mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 19:48:43 +08:00
François Boulogne
64 lines
1.7 KiB
Python
64 lines
1.7 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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If you are not familiar with the details of the different algorithms and the
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underlying assumptions, it is often to know which algorithm will give the best
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results. Therefore, Scikit-image includes a function to test thresholding algorithms
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provided in the library. At a glance, you can select the best algorithm
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for you data, without a deep understanding of their mechanisms.
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.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
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"""
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import matplotlib
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import matplotlib.pyplot as plt
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from skimage import data
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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 algorithm.
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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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"""
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.. image:: PLOT2RST.current_figure
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How to apply a threshold?
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=========================
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Now, 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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"""
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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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#
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#fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
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#ax0, ax1 = axes
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#
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#ax0.imshow(image)
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#ax0.set_title('Original image')
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#
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#ax1.imshow(binary)
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#ax1.set_title('Result')
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#
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#for ax in axes:
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# ax.axis('off')
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#
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#plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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"""
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