neuralib.segmentation.cellpose.run_api.CellPoseEvalResult

final class neuralib.segmentation.cellpose.run_api.CellPoseEvalResult

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)

Parameters:

seg_file (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)

Return type:

Self

save_seg_file(image_file)

Save as seg.npy file`

Parameters:

image_file (str)

Return type:

None

save_roi(output_file)

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()

value 0 in masks to nan

Return type:

ndarray

nan_outlines()

value 0 in outlines to nan

Return type:

ndarray

property points: ndarray

Calculate center of each segmented area in pixel. Array[int, N]