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fixed recursion error
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@keyaloding
keyaloding committed Aug 2, 2024
1 parent 1031247 commit 68c7cfb
Showing 1 changed file with 56 additions and 54 deletions.
110 changes: 56 additions & 54 deletions sleap_io/io/nwb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,7 @@ def pose_training_to_labels(pose_training: PoseTraining) -> Labels: # type: ign
)
skeleton = skeletons[instance.skeleton.name]
instances.append(
Instance.from_numpy(points=instance.node_locations, skeleton=skeleton)
Instance.from_numpy(points=instance.node_locations[:], skeleton=skeleton)
) # `track` field is not stored in `SkeletonInstance` objects
labeled_frames.append(
LabeledFrame(video=video, frame_idx=frame_idx, instances=instances)
Expand Down Expand Up @@ -149,10 +149,10 @@ def labels_to_pose_training(
training_frame_list.append(training_frame)

training_frames = TrainingFrames(training_frames=training_frame_list)
_ = SourceVideos(image_series=source_video_list)
source_videos = SourceVideos(image_series=source_video_list)
pose_training = PoseTraining(
training_frames=training_frames,
source_videos=videos_to_source_videos(labels.videos),
source_videos=source_videos,
)
return pose_training

Expand Down Expand Up @@ -408,8 +408,9 @@ def read_nwb_training(filename: str) -> Labels:
"""
with NWBHDF5IO(filename, mode="r", load_namespaces=True) as io:
read_nwbfile = io.read()
processing_module = read_nwbfile.processing
nwb_pose_training = processing_module["pose_training"]
processing_module = read_nwbfile.processing['SLEAP_VIDEO_000_minimal_instance.pkg']
print(processing_module)
nwb_pose_training = processing_module["PoseTraining"]
labels = pose_training_to_labels(nwb_pose_training)
return labels

Expand Down Expand Up @@ -589,62 +590,63 @@ def append_nwb_training(
default_metadata["original_videos"] = [f"{video.filename}"]
default_metadata["labeled_videos"] = [f"{video.filename}"]
default_metadata.update(pose_estimation_metadata)

# nwb_processing_module = get_processing_module_for_video("behavior_processing", nwbfile)

subject = Subject(subject_id="No specified id", species="No specified species")
nwbfile.subject = subject
subject = Subject(subject_id="No specified id", species="No specified species")
nwbfile.subject = subject

skeletons_list = [slp_skeleton_to_nwb(skeleton) for skeleton in labels.skeletons]
skeletons = Skeletons(skeletons=skeletons_list)
nwb_processing_module.add(skeletons)
skeletons_list = [slp_skeleton_to_nwb(skeleton) for skeleton in labels.skeletons]
skeletons = Skeletons(skeletons=skeletons_list)
nwb_processing_module.add(skeletons)

video_info = write_video_to_path(labels.video, frame_inds)
pose_training = labels_to_pose_training(labels, skeletons_list, video_info)
nwb_processing_module.add(pose_training)
video_info = write_video_to_path(labels.video, frame_inds)
pose_training = labels_to_pose_training(labels, skeletons_list, video_info)
nwb_processing_module.add(pose_training)

camera = nwbfile.create_device(
name="camera",
description="Camera used to record the video",
manufacturer="N/A",
)
camera = nwbfile.create_device(
name="camera",
description="Camera used to record the video",
manufacturer="N/A",
)

data = np.random.rand(100, 2)
timestamps = np.linspace(0, 10, num=100)
confidence = np.random.rand(100)
reference_frame = (
"The coordinates are in (x, y) relative to the top-left of the image."
)
confidence_definition = "Softmax output of the deep neural network"
pose_estimation_series_list = []
for node in skeletons_list[0].nodes:
pose_estimation_series = PoseEstimationSeries(
name=node,
description=f"Sequential trajectory of {node}.",
data=data,
unit="pixels",
reference_frame=reference_frame,
timestamps=timestamps,
confidence=confidence,
confidence_definition=confidence_definition,
data = np.random.rand(100, 2)
timestamps = np.linspace(0, 10, num=100)
confidence = np.random.rand(100)
reference_frame = (
"The coordinates are in (x, y) relative to the top-left of the image."
)
pose_estimation_series_list.append(pose_estimation_series)
confidence_definition = "Softmax output of the deep neural network"
pose_estimation_series_list = []
for node in skeletons_list[0].nodes:
pose_estimation_series = PoseEstimationSeries(
name=node,
description=f"Sequential trajectory of {node}.",
data=data,
unit="pixels",
reference_frame=reference_frame,
timestamps=timestamps,
confidence=confidence,
confidence_definition=confidence_definition,
)
pose_estimation_series_list.append(pose_estimation_series)

dimensions = np.array(
[[labels.videos[0].backend.shape[1], labels.videos[0].backend.shape[2]]]
)
pose_estimation = PoseEstimation(
name="Pose Estimation",
pose_estimation_series=pose_estimation_series_list,
description="Estimated positions of the nodes in the video",
original_videos=[video.filename for video in labels.videos],
labeled_videos=[video.filename for video in labels.videos],
dimensions=dimensions,
devices=[camera],
scorer="No specified scorer",
source_software="SLEAP",
source_software_version=sleap_version,
skeleton=skeletons_list[0],
)
nwb_processing_module.add(pose_estimation)
dimensions = np.array(
[[labels.videos[0].backend.shape[1], labels.videos[0].backend.shape[2]]]
)
pose_estimation = PoseEstimation(
pose_estimation_series=pose_estimation_series_list,
description="Estimated positions of the nodes in the video",
original_videos=[video.filename for video in labels.videos],
labeled_videos=[video.filename for video in labels.videos],
dimensions=dimensions,
devices=[camera],
source_software="SLEAP",
source_software_version=sleap_version,
skeleton=skeletons_list[0],
)
nwb_processing_module.add(pose_estimation)

return nwbfile


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