mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 13:49:56 +08:00
Olivier Debeir
36 lines
849 B
Python
36 lines
849 B
Python
"""
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==============================
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Simplified bilateral filtering
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==============================
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to complete
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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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a8 = (data.coins()).astype('uint8')
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a16 = (data.coins()).astype('uint16')*16
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selem = np.ones((20,20),dtype='uint8')
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f1 = rank.percentile_mean(a8,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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selem = disk(50)
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f3 = rank.equalize(a16,selem = selem)
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# display results
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fig, axes = plt.subplots(nrows=3, figsize=(15,15))
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ax0, ax1, ax2 = axes
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ax0.imshow(np.hstack((a8,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 equalization')
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plt.show()
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