Refactored preprocess.py and pose_utils.py

deepof/pose_utils.py

@@ -405,14 +405,12 @@ def rule_based_tagging(
         - videos (list): list of videos to load, in the same order as tracks
         - coordinates (deepof.preprocessing.coordinates): coordinates object containing the project information
         - vid_index (int): index in videos of the experiment to annotate
-        - animal_ids (list): IDs identifying multiple animals on the arena. None if there's only one
-        - show (bool): if True, enables the display of the annotated video in a separate window
-        - save (bool): if True, saves the annotated video to an mp4 file
+        - mode (str): if show, enables the display of the annotated video in a separate window, saves to mp4 file
+        if save
         - fps (float): frames per second of the analysed video. Same as input by default
         - path (str): directory in which the experimental data is stored
         - frame_limit (float): limit the number of frames to output. Generates all annotated frames by default
         - recog_limit (int): number of frames to use for arena recognition (1 by default)
-        - arena_type (str): type of the arena used in the experiments. Must be one of 'circular'"
         - hparams (dict): dictionary to overwrite the default values of the hyperparameters of the functions
         that the rule-based pose estimation utilizes. Values can be:
             - speed_pause (int): size of the rolling window to use when computing speeds
@@ -456,26 +454,6 @@ def rule_based_tagging(
                 arena[2],
             )
         )
-        tag_dict[animal_ids[0] + "_nose2tail"] = deepof.utils.smooth_boolean_array(
-            close_single_contact(
-                coords,
-                animal_ids[0] + "_Nose",
-                animal_ids[1] + "_Tail_base",
-                hparams["close_contact_tol"],
-                arena_abs,
-                arena[2],
-            )
-        )
-        tag_dict[animal_ids[1] + "_nose2tail"] = deepof.utils.smooth_boolean_array(
-            close_single_contact(
-                coords,
-                animal_ids[1] + "_Nose",
-                animal_ids[0] + "_Tail_base",
-                hparams["close_contact_tol"],
-                arena_abs,
-                arena[2],
-            )
-        )
         tag_dict["sidebyside"] = deepof.utils.smooth_boolean_array(
             close_double_contact(
                 coords,
@@ -502,6 +480,17 @@ def rule_based_tagging(
                 arena_rel=arena[2],
             )
         )
+        for _id in animal_ids:
+            tag_dict[_id + "_nose2tail"] = deepof.utils.smooth_boolean_array(
+                close_single_contact(
+                    coords,
+                    _id + "_Nose",
+                    [i for i in animal_ids if i != _id][0] + "_Tail_base",
+                    hparams["close_contact_tol"],
+                    arena_abs,
+                    arena[2],
+                )
+            )
         for _id in animal_ids:
             tag_dict[_id + "_following"] = deepof.utils.smooth_boolean_array(
                 following_path(

deepof/preprocess.py

@@ -51,7 +51,7 @@ class project:
         smooth_alpha: float = 0.1,
         arena_dims: tuple = (1,),
         model: str = "mouse_topview",
-        animal_ids: List = None,
+        animal_ids: List = (""),
     ):
 
         self.path = path
@@ -368,7 +368,7 @@ class coordinates:
         exp_conditions: dict = None,
         distances: dict = None,
         angles: dict = None,
-        animal_ids: List = None,
+        animal_ids: List = (""),
     ):
         self._tables = tables
         self.distances = distances