offset_labels(Z, seg_idx, labels, this_slice_offset)
this_slice_offset += numregions
total_regions += numregions
condensed_count = condense_labels(Z, numsegs, labels)
print "Labeling depth %d: original %d, condensed %d" % (Z, this_slice_offset, condensed_count)
for seg_idx in range (numsegs):
offset_labels(Z, seg_idx, labels, cross_Z_offset)
cross_Z_offset += condensed_count
# XXX - apply cross-D offset
print "Labeling took", int(time.time() - st), "seconds, ", condense_labels.total_time, "in condensing"
print cross_Z_offset, "total labels", total_regions, "before condensing"
if DEBUG:
assert np.max(labels) == cross_Z_offset
areas, exclusions, links = overlaps.count_overlaps_exclusionsets(numslices, numsegs, labels, link_worth)
num_segments = len(areas)
assert num_segments == cross_Z_offset + 1 # areas includes an area for 0
st = time.time()
model, links_to_segs = build_model(areas, exclusions, links)
print "Building MILP took", int(time.time() - st), "seconds"
# free memory
areas = exclusions = links = None
gc.collect()
print "Solving"
model.solve()
print "Solving took", int(time.time() - st), "seconds"
offset_labels(Z, seg_idx, labels, this_slice_offset)
this_slice_offset += numregions
total_regions += numregions
condensed_count = condense_labels(Z, numsegs, labels)
print "Labeling depth %d: original %d, condensed %d" % (Z, this_slice_offset, condensed_count)
for seg_idx in range (numsegs):
offset_labels(Z, seg_idx, labels, cross_Z_offset)
cross_Z_offset += condensed_count
# XXX - apply cross-D offset
print "Labeling took", int(time.time() - st), "seconds, ", condense_labels.total_time, "in condensing"
print cross_Z_offset, "total labels", total_regions, "before condensing"
if DEBUG:
assert np.max(labels) == cross_Z_offset
areas, exclusions, links = overlaps.count_overlaps_exclusionsets(numslices, numsegs, labels, link_worth, maximum_link_distance)
num_segments = len(areas)
assert num_segments == cross_Z_offset + 1 # areas includes an area for 0
st = time.time()
model, links_to_segs = build_model(areas, exclusions, links)
print "Building MILP took", int(time.time() - st), "seconds"
# free memory
areas = exclusions = links = None
gc.collect()
print "Solving"
model.solve()
print "Solving took", int(time.time() - st), "seconds"
Z, this_slice_offset, condensed_count)
for seg_idx in range(numsegs):
rh.offset_labels(Z, seg_idx, labels, cross_Z_offset)
cross_Z_offset += condensed_count
# XXX - apply cross-D offset
print "Labeling took", int(
time.time() -
st), "seconds, ", condense_labels.total_time, "in condensing"
print cross_Z_offset, "total labels", total_regions, "before condensing"
if debugFlag:
assert np.max(labels) == cross_Z_offset
print zdim
print total_regions
if total_regions > zdim: #this happens when there is one label per slice (no fusion to do)
areas, exclusions, links = overlaps.count_overlaps_exclusionsets(
numslices, numsegs, labels, rh.link_worth)
num_segments = len(areas)
assert num_segments == cross_Z_offset + 1 # areas includes an area for 0
st = time.time()
#print 'use this number'
#print exclusions
model, links_to_segs = rh.build_model(areas, exclusions, links)
print "Building MILP took", int(time.time() - st), "seconds"
# free memory
areas = exclusions = links = None
gc.collect()
print "Solving"
model.solve()
total_regions = 0
cross_D_offset = 0
for D in range(depth):
this_D_offset = 0
for Seg in range(numsegs):
temp, numregions = ndimage_label(segmentations[Seg, D, :, :][...], output=np.int32)
labels[Seg, D, :, :] = temp
offset_labels(D, Seg, labels, this_D_offset)
this_D_offset += numregions
total_regions += numregions
condensed_count = condense_labels(D, numsegs, labels)
print "Labeling depth %d: original %d, condensed %d" % (D, this_D_offset, condensed_count)
for Seg in range (numsegs):
offset_labels(D, Seg, labels, cross_D_offset)
cross_D_offset += condensed_count
# XXX - apply cross-D offset
print "Labeling took", int(time.time() - st), "seconds, ", condense_labels.total_time, "in condensing"
print cross_D_offset, "total labels", total_regions, "before condensing"
if DEBUG:
assert np.max(labels) == cross_D_offset
areas, exclusions, overlaps = overlaps.count_overlaps_exclusionsets(depth, numsegs, labels, link_worth)
num_segments = len(areas)
assert num_segments == cross_D_offset + 1 # areas includes an area for 0
st = time.time()
build_model(open("theproblem.lp", "w"), areas, exclusions, overlaps)
print "Building MILP too<k", int(time.time() - st), "seconds"
)
total_regions = 0
cross_D_offset = 0
for D in range(depth):
this_D_offset = 0
for Seg in range(numsegs):
temp, numregions = ndimage_label(segmentations[Seg, D, :, :][...], output=np.int32)
labels[Seg, D, :, :] = temp
offset_labels(D, Seg, labels, this_D_offset)
this_D_offset += numregions
total_regions += numregions
condensed_count = condense_labels(D, numsegs, labels)
print "Labeling depth %d: original %d, condensed %d" % (D, this_D_offset, condensed_count)
for Seg in range(numsegs):
offset_labels(D, Seg, labels, cross_D_offset)
cross_D_offset += condensed_count
# XXX - apply cross-D offset
print "Labeling took", int(time.time() - st), "seconds, ", condense_labels.total_time, "in condensing"
print cross_D_offset, "total labels", total_regions, "before condensing"
if DEBUG:
assert np.max(labels) == cross_D_offset
areas, exclusions, overlaps = overlaps.count_overlaps_exclusionsets(depth, numsegs, labels, link_worth)
num_segments = len(areas)
assert num_segments == cross_D_offset + 1 # areas includes an area for 0
st = time.time()
build_model(open("theproblem.lp", "w"), areas, exclusions, overlaps)
print "Building MILP too<k", int(time.time() - st), "seconds"