neuralib.imglib.labeller.SequenceLabeller
- class neuralib.imglib.labeller.SequenceLabeller[source]
Bases:
object- __init__(seqs_info, output=None)[source]
- Parameters:
seqs_info (list[FrameInfo])
output (str | Path | PathLike[str] | None)
Methods
__init__(seqs_info[, output])True if any changes in notes
enqueue_message(text)go_to(i)goto_end()handle_command(command)load_from_dir(directory[, file_suffix, ...])read image-related notes from file
load_sequences(seqs[, filenames, output])main()main loop for the GUI
save image-related notes to file
write_note(note, *[, append_mode])Attributes
aka.
- window_title: ClassVar[str] = 'SeqLabeller'
- __init__(seqs_info, output=None)[source]
- Parameters:
seqs_info (list[FrameInfo])
output (str | Path | PathLike[str] | None)
- classmethod load_sequences(seqs, filenames=None, output=None)[source]
- Parameters:
seqs (ndarray | list[ndarray])
filenames (list[str] | None)
output (str | Path | PathLike[str] | None)
- Returns:
- Return type:
Self
- classmethod load_from_dir(directory, file_suffix='.tif', sort_func=None, single_frame_per_file=True, output=None)[source]
- Parameters:
directory (str | Path | PathLike[str]) – directory contain image sequences
file_suffix (str) – sequence file suffix
sort_func (Callable[[Path], Any] | None) – sorted function with signature (filename:Path) -> Comparable
single_frame_per_file (bool)
output (str | Path | PathLike[str] | None)
- Returns:
- Return type:
Self
- property n_frames: int
aka. number of images
- property current_frame_index: int
- property text_color: float | tuple[int, int, int]