From 12d2cd6855c473e3eb578a64164c546e6bfa80e7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 22:07:39 +0100
Subject: [PATCH 01/11] be consistant with python implementation

---
 skimage/transform/_hough_transform.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx
index 2b1acc7c..b6ff2bb7 100644
--- a/skimage/transform/_hough_transform.pyx
+++ b/skimage/transform/_hough_transform.pyx
@@ -98,7 +98,7 @@ def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
     cdef cnp.ndarray[ndim=1, dtype=cnp.double_t] stheta
 
     if theta is None:
-        theta = np.linspace(PI_2, NEG_PI_2, 180)
+        theta = np.linspace(NEG_PI_2, PI_2, 180)
 
     ctheta = np.cos(theta)
     stheta = np.sin(theta)

From 22158fc9da297b383465dd0993af3a336b56cb14 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 23:11:29 +0100
Subject: [PATCH 02/11] improve unittests

---
 .../transform/tests/test_hough_transform.py   | 34 +++++++++++++------
 1 file changed, 24 insertions(+), 10 deletions(-)

diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index 00427332..2bf7b30c 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -4,7 +4,7 @@ from numpy.testing import *
 import skimage.transform as tf
 import skimage.transform.hough_transform as ht
 from skimage.transform import probabilistic_hough
-from skimage.draw import circle_perimeter
+from skimage.draw import circle_perimeter, line
 
 
 def append_desc(func, description):
@@ -16,11 +16,11 @@ def append_desc(func, description):
     return func
 
 
-def test_hough():
+def test_hough_line():
     # Generate a test image
-    img = np.zeros((100, 100), dtype=int)
-    for i in range(25, 75):
-        img[100 - i, i] = 1
+    img = np.zeros((100, 150), dtype=int)
+    rr, cc = line(60, 130, 80, 10)
+    img[rr, cc] = 1
 
     out, angles, d = tf.hough_line(img)
 
@@ -28,11 +28,11 @@ def test_hough():
     dist = d[y[0]]
     theta = angles[x[0]]
 
-    assert_equal(dist > 70, dist < 72)
-    assert_equal(theta > 0.78, theta < 0.79)
+    assert_almost_equal(dist, 80.723, 1)
+    assert_almost_equal(theta, 1.41, 1)
 
 
-def test_hough_angles():
+def test_hough_line_angles():
     img = np.zeros((10, 10))
     img[0, 0] = 1
 
@@ -44,8 +44,8 @@ def test_hough_angles():
 def test_py_hough():
     ht._hough, fast_hough = ht._py_hough, ht._hough
 
-    yield append_desc(test_hough, '_python')
-    yield append_desc(test_hough_angles, '_python')
+    yield append_desc(test_hough_line, '_python')
+    yield append_desc(test_hough_line_angles, '_python')
 
     tf._hough = fast_hough
 
@@ -71,6 +71,20 @@ def test_probabilistic_hough():
     assert([(25, 25), (74, 74)] in sorted_lines)
 
 
+def test_hough_peaks():
+    img = np.zeros((100, 150), dtype=int)
+    rr, cc = line(60, 130, 80, 10)
+    img[rr, cc] = 1
+
+    out, angles, d = tf.hough_line(img)
+
+    out, theta, dist = tf.hough_peaks(out, angles, d)
+
+    assert_equal(len(dist), 1)
+    assert_almost_equal(dist[0], 80.723, 1)
+    assert_almost_equal(theta[0], 1.41, 1)
+
+
 def test_hough_peaks_dist():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 30] = True

From f18be31822bf6e9cb22cac8374239d64ffc480d9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 23:12:05 +0100
Subject: [PATCH 03/11] Fix and improve example

---
 doc/source/plots/hough_tf.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/doc/source/plots/hough_tf.py b/doc/source/plots/hough_tf.py
index ea2fc4bb..df1dcd31 100644
--- a/doc/source/plots/hough_tf.py
+++ b/doc/source/plots/hough_tf.py
@@ -2,13 +2,14 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage.transform import hough_line
+from skimage.draw import line
 
 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
+rr, cc = line(60, 130, 80, 10)
+img[rr, cc] = 1
 img += np.random.random(img.shape) > 0.95
 
 out, angles, d = hough_line(img)
@@ -20,8 +21,8 @@ plt.title('Input image')
 
 plt.subplot(1, 2, 2)
 plt.imshow(out, cmap=plt.cm.bone,
-           extent=(np.rad2deg(angles[0]), np.rad2deg(angles[-1]),
-                   d[0], d[-1]))
+           extent=(np.rad2deg(angles[-1]), np.rad2deg(angles[0]),
+                   d[-1], d[0]))
 plt.title('Hough transform')
 plt.xlabel('Angle (degree)')
 plt.ylabel('Distance (pixel)')

From 33060e334386dde0a696914cfe27a0b36b032d65 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 23:12:18 +0100
Subject: [PATCH 04/11] Fix docstring

---
 skimage/transform/hough_transform.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index f112a025..079ce54e 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -127,7 +127,7 @@ def hough_line(img, theta=None):
     Notes
     -----
     The origin is the top left corner of the original image.
-    The angle is counted clockwise from 9 o'clock.
+    X and Y axis are horizontal and vertical edges respectively.
     The distance is the minimal algebraic distance from this origin to the line.
 
     Examples

From 29b91ca01d2b7195063fc1c354b683ad97af623a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 23:16:13 +0100
Subject: [PATCH 05/11] minor doc improv.

---
 skimage/transform/hough_transform.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 079ce54e..1c36e799 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -128,7 +128,7 @@ def hough_line(img, theta=None):
     -----
     The origin is the top left corner of the original image.
     X and Y axis are horizontal and vertical edges respectively.
-    The distance is the minimal algebraic distance from this origin to the line.
+    The distance is the minimal algebraic distance from the origin to the detected line.
 
     Examples
     --------

From caee53c83c35fa0b82f7a897807e540d2f594375 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Fri, 22 Mar 2013 23:31:06 +0100
Subject: [PATCH 06/11] doc, minor improv.

---
 skimage/transform/hough_transform.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 1c36e799..bd5e3a2b 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -120,7 +120,7 @@ def hough_line(img, theta=None):
     H : 2-D ndarray of uint64
         Hough transform accumulator.
     theta : ndarray
-        Angles at which the transform was computed.
+        Angles at which the transform was computed, in radians.
     distances : ndarray
         Distance values.
 
@@ -186,8 +186,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
     hspace : (N, M) array
         Hough space returned by the `hough_line` function.
     angles : (M,) array
-        Angles returned by the `hough_line` function. Assumed to be continuous
-        (`angles[-1] - angles[0] == PI`).
+        Angles returned by the `hough_line` function. Assumed to be continuous.
     dists : (N, ) array
         Distances returned by the `hough_line` function.
     min_distance : int

From a68434b330b72173cb063273d98f87e0f8690c7f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Sun, 24 Mar 2013 15:11:55 +0100
Subject: [PATCH 07/11] big clean-up!

---
 skimage/transform/__init__.py                 |   3 +
 skimage/transform/_hough_transform.pyx        |  77 +++++++-
 skimage/transform/hough_transform.py          | 172 ++----------------
 .../transform/tests/test_hough_transform.py   |   9 -
 4 files changed, 90 insertions(+), 171 deletions(-)

diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py
index 089487ee..efe8f0b4 100644
--- a/skimage/transform/__init__.py
+++ b/skimage/transform/__init__.py
@@ -1,3 +1,6 @@
+from ._hough_transform import hough_circle,
+                              hough_line,
+                              probabilistic_hough_line
 from .hough_transform import *
 from .radon_transform import *
 from .finite_radon_transform import *
diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx
index b6ff2bb7..a8dabcfc 100644
--- a/skimage/transform/_hough_transform.pyx
+++ b/skimage/transform/_hough_transform.pyx
@@ -20,7 +20,7 @@ cdef inline Py_ssize_t round(double r):
     return <Py_ssize_t>((r + 0.5) if (r > 0.0) else (r - 0.5))
 
 
-def _hough_circle(cnp.ndarray img,
+def hough_circle(cnp.ndarray img,
                   cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
                   char normalize=True):
     """Perform a circular Hough transform.
@@ -88,8 +88,51 @@ def _hough_circle(cnp.ndarray img,
     return acc
 
 
-def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
+def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
+    """Perform a straight line Hough transform.
 
+    Parameters
+    ----------
+    img : (M, N) ndarray
+        Input image with nonzero values representing edges.
+    theta : 1D ndarray of double
+        Angles at which to compute the transform, in radians.
+        Defaults to -pi/2 .. pi/2
+
+    Returns
+    -------
+    H : 2-D ndarray of uint64
+        Hough transform accumulator.
+    theta : ndarray
+        Angles at which the transform was computed, in radians.
+    distances : ndarray
+        Distance values.
+
+    Notes
+    -----
+    The origin is the top left corner of the original image.
+    X and Y axis are horizontal and vertical edges respectively.
+    The distance is the minimal algebraic distance from the origin to the detected line.
+
+    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
+
+    Apply the Hough transform:
+
+    >>> out, angles, d = hough_line(img)
+
+    .. plot:: hough_tf.py
+
+    """
     if img.ndim != 2:
         raise ValueError('The input image must be 2D.')
 
@@ -131,10 +174,38 @@ def _hough(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
     return accum, theta, bins
 
 
-def _probabilistic_hough(cnp.ndarray img, int value_threshold,
+def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
                          int line_length, int line_gap,
                          cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
+    """Return lines from a progressive probabilistic line Hough transform.
 
+    Parameters
+    ----------
+    img : (M, N) ndarray
+        Input image with nonzero values representing edges.
+    threshold : int
+        Threshold
+    line_length : int, optional (default 50)
+        Minimum accepted length of detected lines.
+        Increase the parameter to extract longer lines.
+    line_gap : int, optional, (default 10)
+        Maximum gap between pixels to still form a line.
+        Increase the parameter to merge broken lines more aggresively.
+    theta : 1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
+        Angles at which to compute the transform, in radians.
+
+    Returns
+    -------
+    lines : list
+      List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating
+      line start and end.
+
+    References
+    ----------
+    .. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic
+           Hough transform for line detection", in IEEE Computer Society
+           Conference on Computer Vision and Pattern Recognition, 1999.
+    """
     if img.ndim != 2:
         raise ValueError('The input image must be 2D.')
 
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index bd5e3a2b..0c1747d4 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -1,179 +1,32 @@
-__all__ = ['hough', 'hough_line', 'hough_circle', 'hough_peaks', 'probabilistic_hough']
+__all__ = ['hough_peaks']
 
 from itertools import izip as zip
 
 import numpy as np
 from scipy import ndimage
-from ._hough_transform import _probabilistic_hough
 from skimage import measure, morphology
 
 
-def _hough(img, theta=None):
-    if img.ndim != 2:
-        raise ValueError('The input image must be 2-D')
-
-    if theta is None:
-        theta = np.linspace(-np.pi / 2, np.pi / 2, 180)
-
-    # compute the vertical bins (the distances)
-    d = np.ceil(np.hypot(*img.shape))
-    nr_bins = 2 * d
-    bins = np.linspace(-d, d, nr_bins)
-
-    # allocate the output image
-    out = np.zeros((nr_bins, len(theta)), dtype=np.uint64)
-
-    # precompute the sin and cos of the angles
-    cos_theta = np.cos(theta)
-    sin_theta = np.sin(theta)
-
-    # find the indices of the non-zero values in
-    # the input image
-    y, x = np.nonzero(img)
-
-    # x and y can be large, so we can't just broadcast to 2D
-    # arrays as we may run out of memory. Instead we process
-    # one vertical slice at a time.
-    for i, (cT, sT) in enumerate(zip(cos_theta, sin_theta)):
-
-        # compute the base distances
-        distances = x * cT + y * sT
-
-        # round the distances to the nearest integer
-        # and shift them to a nonzero bin
-        shifted = np.round(distances) - bins[0]
-
-        # cast the shifted values to ints to use as indices
-        indices = shifted.astype(np.int)
-
-        # use bin count to accumulate the coefficients
-        bincount = np.bincount(indices)
-
-        # finally assign the proper values to the out array
-        out[:len(bincount), i] = bincount
-
-    return out, theta, bins
-
-_py_hough = _hough
-
-# try to import and use the faster Cython version if it exists
-try:
-    from ._hough_transform import _hough
-except ImportError:
-    pass
-
-
-def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
-                        theta=None):
-    """Return lines from a progressive probabilistic line Hough transform.
-
-    Parameters
-    ----------
-    img : (M, N) ndarray
-        Input image with nonzero values representing edges.
-    threshold : int
-        Threshold
-    line_length : int, optional (default 50)
-        Minimum accepted length of detected lines.
-        Increase the parameter to extract longer lines.
-    line_gap : int, optional, (default 10)
-        Maximum gap between pixels to still form a line.
-        Increase the parameter to merge broken lines more aggresively.
-    theta : 1D ndarray, dtype=double, optional, default (-pi/2 .. pi/2)
-        Angles at which to compute the transform, in radians.
-
-    Returns
-    -------
-    lines : list
-      List of lines identified, lines in format ((x0, y0), (x1, y0)), indicating
-      line start and end.
-
-    References
-    ----------
-    .. [1] C. Galamhos, J. Matas and J. Kittler, "Progressive probabilistic
-           Hough transform for line detection", in IEEE Computer Society
-           Conference on Computer Vision and Pattern Recognition, 1999.
-    """
-    return _probabilistic_hough(img, threshold, line_length, line_gap, theta)
-
+from ._hough_transform import hough_line, probabilistic_hough_line
 from skimage._shared.utils import deprecated
 
 @deprecated('hough_line')
 def hough(img, theta=None):
     return hough_line(img, theta)
 
-from ._hough_transform import _hough_circle
-
-def hough_line(img, theta=None):
-    """Perform a straight line Hough transform.
-
-    Parameters
-    ----------
-    img : (M, N) ndarray
-        Input image with nonzero values representing edges.
-    theta : 1D ndarray of double
-        Angles at which to compute the transform, in radians.
-        Defaults to -pi/2 .. pi/2
-
-    Returns
-    -------
-    H : 2-D ndarray of uint64
-        Hough transform accumulator.
-    theta : ndarray
-        Angles at which the transform was computed, in radians.
-    distances : ndarray
-        Distance values.
-
-    Notes
-    -----
-    The origin is the top left corner of the original image.
-    X and Y axis are horizontal and vertical edges respectively.
-    The distance is the minimal algebraic distance from the origin to the detected line.
-
-    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
-
-    Apply the Hough transform:
-
-    >>> out, angles, d = hough_line(img)
-
-    .. plot:: hough_tf.py
-
-    """
-    return _hough(img, theta)
-
-def hough_circle(img, radius, normalize=True):
-    """Perform a circular Hough transform.
-
-    Parameters
-    ----------
-    img : (M, N) ndarray
-        Input image with nonzero values representing edges.
-    radius : ndarray
-        Radii at which to compute the Hough transform.
-    normalize : boolean, optional
-        Normalize the accumulator with the number
-        of pixels used to draw the radius
-
-    Returns
-    -------
-    H : 3D ndarray (radius index, (M, N) ndarray)
-        Hough transform accumulator for each radius
-
-    """
-    return _hough_circle(img, radius.astype(np.intp), normalize)
+@deprecated('probabilistic_hough')
+def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
+                        theta=None):
+    return probabilistic_hough_line(img, threshold, line_length, line_gap, theta)
 
+@deprecated('hough_peaks')
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
                 threshold=None, num_peaks=np.inf):
+    return hough_line_peaks(hspace, angles, dists, min_distance, min_angle,
+                threshold, num_peaks)
+
+def hough_line_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
+                threshold=None, num_peaks=np.inf):
     """Return peaks in hough transform.
 
     Identifies most prominent lines separated by a certain angle and distance in
@@ -187,6 +40,7 @@ def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
         Hough space returned by the `hough_line` function.
     angles : (M,) array
         Angles returned by the `hough_line` function. Assumed to be continuous.
+        (`angles[-1] - angles[0] == PI`).
     dists : (N, ) array
         Distances returned by the `hough_line` function.
     min_distance : int
diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index 2bf7b30c..5e175945 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -41,15 +41,6 @@ def test_hough_line_angles():
     assert_equal(len(angles), 10)
 
 
-def test_py_hough():
-    ht._hough, fast_hough = ht._py_hough, ht._hough
-
-    yield append_desc(test_hough_line, '_python')
-    yield append_desc(test_hough_line_angles, '_python')
-
-    tf._hough = fast_hough
-
-
 def test_probabilistic_hough():
     # Generate a test image
     img = np.zeros((100, 100), dtype=int)

From 42e872f926bb4cf8050d91560a2e9180f5ada79f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Sun, 24 Mar 2013 15:22:42 +0100
Subject: [PATCH 08/11] fix names

---
 skimage/transform/__init__.py                 |  4 +--
 skimage/transform/hough_transform.py          |  2 +-
 .../transform/tests/test_hough_transform.py   | 33 +++++++++----------
 3 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py
index efe8f0b4..92858010 100644
--- a/skimage/transform/__init__.py
+++ b/skimage/transform/__init__.py
@@ -1,6 +1,6 @@
-from ._hough_transform import hough_circle,
+from ._hough_transform import (hough_circle,
                               hough_line,
-                              probabilistic_hough_line
+                              probabilistic_hough_line)
 from .hough_transform import *
 from .radon_transform import *
 from .finite_radon_transform import *
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 0c1747d4..8e680c94 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -1,4 +1,4 @@
-__all__ = ['hough_peaks']
+__all__ = ['hough_line_peaks']
 
 from itertools import izip as zip
 
diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index 5e175945..2a2f57d2 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -3,7 +3,6 @@ from numpy.testing import *
 
 import skimage.transform as tf
 import skimage.transform.hough_transform as ht
-from skimage.transform import probabilistic_hough
 from skimage.draw import circle_perimeter, line
 
 
@@ -50,7 +49,7 @@ def test_probabilistic_hough():
     # decrease default theta sampling because similar orientations may confuse
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
-    lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10,
+    lines = tf.probabilistic_hough_line(img, theta=theta, threshold=10, line_length=10,
                                 line_gap=1)
     # sort the lines according to the x-axis
     sorted_lines = []
@@ -62,59 +61,59 @@ def test_probabilistic_hough():
     assert([(25, 25), (74, 74)] in sorted_lines)
 
 
-def test_hough_peaks():
+def test_hough_line_peaks():
     img = np.zeros((100, 150), dtype=int)
     rr, cc = line(60, 130, 80, 10)
     img[rr, cc] = 1
 
     out, angles, d = tf.hough_line(img)
 
-    out, theta, dist = tf.hough_peaks(out, angles, d)
+    out, theta, dist = tf.hough_line_peaks(out, angles, d)
 
     assert_equal(len(dist), 1)
     assert_almost_equal(dist[0], 80.723, 1)
     assert_almost_equal(theta[0], 1.41, 1)
 
 
-def test_hough_peaks_dist():
+def test_hough_line_peaks_dist():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 30] = True
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(img)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2
-    assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=5)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=15)[0]) == 1
 
 
-def test_hough_peaks_angle():
+def test_hough_line_peaks_angle():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 0] = True
     img[0, :] = True
 
     hspace, angles, dists = tf.hough_line(img)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
 
     theta = np.linspace(0, np.pi, 100)
     hspace, angles, dists = tf.hough_line(img, theta)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
 
     theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100)
     hspace, angles, dists = tf.hough_line(img, theta)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
-    assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
 
 
-def test_hough_peaks_num():
+def test_hough_line_peaks_num():
     img = np.zeros((100, 100), dtype=np.bool_)
     img[:, 30] = True
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(img)
-    assert len(tf.hough_peaks(hspace, angles, dists, min_distance=0,
+    assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0,
                               min_angle=0, num_peaks=1)[0]) == 1
 
 
-def test_houghcircle():
+def test_hough_circle():
     # Prepare picture
     img = np.zeros((120, 100), dtype=int)
     radius = 20

From 0e4d10775483ec183b31ab02a0778f341b35da59 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Sun, 24 Mar 2013 15:39:07 +0100
Subject: [PATCH 09/11] fix signature

---
 skimage/transform/_hough_transform.pyx          | 10 +++++-----
 skimage/transform/hough_transform.py            |  3 ++-
 skimage/transform/tests/test_hough_transform.py |  4 ++--
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx
index a8dabcfc..2d5d0230 100644
--- a/skimage/transform/_hough_transform.pyx
+++ b/skimage/transform/_hough_transform.pyx
@@ -174,8 +174,8 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
     return accum, theta, bins
 
 
-def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
-                         int line_length, int line_gap,
+def probabilistic_hough_line(cnp.ndarray img, int threshold=10,
+                         int line_length=50, int line_gap=10,
                          cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
     """Return lines from a progressive probabilistic line Hough transform.
 
@@ -183,7 +183,7 @@ def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
     ----------
     img : (M, N) ndarray
         Input image with nonzero values representing edges.
-    threshold : int
+    threshold : int, optional (default 10)
         Threshold
     line_length : int, optional (default 50)
         Minimum accepted length of detected lines.
@@ -267,7 +267,7 @@ def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
             continue
 
         value = 0
-        max_value = value_threshold - 1
+        max_value = threshold - 1
         max_theta = -1
 
         # apply hough transform on point
@@ -278,7 +278,7 @@ def probabilistic_hough_line(cnp.ndarray img, int value_threshold,
             if value > max_value:
                 max_value = value
                 max_theta = j
-        if max_value < value_threshold:
+        if max_value < threshold:
             continue
 
         # from the random point walk in opposite directions and find line
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 8e680c94..1b356127 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -17,7 +17,8 @@ def hough(img, theta=None):
 @deprecated('probabilistic_hough')
 def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
                         theta=None):
-    return probabilistic_hough_line(img, threshold, line_length, line_gap, theta)
+    return probabilistic_hough_line(img, threshold=threshold,
+                                    line_length=line_length, line_gap=line_gap, theta=theta)
 
 @deprecated('hough_peaks')
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index 2a2f57d2..395e6faa 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -49,8 +49,8 @@ def test_probabilistic_hough():
     # decrease default theta sampling because similar orientations may confuse
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
-    lines = tf.probabilistic_hough_line(img, theta=theta, threshold=10, line_length=10,
-                                line_gap=1)
+    lines = tf.probabilistic_hough_line(img, threshold=10, line_length=10,
+                                line_gap=1, theta=theta)
     # sort the lines according to the x-axis
     sorted_lines = []
     for line in lines:

From 96d0e677dd1c5be440be7417be7506c87c5c9325 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Sun, 24 Mar 2013 15:52:14 +0100
Subject: [PATCH 10/11] cosmectics (flake8)

---
 skimage/transform/_hough_transform.pyx        | 13 ++++++-----
 skimage/transform/hough_transform.py          | 23 +++++++++++--------
 .../transform/tests/test_hough_transform.py   |  5 ++--
 3 files changed, 22 insertions(+), 19 deletions(-)

diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx
index 2d5d0230..b2250ef3 100644
--- a/skimage/transform/_hough_transform.pyx
+++ b/skimage/transform/_hough_transform.pyx
@@ -21,8 +21,8 @@ cdef inline Py_ssize_t round(double r):
 
 
 def hough_circle(cnp.ndarray img,
-                  cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
-                  char normalize=True):
+                 cnp.ndarray[ndim=1, dtype=cnp.intp_t] radius,
+                 char normalize=True):
     """Perform a circular Hough transform.
 
     Parameters
@@ -112,7 +112,8 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
     -----
     The origin is the top left corner of the original image.
     X and Y axis are horizontal and vertical edges respectively.
-    The distance is the minimal algebraic distance from the origin to the detected line.
+    The distance is the minimal algebraic distance from the origin
+    to the detected line.
 
     Examples
     --------
@@ -163,7 +164,7 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
 
     # finally, run the transform
     cdef Py_ssize_t nidxs, nthetas, i, j, x, y, accum_idx
-    nidxs = y_idxs.shape[0] # x and y are the same shape
+    nidxs = y_idxs.shape[0]  # x and y are the same shape
     nthetas = theta.shape[0]
     for i in range(nidxs):
         x = x_idxs[i]
@@ -175,8 +176,8 @@ def hough_line(cnp.ndarray img, cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=No
 
 
 def probabilistic_hough_line(cnp.ndarray img, int threshold=10,
-                         int line_length=50, int line_gap=10,
-                         cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
+                             int line_length=50, int line_gap=10,
+                             cnp.ndarray[ndim=1, dtype=cnp.double_t] theta=None):
     """Return lines from a progressive probabilistic line Hough transform.
 
     Parameters
diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 1b356127..8f56efe9 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -1,7 +1,5 @@
 __all__ = ['hough_line_peaks']
 
-from itertools import izip as zip
-
 import numpy as np
 from scipy import ndimage
 from skimage import measure, morphology
@@ -10,30 +8,35 @@ from skimage import measure, morphology
 from ._hough_transform import hough_line, probabilistic_hough_line
 from skimage._shared.utils import deprecated
 
+
 @deprecated('hough_line')
 def hough(img, theta=None):
     return hough_line(img, theta)
 
+
 @deprecated('probabilistic_hough')
 def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
                         theta=None):
     return probabilistic_hough_line(img, threshold=threshold,
-                                    line_length=line_length, line_gap=line_gap, theta=theta)
+                                    line_length=line_length, line_gap=line_gap,
+                                    theta=theta)
+
 
 @deprecated('hough_peaks')
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
                 threshold=None, num_peaks=np.inf):
     return hough_line_peaks(hspace, angles, dists, min_distance, min_angle,
-                threshold, num_peaks)
+                            threshold, num_peaks)
 
-def hough_line_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
-                threshold=None, num_peaks=np.inf):
+
+def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10,
+                     threshold=None, num_peaks=np.inf):
     """Return peaks in hough transform.
 
-    Identifies most prominent lines separated by a certain angle and distance in
-    a hough transform. Non-maximum suppression with different sizes is applied
-    separately in the first (distances) and second (angles) dimension of the
-    hough space to identify peaks.
+    Identifies most prominent lines separated by a certain angle and distance
+    in a hough transform. Non-maximum suppression with different sizes is
+    applied separately in the first (distances) and second (angles) dimension
+    of the hough space to identify peaks.
 
     Parameters
     ----------
diff --git a/skimage/transform/tests/test_hough_transform.py b/skimage/transform/tests/test_hough_transform.py
index 395e6faa..256c88a8 100644
--- a/skimage/transform/tests/test_hough_transform.py
+++ b/skimage/transform/tests/test_hough_transform.py
@@ -2,7 +2,6 @@ import numpy as np
 from numpy.testing import *
 
 import skimage.transform as tf
-import skimage.transform.hough_transform as ht
 from skimage.draw import circle_perimeter, line
 
 
@@ -50,7 +49,7 @@ def test_probabilistic_hough():
     # as mentioned in article of Galambos et al
     theta = np.linspace(0, np.pi, 45)
     lines = tf.probabilistic_hough_line(img, threshold=10, line_length=10,
-                                line_gap=1, theta=theta)
+                                        line_gap=1, theta=theta)
     # sort the lines according to the x-axis
     sorted_lines = []
     for line in lines:
@@ -110,7 +109,7 @@ def test_hough_line_peaks_num():
     img[:, 40] = True
     hspace, angles, dists = tf.hough_line(img)
     assert len(tf.hough_line_peaks(hspace, angles, dists, min_distance=0,
-                              min_angle=0, num_peaks=1)[0]) == 1
+                                   min_angle=0, num_peaks=1)[0]) == 1
 
 
 def test_hough_circle():

From 35d69732155b7df87af1a77b7312a627ae6f31ab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Boulogne?= <fboulogne sciunto org>
Date: Sun, 24 Mar 2013 16:34:27 +0100
Subject: [PATCH 11/11] fix deprecated variable

---
 skimage/transform/hough_transform.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/skimage/transform/hough_transform.py b/skimage/transform/hough_transform.py
index 8f56efe9..a3c010cd 100644
--- a/skimage/transform/hough_transform.py
+++ b/skimage/transform/hough_transform.py
@@ -14,7 +14,7 @@ def hough(img, theta=None):
     return hough_line(img, theta)
 
 
-@deprecated('probabilistic_hough')
+@deprecated('probabilistic_hough_line')
 def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
                         theta=None):
     return probabilistic_hough_line(img, threshold=threshold,
@@ -22,7 +22,7 @@ def probabilistic_hough(img, threshold=10, line_length=50, line_gap=10,
                                     theta=theta)
 
 
-@deprecated('hough_peaks')
+@deprecated('hough_line_peaks')
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
                 threshold=None, num_peaks=np.inf):
     return hough_line_peaks(hspace, angles, dists, min_distance, min_angle,