Filtering data

The qim3d library provides a set of methods for filtering volumes.

qim3d.filters

qim3d.filters.gaussian

gaussian(vol, sigma, dask=False, chunks='auto', **kwargs)

Applies a Gaussian filter to the input volume using scipy.ndimage.gaussian_filter or dask_image.ndfilters.gaussian_filter.

Parameters:

Name	Type	Description	Default
vol	ndarray	The input image or volume.	required
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.	required
dask	bool	Whether to use Dask for the Gaussian filter.	False
chunks	int or tuple or 'auto'	Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.	'auto'
**kwargs	Any	Additional keyword arguments for the Gaussian filter.	{}

Returns:

Name	Type	Description
filtered_vol	ndarray	The filtered image or volume.

Source code in qim3d/filters/_common_filter_methods.py

def gaussian(
    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.
        **kwargs (Any): Additional keyword arguments for the Gaussian filter.

    Returns:
        filtered_vol (np.ndarray): The filtered image or volume.

    """

    if dask:
        if not isinstance(vol, da.Array):
            vol = da.from_array(vol, chunks=chunks)
        dask_vol = dask_ndfilters.gaussian_filter(vol, sigma, **kwargs)
        res = dask_vol.compute()
        return res
    else:
        res = ndimage.gaussian_filter(vol, sigma, **kwargs)
        return res

qim3d.filters.median

median(vol, size=None, footprint=None, dask=False, chunks='auto', **kwargs)

Applies a median filter to the input volume using scipy.ndimage.median_filter or dask_image.ndfilters.median_filter.

Parameters:

Name	Type	Description	Default
vol	ndarray	The input image or volume.	required
size	scalar or tuple	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.	None
footprint	ndarray	Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.	None
dask	bool	Whether to use Dask for the median filter.	False
chunks	int or tuple or 'auto'	Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.	'auto'
**kwargs	Any	Additional keyword arguments for the median filter.	{}

Returns:

Name	Type	Description
filtered_vol	ndarray	The filtered image or volume.

Raises:

Type	Description
RuntimeError	If neither size nor footprint is defined

Source code in qim3d/filters/_common_filter_methods.py

def median(
    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, size, footprint, **kwargs)
        res = dask_vol.compute()
        return res
    else:
        res = ndimage.median_filter(vol, size, footprint, **kwargs)
        return res

qim3d.filters.maximum

maximum(vol, size=None, footprint=None, dask=False, chunks='auto', **kwargs)

Applies a maximum filter to the input volume using scipy.ndimage.maximum_filter or dask_image.ndfilters.maximum_filter.

Parameters:

Name	Type	Description	Default
vol	ndarray	The input image or volume.	required
size	scalar or tuple	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.	None
footprint	ndarray	Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.	None
dask	bool	Whether to use Dask for the maximum filter.	False
chunks	int or tuple or 'auto'	Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.	'auto'
**kwargs	Any	Additional keyword arguments for the maximum filter.	{}

Returns:

Name	Type	Description
filtered_vol	ndarray	The filtered image or volume.

Raises:

Type	Description
RuntimeError	If neither size nor footprint is defined

Source code in qim3d/filters/_common_filter_methods.py

def maximum(
    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, size, footprint, **kwargs)
        res = dask_vol.compute()
        return res
    else:
        res = ndimage.maximum_filter(vol, size, footprint, **kwargs)
        return res

qim3d.filters.minimum

minimum(vol, size=None, footprint=None, dask=False, chunks='auto', **kwargs)

Applies a minimum filter to the input volume using scipy.ndimage.minimum_filter or dask_image.ndfilters.minimum_filter.

Parameters:

Name	Type	Description	Default
vol	ndarray	The input image or volume.	required
size	scalar or tuple	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.	None
footprint	ndarray	Boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function.	None
dask	bool	Whether to use Dask for the minimum filter.	False
chunks	int or tuple or 'auto'	Defines how to divide the array into blocks when using Dask. Can be an integer, tuple, size in bytes, or "auto" for automatic sizing.	'auto'
**kwargs	Any	Additional keyword arguments for the minimum filter.	{}

Returns:

Name	Type	Description
filtered_vol	ndarray	The filtered image or volume.

Raises:

Type	Description
RuntimeError	If neither size nor footprint is defined

Source code in qim3d/filters/_common_filter_methods.py

def minimum(
    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, size, footprint, **kwargs)
        res = dask_vol.compute()
        return res
    else:
        res = ndimage.minimum_filter(vol, size, footprint, **kwargs)
        return res

qim3d.filters.tophat

tophat(vol, dask=False, **kwargs)

Remove background from the volume.

Parameters:

Name	Type	Description	Default
vol	ndarray	The volume to remove background from.	required
dask	bool	Whether to use Dask for the tophat filter (not supported, will default to SciPy).	False
**kwargs	Any	Additional keyword arguments. radius (float): The radius of the structuring element (default: 3). background (str): Color of the background, 'dark' or 'bright' (default: 'dark'). If 'bright', volume will be inverted.	{}

Returns:

Name	Type	Description
filtered_vol	ndarray	The volume with background removed.

Source code in qim3d/filters/_common_filter_methods.py

def tophat(vol: np.ndarray, dask: bool = False, **kwargs):
    """
    Remove background from the volume.

    Args:
        vol (np.ndarray): The volume to remove background from.
        dask (bool, optional): Whether to use Dask for the tophat filter (not supported, will default to SciPy).
        **kwargs (Any): Additional keyword arguments.
            `radius` (float): The radius of the structuring element (default: 3).
            `background` (str): Color of the background, 'dark' or 'bright' (default: 'dark'). If 'bright', volume will be inverted.

    Returns:
        filtered_vol (np.ndarray): The volume with background removed.

    """

    radius = kwargs['radius'] if 'radius' in kwargs else 3
    background = kwargs['background'] if 'background' in kwargs else 'dark'

    if dask:
        log.info('Dask not supported for tophat filter, switching to scipy.')

    if background == 'bright':
        log.info(
            'Bright background selected, volume will be temporarily inverted when applying white_tophat'
        )
        vol = np.invert(vol)

    selem = morphology.ball(radius)
    vol = vol - morphology.white_tophat(vol, selem)

    if background == 'bright':
        vol = np.invert(vol)

    return vol

qim3d.filters.Pipeline

Example

import qim3d
from qim3d.filters import Pipeline, Median, Gaussian, Maximum, Minimum

# Get data
vol = qim3d.examples.fly_150x256x256

# Show original
fig1 = qim3d.viz.slices_grid(vol, num_slices=5, display_figure=True)

# Create filter pipeline
pipeline = Pipeline(
    Median(size=5),
    Gaussian(sigma=3, dask = True)
)

# Append a third filter to the pipeline
pipeline.append(Maximum(size=3))

# Apply filter pipeline
vol_filtered = pipeline(vol)

# Show filtered
fig2 = qim3d.viz.slices_grid(vol_filtered, num_slices=5, display_figure=True)

Source code in qim3d/filters/_common_filter_methods.py

class Pipeline:

    """
    Example:
        ```python
        import qim3d
        from qim3d.filters import Pipeline, Median, Gaussian, Maximum, Minimum

        # Get data
        vol = qim3d.examples.fly_150x256x256

        # Show original
        fig1 = qim3d.viz.slices_grid(vol, num_slices=5, display_figure=True)

        # Create filter pipeline
        pipeline = Pipeline(
            Median(size=5),
            Gaussian(sigma=3, dask = True)
        )

        # Append a third filter to the pipeline
        pipeline.append(Maximum(size=3))

        # Apply filter pipeline
        vol_filtered = pipeline(vol)

        # Show filtered
        fig2 = qim3d.viz.slices_grid(vol_filtered, num_slices=5, display_figure=True)
        ```
        ![original volume](../../assets/screenshots/filter_original.png)
        ![filtered volume](../../assets/screenshots/filter_processed.png)

    """

    def __init__(self, *args: Type[FilterBase]):
        """
        Represents a sequence of image filters.

        Args:
            *args: Variable number of filter instances to be applied sequentially.

        """
        self.filters = {}

        for idx, fn in enumerate(args):
            self._add_filter(str(idx), fn)

    def _add_filter(self, name: str, fn: Type[FilterBase]):
        """
        Adds a filter to the sequence.

        Args:
            name: A string representing the name or identifier of the filter.
            fn: An instance of a FilterBase subclass.

        Raises:
            AssertionError: If `fn` is not an instance of the FilterBase class.

        """
        if not isinstance(fn, FilterBase):
            filter_names = [
                subclass.__name__ for subclass in FilterBase.__subclasses__()
            ]
            raise AssertionError(
                f'filters should be instances of one of the following classes: {filter_names}'
            )
        self.filters[name] = fn

    def append(self, fn: FilterBase):
        """
        Appends a filter to the end of the sequence.

        Args:
            fn (FilterBase): An instance of a FilterBase subclass to be appended.

        Example:
            ```python
            import qim3d
            from qim3d.filters import Pipeline, Maximum, Median

            # Create filter pipeline
            pipeline = Pipeline(
                Maximum(size=3, dask=True),
            )

            # Append a second filter to the pipeline
            pipeline.append(Median(size=5))
            ```

        """
        self._add_filter(str(len(self.filters)), fn)

    def __call__(self, input):
        """
        Applies the sequential filters to the input in order.

        Args:
            input: The input image or volume.

        Returns:
            The filtered image or volume after applying all sequential filters.

        """
        for fn in self.filters.values():
            input = fn(input)
        return input

qim3d.filters.Pipeline.append

append(fn)

Appends a filter to the end of the sequence.

Parameters:

Name	Type	Description	Default
fn	FilterBase	An instance of a FilterBase subclass to be appended.	required

Example

import qim3d
from qim3d.filters import Pipeline, Maximum, Median

# Create filter pipeline
pipeline = Pipeline(
    Maximum(size=3, dask=True),
)

# Append a second filter to the pipeline
pipeline.append(Median(size=5))

Source code in qim3d/filters/_common_filter_methods.py

def append(self, fn: FilterBase):
    """
    Appends a filter to the end of the sequence.

    Args:
        fn (FilterBase): An instance of a FilterBase subclass to be appended.

    Example:
        ```python
        import qim3d
        from qim3d.filters import Pipeline, Maximum, Median

        # Create filter pipeline
        pipeline = Pipeline(
            Maximum(size=3, dask=True),
        )

        # Append a second filter to the pipeline
        pipeline.append(Median(size=5))
        ```

    """
    self._add_filter(str(len(self.filters)), fn)