deruyter92 · Jul 20, 2026 · Jul 15, 2026 · Jul 15, 2026 · Jul 15, 2026 · Jul 15, 2026
diff --git a/deeplabcut/core/trackingutils.py b/deeplabcut/core/trackingutils.py
@@ -428,7 +428,7 @@ def track(self, poses, identities=None):
             if el is not None:
                 ellipses.append(el)
                 if identities is not None:
-                    pred_ids.append(mode(identities[i])[0][0])
+                    pred_ids.append(mode(identities[i], keepdims=False)[0])
         if not len(trackers):
             matches = np.empty((0, 2), dtype=int)
             unmatched_detections = np.arange(len(ellipses))
@@ -480,7 +480,7 @@ def track(self, poses, identities=None):
         for i in unmatched_detections:
             trk = EllipseTracker(ellipses[i].parameters)
             if identities is not None:
-                trk.id_ = mode(identities[i])[0][0]
+                trk.id_ = mode(identities[i], keepdims=False)[0]
             self.trackers.append(trk)
             animalindex.append(i)
 
@@ -611,7 +611,7 @@ def track(self, poses):
         for tracker in reversed(self.trackers):
             i -= 1
             if tracker.time_since_update > self.max_age:
-                self.trackers.pop()
+                self.trackers.pop(i)
                 continue
             state = tracker.predict()
             states.append(np.r_[state, [tracker.id, int(animalindex[i])]])

diff --git a/deeplabcut/pose_estimation_pytorch/runners/inference.py b/deeplabcut/pose_estimation_pytorch/runners/inference.py
@@ -665,7 +665,7 @@ def add_conditions(
 
         # Extract the conditions
         conds = predictions["bodyparts"][..., :3]
-        pred_mask = ~np.all(np.any(conds <= 0 | np.isnan(conds), axis=2), axis=1)
+        pred_mask = ~np.all(np.any((conds <= 0) | np.isnan(conds), axis=2), axis=1)
         if np.sum(pred_mask) > 0:
             conds = conds[pred_mask]
         else:
@@ -802,7 +802,7 @@ def _ctd_tracking_postprocess(
         else:
             self._prev_pose = None
             self._idx_to_id = None
-            self._idx_ages = None
+            self._ctd_track_ages = None
 
     def _merge_conditions(self, bu_cond: np.ndarray) -> np.ndarray:
         """Merges conditions made by a BU model with existing conditions from CTD

diff --git a/deeplabcut/pose_estimation_tensorflow/core/predict_multianimal.py b/deeplabcut/pose_estimation_tensorflow/core/predict_multianimal.py
@@ -12,7 +12,7 @@
 
 import numpy as np
 import tensorflow as tf
-from scipy.ndimage import measurements
+from scipy.ndimage import center_of_mass, label
 from skimage.feature import peak_local_max
 
 
@@ -263,8 +263,8 @@ def find_local_maxima(scmap, radius, threshold):
     peak_idx = peak_local_max(scmap, min_distance=radius, threshold_abs=threshold, exclude_border=False)
     grid = np.zeros_like(scmap, dtype=bool)
     grid[tuple(peak_idx.T)] = True
-    labels = measurements.label(grid)[0]
-    xy = measurements.center_of_mass(grid, labels, range(1, np.max(labels) + 1))
+    labels = label(grid)[0]
+    xy = center_of_mass(grid, labels, range(1, np.max(labels) + 1))
     return np.asarray(xy, dtype=int).reshape((-1, 2))
 
 

diff --git a/deeplabcut/pose_estimation_tensorflow/core/test.py b/deeplabcut/pose_estimation_tensorflow/core/test.py
@@ -48,7 +48,7 @@ def test_net(visualise, cache_scoremaps):
             Path(out_dir).mkdir(parents=True)
 
     num_images = dataset.num_images
-    predictions = np.zeros((num_images,), dtype=np.object)
+    predictions = np.zeros((num_images,), dtype=object)
 
     for k in range(num_images):
         print(f"processing image {k}/{num_images - 1}")

diff --git a/deeplabcut/post_processing/analyze_skeleton.py b/deeplabcut/post_processing/analyze_skeleton.py
@@ -162,7 +162,7 @@ def analyzebone(bp1, bp2):
     bone_orientation = calc_angle_between_vectors_of_points_2d(bp1_pos.T, bp2_pos.T)
 
     # keep the smallest of the two likelihoods
-    likelihoods = np.vstack([bp2.likelihood.values, bp2.likelihood.values]).T
+    likelihoods = np.vstack([bp1.likelihood.values, bp2.likelihood.values]).T
     likelihood = np.min(likelihoods, 1)
 
     # Create dataframe and return
@@ -256,13 +256,14 @@ def analyzeskeleton(
     Videos = collect_video_paths(videos, extensions=video_extensions)
     for video in Videos:
         print(f"Processing {video}")
-        if destfolder is None:
-            destfolder = str(Path(video).parents[0])
+        videofolder = destfolder
+        if videofolder is None:
+            videofolder = str(Path(video).parents[0])
 
         vname = Path(video).stem
         try:
             df, filepath, scorer, _ = auxiliaryfunctions.load_analyzed_data(
-                destfolder, vname, DLCscorer, filtered, track_method
+                videofolder, vname, DLCscorer, filtered, track_method
             )
         except FileNotFoundError as e:
             print(e)

diff --git a/deeplabcut/post_processing/filtering.py b/deeplabcut/post_processing/filtering.py
@@ -197,14 +197,17 @@ def filterpredictions(
             return video_to_filtered_df
 
     for video in Videos:
-        if destfolder is None:
-            destfolder = str(Path(video).parents[0])
+        videofolder = destfolder
+        if videofolder is None:
+            videofolder = str(Path(video).parents[0])
 
         print(f"Filtering with {filtertype} model {video}")
         vname = Path(video).stem
 
         try:
-            df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(destfolder, vname, DLCscorer, True, track_method)
+            df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(
+                videofolder, vname, DLCscorer, True, track_method
+            )
             print(f"Data from {vname} were already filtered. Skipping...")
             video_to_filtered_df[video] = df
             # Data has been filtered so continue to the next video
@@ -215,7 +218,7 @@ def filterpredictions(
         # Data haven't been filtered yet
         try:
             df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(
-                destfolder, vname, DLCscorer, track_method=track_method
+                videofolder, vname, DLCscorer, track_method=track_method
             )
         except FileNotFoundError as e:
             video_to_filtered_df[video] = None

diff --git a/deeplabcut/refine_training_dataset/outlier_frames.py b/deeplabcut/refine_training_dataset/outlier_frames.py
@@ -432,7 +432,7 @@ def extract_outlier_frames(
                 if frames2use is not None:
                     try:
                         frames2use = np.array(frames2use).astype("int")
-                    except ValueError():
+                    except ValueError:
                         print(
                             "Could not cast frames2use into np array, "
                             "please check that frames2use is a simply a list of integers!"