gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False)

Parameters

image : 2-D array (integer or float)

Input image.

selem : 2-D array (integer or float)

The neighborhood expressed as a 2-D array of 1's and 0's.

out : 2-D array (integer or float), optional

If None, a new array is allocated.

mask : ndarray (integer or float), optional

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int, optional

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

out : 2-D array (same dtype as input image)

Output image.

Return local gradient of an image (i.e. local maximum - local minimum).

Examples

This example is valid syntax, but we were not able to check execution

>>> from skimage import data
... from skimage.morphology import disk
... from skimage.filters.rank import gradient
... img = data.camera()
... out = gradient(img, disk(5))

See :

Back References

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

skimage.measure._structural_similarity.compare_ssim skimage.filters.rank.generic.gradient skimage.metrics._structural_similarity.structural_similarity

Local connectivity graph

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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

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