bezier_curve(r0, c0, r1, c1, r2, c2, weight, shape=None)

Notes

The algorithm is the rational quadratic algorithm presented in reference .

Parameters

r0, c0 : int

Coordinates of the first control point.

r1, c1 : int

Coordinates of the middle control point.

r2, c2 : int

Coordinates of the last control point.

weight : double

Middle control point weight, it describes the line tension.

shape : tuple, optional

Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for curves that exceed the image size. If None, the full extent of the curve is used.

Returns

rr, cc : (N,) ndarray of int

Indices of pixels that belong to the Bezier curve. May be used to directly index into an array, e.g. img[rr, cc] = 1 .

Generate Bezier curve coordinates.

Examples

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

>>> import numpy as np
... from skimage.draw import bezier_curve
... img = np.zeros((10, 10), dtype=np.uint8)
... rr, cc = bezier_curve(1, 5, 5, -2, 8, 8, 2)
... img[rr, cc] = 1
... img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 1, 1, 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.draw.draw.bezier_curve

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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