wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-27 18:25:32 +08:00
Code Issues Packages Projects Releases Wiki Activity

swap examples to show a different image inthe gallery

Browse Source
This commit is contained in:
François Boulogne
2016-06-12 22:09:42 +02:00
parent f25d93c511
commit 2137aed5ec
1 changed files with 29 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/segmentation/plot_thresholding.py
+29 -23
View File
@@ -4,41 +4,21 @@ Thresholding
============
Thresholding is used to create a binary image from a grayscale image [1]_.
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 you data without a deep understanding of their
mechanisms.
.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
.. seealso::
A more comprehensive presentation on
:ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py`
"""
import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import thresholding
img = data.page()
# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = thresholding.try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
plt.show()
######################################################################
# How to apply a threshold?
# =========================
#
# Now, we illustrate how to apply one of these thresholding algorithms.
# We illustrate how to apply one of these thresholding algorithms.
# This example uses the mean value of pixel intensities. It is a simple
# and naive threshold value, which is sometimes used as a guess value.
import matplotlib.pyplot as plt
from skimage.filters.thresholding import threshold_mean
from skimage import data
@@ -59,3 +39,29 @@ for a in ax:
a.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 you data without a deep understanding of their
# mechanisms.
import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import thresholding
img = data.page()
# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = thresholding.try_all_threshold(img, radius=20,
figsize=(10, 8), verbose=False)
plt.show()