# Thresholding¶

Thresholding is used to create a binary image from a grayscale image 1.

1

https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29

A more comprehensive presentation on Thresholding

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 = plt.subplot(1, 3, 1)
ax = plt.subplot(1, 3, 2)
ax = plt.subplot(1, 3, 3, sharex=ax, sharey=ax)

ax.imshow(image, cmap=plt.cm.gray)
ax.set_title('Original')
ax.axis('off')

ax.hist(image.ravel(), bins=256)
ax.set_title('Histogram')
ax.axvline(thresh, color='r')

ax.imshow(binary, cmap=plt.cm.gray)
ax.set_title('Thresholded')
ax.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() Total running time of the script: ( 0 minutes 0.788 seconds)

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