NAPS Functions

sleap_utils.py: General helper functions for interfacing with SLEAP

naps.sleap_utils.get_location_matrix(labels: Labels, all_frames: bool, video: Optional[Video] = None) → Tuple[ndarray, List]

Builds numpy matrix with point location data. Note: This function assumes either all instances have tracks or no instances have tracks.

Parameters:

• labels (sleap.Labels) – The sleap.Labels from which to get data.

• 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.

• video (sleap.Video, optional) – The 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. Defaults to None.

Returns:

point location array with shape (frames, nodes, 2, tracks) List[str]: list of node names

Return type:

Tuple[np.ndarray, List[str]]

naps.sleap_utils.load_tracks_from_slp(slp_path: str) → Tuple[ndarray, List[str]]

Loads tracks from a sleap project file or analysis h5

Parameters:

slp_path (str) – Path to SLEAP project file or analysis h5. This file is typically generated using sleap-convert. Analysis h5s are much faster to load and thus preferable.

Returns:

Tuple of (locations_matrix, node_names) where location matrix is a numpy array with shape (frames, nodes, 2, tracks) and node_names is a list of node names.

Return type:

Tuple[np.ndarray, List[str]]

naps.sleap_utils.update_labeled_frames(slp_path: str, matching_dict: dict, first_frame_idx: int, last_frame_idx: int) → List[LabeledFrame]

Generates a list of labeled frames from a sleap project file with tracks updated from the provided matching_dict.

Parameters:

• slp_path (str) – Path to SLEAP project file (.slp) file.

• matching_dict (dict) – Dictionary of format [Frame][Track] containing the tag number for each track in each frame.

• first_frame_idx (int) – First frame index to include in the reconstructed h5.

• last_frame_idx (int) – Last frame index to include in the reconstructed h5.

Returns:

List of labeled frames with updated tracks.

Return type:

List[sleap.LabeledFrame]

class naps.matching.MatchFrame(frame_exists: bool, frame: ndarray, *args, **kwargs)

Class used to assign markers for a frame and associated data.

frame

Value array of the frame image.

Type:

np.ndarray

frame_exists

Boolean indicating if the frame was read correctly.

Type:

bool

frame_images

Dictionary to store value arrays for each marker image.

Type:

dict

cropMarkerWithCoordsArray(coords_dict, crop_size: int)

Creates cropped images for each marker in the coords_dict.

Parameters:

• coords_dict (dict) – Dictionary of marker coordinates to crop for each track.

• crop_size (float) – Crop size.

classmethod fromCV2(*args, **kwargs)

Class methods to create a MatchFrame from CV2.read()

Parameters:

• bool – Boolean indicating if the frame was read correctly.

• np.ndarray – Value array of the frame image.

Returns:

A MatchFrame object.

Return type:

MatchFrame

returnMarkerTags(marker_detect: Callable)

Detect marker tags using the specified marker_detect function

Parameters:

• marker_detect (Callable) – Marker detector function. Function is expected

• assignments. (to return a list of marker) –

Returns:

Dictionary of matching results for a single frame.

Return type:

defaultdict(list)

class naps.matching.Matching(video_filename: str, video_first_frame: int, video_last_frame: int, marker_detector: Callable, aruco_crop_size: int, half_rolling_window_size: int, tag_node_dict: dict, min_sleap_score: float = 0.1, **kwargs)

Matching pipeline.

video_filename

Matching video filename (i.e. path).

Type:

str

video_first_frame

Frame to start matching.

Type:

str

video_last_frame

Frame to end matching.

Type:

str

marker_detector

Function used to detect/assign markers.

Type:

Callable

aruco_crop_size

Crop size used for marker detection.

Type:

int

half_rolling_window_size

Window size (upstream/downstream) used by the cost matrix.

Type:

int

tag_node_dict

Dictionary of frames, tracks, and node coordinates.

Type:

dict

threads

The number of CPU threads to use.

Type:

int

min_sleap_score

Minimum sleap score required for matching. Should this be removed?

Type:

float

match() -> defaultdict(<function Matching.<lambda> at 0x7fe4a3d25a70>, {})

Performs matching.

Returns:

defaultdict(str)): Dictionary of matching results with the form dictionary[frame][track] = tag.

Return type:

defaultdict(lambda

class naps.aruco.ArUcoModel(tag_set: str, adaptiveThreshWinSizeMin: int, adaptiveThreshWinSizeMax: int, adaptiveThreshWinSizeStep: int, adaptiveThreshConstant: float, perspectiveRemoveIgnoredMarginPerCell: float, errorCorrectionRate: float, **kwargs)

Class providing a wrapper around the cv2.aruco library

perspectiveRemoveIgnoredMarginPerCell

Set the ArUco dict and params to None. Create w/ buildModel as we cannot pickle them when using multiprocessing

naps.naps_track.main(argv=None)

Main function for the NAPS tracking script.