mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:34:59 +08:00
Tony S Yu
fa01b936a6
The name change reflects a switch in the argument order (and makes the image an optional argument).
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
"""
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===================
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Label image regions
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===================
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This example shows how to segment an image with image labelling. The following
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steps are applied:
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1. Thresholding with automatic Otsu method
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2. Close small holes with binary closing
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3. Remove artifacts touching image border
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4. Measure image regions to filter small objects
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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from skimage import data
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from skimage.filter import threshold_otsu
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from skimage.segmentation import clear_border
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from skimage.morphology import label, closing, square
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from skimage.measure import regionprops
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from skimage.color import label2rgb
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image = data.coins()[50:-50, 50:-50]
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# apply threshold
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thresh = threshold_otsu(image)
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bw = closing(image > thresh, square(3))
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# remove artifacts connected to image border
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cleared = bw.copy()
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clear_border(cleared)
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# label image regions
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label_image = label(cleared)
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borders = np.logical_xor(bw, cleared)
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label_image[borders] = -1
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image_label_overlay = label2rgb(label_image, image=image)
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fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
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ax.imshow(image_label_overlay)
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for region in regionprops(label_image, ['Area', 'BoundingBox']):
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# skip small images
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if region['Area'] < 100:
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continue
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# draw rectangle around segmented coins
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minr, minc, maxr, maxc = region['BoundingBox']
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rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
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fill=False, edgecolor='red', linewidth=2)
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ax.add_patch(rect)
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plt.show()
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