mirror of
https://github.com/wassname/scikit-image.git
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François Boulogne
88 lines
2.3 KiB
Python
88 lines
2.3 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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Thresholding algorithms can be separated in two categories:
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- Histogram-based. The histogram of the pixel intensity is used and
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assumptions may be made on the properties of this histogram (e.g. bimodal).
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- Local. To process a pixel, only the neighboring pixels are used.
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These algorithms often require more computation time.
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Scikit-image includes a function to test thresholding algorithms provided
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in the library. Therefore, in 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.data import page
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from skimage.filters import thresholding
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img = 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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Now, we illustrate how to apply one of these thresholding algorithms
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This example uses Otsu's method [2]_.
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Otsu's method calculates an "optimal" threshold (marked by a red line in the
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histogram below) by maximizing the variance between two classes of pixels,
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which are separated by the threshold. Equivalently, this threshold minimizes
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the intra-class variance.
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.. [2] http://en.wikipedia.org/wiki/Otsu's_method
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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.data import camera
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from skimage.filters import threshold_otsu
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matplotlib.rcParams['font.size'] = 9
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image = camera()
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thresh = threshold_otsu(image)
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binary = image > thresh
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fig = plt.figure(figsize=(8, 2.5))
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax2 = plt.subplot(1, 3, 2)
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ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
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ax1.imshow(image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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ax2.hist(image)
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ax2.set_title('Histogram')
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ax2.axvline(thresh, color='r')
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ax3.imshow(binary, cmap=plt.cm.gray)
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ax3.set_title('Thresholded')
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ax3.axis('off')
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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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