scipy.ndimage.correlate#

scipy.ndimage.correlate(input, weights, output=None, mode='reflect', cval=0.0, origin=0)[source]#

Multidimensional correlation.

The array is correlated with the given kernel.

Parameters:

inputarray_like

The input array.

weightsndarray

array of weights, same number of dimensions as input

outputarray 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’.

cvalscalar, optional

Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

originint 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.

Returns:

resultndarray: 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]])