73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
"""
|
|
============
|
|
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.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()
|
|
|
|
# Here, we specify a radius for local thresholding algorithms.
|
|
# If it is not specified, only global algorithms are called.
|
|
fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
|
|
plt.show()
|