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Module « scipy.ndimage »

Fonction correlate - module scipy.ndimage

Signature de la fonction correlate

def correlate(input, weights, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None) 

Description

help(scipy.ndimage.correlate)

Multidimensional correlation.

The array is correlated with the given kernel.

Parameters
----------
input : array_like
    The input array.
weights : ndarray
    array of weights, same number of dimensions as input
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.
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 `mode` or `origin` must match the length
    of `axes`. The ith entry in any of these tuples corresponds to the ith
    entry in `axes`.

Returns
-------
result : ndarray
    The result of correlation of `input` with `weights`.

See Also
--------
convolve : Convolve an image with a kernel.

Examples
--------
Correlation is the process of moving a filter mask often referred to
as kernel over the image and computing the sum of products at each location.

>>> from scipy.ndimage import correlate
>>> import numpy as np
>>> input_img = np.arange(25).reshape(5,5)
>>> print(input_img)
[[ 0  1  2  3  4]
[ 5  6  7  8  9]
[10 11 12 13 14]
[15 16 17 18 19]
[20 21 22 23 24]]

Define a kernel (weights) for correlation. In this example, it is for sum of
center and up, down, left and right next elements.

>>> weights = [[0, 1, 0],
...            [1, 1, 1],
...            [0, 1, 0]]

We can calculate a correlation result:
For example, element ``[2,2]`` is ``7 + 11 + 12 + 13 + 17 = 60``.

>>> correlate(input_img, weights)
array([[  6,  10,  15,  20,  24],
    [ 26,  30,  35,  40,  44],
    [ 51,  55,  60,  65,  69],
    [ 76,  80,  85,  90,  94],
    [ 96, 100, 105, 110, 114]])

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