mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-30 04:52:35 +08:00
Johannes Schönberger
70 lines
1.8 KiB
Python
70 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.
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This example uses Otsu's method to calculate the threshold value. Otsu's method
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calculates an "optimal" threshold (marked by a red line in the histogram below)
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by maximizing the variance between two classes of pixels, which are separated by
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the threshold. Equivalently, this threshold minimizes the intra-class variance.
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Additionally an adaptive thresholding is applied. Also known as local or
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dynamic thresholding where the threshold value is the weighted mean for the
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local neighborhood of a pixel subtracted by a constant. Small filter block sizes
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are suitable for thresholding edges, large filter block sizes suitable for
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thresholding larger homogeneous regions.
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.. [1] http://en.wikipedia.org/wiki/Otsu's_method
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"""
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import matplotlib.pyplot as plt
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from skimage.data import camera
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from skimage.filter import threshold_otsu, threshold_adaptive
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image = camera()
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#: Otsu thresholding
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thresh = threshold_otsu(image)
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otsu_binary = image > thresh
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plt.figure(figsize=(8, 6))
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plt.subplot(2, 3, 1)
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plt.imshow(image, cmap=plt.cm.gray)
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plt.title('Original')
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plt.axis('off')
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plt.subplot(2, 3, 2, aspect='equal')
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plt.hist(image)
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plt.title('Histogram')
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plt.axvline(thresh, color='r')
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plt.subplot(2, 3, 3)
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plt.imshow(otsu_binary, cmap=plt.cm.gray)
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plt.title('Thresholded with Otsu')
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plt.axis('off')
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#: Adaptive thresholding
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plt.subplot(2, 3, 4)
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plt.imshow(threshold_adaptive(image, 11, 5, 'gaussian'), cmap=plt.cm.gray)
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plt.title('Adaptive edge thresholding')
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plt.axis('off')
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plt.subplot(2, 3, 5)
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plt.imshow(threshold_adaptive(image, 125, 7.5, 'gaussian'), cmap=plt.cm.gray)
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plt.title('Adaptive Gaussian')
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plt.axis('off')
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plt.subplot(2, 3, 6)
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plt.imshow(threshold_adaptive(image, 125, 7.5, 'mean'), cmap=plt.cm.gray)
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plt.title('Adaptive Mean')
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plt.axis('off')
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plt.show()
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