W3cubDocs

/scikit-image

Module: draw

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.bezier_curve(r0, c0, r1, c1, …) Generate Bezier curve coordinates.
skimage.draw.polygon(r, c[, shape]) Generate coordinates of pixels within polygon.
skimage.draw.polygon_perimeter(r, c[, …]) Generate polygon perimeter coordinates.
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.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.set_color(image, coords, color) Set pixel color in the image at the given 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.

line

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

Generate line pixel coordinates.

Parameters:
r0, c0 : int

Starting position (row, column).

r1, c1 : int

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, c0 : int

Starting position (row, column).

r1, c1 : int

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)

bezier_curve

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

Generate Bezier curve coordinates.

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.

Notes

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

References

[1] (1, 2) 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)

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.

shape : tuple, 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.
Returns:
rr, cc : ndarray 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, 1])
>>> c = np.array([1, 7, 4, 1])
>>> rr, cc = polygon(r, c)
>>> 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, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 1, 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],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

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.

shape : tuple, 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.

clip : bool, 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, cc : ndarray 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)

ellipse

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

Generate coordinates of pixels within ellipse.

Parameters:
r, c : double

Centre coordinate of ellipse.

r_radius, c_radius : double

Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.

shape : tuple, 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.

rotation : float, 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, cc : ndarray 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)

ellipse_perimeter

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

Generate ellipse perimeter coordinates.

Parameters:
r, c : int

Centre coordinate of ellipse.

r_radius, c_radius : int

Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.

orientation : double, optional

Major axis orientation in clockwise direction as radians.

shape : tuple, 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.
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)

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_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:
a : float

Length of semimajor axis aligned with x-axis.

b : float

Length of semimajor axis aligned with y-axis.

c : float

Length of semimajor axis aligned with z-axis.

spacing : tuple of floats, length 3

Spacing in (x, y, z) spatial dimensions.

levelset : bool

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:
a : float

Length of semimajor axis aligned with x-axis.

b : float

Length of semimajor axis aligned with y-axis.

c : float

Length of semimajor axis aligned with z-axis.
Returns:
vol : float

Calculated volume of ellipsoid.

surf : float

Calculated surface area of ellipsoid.

circle

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

Generate coordinates of pixels within circle.

Parameters:
r, c : double

Centre coordinate of circle.

radius : double

Radius of circle.

shape : tuple, 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.
Returns:
rr, cc : ndarray of int

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

Examples

>>> from skimage.draw import circle
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = circle(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)

circle_perimeter

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

Generate circle perimeter coordinates.

Parameters:
r, c : int

Centre coordinate of circle.

radius: int

Radius of circle.

method : {‘bresenham’, ‘andres’}, optional

bresenham : Bresenham method (default) andres : Andres method

shape : tuple, 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.
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, c : int

Centre coordinate of circle.

radius: int

Radius of circle.

shape : tuple, 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.
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.

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)

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

coords : tuple 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.

alpha : scalar 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)

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_shape : tuple

The number of rows and columns of the image to generate.

max_shapes : int

The maximum number of shapes to (attempt to) fit into the shape.

min_shapes : int, optional

The minimum number of shapes to (attempt to) fit into the shape.

min_size : int, optional

The minimum dimension of each shape to fit into the image.

max_size : int, optional

The maximum dimension of each shape to fit into the image.

multichannel : bool, optional

If True, the generated image has num_channels color channels, otherwise generates grayscale image.

num_channels : int, 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, 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_overlap : bool, optional

If True, allow shapes to overlap.

num_trials : int, optional

How often to attempt to fit a shape into the image before skipping it.

seed : int, optional

Seed to initialize the random number generator. If None, a random seed from the operating system is used.
Returns:
image : uint8 array

An image with the fitted shapes.

labels : list

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 # doctest: +SKIP
array([
   [[255, 255, 255],
    [255, 255, 255],
    [255, 255, 255],
    ...,
    [255, 255, 255],
    [255, 255, 255],
    [255, 255, 255]]], dtype=uint8)
>>> labels # doctest: +SKIP
[('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:
start : tuple

Origin point of the rectangle, e.g., ([plane,] row, column).

end : tuple

End point of the rectangle ([plane,] row, column). Either end or extent must be specified.

extent : tuple

The extent (size) of the drawn rectangle. E.g., ([num_planes,] num_rows, num_cols). Either end or extent must be specified.

shape : tuple, 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:
coords : array 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)

© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.14.x/api/skimage.draw.html