def run(self, workspace):
objects_name = self.objects_name.value
objects = workspace.object_set.get_objects(objects_name)
assert isinstance(objects, cpo.Objects)
labels = objects.segmented
if self.relabel_option == OPTION_SPLIT:
output_labels, count = scind.label(labels > 0, np.ones((3, 3),
bool))
else:
if self.merge_option == UNIFY_DISTANCE:
mask = labels > 0
if self.distance_threshold.value > 0:
#
# Take the distance transform of the reverse of the mask
# and figure out what points are less than 1/2 of the
# distance from an object.
#
d = scind.distance_transform_edt(~mask)
mask = d < self.distance_threshold.value / 2 + 1
output_labels, count = scind.label(mask, np.ones((3, 3), bool))
output_labels[labels == 0] = 0
if self.wants_image:
output_labels = self.filter_using_image(workspace, mask)
elif self.merge_option == UNIFY_PARENT:
parents_name = self.parent_object.value
parents_of = workspace.measurements[objects_name, "_".join(
(C_PARENT, parents_name))]
output_labels = labels.copy().astype(np.uint32)
output_labels[labels > 0] = parents_of[labels[labels > 0] - 1]
if self.merging_method == UM_CONVEX_HULL:
ch_pts, n_pts = morph.convex_hull(output_labels)
ijv = morph.fill_convex_hulls(ch_pts, n_pts)
output_labels[ijv[:, 0], ijv[:, 1]] = ijv[:, 2]
output_objects = cpo.Objects()
output_objects.segmented = output_labels
if objects.has_small_removed_segmented:
output_objects.small_removed_segmented = \
copy_labels(objects.small_removed_segmented, output_labels)
if objects.has_unedited_segmented:
output_objects.unedited_segmented = \
copy_labels(objects.unedited_segmented, output_labels)
output_objects.parent_image = objects.parent_image
workspace.object_set.add_objects(output_objects,
self.output_objects_name.value)
measurements = workspace.measurements
add_object_count_measurements(measurements,
self.output_objects_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(measurements,
self.output_objects_name.value,
output_objects.segmented)
#
# Relate the output objects to the input ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(output_objects)
measurements.add_measurement(
self.objects_name.value,
FF_CHILDREN_COUNT % self.output_objects_name.value,
children_per_parent)
measurements.add_measurement(self.output_objects_name.value,
FF_PARENT % self.objects_name.value,
parents_of_children)
if self.show_window:
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.output_labels = output_objects.segmented
if self.merge_option == UNIFY_PARENT:
workspace.display_data.parent_labels = \
workspace.object_set.get_objects(self.parent_object.value).segmented
def run(self, workspace):
objects_name = self.objects_name.value
objects = workspace.object_set.get_objects(objects_name)
assert isinstance(objects, cpo.Objects)
labels = objects.segmented
if self.relabel_option == OPTION_SPLIT:
output_labels, count = scind.label(labels > 0, np.ones((3,3),bool))
else:
if self.unify_option == UNIFY_DISTANCE:
mask = labels > 0
if self.distance_threshold.value > 0:
#
# Take the distance transform of the reverse of the mask
# and figure out what points are less than 1/2 of the
# distance from an object.
#
d = scind.distance_transform_edt(~mask)
mask = d < self.distance_threshold.value/2+1
output_labels, count = scind.label(mask, np.ones((3,3), bool))
output_labels[labels == 0] = 0
if self.wants_image:
output_labels = self.filter_using_image(workspace, mask)
elif self.unify_option == UNIFY_PARENT:
parents_name = self.parent_object.value
parents_of = workspace.measurements[
objects_name, "_".join((C_PARENT, parents_name))]
output_labels = labels.copy().astype(np.uint32)
output_labels[labels > 0] = parents_of[labels[labels > 0]-1]
if self.unification_method == UM_CONVEX_HULL:
ch_pts, n_pts = morph.convex_hull(output_labels)
ijv = morph.fill_convex_hulls(ch_pts, n_pts)
output_labels[ijv[:, 0], ijv[:, 1]] = ijv[:, 2]
output_objects = cpo.Objects()
output_objects.segmented = output_labels
if objects.has_small_removed_segmented:
output_objects.small_removed_segmented = \
copy_labels(objects.small_removed_segmented, output_labels)
if objects.has_unedited_segmented:
output_objects.unedited_segmented = \
copy_labels(objects.unedited_segmented, output_labels)
output_objects.parent_image = objects.parent_image
workspace.object_set.add_objects(output_objects, self.output_objects_name.value)
measurements = workspace.measurements
add_object_count_measurements(measurements,
self.output_objects_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(measurements,
self.output_objects_name.value,
output_objects.segmented)
#
# Relate the output objects to the input ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(output_objects)
measurements.add_measurement(self.objects_name.value,
FF_CHILDREN_COUNT %
self.output_objects_name.value,
children_per_parent)
measurements.add_measurement(self.output_objects_name.value,
FF_PARENT%self.objects_name.value,
parents_of_children)
if self.wants_outlines:
outlines = centrosome.outline.outline(output_labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(self.outlines_name.value,
outline_image)
if self.show_window:
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.output_labels = output_objects.segmented
if self.unify_option == UNIFY_PARENT:
workspace.display_data.parent_labels = \
workspace.object_set.get_objects(self.parent_object.value).segmented
