def condense_labels(Z, numsegs, labels):
# project all labels at a given Z into a single plane, then merge any that
# end up mapping to the same projected subregions (merge = remove one of
# them)
#
# Regions in the presegmentations tend to be similar. This step merges
# segments that differ only near the boundaries.
# work by chunks for speed. Note that this approach splits projected
# segments at chunk boundaries, but since we're just using them for
# identifying similar regions, this is fine.
overlap_counter = fast64counter.ValueCountInt64()
sublabel_offset = 0
for xslice, yslice in work_by_chunks(labels):
chunklabels = [labels[yslice, xslice, S, Z][...] for S in range(numsegs)]
projected = chunklabels[0] > 0
for S in range(1, numsegs):
projected &= chunklabels[S] > 0 # Mask out boundaries
labeled_projected, num_found = ndimage_label(projected, output=np.int32)
labeled_projected[labeled_projected > 0] += sublabel_offset
sublabel_offset += num_found
for sub in chunklabels:
overlap_counter.add_values_pair32(labeled_projected.ravel(), sub.ravel())
# Build original label to set of projected label map
projected_labels, original_labels, areas = overlap_counter.get_counts_pair32()
label_to_projected = defaultdict(set)
for pl, ol in zip(projected_labels, original_labels):
if ol and pl:
label_to_projected[ol].add(pl)
# Reverse the map
projected_to_label = defaultdict(list)
for ol, plset in label_to_projected.iteritems():
projected_to_label[tuple(sorted(plset))].append(ol)
# Build a remapper to remove merged labels
remapper = np.arange(np.max(original_labels) + 1)
for original_label_list in projected_to_label.itervalues():
# keep the first, but zero the rest
if len(original_label_list) > 1:
remapper[original_label_list[1:]] = 0
# pack the labels in the remapper
final_label_count = np.sum(remapper > 0)
remapper[0] = 1 # simplify next line
remapper[remapper > 0] = np.arange(final_label_count + 1, dtype=np.int32)
# remap the labels by chunk
for xslice, yslice in work_by_chunks(labels):
for S in range(numsegs):
l = labels[yslice, xslice, S, Z]
labels[yslice, xslice, S, Z] = remapper[l]
if DEBUG:
assert len(np.unique(labels[:, :, :, Z].ravel())) == final_label_count + 1
return final_label_count
sys.exit(0)
except Exception, e:
print e
pass
condense_labels = timed(overlaps.condense_labels)
lf = h5py.File(output_path + '_partial', 'w')
chunking = [chunksize, chunksize, 1, 1]
labels = lf.create_dataset('seglabels', segmentations.shape, dtype=np.int32, chunks=tuple(chunking), compression='gzip')
total_regions = 0
cross_Z_offset = 0
for Z in range(numslices):
this_slice_offset = 0
for seg_idx in range(numsegs):
temp, numregions = ndimage_label(1 - segmentations[:, :, seg_idx, Z][...], output=np.int32)
labels[:, :, seg_idx, Z] = temp
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
sys.exit(0)
except Exception, e:
print e
pass
condense_labels = timed(overlaps.condense_labels)
lf = h5py.File(output_path + '_partial', 'w')
chunking = [chunksize, chunksize, 1, 1]
labels = lf.create_dataset('seglabels', segmentations.shape, dtype=np.int32, chunks=tuple(chunking), compression='gzip')
total_regions = 0
cross_Z_offset = 0
for Z in range(numslices):
this_slice_offset = 0
for seg_idx in range(numsegs):
temp, numregions = ndimage_label(1 - segmentations[:, :, seg_idx, Z][...], output=np.int32)
labels[:, :, seg_idx, Z] = temp
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
def condense_labels(Z, numsegs, labels):
# project all labels at a given Z into a single plane, then merge any that
# end up mapping to the same projected subregions (merge = remove one of
# them)
#
# Regions in the presegmentations tend to be similar. This step merges
# segments that differ only near the boundaries.
# work by chunks for speed. Note that this approach splits projected
# segments at chunk boundaries, but since we're just using them for
# identifying similar regions, this is fine.
overlap_counter = fast64counter.ValueCountInt64()
sublabel_offset = 0
for xslice, yslice in work_by_chunks(labels):
chunklabels = [
labels[yslice, xslice, S, Z][...] for S in range(numsegs)
]
projected = chunklabels[0] > 0
for S in range(1, numsegs):
projected &= chunklabels[S] > 0 # Mask out boundaries
labeled_projected, num_found = ndimage_label(projected,
output=np.int32)
labeled_projected[labeled_projected > 0] += sublabel_offset
sublabel_offset += num_found
for sub in chunklabels:
overlap_counter.add_values_pair32(labeled_projected.ravel(),
sub.ravel())
# Build original label to set of projected label map
projected_labels, original_labels, areas = overlap_counter.get_counts_pair32(
)
label_to_projected = defaultdict(set)
for pl, ol in zip(projected_labels, original_labels):
if ol and pl:
label_to_projected[ol].add(pl)
# Reverse the map
projected_to_label = defaultdict(list)
for ol, plset in label_to_projected.iteritems():
projected_to_label[tuple(sorted(plset))].append(ol)
# Build a remapper to remove merged labels
remapper = np.arange(np.max(original_labels) + 1)
for original_label_list in projected_to_label.itervalues():
# keep the first, but zero the rest
if len(original_label_list) > 1:
remapper[original_label_list[1:]] = 0
# pack the labels in the remapper
final_label_count = np.sum(remapper > 0)
remapper[0] = 1 # simplify next line
remapper[remapper > 0] = np.arange(final_label_count + 1, dtype=np.int32)
# remap the labels by chunk
for xslice, yslice in work_by_chunks(labels):
for S in range(numsegs):
l = labels[yslice, xslice, S, Z]
labels[yslice, xslice, S, Z] = remapper[l]
if DEBUG:
assert len(np.unique(labels[:, :, :,
Z].ravel())) == final_label_count + 1
return final_label_count
lf.close()
sys.exit(0)
except Exception, e:
pass
condense_labels = timed(overlaps.condense_labels)
lf = h5py.File(output_path + '_partial', 'w')
chunking = [1, 1, chunksize, chunksize]
labels = lf.create_dataset('seglabels', segmentations.shape, dtype=np.int32, chunks=tuple(chunking), compression='gzip')
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
except Exception, e:
pass
condense_labels = timed(overlaps.condense_labels)
lf = h5py.File(output_path + "_partial", "w")
chunking = [1, 1, chunksize, chunksize]
labels = lf.create_dataset(
"seglabels", segmentations.shape, dtype=np.int32, chunks=tuple(chunking), compression="gzip"
)
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
