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https://github.com/wassname/scikit-image.git
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Johannes Schönberger
46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
"""
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============
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Mean filters
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============
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This example compares the following mean filters of the rank filter package:
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* **local mean**: all pixels belonging to the structuring element to compute
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average gray level
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* **percentile mean**: only use values between percentiles p0 and p1
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(here 10% and 90%)
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* **bilateral mean**: only use pixels of the structuring element having a gray
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level situated inside g-s0 and g+s1 (here g-500 and g+500)
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Percentile and usual mean give here similar results, these filters smooth the
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complete image (background and details). Bilateral mean exhibits a high
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filtering rate for continuous area (i.e. background) while higher image
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frequencies remain untouched.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import data
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from skimage.morphology import disk
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import skimage.filter.rank as rank
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a16 = (data.coins()).astype(np.uint16) * 16
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selem = disk(20)
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f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
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f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
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f3 = rank.mean(a16, selem=selem)
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# display results
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fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
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ax0, ax1, ax2 = axes
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ax0.imshow(np.hstack((a16, f1)))
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ax0.set_title('percentile mean')
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ax1.imshow(np.hstack((a16, f2)))
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ax1.set_title('bilateral mean')
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ax2.imshow(np.hstack((a16, f3)))
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ax2.set_title('local mean')
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plt.show()
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