Added testing examples for multi animal deepof pipeline

deepof/pose_utils.py

@@ -381,10 +381,10 @@ def get_hparameters(hparams: dict = {}) -> dict:
 
     defaults = {
         "speed_pause": 3,
-        "close_contact_tol": 15,
-        "side_contact_tol": 15,
-        "follow_frames": 20,
-        "follow_tol": 20,
+        "close_contact_tol": 35,
+        "side_contact_tol": 80,
+        "follow_frames": 10,
+        "follow_tol": 5,
         "huddle_forward": 15,
         "huddle_spine": 10,
         "huddle_speed": 0.1,
@@ -527,10 +527,12 @@ def rule_based_tagging(
 
         tag_dict["sidereside"] = twobytwo_contact(rev=True)
 
-        for i, _id in enumerate(animal_ids):
-            tag_dict[_id + "_nose2tail"] = onebyone_contact(
-                bparts=["_Nose", "_Tail_base"]
-            )
+        tag_dict[animal_ids[0] + "_nose2tail"] = onebyone_contact(
+            bparts=["_Nose", "_Tail_base"]
+        )
+        tag_dict[animal_ids[1] + "_nose2tail"] = onebyone_contact(
+            bparts=["_Tail_base", "_Nose"]
+        )
 
         for _id in animal_ids:
             tag_dict[_id + "_following"] = deepof.utils.smooth_boolean_array(
@@ -577,7 +579,7 @@ def tag_rulebased_frames(
     undercond,
     hparams,
 ):
-    """Helper function for rule_based_video. Annotates a fiven frame with on-screen information
+    """Helper function for rule_based_video. Annotates a given frame with on-screen information
     about the recognised patterns"""
 
     w, h = dims
@@ -612,6 +614,8 @@ def tag_rulebased_frames(
 
     if len(animal_ids) > 1:
 
+        cv2.line(frame, (100, 100), (110, 100), (255, 0, 0))
+
         if tag_dict["nose2nose"][fnum] and not tag_dict["sidebyside"][fnum]:
             write_on_frame("Nose-Nose", conditional_pos())
         if (
@@ -634,16 +638,16 @@ def tag_rulebased_frames(
                 "Side-Rside",
                 conditional_pos(),
             )
-        for _id, down_pos, up_pos in zipped_pos:
-            if (
-                tag_dict[_id + "_following"][fnum]
-                and not tag_dict[_id + "_climbing"][fnum]
-            ):
-                write_on_frame(
-                    "*f",
-                    (int(w * 0.3 / 10), int(h / 10)),
-                    conditional_col(),
-                )
+        # for _id, down_pos, up_pos in zipped_pos:
+        #     if (
+        #         tag_dict[_id + "_following"][fnum]
+        #         and not tag_dict[_id + "_climbing"][fnum]
+        #     ):
+        #         write_on_frame(
+        #             "*f",
+        #             (int(w * 0.3 / 10), int(h / 10)),
+        #             conditional_col(),
+        #         )
 
     for _id, down_pos, up_pos in zipped_pos:
 
@@ -792,3 +796,8 @@ def rule_based_video(
     cv2.destroyAllWindows()
 
     return True
+
+
+# TODO:
+#    - Relativise default contact parameters
+#    (right now they only work if 'arena_dims' is correctly set in the project object

tests/test_pose_utils.py

@@ -389,3 +389,7 @@ def test_rule_based_tagging(multi_animal, video_output):
 
     assert type(hardcoded_tags) == deepof.data.table_dict
     assert list(hardcoded_tags.values())[0].shape[1] == (13 if multi_animal else 3)
+
+
+# TODO:
+#     - Test if tagging is working properly!