import numpy as np
from .._shared.filters import gaussian
[文档]
def binary_blobs(
length=512, blob_size_fraction=0.1, n_dim=2, volume_fraction=0.5, rng=None
):
"""
Generate synthetic binary image with several rounded blob-like objects.
Parameters
----------
length : int, optional
Linear size of output image.
blob_size_fraction : float, optional
Typical linear size of blob, as a fraction of ``length``, should be
smaller than 1.
n_dim : int, optional
Number of dimensions of output image.
volume_fraction : float, default 0.5
Fraction of image pixels covered by the blobs (where the output is 1).
Should be in [0, 1].
rng : {`numpy.random.Generator`, int}, optional
Pseudo-random number generator.
By default, a PCG64 generator is used (see :func:`numpy.random.default_rng`).
If `rng` is an int, it is used to seed the generator.
Returns
-------
blobs : ndarray of bools
Output binary image
Examples
--------
>>> from skimage import data
>>> data.binary_blobs(length=5, blob_size_fraction=0.2) # doctest: +SKIP
array([[ True, False, True, True, True],
[ True, True, True, False, True],
[False, True, False, True, True],
[ True, False, False, True, True],
[ True, False, False, False, True]])
>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
>>> # Finer structures
>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
>>> # Blobs cover a smaller volume fraction of the image
>>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
"""
rs = np.random.default_rng(rng)
shape = tuple([length] * n_dim)
mask = np.zeros(shape)
n_pts = max(int(1.0 / blob_size_fraction) ** n_dim, 1)
points = (length * rs.random((n_dim, n_pts))).astype(int)
mask[tuple(indices for indices in points)] = 1
mask = gaussian(
mask, sigma=0.25 * length * blob_size_fraction, preserve_range=False
)
threshold = np.percentile(mask, 100 * (1 - volume_fraction))
return np.logical_not(mask < threshold)
