various minor fixes

skimage/transform/_hough_transform.pyx

@@ -22,55 +22,30 @@ cdef inline Py_ssize_t round(double r):
 def _hough_circle(np.ndarray img, \
                   np.ndarray[ndim=1, dtype=np.npy_intp] 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
+
+    """
     if img.ndim != 2:
         raise ValueError('The input image must be 2D.')
 
     # compute the nonzero indexes
     cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y
-    y, x = np.PyArray_Nonzero(img)
-
-    # Offset the image
-    max_radius = radius.max()
-    x = x + max_radius
-    y = y + max_radius
-
-    cdef list H = list()
-
-    for rad in radius:
-        # Accumulator
-        out = np.zeros((img.shape[0] + 2 * max_radius, img.shape[1] + 2 * max_radius))
-
-        # Store in memory the circle of given radius
-        # centered at (0,0)
-        circle_x, circle_y = circle_perimeter(0, 0, rad)
-
-        # For each non zero pixel
-        for (px, py) in zip(x, y):
-            # Plug the circle at (px, py),
-            # its coordinates are (tx, ty)
-            tx = circle_x + px
-            ty = circle_y + py
-            out[tx, ty] += 1
-
-        if normalize:
-            out = out / len(circle_x)
-
-        H.append(out)
-    return np.array(H)
-
-
-@cython.boundscheck(False)
-def _hough_circle(np.ndarray img, \
-                  np.ndarray[ndim=1, dtype=np.npy_intp] radius, \
-                  normalize=True):
-
-    if img.ndim != 2:
-        raise ValueError('The input image must be 2D.')
-
-    # compute the nonzero indexes
-    cdef np.ndarray[ndim=1, dtype=np.npy_intp] x, y
-    y, x = np.PyArray_Nonzero(img)
+    y, x = np.nonzero(img)
 
     # Offset the image
     max_radius = radius.max()

skimage/transform/hough_transform.py

@@ -146,7 +146,7 @@ def hough_line(img, theta=None):
     return _hough(img, theta)
 
 def hough_circle(img, radius, normalize=True):
-    """Perform a circle Hough transform.
+    """Perform a circular Hough transform.
 
     Parameters
     ----------
@@ -154,7 +154,7 @@ def hough_circle(img, radius, normalize=True):
         Input image with nonzero values representing edges.
     radius : ndarray
         Radii at which to compute the Hough transform.
-    normalize : boolean
+    normalize : boolean, optional
         Normalize the accumulator with the number
         of pixels used to draw the radius
 
@@ -163,9 +163,6 @@ def hough_circle(img, radius, normalize=True):
     H : 3D ndarray (radius index, (M, N) ndarray)
         Hough transform accumulator for each radius
 
-    Examples
-    --------
-
     """
     return _hough_circle(img, radius, normalize)