Add bbox centroid fix

movement/io/load_bboxes.py

@@ -402,6 +402,11 @@ def _numpy_arrays_from_via_tracks_file(file_path: Path) -> dict:
 
         array_dict[key] = np.stack(list_arrays, axis=1).squeeze()
 
+    # Transform position_array to represent centroid of bbox,
+    # rather than top-left corner
+    # (top left corner: corner of the bbox with minimum x and y coordinates)
+    array_dict["position_array"] += array_dict["shape_array"] / 2
+
     # Add remaining arrays to dict
     array_dict["ID_array"] = df["ID"].unique().reshape(-1, 1)
     array_dict["frame_array"] = df["frame_number"].unique().reshape(-1, 1)
@@ -415,14 +420,16 @@ def _df_from_via_tracks_file(file_path: Path) -> pd.DataFrame:
     Read the VIA tracks .csv file as a pandas dataframe with columns:
     - ID: the integer ID of the tracked bounding box.
     - frame_number: the frame number of the tracked bounding box.
-    - x: the x-coordinate of the tracked bounding box centroid.
-    - y: the y-coordinate of the tracked bounding box centroid.
+    - x: the x-coordinate of the tracked bounding box's top-left corner.
+    - y: the y-coordinate of the tracked bounding box's top-left corner.
     - w: the width of the tracked bounding box.
     - h: the height of the tracked bounding box.
     - confidence: the confidence score of the tracked bounding box.
 
     The dataframe is sorted by ID and frame number, and for each ID,
-    empty frames are filled in with NaNs.
+    empty frames are filled in with NaNs. The coordinates of the bboxes
+    are assumed to be in the image coordinate system (i.e., the top-left
+    corner of a bbox is its corner with minimum x and y coordinates).
     """
     # Read VIA tracks .csv file as a pandas dataframe
     df_file = pd.read_csv(file_path, sep=",", header=0)

tests/test_unit/test_load_bboxes.py

@@ -419,3 +419,54 @@ def test_fps_and_time_coords(
     else:
         start_frame = 0
     assert_time_coordinates(ds, expected_fps, start_frame)
+
+
+def test_df_from_via_tracks_file(via_tracks_file):
+    """Test that the helper function correctly reads the VIA tracks .csv file
+    as a dataframe.
+    """
+    df = load_bboxes._df_from_via_tracks_file(via_tracks_file)
+
+    assert isinstance(df, pd.DataFrame)
+    assert len(df.frame_number.unique()) == 5
+    assert (
+        df.shape[0] == len(df.ID.unique()) * 5
+    )  # all individuals in all frames (even if nan)
+    assert list(df.columns) == [
+        "ID",
+        "frame_number",
+        "x",
+        "y",
+        "w",
+        "h",
+        "confidence",
+    ]
+
+
+def test_position_numpy_array_from_via_tracks_file(via_tracks_file):
+    """Test the extracted position array from the VIA tracks .csv file
+    represents the centroid of the bbox.
+    """
+    # Extract numpy arrays from VIA tracks .csv file
+    bboxes_arrays = load_bboxes._numpy_arrays_from_via_tracks_file(
+        via_tracks_file
+    )
+
+    # Read VIA tracks .csv file as a dataframe
+    df = load_bboxes._df_from_via_tracks_file(via_tracks_file)
+
+    # Compute centroid positions from the dataframe
+    # (go thru in the same order as ID array)
+    list_derived_centroids = []
+    for id in bboxes_arrays["ID_array"]:
+        df_one_ID = df[df["ID"] == id.item()]
+        centroid_position = np.array(
+            [df_one_ID.x + df_one_ID.w / 2, df_one_ID.y + df_one_ID.h / 2]
+        ).T  # frames, xy
+        list_derived_centroids.append(centroid_position)
+
+    # Compare to extracted position array
+    assert np.allclose(
+        bboxes_arrays["position_array"],  # frames, individuals, xy
+        np.stack(list_derived_centroids, axis=1),
+    )