mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 02:01:40 +08:00
Olivier Debeir
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""
|
|
==============================
|
|
Bilateral mean
|
|
==============================
|
|
This example compares
|
|
|
|
* local mean
|
|
* percentile mean
|
|
* bilateral mean
|
|
|
|
build on the local histogram distribution
|
|
local mean uses all pixels belonging to the structuring element to compute average gray level,
|
|
percentile mean uses only values between percentiles p0 and p1 (here 10% and 90%),
|
|
whereas bilateral mean uses only pixels of the structuring element having a gray level situated inside
|
|
g-s0 and g+s1 (here g-500 and g+500).
|
|
The filters are applied on a 16 bit image (actual bitdepth is 12bit).
|
|
|
|
Percentile and usual mean give here similar results, these filters smooth the complete image (background and details).
|
|
Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while image higher frequencies
|
|
remains untouched.
|
|
|
|
"""
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
from skimage import data
|
|
from skimage.morphology import disk
|
|
import skimage.filter.rank as rank
|
|
|
|
a16 = (data.coins()).astype('uint16') * 16
|
|
selem = disk(20)
|
|
|
|
f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
|
|
f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
|
|
f3 = rank.mean(a16, selem=selem)
|
|
|
|
# display results
|
|
fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
|
|
ax0, ax1, ax2 = axes
|
|
|
|
ax0.imshow(np.hstack((a16, f1)))
|
|
ax0.set_title('percentile mean')
|
|
ax1.imshow(np.hstack((a16, f2)))
|
|
ax1.set_title('bilateral mean')
|
|
ax2.imshow(np.hstack((a16, f3)))
|
|
ax2.set_title('local mean')
|
|
plt.show()
|