neuralib.segmentation.cellpose.core.CellPoseEvalResult

final class neuralib.segmentation.cellpose.core.CellPoseEvalResult[source]

Bases: object

Cellpose results

Dimension parameters:

N = Number of segmented cell

W = Image width

H = Image height

__init__(filename, image, diameter, chan_choose, masks, flows=NOTHING, styles=NOTHING, *, colors=None, manual_changes=None, est_diam=None, model_path=0, flow_threshold=None, cellprob_threshold=0, normalize_params=None, img_restore=None, restore=None, ratio=1.0, outlines=array([], dtype=float64), ismanual=array([], dtype=float64))

Method generated by attrs for class CellPoseEvalResult.

Parameters:

filename (str)
image (ndarray | list[ndarray])
diameter (float)
chan_choose (list[int])
masks (ndarray)
flows (list[ndarray])
styles (list[ndarray])
colors (ndarray | None)
manual_changes (list[Any] | None)
est_diam (float | None)
model_path (int)
flow_threshold (float | None)
cellprob_threshold (float)
normalize_params (NormParams | None)
img_restore (list[ndarray] | None)
restore (str | None)
ratio (float)
outlines (ndarray)
ismanual (ndarray)

Return type:

None

Methods

`__init__`(filename, image, diameter, ...[, ...])	Method generated by attrs for class CellPoseEvalResult.
`load`(seg_file)
`nan_masks`()	value 0 in `masks` to nan
`nan_outlines`()	value 0 in `outlines` to nan
`save_roi`(output_file)	Save as imageJ `.roi` file.
`save_seg_file`(image_file)	Save as `seg.npy` file`

Attributes

`filename`	image file name
`image`	image array
`diameter`	neuronal diameter
`chan_choose`	[chan_seg, chan_nuclear]
`masks`	each pixel in the image is assigned to an ROI (H, W) list of 2D arrays, labelled image, where 0=no masks; 1,2,...=mask labels
`flows`	flows[0] is XY flow in RGB, flows[1] is the cell probability in range 0-255 instead of 0.0 to 1.0, flows[2] is Z flow in range 0-255 (if it exists, otherwise zeros), flows[3] is [dY, dX, cellprob] (or [dZ, dY, dX, cellprob] for 3D), flows[4] is pixel destinations (for internal use)
`styles`	list of 1D arrays of length 256, style vector summarizing each image, also used to estimate size of objects in image
`colors`	colors for ROIs (N, 3)
`manual_changes`
`est_diam`	estimated diameter (if run on command line)
`model_path`
`flow_threshold`
`cellprob_threshold`
`normalize_params`
`img_restore`
`restore`
`ratio`
`outlines`	outlines of ROIs.
`ismanual`	whether or not mask k was manually drawn or computed by the cellpose algorithm.
`points`	Calculate center of each segmented area in pixel.

filename: str: image file name

image: ndarray | list[ndarray]: image array

diameter: float: neuronal diameter

chan_choose: list[int]: [chan_seg, chan_nuclear]

masks: ndarray: each pixel in the image is assigned to an ROI (H, W) list of 2D arrays, labelled image, where 0=no masks; 1,2,…=mask labels

flows: list[ndarray]: flows[0] is XY flow in RGB, flows[1] is the cell probability in range 0-255 instead of 0.0 to 1.0, flows[2] is Z flow in range 0-255 (if it exists, otherwise zeros), flows[3] is [dY, dX, cellprob] (or [dZ, dY, dX, cellprob] for 3D), flows[4] is pixel destinations (for internal use)

styles: list[ndarray]: list of 1D arrays of length 256, style vector summarizing each image, also used to estimate size of objects in image

colors: ndarray | None: colors for ROIs (N, 3)

manual_changes: list[Any] | None

est_diam: float | None: estimated diameter (if run on command line)

model_path: int

flow_threshold: float | None

cellprob_threshold: float

normalize_params: NormParams | None

img_restore: list[ndarray] | None

restore: str | None

ratio: float

__init__(filename, image, diameter, chan_choose, masks, flows=NOTHING, styles=NOTHING, *, colors=None, manual_changes=None, est_diam=None, model_path=0, flow_threshold=None, cellprob_threshold=0, normalize_params=None, img_restore=None, restore=None, ratio=1.0, outlines=array([], dtype=float64), ismanual=array([], dtype=float64))

Method generated by attrs for class CellPoseEvalResult.

Parameters:

filename (str)
image (ndarray | list[ndarray])
diameter (float)
chan_choose (list[int])
masks (ndarray)
flows (list[ndarray])
styles (list[ndarray])
colors (ndarray | None)
manual_changes (list[Any] | None)
est_diam (float | None)
model_path (int)
flow_threshold (float | None)
cellprob_threshold (float)
normalize_params (NormParams | None)
img_restore (list[ndarray] | None)
restore (str | None)
ratio (float)
outlines (ndarray)
ismanual (ndarray)

Return type:

None

outlines: ndarray: outlines of ROIs. Array[uint16, [H, W]]

ismanual: ndarray: whether or not mask k was manually drawn or computed by the cellpose algorithm. Array[bool, N]

classmethod load(seg_file)[source]

Parameters:: seg_file (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)
Return type:: Self

save_seg_file(image_file)[source]

Save as seg.npy file`

Parameters:: image_file (str)
Return type:: None

save_roi(output_file)[source]

Save as imageJ .roi file. CHECKOUT native BUG: https://github.com/MouseLand/cellpose/issues/969

Parameters:: output_file (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)
Return type:: None

nan_masks()[source]

value 0 in masks to nan

Return type:: ndarray

nan_outlines()[source]

value 0 in outlines to nan

Return type:: ndarray

property points: ndarray: Calculate center of each segmented area in pixel. Array[int, N]