mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:17:50 +08:00
Olivier Debeir
67 lines
1.7 KiB
Python
67 lines
1.7 KiB
Python
"""
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=====================
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Local 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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Added local threshold using rank filter
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to be adjusted ...
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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.filter import threshold_otsu, threshold_adaptive
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from skimage.rank import threshold,morph_contr_enh
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from skimage.morphology import disk
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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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selem = disk(10)
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loc_thresh = threshold(image,selem=selem)
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loc_morph_contr_enh = morph_contr_enh(image,selem=selem)
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fig, axes = plt.subplots(nrows=5, figsize=(7, 8))
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ax0, ax1, ax2, ax3, ax4 = 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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ax3.imshow(loc_thresh)
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ax3.set_title('Local thresholding')
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ax4.imshow(loc_morph_contr_enh)
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ax4.set_title('Local morphological contrast enhancement')
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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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