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
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
"""
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==================================
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Approximate and subdivide polygons
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==================================
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This example shows how to approximate (Douglas-Peucker algorithm) and subdivide
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(B-Splines) polygonal chains.
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"""
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from __future__ import print_function
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage.draw import ellipse
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from skimage.measure import find_contours, approximate_polygon, \
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subdivide_polygon
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hand = np.array([[1.64516129, 1.16145833],
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[1.64516129, 1.59375],
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[1.35080645, 1.921875],
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[1.375, 2.18229167],
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[1.68548387, 1.9375],
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[1.60887097, 2.55208333],
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[1.68548387, 2.69791667],
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[1.76209677, 2.56770833],
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[1.83064516, 1.97395833],
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[1.89516129, 2.75],
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[1.9516129, 2.84895833],
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[2.01209677, 2.76041667],
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[1.99193548, 1.99479167],
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[2.11290323, 2.63020833],
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[2.2016129, 2.734375],
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[2.25403226, 2.60416667],
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[2.14919355, 1.953125],
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[2.30645161, 2.36979167],
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[2.39112903, 2.36979167],
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[2.41532258, 2.1875],
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[2.1733871, 1.703125],
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[2.07782258, 1.16666667]])
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# subdivide polygon using 2nd degree B-Splines
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new_hand = hand.copy()
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for _ in range(5):
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new_hand = subdivide_polygon(new_hand, degree=2, preserve_ends=True)
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# approximate subdivided polygon with Douglas-Peucker algorithm
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appr_hand = approximate_polygon(new_hand, tolerance=0.02)
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print("Number of coordinates:", len(hand), len(new_hand), len(appr_hand))
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fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(9, 4))
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ax1.plot(hand[:, 0], hand[:, 1])
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ax1.plot(new_hand[:, 0], new_hand[:, 1])
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ax1.plot(appr_hand[:, 0], appr_hand[:, 1])
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# create two ellipses in image
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img = np.zeros((800, 800), 'int32')
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rr, cc = ellipse(250, 250, 180, 230, img.shape)
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img[rr, cc] = 1
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rr, cc = ellipse(600, 600, 150, 90, img.shape)
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img[rr, cc] = 1
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plt.gray()
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ax2.imshow(img)
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# approximate / simplify coordinates of the two ellipses
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for contour in find_contours(img, 0):
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coords = approximate_polygon(contour, tolerance=2.5)
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ax2.plot(coords[:, 1], coords[:, 0], '-r', linewidth=2)
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coords2 = approximate_polygon(contour, tolerance=39.5)
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ax2.plot(coords2[:, 1], coords2[:, 0], '-g', linewidth=2)
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print("Number of coordinates:", len(contour), len(coords), len(coords2))
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ax2.axis((0, 800, 0, 800))
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plt.show()
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