add an only_predicted_instances option to include user-defined instan…

…ces in the tracking

sleap/gui/learning/runners.py

@@ -261,6 +261,7 @@ def make_predict_cli_call(
             "tracking.post_connect_single_breaks",
             "tracking.save_shifted_instances",
             "tracking.oks_score_weighting",
+            "tracking.only_predicted_instances",
         )
 
         for key in bool_items_as_ints:

sleap/instance.py

@@ -1177,6 +1177,25 @@ def from_numpy(
         )
 
 
+def convert_to_predicted_instance(
+    inst: Union[Instance, PredictedInstance],
+    *,
+    score: float = 1.0,
+    tracking_score: float = 0.0,
+) -> PredictedInstance:
+    """Convert an Instance to a PredictedInstance, if it's not one already.
+
+    Score is by default 1.0, like a user-defined instance.
+    """
+    if isinstance(inst, PredictedInstance):
+        return inst
+
+    kwargs = attr.asdict(inst)
+    kwargs["score"] = score
+    kwargs["tracking_score"] = tracking_score
+    return PredictedInstance(**kwargs)
+
+
 def make_instance_cattr() -> cattr.Converter:
     """Create a cattr converter for Lists of Instances/PredictedInstances.
 

sleap/nn/tracking.py

@@ -9,6 +9,7 @@
 from typing import Callable, Deque, Dict, Iterable, List, Optional, Tuple
 
 from sleap import Track, LabeledFrame, Skeleton
+from sleap.instance import convert_to_predicted_instance
 
 from sleap.nn.tracker.components import (
     factory_object_keypoint_similarity,
@@ -22,7 +23,6 @@
     cull_frame_instances,
     connect_single_track_breaks,
     InstanceType,
-    PredictedInstance,
     FrameMatches,
     Match,
 )
@@ -581,6 +581,7 @@ class Tracker(BaseTracker):
     robust_best_instance: float = 1.0
 
     min_new_track_points: int = 0
+    only_predicted_instances: bool = True
 
     track_matching_queue: Deque[MatchedFrameInstances] = attr.ib()
 
@@ -843,6 +844,7 @@ def make_tracker_by_name(
         robust: float = 1.0,
         min_new_track_points: int = 0,
         min_match_points: int = 0,
+        only_predicted_instances: bool = True,
         # Optical flow options
         img_scale: float = 1.0,
         of_window_size: int = 21,
@@ -941,6 +943,7 @@ def pre_cull_function(inst_list):
             max_tracks=max_tracks,
             target_instance_count=target_instance_count,
             post_connect_single_breaks=post_connect_single_breaks,
+            only_predicted_instances=only_predicted_instances,
         )
 
         if target_instance_count and kf_init_frame_count:
@@ -1057,6 +1060,11 @@ def get_by_name_factory_options(cls):
         option["help"] = "Minimum points for match candidates"
         options.append(option)
 
+        option = dict(name="only_predicted_instances", default=1)
+        option["type"] = int
+        option["help"] = "Track only predicted instances, not user-defined instances."
+        options.append(option)
+
         option = dict(name="img_scale", default=1.0)
         option["type"] = float
         option["help"] = "For optical-flow: Image scale"
@@ -1518,9 +1526,10 @@ def run_tracker(frames: List[LabeledFrame], tracker: BaseTracker) -> List[Labele
     # Run tracking on every frame
     for lf in frames:
         # Use only the predicted instances
-        instances = [
-            inst for inst in lf.instances if isinstance(inst, PredictedInstance)
-        ]
+        if tracker.only_predicted_instances:
+            instances = lf.predicted_instances
+        else:
+            instances = [convert_to_predicted_instance(inst) for inst in lf.instances]
 
         # Clear the tracks
         for inst in instances: