mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 05:43:17 +08:00
Johannes Schönberger
72 lines
2.2 KiB
Python
Executable File
72 lines
2.2 KiB
Python
Executable File
"""
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========================
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Circular Hough Transform
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========================
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The Hough transform in its simplest form is a `method to detect
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straight lines <http://en.wikipedia.org/wiki/Hough_transform>`__
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but it can also be used to detect circles.
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In the following example, the Hough transform is used to detect
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coin positions and match their edges. We provide a range of
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plausible radii. For each radius, two circles are extracted and
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we finally keep the five most prominent candidates.
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The result shows that coin positions are well-detected.
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Algorithm overview
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------------------
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Given a black circle on a white background, we first guess its
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radius (or a range of radii) to construct a new circle.
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This circle is applied on each black pixel of the original picture
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and the coordinates of this circle are voting in an accumulator.
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From this geometrical construction, the original circle center
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position receives the highest score.
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Note that the accumulator size is built to be larger than the
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original picture in order to detect centers outside the frame.
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Its size is extended by two times the larger radius.
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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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from skimage import data, filter, color
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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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from skimage.draw import circle_perimeter
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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(image, sigma=3, low_threshold=10, high_threshold=50)
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fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
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# Detect two radii
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hough_radii = np.arange(15, 30, 2)
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hough_res = hough_circle(edges, hough_radii)
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centers = []
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accums = []
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radii = []
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for radius, h in zip(hough_radii, hough_res):
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# For each radius, extract two circles
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peaks = peak_local_max(h, num_peaks=2)
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centers.extend(peaks)
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accums.extend(h[peaks[:, 0], peaks[:, 1]])
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radii.extend([radius, radius])
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# Draw the most prominent 5 circles
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image = color.gray2rgb(image)
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for idx in np.argsort(accums)[::-1][:5]:
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center_x, center_y = centers[idx]
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radius = radii[idx]
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cx, cy = circle_perimeter(center_y, center_x, radius)
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image[cy, cx] = (220, 20, 20)
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ax.imshow(image, cmap=plt.cm.gray)
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plt.show()
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