write_tracking_h5

sleap.info.write_tracking_h5

Generate an HDF5 or CSV file with track occupancy and point location data.

Ignores tracks that are entirely empty. By default will also ignore empty frames from the beginning and end of video, although --all-frames argument will make it include empty frames from beginning of video.

The HDF5 file has these datasets:

• "track_occupancy" (shape: tracks * frames)
• "tracks" (shape: frames * nodes * 2 * tracks)
• "track_names" (shape: tracks)
• "node_names" (shape: nodes)
• "edge_names" (shape: nodes - 1)
• "edge_inds" (shape: nodes - 1)
• "point_scores" (shape: frames * nodes * tracks)
• "instance_scores" (shape: frames * tracks)
• "tracking_scores" (shape: frames * tracks)
• "labels_path": Path to the source .slp file (if available from GUI context)
• "video_path": Path to the source class:Video.
• "video_ind": Scalar integer index of the video within the class:Labels.
• "provenance": Dictionary that denotes the origin of the class:Labels.

Note: the datasets are stored column-major as expected by MATLAB.

Functions:

Name	Description
get_edges_as_np_strings	Get list of edge names as bytes.
get_nodes_as_np_strings	Get list of node names as bytes.
get_occupancy_and_points_matrices	Builds numpy matrices with track occupancy and point location data.
get_tracks_as_np_strings	Get list of track names as bytes.
main	Writes HDF5 file with matrices of track occupancy and coordinates.
remove_empty_tracks_from_matrices	Removes matrix rows/columns for unoccupied tracks.
write_csv_file	Write CSV file with data from given dictionary.
write_occupancy_file	Write HDF5 file with data from given dictionary.

get_edges_as_np_strings(labels)

Get list of edge names as bytes.

Source code in sleap/info/write_tracking_h5.py

def get_edges_as_np_strings(labels: Labels) -> List[Tuple[bytes, bytes]]:
    """Get list of edge names as bytes."""
    return [
        (src_name.encode("utf-8"), dst_name.encode("utf-8"))
        for (src_name, dst_name) in labels.skeletons[0].edge_names
    ]

get_nodes_as_np_strings(labels)

Get list of node names as bytes.

Source code in sleap/info/write_tracking_h5.py

def get_nodes_as_np_strings(labels: Labels) -> List[bytes]:
    """Get list of node names as bytes."""
    return [node.name.encode("utf-8") for node in labels.skeletons[0].nodes]

get_occupancy_and_points_matrices(labels, all_frames, video=None)

Builds numpy matrices with track occupancy and point location data.

Note: This function assumes either all instances have tracks or no instances have tracks.

Parameters:

Name	Type	Description	Default
labels	Labels	The class:Labels from which to get data.	required
all_frames	bool	If True, then includes zeros so that frame index will line up with columns in the output. Otherwise, there will only be columns for the frames between the first and last frames with labeling data.	required
video	Video	The class:Video from which to get data. If no video is specified, then the first video in source_object videos list will be used. If there are no labeled frames in the video, then None will be returned.	None

Returns:

Type	Description
Tuple[ndarray, ndarray, ndarray, ndarray, ndarray]	tuple of arrays: occupancy matrix with shape (tracks, frames) point location array with shape (frames, nodes, 2, tracks) point scores array with shape (frames, nodes, tracks) instance scores array with shape (frames, tracks) tracking scores array with shape (frames, tracks)

Source code in sleap/info/write_tracking_h5.py

def get_occupancy_and_points_matrices(
    labels: Labels, all_frames: bool, video: Video = None
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Builds numpy matrices with track occupancy and point location data.

    Note: This function assumes either all instances have tracks or no instances have
    tracks.

    Args:
        labels: The :py:class:`Labels` from which to get data.
        all_frames: If True, then includes zeros so that frame index
            will line up with columns in the output. Otherwise,
            there will only be columns for the frames between the
            first and last frames with labeling data.
        video: The :py:class:`Video` from which to get data. If no `video` is specified,
            then the first video in `source_object` videos list will be used. If there
            are no labeled frames in the `video`, then None will be returned.

    Returns:
        tuple of arrays:

        * occupancy matrix with shape (tracks, frames)
        * point location array with shape (frames, nodes, 2, tracks)
        * point scores array with shape (frames, nodes, tracks)
        * instance scores array with shape (frames, tracks)
        * tracking scores array with shape (frames, tracks)
    """
    # Assumes either all instances have tracks or no instances have tracks
    track_count = len(labels.tracks) or 1
    node_count = len(labels.skeletons[0].nodes)

    # Retrieve frames from current video only
    try:
        if video is None:
            video = labels.videos[0]
    except IndexError:
        print("There are no videos in this project. No occupancy matrix to return.")
        return
    labeled_frames = labels.find(video)

    frame_idxs = [lf.frame_idx for lf in labeled_frames]
    frame_idxs.sort()

    try:
        first_frame_idx = 0 if all_frames else frame_idxs[0]

        frame_count = (
            frame_idxs[-1] - first_frame_idx + 1
        )  # count should include unlabeled frames
    except IndexError:
        print(f"No labeled frames in {video.filename}. No occupancy matrix to return.")
        return

    # Desired MATLAB format:
    # "track_occupancy"     tracks * frames
    # "tracks"              frames * nodes * 2 * tracks
    # "track_names"         tracks
    # "point_scores"        frames * nodes * tracks
    # "instance_scores"     frames * tracks
    # "tracking_scores"     frames * tracks

    occupancy_matrix = np.zeros((track_count, frame_count), dtype=np.uint8)
    locations_matrix = np.full(
        (frame_count, node_count, 2, track_count), np.nan, dtype=float
    )
    point_scores = np.full((frame_count, node_count, track_count), np.nan, dtype=float)
    instance_scores = np.full((frame_count, track_count), np.nan, dtype=float)
    tracking_scores = np.full((frame_count, track_count), np.nan, dtype=float)

    # Assumes either all instances have tracks or no instances have tracks
    # Prefer user-labeled instances over predicted instances
    tracks = labels.tracks or [None]  # Comparator in case of project with no tracks
    lfs_instances = list()
    warning_flag = False
    for lf in labeled_frames:
        user_instances = lf.user_instances
        predicted_instances = lf.predicted_instances
        for track in tracks:
            track_instances = list()
            # If a user-instance exists for this track, then use user-instance
            user_track_instances = [
                inst for inst in user_instances if inst.track == track
            ]
            if len(user_track_instances) > 0:
                track_instances = user_track_instances
            else:
                # Otherwise, if a predicted instance exists, then use the predicted
                predicted_track_instances = [
                    inst for inst in predicted_instances if inst.track == track
                ]
                if len(predicted_track_instances) > 0:
                    track_instances = predicted_track_instances

            lfs_instances.extend([(lf, inst) for inst in track_instances])

            # Set warning flag if more than one instances on a track in a single frame
            warning_flag = warning_flag or (
                (track is not None) and (len(track_instances) > 1)
            )

    if warning_flag:
        print(
            "\nWarning! There are more than one instances per track on a "
            "single frame.\n"
        )

    for lf, inst in lfs_instances:
        frame_i = lf.frame_idx - first_frame_idx
        # Assumes either all instances have tracks or no instances have tracks
        if inst.track is None:
            # We could use lf.instances.index(inst) but then we'd need
            # to calculate the number of "tracks" based on the max number of
            # instances in any frame, so for now we'll assume that there's
            # a single instance if we aren't using tracks.
            track_i = 0
        else:
            track_i = labels.tracks.index(inst.track)

        occupancy_matrix[track_i, frame_i] = 1

        locations_matrix[frame_i, ..., track_i] = inst.numpy()
        tracking_scores[frame_i, ..., track_i] = inst.tracking_score
        if type(inst) == PredictedInstance:
            from sleap.sleap_io_adaptors.instance_utils import instance_get_scores

            scores = instance_get_scores(inst)
            if scores is not None:
                point_scores[frame_i, ..., track_i] = scores
            else:
                # Fallback to NaN if no scores available
                point_scores[frame_i, ..., track_i] = np.nan
            instance_scores[frame_i, ..., track_i] = inst.score

    return (
        occupancy_matrix,
        locations_matrix,
        point_scores,
        instance_scores,
        tracking_scores,
    )

get_tracks_as_np_strings(labels)

Get list of track names as bytes.

Source code in sleap/info/write_tracking_h5.py

def get_tracks_as_np_strings(labels: Labels) -> List[bytes]:
    """Get list of track names as bytes."""
    return [track.name.encode("utf-8") for track in labels.tracks]

main(labels, output_path, labels_path=None, all_frames=True, video=None, csv=False)

Writes HDF5 file with matrices of track occupancy and coordinates.

Parameters:

Name	Type	Description	Default
labels	Labels	The :class:Labels from which to get data.	required
output_path	str	Path of HDF5 file to create.	required
labels_path	str	Path of labels .slp file.	None
all_frames	bool	If True, then includes zeros so that frame index will line up with columns in the output. Otherwise, there will only be columns for the frames between the first and last frames with labeling data.	True
video	Video	The class:Video from which to get data. If no video is specified, then the first video in source_object videos list will be used. If there are no labeled frames in the video, then no output file will be written.	None
csv	bool	Bool to save the analysis as a csv file if set to True	False

Returns:

Type	Description
	None

Source code in sleap/info/write_tracking_h5.py

def main(
    labels: Labels,
    output_path: str,
    labels_path: str = None,
    all_frames: bool = True,
    video: Video = None,
    csv: bool = False,
):
    """Writes HDF5 file with matrices of track occupancy and coordinates.

    Args:
        labels: The :class:`Labels` from which to get data.
        output_path: Path of HDF5 file to create.
        labels_path: Path of `labels` .slp file.
        all_frames: If True, then includes zeros so that frame index
            will line up with columns in the output. Otherwise,
            there will only be columns for the frames between the
            first and last frames with labeling data.
        video: The :py:class:`Video` from which to get data. If no `video` is specified,
            then the first video in `source_object` videos list will be used. If there
            are no labeled frames in the `video`, then no output file will be written.
        csv: Bool to save the analysis as a csv file if set to True

    Returns:
        None
    """
    track_names = get_tracks_as_np_strings(labels)

    # Export analysis of current video only
    try:
        if video is None:
            video = labels.videos[0]
    except IndexError:
        print("There are no videos in this project. Output file will not be written.")
        return

    try:
        (
            occupancy_matrix,
            locations_matrix,
            point_scores,
            instance_scores,
            tracking_scores,
        ) = get_occupancy_and_points_matrices(labels, all_frames, video)
    except TypeError:
        print(
            f"No labeled frames in {video.filename}. Skipping the analysis for "
            "this video."
        )
        return

    (
        track_names,
        occupancy_matrix,
        locations_matrix,
        point_scores,
        instance_scores,
        tracking_scores,
    ) = remove_empty_tracks_from_matrices(
        track_names,
        occupancy_matrix,
        locations_matrix,
        point_scores,
        instance_scores,
        tracking_scores,
    )

    data_dict = dict(
        track_names=track_names,
        node_names=get_nodes_as_np_strings(labels),
        edge_names=get_edges_as_np_strings(labels),
        edge_inds=labels.skeletons[0].edge_inds,
        tracks=locations_matrix,
        track_occupancy=occupancy_matrix,
        point_scores=point_scores,
        instance_scores=instance_scores,
        tracking_scores=tracking_scores,
        labels_path=str(labels_path),  # NoneType cannot be written to hdf5.
        video_path=video.filename,
        video_ind=labels.videos.index(video),
        provenance=json.dumps(labels.provenance),  # dict cannot be written to hdf5.
    )

    if csv:
        write_csv_file(output_path, data_dict)
    else:
        write_occupancy_file(output_path, data_dict, transpose=True)

remove_empty_tracks_from_matrices(track_names, occupancy_matrix, locations_matrix, point_scores, instance_scores, tracking_scores)

Removes matrix rows/columns for unoccupied tracks.

Parameters:

Name	Type	Description	Default
track_names	List	List of track names	required
occupancy_matrix	ndarray	2d numpy matrix, rows correspond to tracks	required
locations_matrix	ndarray	4d numpy matrix, last index is track	required
point_scores	ndarray	3d numpy matrix, last index is track	required
instance_scores	ndarray	2d numpy matrix, last index is track	required
tracking_scores	ndarray	2d numpy matrix, last index is track	required

Returns:

Type	Description
Tuple[List, ndarray, ndarray, ndarray, ndarray, ndarray]	track_names, occupancy_matrix, locations_matrix, point_scores, instance_scores tracking_scores but without the rows/columns corresponding to unoccupied tracks.

Source code in sleap/info/write_tracking_h5.py

def remove_empty_tracks_from_matrices(
    track_names: List,
    occupancy_matrix: np.ndarray,
    locations_matrix: np.ndarray,
    point_scores: np.ndarray,
    instance_scores: np.ndarray,
    tracking_scores: np.ndarray,
) -> Tuple[List, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Removes matrix rows/columns for unoccupied tracks.

    Args:
        track_names: List of track names
        occupancy_matrix: 2d numpy matrix, rows correspond to tracks
        locations_matrix: 4d numpy matrix, last index is track
        point_scores: 3d numpy matrix, last index is track
        instance_scores: 2d numpy matrix, last index is track
        tracking_scores: 2d numpy matrix, last index is track

    Returns:
        track_names, occupancy_matrix, locations_matrix, point_scores, instance_scores
        tracking_scores but without the rows/columns corresponding to unoccupied tracks.
    """
    # Make mask with only the occupied tracks
    occupied_track_mask = np.sum(occupancy_matrix, axis=1) > 0

    # Ignore unoccupied tracks
    if np.sum(~occupied_track_mask):
        print(f"ignoring {np.sum(~occupied_track_mask)} empty tracks")

        occupancy_matrix = occupancy_matrix[occupied_track_mask]
        locations_matrix = locations_matrix[..., occupied_track_mask]
        point_scores = point_scores[..., occupied_track_mask]
        instance_scores = instance_scores[..., occupied_track_mask]
        tracking_scores = tracking_scores[..., occupied_track_mask]
        track_names = [
            track_names[i] for i in range(len(track_names)) if occupied_track_mask[i]
        ]

    return (
        track_names,
        occupancy_matrix,
        locations_matrix,
        point_scores,
        instance_scores,
        tracking_scores,
    )

write_csv_file(output_path, data_dict)

Write CSV file with data from given dictionary.

Parameters:

Name	Type	Description	Default
output_path		Path of HDF5 file.	required
data_dict		Dictionary with data to save. Keys are dataset names, values are the data.	required

Returns:

Type	Description
	None

Source code in sleap/info/write_tracking_h5.py

def write_csv_file(output_path, data_dict):
    """Write CSV file with data from given dictionary.

    Args:
        output_path: Path of HDF5 file.
        data_dict: Dictionary with data to save. Keys are dataset names,
            values are the data.

    Returns:
        None
    """

    if data_dict["tracks"].shape[-1] == 0:
        print(f"No tracks to export in {data_dict['video_path']}. Skipping the export")
        return

    data_dict["node_names"] = [s.decode() for s in data_dict["node_names"]]
    data_dict["track_names"] = [s.decode() for s in data_dict["track_names"]]
    data_dict["track_occupancy"] = np.transpose(data_dict["track_occupancy"]).astype(
        bool
    )

    # Find frames with at least one animal tracked.
    valid_frame_idxs = np.argwhere(data_dict["track_occupancy"].any(axis=1)).flatten()

    tracks = []
    for frame_idx in valid_frame_idxs:
        frame_tracks = data_dict["tracks"][frame_idx]

        for i in range(frame_tracks.shape[-1]):
            pts = frame_tracks[..., i]
            conf_scores = data_dict["point_scores"][frame_idx][..., i]

            if np.isnan(pts).all():
                # Skip if animal wasn't detected in the current frame.
                continue
            if data_dict["track_names"]:
                track = data_dict["track_names"][i]
            else:
                track = None

            instance_score = data_dict["instance_scores"][frame_idx][i]

            detection = {
                "track": track,
                "frame_idx": frame_idx,
                "instance.score": instance_score,
            }

            # Coordinates for each body part.
            for node_name, score, (x, y) in zip(
                data_dict["node_names"], conf_scores, pts
            ):
                detection[f"{node_name}.x"] = x
                detection[f"{node_name}.y"] = y
                detection[f"{node_name}.score"] = score

            tracks.append(detection)

    tracks = pd.DataFrame(tracks)
    tracks.to_csv(output_path, index=False)

write_occupancy_file(output_path, data_dict, transpose=True)

Write HDF5 file with data from given dictionary.

Parameters:

Name	Type	Description	Default
output_path	str	Path of HDF5 file.	required
data_dict	Dict[str, Any]	Dictionary with data to save. Keys are dataset names, values are the data.	required
transpose	bool	If True, then any ndarray in data dictionary will be transposed before saving. This is useful for writing files that will be imported into MATLAB, which expects data in column-major format.	True

Returns:

Type	Description
	None

Source code in sleap/info/write_tracking_h5.py

def write_occupancy_file(
    output_path: str, data_dict: Dict[str, Any], transpose: bool = True
):
    """Write HDF5 file with data from given dictionary.

    Args:
        output_path: Path of HDF5 file.
        data_dict: Dictionary with data to save. Keys are dataset names,
            values are the data.
        transpose: If True, then any ndarray in data dictionary will be
            transposed before saving. This is useful for writing files
            that will be imported into MATLAB, which expects data in
            column-major format.

    Returns:
        None
    """

    with h5.File(output_path, "w") as f:
        print("\nExporting to SLEAP Analysis file...")
        for key, val in data_dict.items():
            print(f"\t{key}: ", end="")
            if isinstance(val, np.ndarray):
                print(f"{val.shape}")

                if transpose:
                    # Transpose since MATLAB expects column-major
                    f.create_dataset(
                        key,
                        data=np.transpose(val),
                        compression="gzip",
                        compression_opts=9,
                    )
                else:
                    f.create_dataset(
                        key, data=val, compression="gzip", compression_opts=9
                    )
            else:
                if isinstance(val, (str, int, type(None))):
                    print(f"{val}")
                else:
                    print(f"{len(val)}")
                f.create_dataset(key, data=val)

    print(f"Saved as {output_path}")