skimage 0.17.2

ParametersReturns
_prominent_peaks(image, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=inf)

Identifies most prominent features separated by certain distances. Non-maximum suppression with different sizes is applied separately in the first and second dimension of the image to identify peaks.

Parameters

image : (M, N) ndarray

Input image.

min_xdistance : int

Minimum distance separating features in the x dimension.

min_ydistance : int

Minimum distance separating features in the y dimension.

threshold : float

Minimum intensity of peaks. Default is :None:None:`0.5 * max(image)`.

num_peaks : int

Maximum number of peaks. When the number of peaks exceeds :None:None:`num_peaks`, return :None:None:`num_peaks` coordinates based on peak intensity.

Returns

intensity, xcoords, ycoords : tuple of array

Peak intensity values, x and y indices.

Return peaks with non-maximum suppression.

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


File: /skimage/feature/peak.py#257
type: <class 'function'>
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