def __init__(
self,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
) -> None:
super().__init__()
self.preprocessor = preprocessor
def from_ilp_scale(cls: Type[FE],
*,
preprocessor: Operator[DataRoi,
Array5D] = OpRetriever(),
scale: float,
axis_2d: Optional[str] = None) -> FE:
pass
def __init__(self,
*,
threshold: float,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever()):
self.preprocessor = preprocessor
self.threshold = threshold
super().__init__()
def __init__(self,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
axis_2d: Optional[str] = None,
presmooth_sigma: float = 0):
self.preprocessor = preprocessor
self.presmooth_sigma = presmooth_sigma
super().__init__(axis_2d=axis_2d)
def __init__(
self,
*,
preprocessor: Operator[DataRoi,
Array5D] = OpRetriever(axiskeys_hint="ctzyx"),
axis_2d: Optional[Axis2D],
):
super().__init__()
self.preprocessor = preprocessor
self.axis_2d = axis_2d
def from_ilp_scale(cls: Type[SIGMA_FILTER],
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(
axiskeys_hint="ctzyx"),
scale: float,
axis_2d: Optional[Axis2D]) -> SIGMA_FILTER:
return cls(
preprocessor=preprocessor,
sigma=min(scale, 1.0),
axis_2d=axis_2d,
)
def __init__(
self,
*,
preprocessor: Operator[DataRoi,
Array5D] = OpRetriever(axiskeys_hint="ctzyx"),
scale: float,
window_size: float = 0,
axis_2d: Optional[Axis2D],
):
super().__init__(preprocessor=preprocessor, axis_2d=axis_2d)
self.scale = scale
self.window_size = window_size
def from_ilp_scale(cls: Type[ScaleFilter],
*,
preprocessor: Operator[DataRoi,
Array5D] = OpRetriever(),
scale: float,
axis_2d: Optional[str] = None) -> ScaleFilter:
return cls(
preprocessor=preprocessor,
scale=min(scale, 1.0),
axis_2d=axis_2d,
presmooth_sigma=cls.calc_presmooth_sigma(scale),
)
def __init__(
self,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
sigma: float,
window_size: float = 0,
axis_2d: Optional[str] = None,
presmooth_sigma: float = 0,
):
super().__init__(axis_2d=axis_2d, presmooth_sigma=presmooth_sigma)
self.sigma = sigma
self.window_size = window_size
def __init__(
self,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(), #FIXME: ScalarData instead of Array5D?
object_channel_idx: int,
expansion_step: Optional[Shape5D] = None,
maximum_tile_size: Optional[Shape5D] = None
):
self.preprocessor = preprocessor
self.object_channel_idx = object_channel_idx
self.expansion_step = expansion_step
self.maximum_tile_size = maximum_tile_size
def from_ilp_scale(
cls,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
scale: float,
axis_2d: Optional[str] = None) -> "DifferenceOfGaussians":
capped_scale = min(scale, 1.0)
return cls(
preprocessor=preprocessor,
sigma0=capped_scale,
sigma1=capped_scale * 0.66,
axis_2d=axis_2d,
presmooth_sigma=cls.calc_presmooth_sigma(scale),
)
def from_ilp_scale(
cls,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
scale: float,
axis_2d: Optional[str] = None) -> "StructureTensorEigenvalues":
capped_scale = min(scale, 1.0)
return cls(
preprocessor=preprocessor,
innerScale=capped_scale,
outerScale=0.5 * capped_scale,
axis_2d=axis_2d,
presmooth_sigma=cls.calc_presmooth_sigma(scale),
)
def from_ilp_scale(
cls,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(
axiskeys_hint="ctzyx"),
scale: float,
axis_2d: Optional[Axis2D]) -> "StructureTensorEigenvalues":
capped_scale = min(scale, 1.0)
return cls(
preprocessor=preprocessor,
innerScale=capped_scale,
outerScale=0.5 * capped_scale,
axis_2d=axis_2d,
)
def __init__(
self,
*,
preprocessor: Operator[DataRoi, Array5D] = OpRetriever(),
innerScale: float,
outerScale: float,
window_size: float = 0,
axis_2d: Optional[str] = None,
presmooth_sigma: float = 0,
):
super().__init__(preprocessor=preprocessor,
axis_2d=axis_2d,
presmooth_sigma=presmooth_sigma)
self.innerScale = innerScale
self.outerScale = outerScale
self.window_size = window_size
def __init__(
self,
*,
ilp_scale: float,
axis_2d: Optional[Axis2D],
preprocessor: Operator[DataRoi,
Array5D] = OpRetriever(axiskeys_hint="ctzyx"),
):
self.ilp_scale = ilp_scale
self.presmoother = GaussianSmoothing(
preprocessor=preprocessor,
axis_2d=axis_2d,
window_size=WINDOW_SIZE,
sigma=math.sqrt(ilp_scale**2 -
1.0) if ilp_scale > 1.0 else ilp_scale,
)
self.ilp_scale = ilp_scale
self.axis_2d = axis_2d
super().__init__()
def __init__(
self, ilp_scale: float, axis_2d: Optional[Axis2D], preprocessor: Operator[DataRoi, Array5D] = OpRetriever(axiskeys_hint="ctzyx")
):
super().__init__(ilp_scale=ilp_scale, axis_2d=axis_2d, preprocessor=preprocessor)
capped_scale = min(ilp_scale, 1.0)
self._op = DifferenceOfGaussians(
preprocessor=self.presmoother,
sigma0=capped_scale,
sigma1=capped_scale * 0.66,
axis_2d=axis_2d,
)
def __init__(
self, ilp_scale: float, axis_2d: Optional[Axis2D], preprocessor: Operator[DataRoi, Array5D] = OpRetriever(axiskeys_hint="ctzyx")
):
super().__init__(ilp_scale=ilp_scale, axis_2d=axis_2d, preprocessor=preprocessor)
self._op = GaussianGradientMagnitude(
preprocessor=self.presmoother,
sigma=min(ilp_scale, 1.0),
axis_2d=axis_2d,
)
def __init__(
self, *, ilp_scale: float, axis_2d: Optional[Axis2D], preprocessor: Operator[DataRoi, Array5D] = OpRetriever(axiskeys_hint="ctzyx")
):
super().__init__(ilp_scale=ilp_scale, preprocessor=preprocessor, axis_2d=axis_2d)
capped_scale = min(ilp_scale, 1.0)
self._op = StructureTensorEigenvalues(
innerScale=capped_scale,
outerScale=0.5 * capped_scale,
axis_2d=axis_2d,
preprocessor=self.presmoother,
)