Add Hough transform.

scikits/image/transform/__init__.py

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+from hough_transform import *

scikits/image/transform/hough_transform.py

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

scikits/image/transform/tests/test_hough_transform.py

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+import numpy as np
+from numpy.testing import *
+
+from scikits.image.transform import *
+
+def test_hough():
+    # Generate a test image
+    img = np.zeros((100, 150), dtype=bool)
+    img[30, :] = 1
+    img[:, 65] = 1
+    img[35:45, 35:50] = 1
+    for i in range(90):
+        img[i, i] = 1
+
+    out, angles, d = hough(img)
+
+    assert_equal(out.max(), 100)
+    assert_equal(len(angles), 180)