> List[InstanceType]:
         results = []
         for inst in unmatched_instances:
-
             # Skip if this instance is too small to spawn a new track with.
             if inst.n_visible_points < self.min_new_track_points:
                 continue
@@ -1108,7 +1103,6 @@ def pre_cull_function(inst_list):
 
     @classmethod
     def get_by_name_factory_options(cls):
-
         options = []
 
         option = dict(name="tracker", default="None")
@@ -1212,9 +1206,9 @@ def get_by_name_factory_options(cls):
 
         option = dict(name="of_window_size", default=21)
         option["type"] = int
-        option[
-            "help"
-        ] = "For optical-flow: Optical flow window size to consider at each pyramid "
+        option["help"] = (
+            "For optical-flow: Optical flow window size to consider at each pyramid "
+        )
         "scale level"
         options.append(option)
 
@@ -1390,7 +1384,6 @@ def add_frame_instances(
         # "usuable" instances—i.e., instances with the nodes that we'll track
         # using Kalman filters.
         elif frame_match.has_only_first_choice_matches:
-
             good_instances = [
                 inst for inst in instances if self.is_usable_instance(inst)
             ]
@@ -1610,7 +1603,6 @@ def track(
         # Check whether we've been getting good results from the Kalman filters.
         # First, has it been a while since the filters were initialized?
         if self.init_done and (t - self.last_init_t) > self.re_init_cooldown:
-
             # If it's been a while, then see if it's also been a while since
             # the filters successfully matched tracks to the instances.
             if self.kalman_tracker.last_frame_with_tracks < t - self.re_init_after: