Module: draw

`skimage.draw.bezier_curve`(r0, c0, r1, c1, …)	Generate Bezier curve coordinates.
`skimage.draw.circle`(r, c, radius[, shape])	Generate coordinates of pixels within circle.
`skimage.draw.circle_perimeter`(r, c, radius)	Generate circle perimeter coordinates.
`skimage.draw.circle_perimeter_aa`(r, c, radius)	Generate anti-aliased circle perimeter coordinates.
`skimage.draw.disk`(center, radius, *[, shape])	Generate coordinates of pixels within circle.
`skimage.draw.ellipse`(r, c, r_radius, c_radius)	Generate coordinates of pixels within ellipse.
`skimage.draw.ellipse_perimeter`(r, c, …[, …])	Generate ellipse perimeter coordinates.
`skimage.draw.ellipsoid`(a, b, c[, spacing, …])	Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified `spacing`.
`skimage.draw.ellipsoid_stats`(a, b, c)	Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified `spacing`.
`skimage.draw.line`(r0, c0, r1, c1)	Generate line pixel coordinates.
`skimage.draw.line_aa`(r0, c0, r1, c1)	Generate anti-aliased line pixel coordinates.
`skimage.draw.line_nd`(start, stop, *[, …])	Draw a single-pixel thick line in n dimensions.
`skimage.draw.polygon`(r, c[, shape])	Generate coordinates of pixels within polygon.
`skimage.draw.polygon2mask`(image_shape, polygon)	Compute a mask from polygon.
`skimage.draw.polygon_perimeter`(r, c[, …])	Generate polygon perimeter coordinates.
`skimage.draw.random_shapes`(image_shape, …)	Generate an image with random shapes, labeled with bounding boxes.
`skimage.draw.rectangle`(start[, end, extent, …])	Generate coordinates of pixels within a rectangle.
`skimage.draw.rectangle_perimeter`(start[, …])	Generate coordinates of pixels that are exactly around a rectangle.
`skimage.draw.set_color`(image, coords, color)	Set pixel color in the image at the given coordinates.

bezier_curve

skimage.draw.bezier_curve(r0, c0, r1, c1, r2, c2, weight, shape=None) [source]

Generate Bezier curve coordinates.

Parameters

r0, c0int: Coordinates of the first control point.
r1, c1int: Coordinates of the middle control point.
r2, c2int: Coordinates of the last control point.
weightdouble: Middle control point weight, it describes the line tension.
shapetuple, 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.

Notes

The algorithm is the rational quadratic algorithm presented in reference [1].

References

1: A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf

Examples

>>> 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)

circle

skimage.draw.circle(r, c, radius, shape=None) [source]

Generate coordinates of pixels within circle.

Parameters

r, cdouble: Center coordinate of disk.
radiusdouble: Radius of disk.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for disks that exceed the image size. If None, the full extent of the disk is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, ccndarray of int: Pixel coordinates of disk. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Warns

Deprecated:: New in version 0.17: This function is deprecated and will be removed in scikit-image 0.19. Please use the function named disk instead.

circle_perimeter

skimage.draw.circle_perimeter(r, c, radius, method='bresenham', shape=None) [source]

Generate circle perimeter coordinates.

Parameters

r, cint: Centre coordinate of circle.
radiusint: Radius of circle.
method{‘bresenham’, ‘andres’}, optional: bresenham : Bresenham method (default) andres : Andres method
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, cc(N,) ndarray of int: Bresenham and Andres’ method: Indices of pixels that belong to the circle perimeter. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Notes

Andres method presents the advantage that concentric circles create a disc whereas Bresenham can make holes. There is also less distortions when Andres circles are rotated. Bresenham method is also known as midpoint circle algorithm. Anti-aliased circle generator is available with circle_perimeter_aa.

References

1: J.E. Bresenham, “Algorithm for computer control of a digital plotter”, IBM Systems journal, 4 (1965) 25-30.
2: E. Andres, “Discrete circles, rings and spheres”, Computers & Graphics, 18 (1994) 695-706.

Examples

>>> from skimage.draw import circle_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = circle_perimeter(4, 4, 3)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

circle_perimeter_aa

skimage.draw.circle_perimeter_aa(r, c, radius, shape=None) [source]

Generate anti-aliased circle perimeter coordinates.

Parameters

r, cint: Centre coordinate of circle.
radiusint: Radius of circle.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, cc, val(N,) ndarray (int, int, float): Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val.

Notes

Wu’s method draws anti-aliased circle. This implementation doesn’t use lookup table optimization.

Use the function draw.set_color to apply circle_perimeter_aa results to color images.

References

1: X. Wu, “An efficient antialiasing technique”, In ACM SIGGRAPH Computer Graphics, 25 (1991) 143-152.

Examples

>>> from skimage.draw import circle_perimeter_aa
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc, val = circle_perimeter_aa(4, 4, 3)
>>> img[rr, cc] = val * 255
>>> img
array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
       [  0,   0,  60, 211, 255, 211,  60,   0,   0,   0],
       [  0,  60, 194,  43,   0,  43, 194,  60,   0,   0],
       [  0, 211,  43,   0,   0,   0,  43, 211,   0,   0],
       [  0, 255,   0,   0,   0,   0,   0, 255,   0,   0],
       [  0, 211,  43,   0,   0,   0,  43, 211,   0,   0],
       [  0,  60, 194,  43,   0,  43, 194,  60,   0,   0],
       [  0,   0,  60, 211, 255, 211,  60,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=uint8)

>>> from skimage import data, draw
>>> image = data.chelsea()
>>> rr, cc, val = draw.circle_perimeter_aa(r=100, c=100, radius=75)
>>> draw.set_color(image, (rr, cc), [1, 0, 0], alpha=val)

disk

skimage.draw.disk(center, radius, *, shape=None) [source]

Generate coordinates of pixels within circle.

Parameters

centertuple: Center coordinate of disk.
radiusdouble: Radius of disk.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for disks that exceed the image size. If None, the full extent of the disk is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, ccndarray of int: Pixel coordinates of disk. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Examples

>>> from skimage.draw import disk
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = disk((4, 4), 5)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

ellipse

skimage.draw.ellipse(r, c, r_radius, c_radius, shape=None, rotation=0.0) [source]

Generate coordinates of pixels within ellipse.

Parameters

r, cdouble: Centre coordinate of ellipse.
r_radius, c_radiusdouble: Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed the image size. By default the full extent of the ellipse are used. Must be at least length 2. Only the first two values are used to determine the extent.
rotationfloat, optional (default 0.): Set the ellipse rotation (rotation) in range (-PI, PI) in contra clock wise direction, so PI/2 degree means swap ellipse axis

Returns

rr, ccndarray of int: Pixel coordinates of ellipse. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Notes

The ellipse equation:

((x * cos(alpha) + y * sin(alpha)) / x_radius) ** 2 +
((x * sin(alpha) - y * cos(alpha)) / y_radius) ** 2 = 1

Note that the positions of ellipse without specified shape can have also, negative values, as this is correct on the plane. On the other hand using these ellipse positions for an image afterwards may lead to appearing on the other side of image, because image[-1, -1] = image[end-1, end-1]

>>> rr, cc = ellipse(1, 2, 3, 6)
>>> img = np.zeros((6, 12), dtype=np.uint8)
>>> img[rr, cc] = 1
>>> img
array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1]], dtype=uint8)

Examples

>>> from skimage.draw import ellipse
>>> img = np.zeros((10, 12), dtype=np.uint8)
>>> rr, cc = ellipse(5, 6, 3, 5, rotation=np.deg2rad(30))
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

Examples using `skimage.draw.ellipse`

Masked Normalized Cross-Correlation

Measure region properties

ellipse_perimeter

skimage.draw.ellipse_perimeter(r, c, r_radius, c_radius, orientation=0, shape=None) [source]

Generate ellipse perimeter coordinates.

Parameters

r, cint: Centre coordinate of ellipse.
r_radius, c_radiusint: Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.
orientationdouble, optional: Major axis orientation in clockwise direction as radians.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses that exceed the image size. If None, the full extent of the ellipse is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, cc(N,) ndarray of int: Indices of pixels that belong to the ellipse perimeter. May be used to directly index into an array, e.g. img[rr, cc] = 1.

References

1: A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf

Examples

>>> from skimage.draw import ellipse_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = ellipse_perimeter(5, 5, 3, 4)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

>>> rr, cc = ellipse_perimeter(2, 3, 4, 5)
>>> img = np.zeros((9, 12), dtype=np.uint8)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
       [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
       [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], dtype=uint8)

ellipsoid

skimage.draw.ellipsoid(a, b, c, spacing=(1.0, 1.0, 1.0), levelset=False) [source]

Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified spacing.

Parameters

afloat: Length of semimajor axis aligned with x-axis.
bfloat: Length of semimajor axis aligned with y-axis.
cfloat: Length of semimajor axis aligned with z-axis.
spacingtuple of floats, length 3: Spacing in (x, y, z) spatial dimensions.
levelsetbool: If True, returns the level set for this ellipsoid (signed level set about zero, with positive denoting interior) as np.float64. False returns a binarized version of said level set.

Returns

ellip(N, M, P) array: Ellipsoid centered in a correctly sized array for given spacing. Boolean dtype unless levelset=True, in which case a float array is returned with the level set above 0.0 representing the ellipsoid.

ellipsoid_stats

skimage.draw.ellipsoid_stats(a, b, c) [source]

Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified spacing.

Parameters

afloat: Length of semimajor axis aligned with x-axis.
bfloat: Length of semimajor axis aligned with y-axis.
cfloat: Length of semimajor axis aligned with z-axis.

Returns

volfloat: Calculated volume of ellipsoid.
surffloat: Calculated surface area of ellipsoid.

line

skimage.draw.line(r0, c0, r1, c1) [source]

Generate line pixel coordinates.

Parameters

r0, c0int: Starting position (row, column).
r1, c1int: End position (row, column).

Returns

rr, cc(N,) ndarray of int: Indices of pixels that belong to the line. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Notes

Anti-aliased line generator is available with line_aa.

Examples

>>> from skimage.draw import line
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = line(1, 1, 8, 8)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

line_aa

skimage.draw.line_aa(r0, c0, r1, c1) [source]

Generate anti-aliased line pixel coordinates.

Parameters

r0, c0int: Starting position (row, column).
r1, c1int: End position (row, column).

Returns

rr, cc, val(N,) ndarray (int, int, float): Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val.

References

1: A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf

Examples

>>> from skimage.draw import line_aa
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc, val = line_aa(1, 1, 8, 8)
>>> img[rr, cc] = val * 255
>>> img
array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
       [  0, 255,  74,   0,   0,   0,   0,   0,   0,   0],
       [  0,  74, 255,  74,   0,   0,   0,   0,   0,   0],
       [  0,   0,  74, 255,  74,   0,   0,   0,   0,   0],
       [  0,   0,   0,  74, 255,  74,   0,   0,   0,   0],
       [  0,   0,   0,   0,  74, 255,  74,   0,   0,   0],
       [  0,   0,   0,   0,   0,  74, 255,  74,   0,   0],
       [  0,   0,   0,   0,   0,   0,  74, 255,  74,   0],
       [  0,   0,   0,   0,   0,   0,   0,  74, 255,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=uint8)

line_nd

skimage.draw.line_nd(start, stop, *, endpoint=False, integer=True) [source]

Draw a single-pixel thick line in n dimensions.

The line produced will be ndim-connected. That is, two subsequent pixels in the line will be either direct or diagonal neighbours in n dimensions.

Parameters

startarray-like, shape (N,): The start coordinates of the line.
stoparray-like, shape (N,): The end coordinates of the line.
endpointbool, optional: Whether to include the endpoint in the returned line. Defaults to False, which allows for easy drawing of multi-point paths.
integerbool, optional: Whether to round the coordinates to integer. If True (default), the returned coordinates can be used to directly index into an array. False could be used for e.g. vector drawing.

Returns

coordstuple of arrays: The coordinates of points on the line.

Examples

>>> lin = line_nd((1, 1), (5, 2.5), endpoint=False)
>>> lin
(array([1, 2, 3, 4]), array([1, 1, 2, 2]))
>>> im = np.zeros((6, 5), dtype=int)
>>> im[lin] = 1
>>> im
array([[0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0],
       [0, 1, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])
>>> line_nd([2, 1, 1], [5, 5, 2.5], endpoint=True)
(array([2, 3, 4, 4, 5]), array([1, 2, 3, 4, 5]), array([1, 1, 2, 2, 2]))

polygon

skimage.draw.polygon(r, c, shape=None) [source]

Generate coordinates of pixels within polygon.

Parameters

r(N,) ndarray: Row coordinates of vertices of polygon.
c(N,) ndarray: Column coordinates of vertices of polygon.
shapetuple, optional: Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extent of the polygon is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.

Returns

rr, ccndarray of int: Pixel coordinates of polygon. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Examples

>>> from skimage.draw import polygon
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> r = np.array([1, 2, 8])
>>> c = np.array([1, 7, 4])
>>> rr, cc = polygon(r, c)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0]], dtype=uint8)

polygon2mask

skimage.draw.polygon2mask(image_shape, polygon) [source]

Compute a mask from polygon.

Parameters

image_shapetuple of size 2.: The shape of the mask.
polygonarray_like.: The polygon coordinates of shape (N, 2) where N is the number of points.

Returns

mask2-D ndarray of type ‘bool’.: The mask that corresponds to the input polygon.

Notes

This function does not do any border checking, so that all the vertices need to be within the given shape.

Examples

>>> image_shape = (128, 128)
>>> polygon = np.array([[60, 100], [100, 40], [40, 40]])
>>> mask = polygon2mask(image_shape, polygon)
>>> mask.shape
(128, 128)

polygon_perimeter

skimage.draw.polygon_perimeter(r, c, shape=None, clip=False) [source]

Generate polygon perimeter coordinates.

Parameters

r(N,) ndarray: Row coordinates of vertices of polygon.
c(N,) ndarray: Column coordinates of vertices of polygon.
shapetuple, optional: Image shape which is used to determine maximum extents of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extents of the polygon is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.
clipbool, optional: Whether to clip the polygon to the provided shape. If this is set to True, the drawn figure will always be a closed polygon with all edges visible.

Returns

rr, ccndarray of int: Pixel coordinates of polygon. May be used to directly index into an array, e.g. img[rr, cc] = 1.

Examples

>>> from skimage.draw import polygon_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = polygon_perimeter([5, -1, 5, 10],
...                            [-1, 5, 11, 5],
...                            shape=img.shape, clip=True)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
       [0, 0, 0, 1, 0, 0, 0, 1, 1, 0],
       [0, 0, 0, 0, 1, 1, 1, 0, 0, 0]], dtype=uint8)

random_shapes

skimage.draw.random_shapes(image_shape, max_shapes, min_shapes=1, min_size=2, max_size=None, multichannel=True, num_channels=3, shape=None, intensity_range=None, allow_overlap=False, num_trials=100, random_seed=None) [source]

Generate an image with random shapes, labeled with bounding boxes.

The image is populated with random shapes with random sizes, random locations, and random colors, with or without overlap.

Shapes have random (row, col) starting coordinates and random sizes bounded by min_size and max_size. It can occur that a randomly generated shape will not fit the image at all. In that case, the algorithm will try again with new starting coordinates a certain number of times. However, it also means that some shapes may be skipped altogether. In that case, this function will generate fewer shapes than requested.

Parameters

image_shapetuple: The number of rows and columns of the image to generate.
max_shapesint: The maximum number of shapes to (attempt to) fit into the shape.
min_shapesint, optional: The minimum number of shapes to (attempt to) fit into the shape.
min_sizeint, optional: The minimum dimension of each shape to fit into the image.
max_sizeint, optional: The maximum dimension of each shape to fit into the image.
multichannelbool, optional: If True, the generated image has num_channels color channels, otherwise generates grayscale image.
num_channelsint, optional: Number of channels in the generated image. If 1, generate monochrome images, else color images with multiple channels. Ignored if multichannel is set to False.
shape{rectangle, circle, triangle, ellipse, None} str, optional: The name of the shape to generate or None to pick random ones.
intensity_range{tuple of tuples of uint8, tuple of uint8}, optional: The range of values to sample pixel values from. For grayscale images the format is (min, max). For multichannel - ((min, max),) if the ranges are equal across the channels, and ((min_0, max_0), … (min_N, max_N)) if they differ. As the function supports generation of uint8 arrays only, the maximum range is (0, 255). If None, set to (0, 254) for each channel reserving color of intensity = 255 for background.
allow_overlapbool, optional: If True, allow shapes to overlap.
num_trialsint, optional: How often to attempt to fit a shape into the image before skipping it.
random_seedint, optional: Seed to initialize the random number generator. If None, a random seed from the operating system is used.

Returns

imageuint8 array: An image with the fitted shapes.
labelslist: A list of labels, one per shape in the image. Each label is a (category, ((r0, r1), (c0, c1))) tuple specifying the category and bounding box coordinates of the shape.

Examples

>>> import skimage.draw
>>> image, labels = skimage.draw.random_shapes((32, 32), max_shapes=3)
>>> image 
array([
   [[255, 255, 255],
    [255, 255, 255],
    [255, 255, 255],
    ...,
    [255, 255, 255],
    [255, 255, 255],
    [255, 255, 255]]], dtype=uint8)
>>> labels 
[('circle', ((22, 18), (25, 21))),
 ('triangle', ((5, 6), (13, 13)))]

rectangle

skimage.draw.rectangle(start, end=None, extent=None, shape=None) [source]

Generate coordinates of pixels within a rectangle.

Parameters

starttuple: Origin point of the rectangle, e.g., ([plane,] row, column).
endtuple: End point of the rectangle ([plane,] row, column). For a 2D matrix, the slice defined by the rectangle is [start:(end+1)]. Either end or extent must be specified.
extenttuple: The extent (size) of the drawn rectangle. E.g., ([num_planes,] num_rows, num_cols). Either end or extent must be specified. A negative extent is valid, and will result in a rectangle going along the opposite direction. If extent is negative, the start point is not included.
shapetuple, optional: Image shape used to determine the maximum bounds of the output coordinates. This is useful for clipping rectangles that exceed the image size. By default, no clipping is done.

Returns

coordsarray of int, shape (Ndim, Npoints): The coordinates of all pixels in the rectangle.

Notes

This function can be applied to N-dimensional images, by passing start and end or extent as tuples of length N.

Examples

>>> import numpy as np
>>> from skimage.draw import rectangle
>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> start = (1, 1)
>>> extent = (3, 3)
>>> rr, cc = rectangle(start, extent=extent, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
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=uint8)

>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> start = (0, 1)
>>> end = (3, 3)
>>> rr, cc = rectangle(start, end=end, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
array([[0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 1, 1, 1, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)

>>> import numpy as np
>>> from skimage.draw import rectangle
>>> img = np.zeros((6, 6), dtype=np.uint8)
>>> start = (3, 3)
>>>
>>> rr, cc = rectangle(start, extent=(2, 2))
>>> img[rr, cc] = 1
>>> rr, cc = rectangle(start, extent=(-2, 2))
>>> img[rr, cc] = 2
>>> rr, cc = rectangle(start, extent=(-2, -2))
>>> img[rr, cc] = 3
>>> rr, cc = rectangle(start, extent=(2, -2))
>>> img[rr, cc] = 4
>>> print(img)
[[0 0 0 0 0 0]
 [0 3 3 2 2 0]
 [0 3 3 2 2 0]
 [0 4 4 1 1 0]
 [0 4 4 1 1 0]
 [0 0 0 0 0 0]]

rectangle_perimeter

skimage.draw.rectangle_perimeter(start, end=None, extent=None, shape=None, clip=False) [source]

Generate coordinates of pixels that are exactly around a rectangle.

Parameters

starttuple: Origin point of the inner rectangle, e.g., (row, column).
endtuple: End point of the inner rectangle (row, column). For a 2D matrix, the slice defined by inner the rectangle is [start:(end+1)]. Either end or extent must be specified.
extenttuple: The extent (size) of the inner rectangle. E.g., (num_rows, num_cols). Either end or extent must be specified. Negative extents are permitted. See rectangle to better understand how they behave.
shapetuple, optional: Image shape used to determine the maximum bounds of the output coordinates. This is useful for clipping perimeters that exceed the image size. By default, no clipping is done. Must be at least length 2. Only the first two values are used to determine the extent of the input image.
clipbool, optional: Whether to clip the perimeter to the provided shape. If this is set to True, the drawn figure will always be a closed polygon with all edges visible.

Returns

coordsarray of int, shape (2, Npoints): The coordinates of all pixels in the rectangle.

Examples

>>> import numpy as np
>>> from skimage.draw import rectangle_perimeter
>>> img = np.zeros((5, 6), dtype=np.uint8)
>>> start = (2, 3)
>>> end = (3, 4)
>>> rr, cc = rectangle_perimeter(start, end=end, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 1, 1],
       [0, 0, 1, 0, 0, 1],
       [0, 0, 1, 0, 0, 1],
       [0, 0, 1, 1, 1, 1]], dtype=uint8)

>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> r, c = rectangle_perimeter(start, (10, 10), shape=img.shape, clip=True)
>>> img[r, c] = 1
>>> img
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 1, 1],
       [0, 0, 1, 0, 1],
       [0, 0, 1, 0, 1],
       [0, 0, 1, 1, 1]], dtype=uint8)

set_color

skimage.draw.set_color(image, coords, color, alpha=1) [source]

Set pixel color in the image at the given coordinates.

Note that this function modifies the color of the image in-place. Coordinates that exceed the shape of the image will be ignored.

Parameters

image(M, N, D) ndarray: Image
coordstuple of ((P,) ndarray, (P,) ndarray): Row and column coordinates of pixels to be colored.
color(D,) ndarray: Color to be assigned to coordinates in the image.
alphascalar or (N,) ndarray: Alpha values used to blend color with image. 0 is transparent, 1 is opaque.

Examples

>>> from skimage.draw import line, set_color
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = line(1, 1, 20, 20)
>>> set_color(img, (rr, cc), 1)
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=uint8)

© 2019 the scikit-image team
Licensed under the BSD 3-clause License.
https://scikit-image.org/docs/0.18.x/api/skimage.draw.html

Module: draw

bezier_curve

Notes

References

Examples

circle

circle_perimeter

Notes

References

Examples

circle_perimeter_aa

Notes

References

Examples

disk

Examples

ellipse

Notes

Examples

Examples using skimage.draw.ellipse

ellipse_perimeter

References

Examples

ellipsoid

ellipsoid_stats

line

Notes

Examples

line_aa

References

Examples

line_nd

Examples

polygon

Examples

polygon2mask

Notes

Examples

polygon_perimeter

Examples

random_shapes

Examples

rectangle

Notes

Examples

rectangle_perimeter

Examples

set_color

Examples

Examples using `skimage.draw.ellipse`