neuralib.segmentation.cellpose.run_api.CellPoseAPIOption

class neuralib.segmentation.cellpose.run_api.CellPoseAPIOption[source]

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_cellpose_gui`()	AttributeError: 'MainW' object has no attribute 'load_3D'.
`launch_napari`()	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`
`RESTORE_RATIO`
`RESTORE_TYPE`
`USAGE`	parser usage.
`batch_mode`	Flag batch mode
`cellpose_view`	launch cellpose gui for the analyzed result
`chan_nuclear`	nuclear channel
`chan_seg`	channel for segmentation default:{'none': -1, 'gray': 0, 'red': 1, 'green': 2, 'blue': 3}
`diameter`	diameter for each neuron (number of each pixel)
`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 = 'run cell pose for directly call the api': parser description. Could be override as a method if its content is dynamic-generated.

eval()[source]: eval the model in single file or batch files, and save the results