1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
|
"""
============
Thresholding
============
Thresholding is used to create a binary image from a grayscale image [1]_.
.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
.. seealso::
A more comprehensive presentation on
:ref:`sphx_glr_auto_examples_applications_plot_thresholding_guide.py`
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import threshold_otsu
######################################################################
# We illustrate how to apply one of these thresholding algorithms.
# Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the
# histogram below) by maximizing the variance between two classes of pixels,
# which are separated by the threshold. Equivalently, this threshold minimizes
# the intra-class variance.
#
# .. [2] https://en.wikipedia.org/wiki/Otsu's_method
#
image = data.camera()
thresh = threshold_otsu(image)
binary = image > thresh
fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
ax = axes.ravel()
ax[0] = plt.subplot(1, 3, 1)
ax[1] = plt.subplot(1, 3, 2)
ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0])
ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_title('Original')
ax[0].axis('off')
ax[1].hist(image.ravel(), bins=256)
ax[1].set_title('Histogram')
ax[1].axvline(thresh, color='r')
ax[2].imshow(binary, cmap=plt.cm.gray)
ax[2].set_title('Thresholded')
ax[2].axis('off')
plt.show()
######################################################################
# If you are not familiar with the details of the different algorithms and the
# underlying assumptions, it is often difficult to know which algorithm will give
# the best results. Therefore, Scikit-image includes a function to evaluate
# thresholding algorithms provided by the library. At a glance, you can select
# the best algorithm for your data without a deep understanding of their
# mechanisms.
#
from skimage.filters import try_all_threshold
img = data.page()
fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
plt.show()
|
