mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:17:18 +08:00
François Boulogne
46 lines
1.0 KiB
Python
Executable File
46 lines
1.0 KiB
Python
Executable File
#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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# Author: Francois Boulogne
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# License: GPL
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"""
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========================
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Circular Hough Transform
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========================
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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, filter
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from skimage.transform import hough_circle
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from skimage.feature import peak_local_max
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# Load picture and detect edges
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image = data.coins()[0:95, 70:370]
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edges = filter.canny(filter.sobel(image), sigma=2.8)
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fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
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ax.imshow(image, cmap=plt.cm.gray)
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# Detect two radii
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radii = np.array([21, 25])
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hough_res = hough_circle(edges, radii)
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for radius, h in zip(radii, hough_res):
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# For each radius, keep two circles
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maxima = peak_local_max(h, num_peaks=2)
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for maximum in maxima:
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center_x, center_y = maximum - radii.max()
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circ = mpatches.Circle((center_y, center_x), radius,
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fill=False, edgecolor='red', linewidth=2)
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ax.add_patch(circ)
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plt.show()
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