neuralib.segmentation.cellpose.core.AbstractSegmentationOption

class neuralib.segmentation.cellpose.core.AbstractSegmentationOption

Bases: AbstractParser

DESCRIPTION: str = 'ABC for cellular segmentation'

parser description.

EX_GROUP_SOURCE = 'EX_GROUP_SOURCE'

file: Path

image file path

directory: Path

images directory for batch processing

directory_suffix: str

suffix in batch mode

save_ij_roi: bool

if save also the imageJ/Fiji compatible .roi file

force_re_eval: bool

force re-evaluate the result

model: str

which pretrained model

no_normalize: bool

NOT DO Percentile-based image normalization for eval

napari_view: bool

view in napari

post_parsing()

check args is valid

property file_mode: bool

Flag file mode

property batch_mode: bool

Flag batch mode

raw_image()

Load image from file and convert to grayscale

Returns:

Array[float, [H, W]]

Return type:

ndarray

normalize_image()

Normalize the image

Returns:

Array[float, [H, W]]

Return type:

ndarray

foreach_raw_image()

Load image from a directory and convert to grayscale

Returns:

Tuple of filepath and image Array[float, [H, W]]

Return type:

Iterable[tuple[Path, ndarray]]

foreach_normalize_image()

Normalize the image in batch mode

Returns:

Tuple of filepath and image Array[float, [H, W]]

Return type:

Iterable[tuple[Path, ndarray]]

abstract seg_output(filepath)

Get segmented output save path

Parameters:

filepath (Path) – filepath for image

Returns:

segmented output save path

Return type:

Path

ij_roi_output(filepath)

Get imageJ/Fiji .roi output save path

Parameters:

filepath (Path) – filepath for image

Returns:

ij roi output save path

Return type:

Path

abstract eval()

eval the model in single file or batch files, and save the results

Return type:

None

abstract launch_napari(**kwargs)

napari viewer

EPILOG: str = None

parser epilog. Could be override as a method if its content is dynamic-generated.

USAGE: str = None

parser usage.

static __new__(cls, *args, **kwargs)

main(args=None, *, exit_on_error=True)

parsing the commandline input args and set the argument attributes, then call run().

Example

if overwrite with the argument default, use args

>>> AbstractParser().main((['--source=allen_mouse_25um', '--region=VISal,VISam,...'], []))

Parameters:

• args (list[str] | tuple[list[str]] | None) – commandline arguments, or a tuple of (prepend, append) arguments

• exit_on_error (bool) – exit when commandline parsed fail. Otherwise, raise a RuntimeError.

classmethod new_parser(**kwargs)

create an argparse.ArgumentParser.

class variable: USAGE, DESCRIPTION and EPILOG are used when creation.

>>> class A(AbstractParser):
...     @classmethod
...     def new_parser(cls, **kwargs) -> argparse.ArgumentParser:
...         return super().new_parser(**kwargs)

Parameters:

kwargs – keyword parameters to ArgumentParser

Returns:

an ArgumentParser.

Return type:

ArgumentParser

run()

called when all argument attributes are set