added cameras

pyproject.toml

@@ -24,7 +24,7 @@ dependencies = [
     "attrs",
     "h5py>=3.8.0",
     "pynwb",
-    "ndx-pose @ git+https://github.com/rly/ndx-pose@a847ad4be75e60ef9e413b8cbfc99c616fc9fd05",
+    "ndx-pose",
     "pandas",
     "simplejson",
     "imageio",

sleap_io/io/main.py

@@ -60,15 +60,15 @@ def load_nwb(filename: str) -> Labels:
     """
     with NWBHDF5IO(filename, "r", load_namespaces=True) as io:
         nwb_processing = io.read().processing
-        if any('PoseTraining' in module for module in nwb_processing.values()):
+        if any("PoseTraining" in module for module in nwb_processing.values()):
             return nwb.read_nwb_training(nwb_processing)
         return nwb.read_nwb(filename)
 
 
 def save_nwb(
     labels: Labels,
     filename: str,
-    as_training: bool = True,
+    as_training: bool = False,
     append: bool = True,
     frame_inds: Optional[list[int]] = None,
     frame_path: Optional[str] = None,

sleap_io/io/nwb.py

@@ -454,7 +454,7 @@ def write_nwb(
     nwbfile_path: str,
     nwb_file_kwargs: Optional[dict] = None,
     pose_estimation_metadata: Optional[dict] = None,
-    as_training: bool = True,
+    as_training: bool = False,
     frame_inds: Optional[list[int]] = None,
     frame_path: Optional[str] = None,
 ):
@@ -813,12 +813,27 @@ def build_pose_estimation_container_for_track(
         track_data_df, timestamps
     )
 
+    cameras = []
+    nwbfile = NWBFile(
+        session_description="Processed SLEAP pose data",
+        session_start_time=datetime.datetime.now(datetime.timezone.utc),
+        identifier=str(uuid.uuid1()),
+    )
+    for i, video in enumerate(labels.videos):
+        camera = nwbfile.create_device(
+            name=f"camera {i}",
+            description="Camera used to record video {i}",
+            manufacturer="No specified manufacturer",
+        )
+        cameras.append(camera)
+
     # Arrange and mix metadata
     pose_estimation_container_kwargs = dict(
         name=f"track={track_name}",
         description=f"Estimated positions of {skeleton.name} in video {video_path.name}",
         pose_estimation_series=pose_estimation_series_list,
         nodes=skeleton.node_names,
+        devices=cameras,
         edges=np.array(skeleton.edge_inds).astype("uint64"),
         source_software="SLEAP",
         # dimensions=np.array([[video.backend.height, video.backend.width]]),