mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 00:23:43 +08:00
Andreas Mueller
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from scipy import ndimage
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#from skimage.data import lena
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#from skimage.util import img_as_float
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from skimage.segmentation import quickshift
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from IPython.core.debugger import Tracer
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tracer = Tracer()
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def microstructure(l=256):
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"""
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Synthetic binary data: binary microstructure with blobs.
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Parameters
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----------
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l: int, optional
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linear size of the returned image
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"""
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n = 5
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x, y = np.ogrid[0:l, 0:l]
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mask = np.zeros((l, l))
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generator = np.random.RandomState(1)
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points = l * generator.rand(2, n ** 2)
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mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
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mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
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return (mask > mask.mean()).astype(np.float)
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#img = img_as_float(lena()[250:300, 250:300])
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img = microstructure(l=50)
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segments = quickshift(img.reshape(50, 50, 1))
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segments = np.unique(segments, return_inverse=True)[1].reshape(50, 50)
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intensities = np.bincount(segments.ravel(), img.ravel())
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counts = np.bincount(segments.ravel())
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intensities /= counts
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plt.imshow(img, interpolation='nearest')
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plt.figure()
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plt.imshow(segments, interpolation='nearest')
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plt.figure()
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plt.imshow(intensities[segments], interpolation='nearest')
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plt.show()
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print("num segments: %d" % len(np.unique(segments)))
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