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]
is7 + 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]])