diff --git a/qim3d/filters/_common_filter_methods.py b/qim3d/filters/_common_filter_methods.py
index bc4b02f0de5e94eb3e41a17786a04b950a36696d..87d6b6096d1b119e1d384f84b7f9fb08ffcf8198 100644
--- a/qim3d/filters/_common_filter_methods.py
+++ b/qim3d/filters/_common_filter_methods.py
@@ -27,7 +27,11 @@ __all__ = [
class FilterBase:
- def __init__(self, *args, dask: bool = False, chunks: str = "auto", **kwargs):
+ def __init__(self,
+ *args,
+ dask: bool = False,
+ chunks: str = "auto",
+ **kwargs):
"""
Base class for image filters.
@@ -42,6 +46,18 @@ class FilterBase:
class Gaussian(FilterBase):
+ def __init__(self, sigma: float, *args, **kwargs):
+ """
+ Gaussian filter initialization.
+
+ Args:
+ sigma (float): Standard deviation for Gaussian kernel.
+ *args: Additional arguments.
+ **kwargs: Additional keyword arguments.
+ """
+ super().__init__(*args, **kwargs)
+ self.sigma = sigma
+
def __call__(self, input: np.ndarray) -> np.ndarray:
"""
Applies a Gaussian filter to the input.
@@ -53,11 +69,27 @@ class Gaussian(FilterBase):
The filtered image or volume.
"""
return gaussian(
- input, dask=self.dask, chunks=self.chunks, *self.args, **self.kwargs
+ input, sigma=self.sigma, dask=self.dask, chunks=self.chunks, *self.args, **self.kwargs
)
class Median(FilterBase):
+ def __init__(self, size: float = None, footprint: np.ndarray = None, *args, **kwargs):
+ """
+ Median filter initialization.
+
+ Args:
+ size (float or tuple, optional): Filter size.
+ footprint (np.ndarray, optional): The structuring element for filtering.
+ *args: Additional arguments.
+ **kwargs: Additional keyword arguments.
+ """
+ if size is None and footprint is None:
+ raise ValueError("Either 'size' or 'footprint' must be provided.")
+ super().__init__(*args, **kwargs)
+ self.size = size
+ self.footprint = footprint
+
def __call__(self, input: np.ndarray) -> np.ndarray:
"""
Applies a median filter to the input.
@@ -68,10 +100,26 @@ class Median(FilterBase):
Returns:
The filtered image or volume.
"""
- return median(input, dask=self.dask, chunks=self.chunks, **self.kwargs)
+ return median(vol=input, size=self.size, footprint=self.footprint, dask=self.dask, chunks=self.chunks, **self.kwargs)
class Maximum(FilterBase):
+ def __init__(self, size: float = None, footprint: np.ndarray = None, *args, **kwargs):
+ """
+ Maximum filter initialization.
+
+ Args:
+ size (float or tuple, optional): Filter size.
+ footprint (np.ndarray, optional): The structuring element for filtering.
+ *args: Additional arguments.
+ **kwargs: Additional keyword arguments.
+ """
+ if size is None and footprint is None:
+ raise ValueError("Either 'size' or 'footprint' must be provided.")
+ super().__init__(*args, **kwargs)
+ self.size = size
+ self.footprint = footprint
+
def __call__(self, input: np.ndarray) -> np.ndarray:
"""
Applies a maximum filter to the input.
@@ -82,10 +130,26 @@ class Maximum(FilterBase):
Returns:
The filtered image or volume.
"""
- return maximum(input, dask=self.dask, chunks=self.chunks, **self.kwargs)
+ return maximum(vol=input, size=self.size, footprint=self.footprint, dask=self.dask, chunks=self.chunks, **self.kwargs)
class Minimum(FilterBase):
+ def __init__(self, size: float = None, footprint: np.ndarray = None, *args, **kwargs):
+ """
+ Minimum filter initialization.
+
+ Args:
+ size (float or tuple, optional): Filter size.
+ footprint (np.ndarray, optional): The structuring element for filtering.
+ *args: Additional arguments.
+ **kwargs: Additional keyword arguments.
+ """
+ if size is None and footprint is None:
+ raise ValueError("Either 'size' or 'footprint' must be provided.")
+ super().__init__(*args, **kwargs)
+ self.size = size
+ self.footprint = footprint
+
def __call__(self, input: np.ndarray) -> np.ndarray:
"""
Applies a minimum filter to the input.
@@ -96,7 +160,7 @@ class Minimum(FilterBase):
Returns:
The filtered image or volume.
"""
- return minimum(input, dask=self.dask, chunks=self.chunks, **self.kwargs)
+ return minimum(vol=input, size=self.size, footprint=self.footprint, dask=self.dask, chunks=self.chunks, **self.kwargs)
class Tophat(FilterBase):
@@ -217,13 +281,18 @@ class Pipeline:
def gaussian(
- vol: np.ndarray, dask: bool = False, chunks: str = "auto", *args, **kwargs
-) -> np.ndarray:
+ vol: np.ndarray,
+ sigma: float,
+ dask: bool = False,
+ chunks: str = "auto",
+ **kwargs
+ ) -> np.ndarray:
"""
Applies a Gaussian filter to the input volume using scipy.ndimage.gaussian_filter or dask_image.ndfilters.gaussian_filter.
Args:
vol (np.ndarray): The input image or volume.
+ sigma (float or sequence of floats): The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
dask (bool, optional): Whether to use Dask for the Gaussian filter.
chunks (int or tuple or "'auto'", optional): Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.
*args (Any): Additional positional arguments for the Gaussian filter.
@@ -236,92 +305,137 @@ def gaussian(
if dask:
if not isinstance(vol, da.Array):
vol = da.from_array(vol, chunks=chunks)
- dask_vol = dask_ndfilters.gaussian_filter(vol, *args, **kwargs)
+ dask_vol = dask_ndfilters.gaussian_filter(vol, sigma, **kwargs)
res = dask_vol.compute()
return res
else:
- res = ndimage.gaussian_filter(vol, *args, **kwargs)
+ res = ndimage.gaussian_filter(vol, sigma, **kwargs)
return res
def median(
- vol: np.ndarray, dask: bool = False, chunks: str = "auto", **kwargs
+ vol: np.ndarray,
+ size: float = None,
+ footprint: np.ndarray = None,
+ dask: bool = False,
+ chunks: str = "auto",
+ **kwargs
) -> np.ndarray:
"""
Applies a median filter to the input volume using scipy.ndimage.median_filter or dask_image.ndfilters.median_filter.
Args:
vol (np.ndarray): The input image or volume.
+ size (scalar or tuple, optional): Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function.
+ footprint (np.ndarray, optional): Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.
dask (bool, optional): Whether to use Dask for the median filter.
chunks (int or tuple or "'auto'", optional): Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.
**kwargs (Any): Additional keyword arguments for the median filter.
Returns:
filtered_vol (np.ndarray): The filtered image or volume.
+
+ Raises:
+ RuntimeError: If neither size nor footprint is defined
"""
+ if size is None:
+ if footprint is None:
+ raise RuntimeError("no footprint or filter size provided")
+
if dask:
if not isinstance(vol, da.Array):
vol = da.from_array(vol, chunks=chunks)
- dask_vol = dask_ndfilters.median_filter(vol, **kwargs)
+ dask_vol = dask_ndfilters.median_filter(vol, size, footprint, **kwargs)
res = dask_vol.compute()
return res
else:
- res = ndimage.median_filter(vol, **kwargs)
+ res = ndimage.median_filter(vol, size, footprint, **kwargs)
return res
def maximum(
- vol: np.ndarray, dask: bool = False, chunks: str = "auto", **kwargs
+ vol: np.ndarray,
+ size: float = None,
+ footprint: np.ndarray = None,
+ dask: bool = False,
+ chunks: str = "auto",
+ **kwargs
) -> np.ndarray:
"""
Applies a maximum filter to the input volume using scipy.ndimage.maximum_filter or dask_image.ndfilters.maximum_filter.
Args:
vol (np.ndarray): The input image or volume.
+ size (scalar or tuple, optional): Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function.
+ footprint (np.ndarray, optional): Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.
dask (bool, optional): Whether to use Dask for the maximum filter.
chunks (int or tuple or "'auto'", optional): Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.
**kwargs (Any): Additional keyword arguments for the maximum filter.
Returns:
filtered_vol (np.ndarray): The filtered image or volume.
+
+ Raises:
+ RuntimeError: If neither size nor footprint is defined
"""
+ if size is None:
+ if footprint is None:
+ raise RuntimeError("no footprint or filter size provided")
+
if dask:
if not isinstance(vol, da.Array):
vol = da.from_array(vol, chunks=chunks)
- dask_vol = dask_ndfilters.maximum_filter(vol, **kwargs)
+ dask_vol = dask_ndfilters.maximum_filter(vol, size, footprint, **kwargs)
res = dask_vol.compute()
return res
else:
- res = ndimage.maximum_filter(vol, **kwargs)
+ res = ndimage.maximum_filter(vol, size, footprint, **kwargs)
return res
def minimum(
- vol: np.ndarray, dask: bool = False, chunks: str = "auto", **kwargs
+ vol: np.ndarray,
+ size: float = None,
+ footprint: np.ndarray = None,
+ dask: bool = False,
+ chunks: str = "auto",
+ **kwargs
) -> np.ndarray:
"""
Applies a minimum filter to the input volume using scipy.ndimage.minimum_filter or dask_image.ndfilters.minimum_filter.
Args:
vol (np.ndarray): The input image or volume.
+ size (scalar or tuple, optional): Either size or footprint must be defined. size gives the shape that is taken from the input array, at every element position, to define the input to the filter function.
+ footprint (np.ndarray, optional): Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.
dask (bool, optional): Whether to use Dask for the minimum filter.
chunks (int or tuple or "'auto'", optional): Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.
**kwargs (Any): Additional keyword arguments for the minimum filter.
Returns:
filtered_vol (np.ndarray): The filtered image or volume.
+
+ Raises:
+ RuntimeError: If neither size nor footprint is defined
"""
+ if size is None:
+ if footprint is None:
+ raise RuntimeError("no footprint or filter size provided")
+
if dask:
if not isinstance(vol, da.Array):
vol = da.from_array(vol, chunks=chunks)
- dask_vol = dask_ndfilters.minimum_filter(vol, **kwargs)
+ dask_vol = dask_ndfilters.minimum_filter(vol, size, footprint, **kwargs)
res = dask_vol.compute()
return res
else:
- res = ndimage.minimum_filter(vol, **kwargs)
+ res = ndimage.minimum_filter(vol, size, footprint, **kwargs)
return res
-def tophat(vol, dask=False, **kwargs):
+def tophat(vol: np.ndarray,
+ dask: bool = False,
+ **kwargs
+ ):
"""
Remove background from the volume.