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https://github.com/wassname/scikit-image.git
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Stefan van der Walt
102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
## Copyright (C) 2006 Stefan van der Walt <stefan@sun.ac.za>
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##
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## Redistribution and use in source and binary forms, with or without
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## modification, are permitted provided that the following conditions are
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## met:
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##
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## 1. Redistributions of source code must retain the above copyright
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## notice, this list of conditions and the following disclaimer.
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## 2. Redistributions in binary form must reproduce the above copyright
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## notice, this list of conditions and the following disclaimer in
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## the documentation and/or other materials provided with the
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## distribution.
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##
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## THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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## IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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## WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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## DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
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## INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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## (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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## SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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## HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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## STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
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## IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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## POSSIBILITY OF SUCH DAMAGE.
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__all__ = ['hough']
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import numpy as np
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itype = np.uint16 # See ticket 225
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def hough(img, angles=None):
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"""Perform a straight line Hough transform.
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Parameters
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----------
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img : (M, N) bool ndarray
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Thresholded input image.
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angles : ndarray or list
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Angles at which to compute the transform.
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Returns
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-------
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H : 2-D ndarray
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Hough transform coefficients.
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distances : ndarray
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Distance values.
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angles : ndarray
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Angle values.
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Examples
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--------
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Generate a test image:
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>>> img = np.zeros((100, 150), dtype=bool)
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>>> img[30, :] = 1
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>>> img[:, 65] = 1
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>>> img[35:45, 35:50] = 1
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>>> for i in range(90):
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>>> img[i, i] = 1
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>>> img += np.random.random(img.shape) > 0.95
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Apply the Hough transform:
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>>> out, angles, d = houghtf(img)
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Plot the results:
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>>> import matplotlib.pyplot as plt
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>>> plt.imshow(out, cmap=plt.cm.bone)
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>>> plt.xlabel('Angle (degree)')
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>>> plt.ylabel('Distance %d (pixel)' % d[0])
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>>> plt.show()
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"""
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if img.ndim != 2:
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raise ValueError("Input must be a two-dimensional array")
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img = img.astype(bool)
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if not angles:
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angles = np.linspace(-90,90,180)
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theta = angles / 180. * np.pi
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d = np.ceil(np.hypot(*img.shape))
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nr_bins = 2*d - 1
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bins = np.linspace(-d, d, nr_bins)
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out = np.zeros((nr_bins, len(theta)), dtype=itype)
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rows, cols = img.shape
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x,y = np.mgrid[:rows, :cols]
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for i, (cT, sT) in enumerate(zip(np.cos(theta), np.sin(theta))):
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rho = np.round_(cT * x[img] + sT * y[img]) - bins[0] + 1
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rho = rho.astype(itype)
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rho[(rho < 0) | (rho > nr_bins)] = 0
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bc = np.bincount(rho.flat)[1:]
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out[:len(bc), i] = bc
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return out, angles, bins
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