mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0)

Parameters

image : (M, N[, 3]) array

Grayscale or RGB image.

label_img : (M, N) array of int

Label array where regions are marked by different integer values.

color : length-3 sequence, optional

RGB color of boundaries in the output image.

outline_color : length-3 sequence, optional

RGB color surrounding boundaries in the output image. If None, no outline is drawn.

mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional

The mode for finding boundaries.

background_label : int, optional

Which label to consider background (this is only useful for modes inner and outer ).

Returns

marked : (M, N, 3) array of float

An image in which the boundaries between labels are superimposed on the original image.

Return image with boundaries between labeled regions highlighted.

See Also

find_boundaries

Examples

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

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File: /skimage/segmentation/boundaries.py#183
type: <class 'function'>
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