utils/draw.py: Merge "_ellipse_in_shape()" to "ellipse()"

* Fix the bug that "ellipse()" generate unexpected results.
* Update the parameters of "ellipse()" also

1 files changed, 24 insertions, 84 deletions

diff --git a/fg21sim/utils/draw.py b/fg21sim/utils/draw.py
index b303f05..25d83c9 100644
--- a/fg21sim/utils/draw.py
+++ b/fg21sim/utils/draw.py
@@ -4,17 +4,6 @@
 
 """
 Generic drawers (a.k.a. painters) that draw some commonly used shapes.
-
-
-Credits
--------
-The ``_ellipse_in_shape`` and ``ellipse()`` functions are originally taken
-from project [scikit-image]_, which are licensed under the *Modified BSD*
-license.
-
-.. [scikit-image] skimage.draw.draw
-   http://scikit-image.org/docs/dev/api/skimage.draw.html
-   https://github.com/scikit-image/scikit-image/blob/master/skimage/draw/draw.py
 """
 
 
@@ -22,99 +11,50 @@ import numpy as np
 import numba as nb
 
 
-@nb.jit([nb.types.UniTuple(nb.int64[:], 2)(nb.types.UniTuple(nb.int64, 2),
-                                           nb.types.UniTuple(nb.int64, 2),
-                                           nb.types.UniTuple(nb.int64, 2)),
-         nb.types.UniTuple(nb.int64[:], 2)(nb.int64[:], nb.int64[:],
-                                           nb.int64[:])],
-        nopython=True)
-def _ellipse_in_shape(shape, center, radii):
-    """Generate coordinates of points within the ellipse bounded by shape."""
-    # XXX: ``numba`` currently does not support ``numpy.meshgrid``
-    nrow, ncol = shape
-    r_lim = np.zeros((nrow, ncol))
-    for i in range(nrow):
-        r_lim[i, :] = np.arange(float(ncol))
-    c_lim = np.zeros((nrow, ncol))
-    for i in range(ncol):
-        c_lim[:, i] = np.arange(float(nrow))
-    #
-    r_o, c_o = center
-    r_r, c_r = radii
-    distances = (((r_lim-r_o) / r_r) * ((r_lim-r_o) / r_r) +
-    xi, yi = np.nonzero(distances < 1.0)
-    return (xi, yi)
-                 ((c_lim-c_o) / c_r) * ((c_lim-c_o) / c_r))
-
-
-@nb.jit(nb.types.UniTuple(nb.int64[:], 2)(nb.int64, nb.int64,
-                                          nb.int64, nb.int64,
+@nb.jit(nb.types.UniTuple(nb.int64[:], 2)(nb.types.UniTuple(nb.int64, 2),
+                                          nb.types.UniTuple(nb.int64, 2),
                                           nb.types.UniTuple(nb.int64, 2)),
         nopython=True)
-def ellipse(r, c, r_radius, c_radius, shape):
-    """Generate coordinates of pixels within the ellipse.
+def ellipse(center, radii, shape):
+    """
+    Generate coordinates of pixels within the ellipse.
 
     XXX/NOTE
     --------
     * Cannot figure out why ``nb.optional(nb.types.UniTuple(nb.int64, 2))``
       does NOT work.  Therefore, make ``shape`` as mandatory parameter
       instead of optional.
-    * Cannot figure out multi-dispatch that allows both int and float types
-      for ``r``, ``c``, ``r_radius`` and ``c_radius``.  Thus only support
-      the int type for the moment.
 
     Parameters
     ----------
-    r, c : int
+    center : int tuple (r0, c0)
         Center coordinate of the ellipse.
-    r_radius, c_radius : int
-        Minor and major semi-axes. ``(r/r_radius)**2 + (c/c_radius)**2 = 1``.
-    shape : tuple
+    radii : int tuple (r_radius, c_radius)
+        Minor and major semi-axes. ``(r/r_radius)**2 + (c/c_radius)**2 <= 1``.
+    shape : int tuple
         Image shape which is used to determine the maximum extent of output
         pixel coordinates.  This is useful for ellipses that exceed the image
         size.  If None, the full extent of the ellipse is used.
 
     Returns
     -------
-    rr, cc : integer `~numpy.ndarray`
+    rr, cc : int `~numpy.ndarray`
         Pixel coordinates of the ellipse.
         May be used to directly index into an array, e.g.
         ``img[rr, cc] = 1``.
-
-    Examples
-    --------
-    >>> from fg21sim.utils.draw import ellipse
-    >>> img = np.zeros((10, 10), dtype=np.uint8)
-    >>> rr, cc = ellipse(5, 5, 3, 4)
-    >>> img[rr, cc] = 1
-    >>> img
-    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
-           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
-           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
-           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
-           [0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
     """
-    center = np.array([r, c])
-    radii = np.array([r_radius, c_radius])
-
-    # The upper_left and lower_right corners of the
-    # smallest rectangle containing the ellipse.
-    upper_left = np.ceil(center - radii).astype(np.int64)
-    lower_right = np.floor(center + radii).astype(np.int64)
-
-    # Constrain upper_left and lower_right by shape boundary.
-    upper_left = np.maximum(upper_left, np.array([0, 0]))
-    lower_right = np.minimum(lower_right, np.array(shape)-1)
-
-    shifted_center = center - upper_left
-    bounding_shape = lower_right - upper_left + 1
+    # XXX: ``numba`` currently does not support ``numpy.meshgrid``
+    nrow, ncol = shape
+    r_lim = np.zeros((nrow, ncol))
+    for i in range(nrow):
+        r_lim[i, :] = np.arange(float(ncol))
+    c_lim = np.zeros((nrow, ncol))
+    for i in range(ncol):
+        c_lim[:, i] = np.arange(float(nrow))
 
-    rr, cc = _ellipse_in_shape(bounding_shape, shifted_center, radii)
-    rr += upper_left[0]
-    cc += upper_left[1]
-    return (rr, cc)
+    r_o, c_o = center
+    r_r, c_r = radii
+    distances = (((r_lim-r_o) / r_r) * ((r_lim-r_o) / r_r) +
+                 ((c_lim-c_o) / c_r) * ((c_lim-c_o) / c_r))
+    r_idx, c_idx = np.nonzero(distances <= 1.0)
+    return (r_idx, c_idx)