Added testing examples for multi animal deepof pipeline

deepof/data.py

@@ -703,7 +703,9 @@ class coordinates:
         tag_dict = {}
         # noinspection PyTypeChecker
         coords = self.get_coords(center=False)
+        dists = self.get_distances()
         speeds = self.get_coords(speed=1)
+
         for key in tqdm(self._tables.keys()):
 
             video = [vid for vid in self._videos if key + "DLC" in vid][0]
@@ -712,6 +714,7 @@ class coordinates:
                 self._videos,
                 self,
                 coords,
+                dists,
                 speeds,
                 self._videos.index(video),
                 arena_type=self._arena,

deepof/pose_utils.py

@@ -429,6 +429,7 @@ def rule_based_tagging(
     videos: List,
     coordinates: Coordinates,
     coords: Any,
+    dists: Any,
     speeds: Any,
     vid_index: int,
     arena_type: str,
@@ -444,6 +445,7 @@ 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
         - coords (deepof.preprocessing.table_dict): table_dict with already processed coordinates
+        - dists (deepof.preprocessing.table_dict): table_dict with already processed distances
         - speeds (deepof.preprocessing.table_dict): table_dict with already processed speeds
         - vid_index (int): index in videos of the experiment to annotate
         - path (str): directory in which the experimental data is stored
@@ -474,6 +476,7 @@ def rule_based_tagging(
         vid_name = tracks[vid_index]
 
     coords = coords[vid_name]
+    dists = dists[vid_name]
     speeds = speeds[vid_name]
     arena_abs = coordinates.get_arenas[1][0]
     arena, h, w = deepof.utils.recognize_arena(
@@ -532,7 +535,7 @@ def rule_based_tagging(
         for _id in animal_ids:
             tag_dict[_id + "_following"] = deepof.utils.smooth_boolean_array(
                 following_path(
-                    coords[vid_name],
+                    dists,
                     coords,
                     follower=_id,
                     followed=[i for i in animal_ids if i != _id][0],
@@ -553,6 +556,7 @@ def rule_based_tagging(
                 hparams["huddle_forward"],
                 hparams["huddle_spine"],
                 hparams["huddle_speed"],
+                animal_id=_id
             )
         )
 

tests/test_pose_utils.py

@@ -364,7 +364,7 @@ def test_frame_corners(w, h):
 )
 def test_rule_based_tagging(multi_animal, video_output):
 
-    multi_animal = False
+    multi_animal = True
 
     if video_output:
         video_output = ["test"]
@@ -388,4 +388,4 @@ def test_rule_based_tagging(multi_animal, video_output):
     hardcoded_tags = prun.rule_based_annotation(video_output=video_output, frame_limit=50)
 
     assert type(hardcoded_tags) == deepof.data.table_dict
-    assert list(hardcoded_tags.values())[0].shape[1] == 3
+    assert list(hardcoded_tags.values())[0].shape[1] == (13 if multi_animal else 3)