Add support for new single animal DLC format (#704)

* Add support for single animal DLC files (#702)

* Add test for new format single animal DLC

* Add test images for visual verification

* Add data and test for old version single animal DLC file

sleap/io/format/deeplabcut.py

@@ -131,7 +131,8 @@ def read_frames(
             # Create the skeleton from the list of nodes in the csv file.
             # Note that DeepLabCut doesn't have edges, so these will need to be
             # added by user later.
-            node_names = [n[0] for n in list(data)[1::2]]
+            start_col = 3 if is_new_format else 1
+            node_names = [n[0] for n in list(data)[start_col::2]]
 
         if skeleton is None:
             skeleton = Skeleton()
@@ -196,14 +197,20 @@ def read_frames(
                         )
             else:
                 # Get points for each node.
+                any_not_missing = False
                 instance_points = dict()
                 for node in node_names:
                     x, y = data[(node, "x")][i], data[(node, "y")][i]
                     instance_points[node] = Point(x, y)
-
-                # Create instance with points assuming there's a single instance per
-                # frame.
-                instances.append(Instance(skeleton=skeleton, points=instance_points))
+                    if ~(np.isnan(x) and np.isnan(y)):
+                        any_not_missing = True
+
+                if any_not_missing:
+                    # Create instance with points assuming there's a single instance per
+                    # frame.
+                    instances.append(
+                        Instance(skeleton=skeleton, points=instance_points)
+                    )
 
             if len(instances) > 0:
                 # Create LabeledFrame and add it to list.

tests/data/dlc/dlc_testdata.csv

@@ -0,0 +1,7 @@
+scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer
+bodyparts,A,A,B,B,C,C
+coords,x,y,x,y,x,y
+labeled-data/video/img000.png,0,1,2,3,4,5
+labeled-data/video/img001.png,12,13,,,15,16
+labeled-data/video/img002.png,,,,,,
+labeled-data/video/img003.png,22,23,24,25,26,27

tests/data/dlc/dlc_testdata_v2.csv

@@ -0,0 +1,7 @@
+scorer,,,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer
+bodyparts,,,A,A,B,B,C,C
+coords,,,x,y,x,y,x,y
+labeled-data,video,img000.png,0,1,2,3,4,5
+labeled-data,video,img001.png,12,13,,,15,16
+labeled-data,video,img002.png,,,,,,
+labeled-data,video,img003.png,22,23,24,25,26,27

tests/data/dlc/madlc_testdata_v2.csv

@@ -2,7 +2,7 @@ scorer,,,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,Scorer,S
 individuals,,,Animal1,Animal1,Animal1,Animal1,Animal1,Animal1,Animal2,Animal2,Animal2,Animal2,Animal2,Animal2
 bodyparts,,,A,A,B,B,C,C,A,A,B,B,C,C
 coords,,,x,y,x,y,x,y,x,y,x,y,x,y
-labeled-data,species,img000.png,0,1,2,3,4,5,6,7,8,9,10,11
-labeled-data,species,img001.png,12,13,,,15,16,17,18,,,20,21
-labeled-data,species,img002.png,,,,,,,,,,,,
-labeled-data,species,img003.png,22,23,24,25,26,27,,,,,,
+labeled-data,video,img000.png,0,1,2,3,4,5,6,7,8,9,10,11
+labeled-data,video,img001.png,12,13,,,15,16,17,18,,,20,21
+labeled-data,video,img002.png,,,,,,,,,,,,
+labeled-data,video,img003.png,22,23,24,25,26,27,,,,,,

tests/io/test_formats.py

@@ -161,7 +161,10 @@ def test_matching_adaptor(centered_pair_predictions_hdf5_path):
 
 @pytest.mark.parametrize(
     "test_data",
-    ["tests/data/dlc/madlc_testdata.csv", "tests/data/dlc/madlc_testdata_v2.csv"],
+    [
+        "tests/data/dlc/madlc_testdata.csv",
+        "tests/data/dlc/madlc_testdata_v2.csv",
+    ],
 )
 def test_madlc(test_data):
     labels = read(
@@ -178,7 +181,10 @@ def test_madlc(test_data):
     assert labels.videos[0].filenames[2].endswith("img002.png")
     assert labels.videos[0].filenames[3].endswith("img003.png")
 
+    # Assert frames without any coor are not labeled
     assert len(labels) == 3
+
+    # Assert number of instances per frame is correct
     assert len(labels[0]) == 2
     assert len(labels[1]) == 2
     assert len(labels[2]) == 1
@@ -191,6 +197,40 @@ def test_madlc(test_data):
     assert labels[2].frame_idx == 3
 
 
+# TODO: Add test data for old single animal DLC format
+@pytest.mark.parametrize(
+    "test_data",
+    ["tests/data/dlc/dlc_testdata.csv", "tests/data/dlc/dlc_testdata_v2.csv"],
+)
+def test_sadlc(test_data):
+    labels = read(
+        test_data,
+        for_object="labels",
+        as_format="deeplabcut",
+    )
+
+    assert labels.skeleton.node_names == ["A", "B", "C"]
+    assert len(labels.videos) == 1
+    assert len(labels.video.filenames) == 4
+    assert labels.videos[0].filenames[0].endswith("img000.png")
+    assert labels.videos[0].filenames[1].endswith("img001.png")
+    assert labels.videos[0].filenames[2].endswith("img002.png")
+    assert labels.videos[0].filenames[3].endswith("img003.png")
+
+    # Assert frames without any coor are not labeled
+    assert len(labels) == 3
+
+    # Assert number of instances per frame is correct
+    assert len(labels[0]) == 1
+    assert len(labels[1]) == 1
+    assert len(labels[2]) == 1
+
+    assert_array_equal(labels[0][0].numpy(), [[0, 1], [2, 3], [4, 5]])
+    assert_array_equal(labels[1][0].numpy(), [[12, 13], [np.nan, np.nan], [15, 16]])
+    assert_array_equal(labels[2][0].numpy(), [[22, 23], [24, 25], [26, 27]])
+    assert labels[2].frame_idx == 3
+
+
 def test_tracking_scores(tmpdir, centered_pair_predictions_slp_path):
 
     # test reading