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Fix watershed failures on Python 3

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This commit is contained in:
cgohlke
2011-10-10 14:35:21 -07:00
parent 0fe16b54ac
commit 7f07f825e8
1 changed files with 9 additions and 11 deletions
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scikits/image/morphology/watershed.py
+9 -11
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@@ -129,7 +129,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
#
# offset to center of connectivity array
#
offset = np.array(c_connectivity.shape) / 2
offset = np.array(c_connectivity.shape) // 2
# pad the image, markers, and mask so that we can use the mask to
# keep from running off the edges
@@ -175,7 +175,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
# and the second through last are the x,y...whatever offsets
# (to do bounds checking).
c = []
image_stride = np.array(image.strides) / image.itemsize
image_stride = np.array(image.strides) // image.itemsize
for i in range(np.product(c_connectivity.shape)):
multiplier = 1
offs = []
@@ -183,7 +183,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
ignore = True
for j in range(len(c_connectivity.shape)):
elems = c_image.shape[j]
idx = (i / multiplier) % c_connectivity.shape[j]
idx = (i // multiplier) % c_connectivity.shape[j]
off = idx - offset[j]
if off:
ignore = False
@@ -282,7 +282,7 @@ def is_local_maximum(image, labels=None, footprint=None):
footprint = np.ones([3] * image.ndim, dtype=np.uint8)
assert((np.all(footprint.shape) & 1) == 1)
footprint = (footprint != 0)
footprint_extent = (np.array(footprint.shape)-1) / 2
footprint_extent = (np.array(footprint.shape)-1) // 2
if np.all(footprint_extent == 0):
return labels > 0
result = (labels > 0).copy()
@@ -296,9 +296,9 @@ def is_local_maximum(image, labels=None, footprint=None):
#
# Find the relative indexes of each footprint element
#
image_strides = np.array(image.strides) / image.dtype.itemsize
big_strides = np.array(big_labels.strides) / big_labels.dtype.itemsize
result_strides = np.array(result.strides) / result.dtype.itemsize
image_strides = np.array(image.strides) // image.dtype.itemsize
big_strides = np.array(big_labels.strides) // big_labels.dtype.itemsize
result_strides = np.array(result.strides) // result.dtype.itemsize
footprint_offsets = np.mgrid[[slice(-fe,fe+1) for fe in footprint_extent]]
fp_image_offsets = np.sum(image_strides[:, np.newaxis] *
@@ -342,7 +342,7 @@ def is_local_maximum(image, labels=None, footprint=None):
def __heapify_markers(markers, image):
"""Create a priority queue heap with the markers on it"""
stride = np.array(image.strides) / image.itemsize
stride = np.array(image.strides) // image.itemsize
coords = np.argwhere(markers != 0)
ncoords = coords.shape[0]
if ncoords > 0:
@@ -442,8 +442,6 @@ def _slow_watershed(image, markers, connectivity=8, mask=None):
# label the pixel
labels[x, y] = pix_label
# put the pixel onto the queue
heappush(pq, (image[x, y], age, 0, x, y))
heappush(pq, [image[x, y], age, 0, x, y])
age += 1
return labels