neuralib.segmentation.base.AbstractSegmentationOption

class neuralib.segmentation.base.AbstractSegmentationOption

Bases: AbstractParser

__init__()

Methods

__init__()
eval()	eval the model in single file or batch files, and save the results
foreach_normalize_image()	Normalize the image in batch mode
foreach_raw_image()	Load image from a directory and convert to grayscale
ij_roi_output(filepath)	Get imageJ/Fiji .roi output save path
launch_napari(**kwargs)	napari viewer
main([args, parse_only, system_exit])	parsing the commandline input args and call run().
new_parser(**kwargs)	create an ArgumentParser.
normalize_image()	Normalize the image
raw_image()	Load image from file and convert to grayscale
run()	called after main()
seg_output(filepath)	Get segmented output save path

Attributes

DESCRIPTION	parser description.
EPILOG	parser epilog.
EX_GROUP_SOURCE
USAGE	parser usage.
batch_mode	Flag batch mode
directory	images directory for batch processing
directory_suffix	suffix in batch mode
file	image file path
file_mode	Flag file mode
force_re_eval	force re-evaluate the result
model	which pretrained model
napari_view	view in napari
no_normalize	NOT DO Percentile-based image normalization for eval
save_ij_roi	if save also the imageJ/Fiji compatible .roi file

DESCRIPTION: str = 'ABC for cellular segmentation'

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

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

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