itasc.cellpose.cellpose_runner

Local Cellpose-SAM runner, Qt-free, used by the napari Cellpose widget.

Functions

device_label()

get_model()

Lazy-load the cpsam model once per process; cached at module level.

infer_layout_from_ndim(ndim)

Best-effort layout from array ndim.

is_model_loaded()

layout_has_time(layout)

layout_has_z(layout)

run_cell_frame(frame, z, params)

Single 2D-slice cell inference.

run_cell_stack(stack, params, *[, ...])

Process a (T, Z, Y, X) stack slice-by-slice in 2D.

run_nucleus_frame(frame, z, params)

Single-frame nucleus inference.

run_nucleus_stack(stack, params, *[, ...])

Process a (T, Z, Y, X) stack frame-by-frame.

to_tzyx(arr, layout)

Normalize an input array to canonical (T, Z, Y, X) for its layout.

write_outputs(prob_3dt, dp_3dt, output_dir, ...)

Write the two canonical TIFFs under output_dir.

Classes

CellParams(diameter, min_size, gamma)

NucleusParams(do_3d, anisotropy, diameter, ...)

Exceptions

CancelledError

Raised by run_*_stack when cancel_cb returns True between frames.
itasc.cellpose.cellpose_runner.layout_has_time(layout)[source]
Return type:

bool

Parameters:

layout (str)

itasc.cellpose.cellpose_runner.layout_has_z(layout)[source]
Return type:

bool

Parameters:

layout (str)

itasc.cellpose.cellpose_runner.infer_layout_from_ndim(ndim)[source]

Best-effort layout from array ndim.

2 -> "2D" and 4 -> "3D+t" are unambiguous; 3 is ambiguous (2D+t vs 3D) so returns None and the caller keeps the user’s explicit choice.

Return type:

str | None

Parameters:

ndim (int)

itasc.cellpose.cellpose_runner.to_tzyx(arr, layout)[source]

Normalize an input array to canonical (T, Z, Y, X) for its layout.

Singleton T and/or Z axes are inserted so 2D/2D+t/3D/3D+t inputs all become 4-D; the runner then iterates uniformly over frames and z-slices.

Return type:

ndarray

Parameters:
class itasc.cellpose.cellpose_runner.NucleusParams(do_3d, anisotropy, diameter, min_size, gamma, cellprob_threshold=0.0, flow_threshold=0.4, niter=0)[source]

Bases: object

Parameters:
do_3d: bool
anisotropy: float
diameter: float
min_size: int
gamma: float
cellprob_threshold: float = 0.0
flow_threshold: float = 0.4
niter: int = 0
class itasc.cellpose.cellpose_runner.CellParams(diameter, min_size, gamma)[source]

Bases: object

Parameters:
diameter: float
min_size: int
gamma: float
itasc.cellpose.cellpose_runner.device_label()[source]
Return type:

str

itasc.cellpose.cellpose_runner.is_model_loaded()[source]
Return type:

bool

itasc.cellpose.cellpose_runner.get_model()[source]

Lazy-load the cpsam model once per process; cached at module level.

itasc.cellpose.cellpose_runner.run_nucleus_frame(frame, z, params)[source]

Single-frame nucleus inference.

If z is None, runs full 3D over (Z, Y, X) and returns prob with shape (Z, Y, X) and dp with shape (3, Z, Y, X). If z is an integer, runs 2D on frame[z] and returns prob with shape (Y, X) and dp with shape (2, Y, X).

Return type:

tuple[ndarray, ndarray]

Parameters:
exception itasc.cellpose.cellpose_runner.CancelledError[source]

Bases: RuntimeError

Raised by run_*_stack when cancel_cb returns True between frames.

itasc.cellpose.cellpose_runner.run_cell_frame(frame, z, params)[source]

Single 2D-slice cell inference. Returns (prob (Y,X), dp (2,Y,X)).

Return type:

tuple[ndarray, ndarray]

Parameters:
itasc.cellpose.cellpose_runner.run_nucleus_stack(stack, params, *, progress_cb=None, cancel_cb=None)[source]

Process a (T, Z, Y, X) stack frame-by-frame.

Returns (prob_3dt, dp_3dt). For do_3d=True dp has shape (T, 3, Z, Y, X); for do_3d=False dp has shape (T, Z, 2, Y, X).

Return type:

tuple[ndarray, ndarray]

Parameters:
itasc.cellpose.cellpose_runner.run_cell_stack(stack, params, *, progress_cb=None, cancel_cb=None)[source]

Process a (T, Z, Y, X) stack slice-by-slice in 2D.

Returns (prob_3dt (T, Z, Y, X), dp_3dt (T, Z, 2, Y, X)).

Return type:

tuple[ndarray, ndarray]

Parameters:
itasc.cellpose.cellpose_runner.write_outputs(prob_3dt, dp_3dt, output_dir, channel)[source]

Write the two canonical TIFFs under output_dir.

Writes {channel}_prob.tif and {channel}_dp.tif. Axis labels are recorded as metadata so singleton T/Z axes (2D / 2D+t / single 3D-stack inputs) survive the TIFF round-trip and are not misread downstream.

Return type:

None

Parameters: