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François Boulogne
2016-06-18 20:20:34 +02:00
parent 6442bf5f0c
commit 476f6bd8f2
2 changed files with 165 additions and 189 deletions
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doc/examples/segmentation/plot_thresholding.py
+31 -36
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@@ -5,10 +5,11 @@ 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 to know which algorithm will give the best
results. Therefore, Scikit-image includes a function to test thresholding algorithms
provided in the library. At a glance, you can select the best algorithm
for you data, without a deep understanding of their mechanisms.
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
@@ -21,43 +22,37 @@ from skimage.filters import thresholding
img = data.page()
# Here, we specify a radius for local thresholding algorithm.
# 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)
figsize=(10, 8), verbose=False)
plt.show()
"""
.. image:: PLOT2RST.current_figure
######################################################################
# How to apply a threshold?
# =========================
#
# Now, 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.
How to apply a threshold?
=========================
from skimage.filters.thresholding import threshold_mean
from skimage import data
Now, 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.
"""
image = data.camera()
thresh = threshold_mean(image)
binary = image > thresh
#from skimage.filters.thresholding import threshold_mean
#from skimage import data
#image = data.camera()
#thresh = threshold_mean(image)
#binary = image > thresh
#
#fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
#ax0, ax1 = axes
#
#ax0.imshow(image)
#ax0.set_title('Original image')
#
#ax1.imshow(binary)
#ax1.set_title('Result')
#
#for ax in axes:
# ax.axis('off')
#
#plt.show()
fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
ax = axes.ravel()
"""
.. image:: PLOT2RST.current_figure
"""
ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_title('Original image')
ax[1].imshow(binary, cmap=plt.cm.gray)
ax[1].set_title('Result')
for a in ax:
a.axis('off')
plt.show()