58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
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import numpy as np
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def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
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volume_fraction=0.5, seed=None):
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"""
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Generate synthetic binary image with several rounded blob-like objects.
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Parameters
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----------
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length : int, optional
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Linear size of output image.
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blob_size_fraction : float, optional
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Typical linear size of blob, as a fraction of ``length``, should be
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smaller than 1.
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n_dim : int, optional
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Number of dimensions of output image.
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volume_fraction : float, default 0.5
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Fraction of image pixels covered by the blobs (where the output is 1).
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Should be in [0, 1].
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seed : int, optional
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Seed to initialize the random number generator.
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If `None`, a random seed from the operating system is used.
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Returns
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-------
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blobs : ndarray of bools
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Output binary image
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Examples
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--------
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>>> from skimage import data
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>>> data.binary_blobs(length=5, blob_size_fraction=0.2, seed=1)
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array([[ True, False, True, True, True],
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[ True, True, True, False, True],
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[False, True, False, True, True],
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[ True, False, False, True, True],
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[ True, False, False, False, True]])
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>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
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>>> # Finer structures
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>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
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>>> # Blobs cover a smaller volume fraction of the image
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>>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
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"""
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# filters is quite an expensive import since it imports all of scipy.signal
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# We lazy import here
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from ..filters import gaussian
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rs = np.random.RandomState(seed)
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shape = tuple([length] * n_dim)
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mask = np.zeros(shape)
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n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
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points = (length * rs.rand(n_dim, n_pts)).astype(np.int)
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mask[tuple(indices for indices in points)] = 1
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mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction)
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threshold = np.percentile(mask, 100 * (1 - volume_fraction))
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return np.logical_not(mask < threshold)
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