mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
"""
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=====================
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Adaptive Thresholding
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=====================
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Thresholding is the simplest way to segment objects from a background. If that
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background is relatively uniform, then you can use a global threshold value to
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binarize the image by pixel-intensity. If there's large variation in the
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background intensity, however, adaptive thresholding (a.k.a. local or dynamic
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thresholding) may produce better results.
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Here, we binarize an image using the `threshold_adaptive` function, which
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calculates thresholds in regions of size `block_size` surrounding each pixel
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(i.e. local neighborhoods). Each threshold value is the weighted mean of the
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local neighborhood minus an offset value.
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"""
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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 threshold_otsu, threshold_adaptive
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image = data.page()
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global_thresh = threshold_otsu(image)
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binary_global = image > global_thresh
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block_size = 40
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binary_adaptive = threshold_adaptive(image, block_size, offset=10)
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fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
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ax0, ax1, ax2 = axes
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plt.gray()
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ax0.imshow(image)
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ax0.set_title('Image')
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ax1.imshow(binary_global)
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ax1.set_title('Global thresholding')
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ax2.imshow(binary_adaptive)
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ax2.set_title('Adaptive thresholding')
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for ax in axes:
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ax.axis('off')
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plt.show()
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