Loading [MathJax]/jax/input/TeX/config.js
scipy 1.10.1 Pypi GitHub Homepage
Other Docs

Parameters
prewitt(input, axis=-1, output=None, mode='reflect', cval=0.0)

Parameters

input : array_like

The input array.

axis : int, optional

The axis of input along which to calculate. Default is -1.

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 : str or sequence, optional

The mode parameter determines how the input array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the input array, different modes can be specified along each axis. Default value is 'reflect'. The valid values and their behavior 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-constant'

This is a synonym for 'constant'.

'grid-mirror'

This is a synonym for 'reflect'.

'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.

Calculate a Prewitt filter.

Examples

from scipy import ndimage, datasets
import matplotlib.pyplot as plt
fig = plt.figure()
plt.gray()  # show the filtered result in grayscale
ax1 = fig.add_subplot(121)  # left side
ax2 = fig.add_subplot(122)  # right side
ascent = datasets.ascent()
result = ndimage.prewitt(ascent)
ax1.imshow(ascent)
ax2.imshow(result)
plt.show()
See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/ndimage/_filters.py#376
type: <class 'function'>
Commit: