mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 00:29:50 +08:00
Stefan van der Walt
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
"""
|
|
============
|
|
Mean filters
|
|
============
|
|
|
|
This example compares the following mean filters of the rank filter package:
|
|
|
|
* **local mean**: all pixels belonging to the structuring element to compute
|
|
average gray level.
|
|
* **percentile mean**: only use values between percentiles p0 and p1
|
|
(here 10% and 90%).
|
|
* **bilateral mean**: only use pixels of the structuring element having a gray
|
|
level situated inside g-s0 and g+s1 (here g-500 and g+500)
|
|
|
|
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 higher image
|
|
frequencies remain untouched.
|
|
|
|
"""
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
from skimage import data
|
|
from skimage.morphology import disk
|
|
from skimage.filters import rank
|
|
|
|
|
|
image = (data.coins()).astype(np.uint16) * 16
|
|
selem = disk(20)
|
|
|
|
percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
|
|
bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
|
|
normal_result = rank.mean(image, selem=selem)
|
|
|
|
|
|
fig, axes = plt.subplots(nrows=3, figsize=(8, 10))
|
|
ax0, ax1, ax2 = axes
|
|
|
|
ax0.imshow(np.hstack((image, percentile_result)))
|
|
ax0.set_title('Percentile mean')
|
|
ax0.axis('off')
|
|
|
|
ax1.imshow(np.hstack((image, bilateral_result)))
|
|
ax1.set_title('Bilateral mean')
|
|
ax1.axis('off')
|
|
|
|
ax2.imshow(np.hstack((image, normal_result)))
|
|
ax2.set_title('Local mean')
|
|
ax2.axis('off')
|
|
|
|
plt.show()
|