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odebeir
64 lines
1.6 KiB
Python
64 lines
1.6 KiB
Python
"""
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====================================================
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Bilateral comparison
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====================================================
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In this example, we compare both bilateral implementation
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* filter.denoise_bilateral
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* filter.rank.bilateral_mean
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The first filter implements a spatial-gaussian and spectral-gaussian kernel bilateral filter whereas the latter implements
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a cylindrical kernel bilateral filter i.e. spatial-flat and spectral-flat kernel.
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The timing comparison is just for information since the kernel are not the same.
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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.filter._denoise import denoise_bilateral
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from skimage.filter.rank import bilateral_mean
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from skimage.morphology import disk
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from skimage.filter import denoise_bilateral
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import time
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def exec_and_timeit(func):
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""" Decorator that returns both function results and execution time
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(result, ms)
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"""
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def wrapper(*arg):
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t1 = time.time()
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res = func(*arg)
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t2 = time.time()
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ms = (t2-t1)*1000.0
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return (res,ms)
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return wrapper
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@exec_and_timeit
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def den_bil(image):
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return denoise_bilateral(a8,win_size=20,sigma_range=255,sigma_spatial=1)[:,:,0]*255
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@exec_and_timeit
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def rank_bil(image):
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return bilateral_mean(a8.astype(np.uint16),disk(20),s0=10,s1=10)
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a8 = data.camera()
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selem = disk(10)
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f1,t1 = den_bil(a8)
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f2,t2 = rank_bil(a8)
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# display results
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fig, axes = plt.subplots(nrows=2, figsize=(15,10))
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ax0, ax1= axes
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ax0.imshow(np.hstack((f1,a8-f1)))
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ax0.set_title('denoise bilateral (%f ms)'%t1)
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ax1.imshow(np.hstack((f2,a8-f1)))
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ax1.set_title('bilateral mean (%f ms)'%t2)
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plt.show()
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