mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 20:06:43 +08:00
François Boulogne
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
"""
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============
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Thresholding
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============
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Thresholding is used to create a binary image from a grayscale image [1]_.
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.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
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.. seealso::
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A more comprehensive presentation on
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:ref:`sphx_glr_auto_examples_xx_applications_plot_thresholding.py`
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"""
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######################################################################
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# We illustrate how to apply one of these thresholding algorithms.
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# This example uses the mean value of pixel intensities. It is a simple
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# and naive threshold value, which is sometimes used as a guess value.
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import matplotlib.pyplot as plt
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from skimage.filters.thresholding import threshold_mean
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from skimage import data
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image = data.camera()
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thresh = threshold_mean(image)
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binary = image > thresh
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fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
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ax = axes.ravel()
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ax[0].imshow(image, cmap=plt.cm.gray)
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ax[0].set_title('Original image')
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ax[1].imshow(binary, cmap=plt.cm.gray)
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ax[1].set_title('Result')
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for a in ax:
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a.axis('off')
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plt.show()
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######################################################################
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# If you are not familiar with the details of the different algorithms and the
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# underlying assumptions, it is often difficult to know which algorithm will give
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# the best results. Therefore, Scikit-image includes a function to evaluate
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# thresholding algorithms provided by the library. At a glance, you can select
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# the best algorithm for you data without a deep understanding of their
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# mechanisms.
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from skimage.filters import thresholding
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img = data.page()
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# Here, we specify a radius for local thresholding algorithms.
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# If it is not specified, only global algorithms are called.
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fig, ax = thresholding.try_all_threshold(img, radius=20,
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figsize=(10, 8), verbose=False)
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plt.show()
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