Implemented outlier interpolation

deepof/data.py

@@ -961,8 +961,8 @@ class table_dict(dict):
             ).reshape(X_train.shape)
 
             if scale == "standard":
-                assert np.allclose(np.mean(X_train), 0)
-                assert np.allclose(np.std(X_train), 1)
+                assert np.allclose(np.nan_to_num(np.mean(X_train), nan=0), 0)
+                assert np.allclose(np.nan_to_num(np.std(X_train), nan=1), 1)
 
             if test_videos:
                 X_test = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(
@@ -1124,3 +1124,5 @@ def merge_tables(*args):
 #   - with the current implementation, preprocess can't fully work on merged table_dict instances.
 #   While some operations (mainly alignment) should be carried out before merging, others require
 #   the whole dataset to function properly.
+#   - Understand how keras handles NA values. Decide whether to do nothing, to mask them or to impute them using
+#   a clear outlier (e.g. -9999)

deepof/pose_utils.py

@@ -660,7 +660,9 @@ def tag_rulebased_frames(
                         (int(coords[bpart]["x"][fnum]), int(coords[bpart]["y"][fnum])),
                         radius=3,
                         color=(
-                            (255, 0, 0) if bpart.startswith(animal_ids[0]) else (0, 0, 255)
+                            (255, 0, 0)
+                            if bpart.startswith(animal_ids[0])
+                            else (0, 0, 255)
                         ),
                         thickness=-1,
                     )