skimage 0.17.2

NotesParametersReturnsBackRef
erosion(image, selem=None, out=None, shift_x=False, shift_y=False)

Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions.

Notes

For uint8 (and uint16 up to a certain bit-depth) data, the lower algorithm complexity makes the skimage.filters.rank.minimum function more efficient for larger images and structuring elements.

Parameters

image : ndarray

Image array.

selem : ndarray, optional

The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1).

out : ndarrays, optional

The array to store the result of the morphology. If None is passed, a new array will be allocated.

shift_x, shift_y : bool, optional

shift structuring element about center point. This only affects eccentric structuring elements (i.e. selem with even numbered sides).

Returns

eroded : array, same shape as `image`

The result of the morphological erosion.

Return greyscale morphological erosion of an image.

Examples

This example is valid syntax, but we were not able to check execution
>>> # Erosion shrinks bright regions
... import numpy as np
... from skimage.morphology import square
... bright_square = np.array([[0, 0, 0, 0, 0],
...  [0, 1, 1, 1, 0],
...  [0, 1, 1, 1, 0],
...  [0, 1, 1, 1, 0],
...  [0, 0, 0, 0, 0]], dtype=np.uint8)
... erosion(bright_square, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8)
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.morphology.grey.erosion skimage.filters.rank.generic.minimum

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