neuralib.segmentation.base

class neuralib.segmentation.base.AbstractSegmentationOption[source]

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()[source]: check args is valid

property file_mode: bool: Flag file mode

property batch_mode: bool: Flag batch mode

raw_image()[source]

Load image from file and convert to grayscale

Returns:: Array[float, [H, W]]
Return type:: ndarray

normalize_image()[source]

Normalize the image

Returns:: Array[float, [H, W]]
Return type:: ndarray

foreach_raw_image()[source]

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()[source]

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)[source]

Get segmented output save path

Parameters:: filepath (Path) – filepath for image
Returns:: segmented output save path
Return type:: Path

ij_roi_output(filepath)[source]

Get imageJ/Fiji .roi output save path

Parameters:: filepath (Path) – filepath for image
Returns:: ij roi output save path
Return type:: Path

abstract eval()[source]

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

Return type:: None

abstract launch_napari(**kwargs)[source]: napari viewer