Merge pull request #453 from cgohlke/patch-1

Fix `ValueError: Buffer dtype mismatch` on win-amd64

skimage/draw/_draw.pyx

@@ -126,7 +126,7 @@ def polygon(y, x, shape=None):
                 rr.append(r)
                 cc.append(c)
 
-    return np.array(rr), np.array(cc)
+    return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp)
 
 
 def ellipse(double cy, double cx, double yradius, double xradius, shape=None):
@@ -174,7 +174,7 @@ def ellipse(double cy, double cx, double yradius, double xradius, shape=None):
                 rr.append(r)
                 cc.append(c)
 
-    return np.array(rr), np.array(cc)
+    return np.array(rr, dtype=np.intp), np.array(cc, dtype=np.intp)
 
 
 def circle(double cy, double cx, double radius, shape=None):
@@ -281,7 +281,7 @@ def circle_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t radius,
                 y = y - 1
                 x = x + 1
 
-    return np.array(rr) + cy, np.array(cc) + cx
+    return np.array(rr, dtype=np.intp) + cy, np.array(cc, dtype=np.intp) + cx
 
 
 def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius,
@@ -368,7 +368,7 @@ def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius,
             err += ychange
             ychange += twobsquared
 
-    return np.array(py) + cy, np.array(px) + cx
+    return np.array(py, dtype=np.intp) + cy, np.array(px, dtype=np.intp) + cx
 
 
 def set_color(img, coords, color):

skimage/morphology/_convex_hull.pyx

@@ -31,7 +31,7 @@ def possible_hull(cnp.ndarray[dtype=cnp.uint8_t, ndim=2, mode="c"] img):
     #         rows storage slots for right boundary pixels
     #         cols storage slots for bottom boundary pixels
     cdef cnp.ndarray[dtype=cnp.intp_t, ndim=2] nonzero = \
-         np.ones((2 * (rows + cols), 2), dtype=np.int)
+         np.ones((2 * (rows + cols), 2), dtype=np.intp)
     nonzero *= -1
 
     for r in range(rows):

skimage/segmentation/_felzenszwalb_cy.pyx

@@ -73,7 +73,7 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20)
     cdef cnp.intp_t *edges_p = <cnp.intp_t*>edges.data
     cdef cnp.float_t *costs_p = <cnp.float_t*>costs.data
     cdef cnp.ndarray[cnp.intp_t, ndim=1] segment_size \
-            = np.ones(width * height, dtype=np.int)
+            = np.ones(width * height, dtype=np.intp)
     # inner cost of segments
     cdef cnp.ndarray[cnp.float_t, ndim=1] cint = np.zeros(width * height)
     cdef int seg0, seg1, seg_new, e

skimage/transform/hough_transform.py

@@ -164,7 +164,7 @@ def hough_circle(img, radius, normalize=True):
         Hough transform accumulator for each radius
 
     """
-    return _hough_circle(img, radius, normalize)
+    return _hough_circle(img, radius.astype(np.intp), normalize)
 
 def hough_peaks(hspace, angles, dists, min_distance=10, min_angle=10,
                 threshold=None, num_peaks=np.inf):