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Module « scipy.ndimage »

Fonction generic_filter - module scipy.ndimage

Signature de la fonction generic_filter

def generic_filter(input, function, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords=None, *, axes=None) 

Description

help(scipy.ndimage.generic_filter)

Calculate a multidimensional filter using the given function.

At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1-D array of double values.

Parameters
----------
input : array_like
    The input array.
function : {callable, scipy.LowLevelCallable}
    Function to apply at each element.
size : scalar or tuple, optional
    See footprint, below. Ignored if footprint is given.
footprint : array, 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` is a boolean array that specifies (implicitly) a
    shape, but also which of the elements within this shape will get
    passed to the filter function. Thus ``size=(n,m)`` is equivalent
    to ``footprint=np.ones((n,m))``.  We adjust `size` to the number
    of dimensions of the input array, so that, if the input array is
    shape (10,10,10), and `size` is 2, then the actual size used is
    (2,2,2). When `footprint` is given, `size` is ignored.
output : array or dtype, optional
    The array in which to place the output, or the dtype of the
    returned array. By default an array of the same dtype as input
    will be created.
mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
    The `mode` parameter determines how the input array is extended
    beyond its boundaries. Default is 'reflect'. Behavior for each valid
    value is as follows:

    'reflect' (`d c b a | a b c d | d c b a`)
        The input is extended by reflecting about the edge of the last
        pixel. This mode is also sometimes referred to as half-sample
        symmetric.

    'constant' (`k k k k | a b c d | k k k k`)
        The input is extended by filling all values beyond the edge with
        the same constant value, defined by the `cval` parameter.

    'nearest' (`a a a a | a b c d | d d d d`)
        The input is extended by replicating the last pixel.

    'mirror' (`d c b | a b c d | c b a`)
        The input is extended by reflecting about the center of the last
        pixel. This mode is also sometimes referred to as whole-sample
        symmetric.

    'wrap' (`a b c d | a b c d | a b c d`)
        The input is extended by wrapping around to the opposite edge.

    For consistency with the interpolation functions, the following mode
    names can also be used:

    'grid-mirror'
        This is a synonym for 'reflect'.

    'grid-constant'
        This is a synonym for 'constant'.

    'grid-wrap'
        This is a synonym for 'wrap'.
cval : scalar, optional
    Value to fill past edges of input if `mode` is 'constant'. Default
    is 0.0.
origin : int or sequence, optional
    Controls the placement of the filter on the input array's pixels.
    A value of 0 (the default) centers the filter over the pixel, with
    positive values shifting the filter to the left, and negative ones
    to the right. By passing a sequence of origins with length equal to
    the number of dimensions of the input array, different shifts can
    be specified along each axis.
extra_arguments : sequence, optional
    Sequence of extra positional arguments to pass to passed function.
extra_keywords : dict, optional
    dict of extra keyword arguments to pass to passed function.
axes : tuple of int or None, optional
    If None, `input` is filtered along all axes. Otherwise,
    `input` is filtered along the specified axes. When `axes` is
    specified, any tuples used for `size` or `origin` must match the length
    of `axes`. The ith entry in any of these tuples corresponds to the ith
    entry in `axes`.

Returns
-------
generic_filter : ndarray
    Filtered array. Has the same shape as `input`.

Notes
-----
This function also accepts low-level callback functions with one of
the following signatures and wrapped in `scipy.LowLevelCallable`:

.. code:: c

   int callback(double *buffer, npy_intp filter_size,
                double *return_value, void *user_data)
   int callback(double *buffer, intptr_t filter_size,
                double *return_value, void *user_data)

The calling function iterates over the elements of the input and
output arrays, calling the callback function at each element. The
elements within the footprint of the filter at the current element are
passed through the ``buffer`` parameter, and the number of elements
within the footprint through ``filter_size``. The calculated value is
returned in ``return_value``. ``user_data`` is the data pointer provided
to `scipy.LowLevelCallable` as-is.

The callback function must return an integer error status that is zero
if something went wrong and one otherwise. If an error occurs, you should
normally set the python error status with an informative message
before returning, otherwise a default error message is set by the
calling function.

In addition, some other low-level function pointer specifications
are accepted, but these are for backward compatibility only and should
not be used in new code.

Examples
--------
Import the necessary modules and load the example image used for
filtering.

>>> import numpy as np
>>> from scipy import datasets
>>> from scipy.ndimage import zoom, generic_filter
>>> import matplotlib.pyplot as plt
>>> ascent = zoom(datasets.ascent(), 0.5)

Compute a maximum filter with kernel size 5 by passing a simple NumPy
aggregation function as argument to `function`.

>>> maximum_filter_result = generic_filter(ascent, np.amax, [5, 5])

While a maximum filter could also directly be obtained using
`maximum_filter`, `generic_filter` allows generic Python function or
`scipy.LowLevelCallable` to be used as a filter. Here, we compute the
range between maximum and minimum value as an example for a kernel size
of 5.

>>> def custom_filter(image):
...     return np.amax(image) - np.amin(image)
>>> custom_filter_result = generic_filter(ascent, custom_filter, [5, 5])

Plot the original and filtered images.

>>> fig, axes = plt.subplots(3, 1, figsize=(3, 9))
>>> plt.gray()  # show the filtered result in grayscale
>>> top, middle, bottom = axes
>>> for ax in axes:
...     ax.set_axis_off()  # remove coordinate system
>>> top.imshow(ascent)
>>> top.set_title("Original image")
>>> middle.imshow(maximum_filter_result)
>>> middle.set_title("Maximum filter, Kernel: 5x5")
>>> bottom.imshow(custom_filter_result)
>>> bottom.set_title("Custom filter, Kernel: 5x5")
>>> fig.tight_layout()



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