def run(self, workspace):
image_name = self.image_name.value
objects_name = self.objects_name.value
image = workspace.image_set.get_image(image_name)
pixel_data = image.pixel_data
labels = workspace.interaction_request(
self, pixel_data, workspace.measurements.image_set_number)
if labels is None:
# User cancelled. Soldier on as best we can.
workspace.cancel_request()
labels = np.zeros(pixel_data.shape[:2], int)
objects = cpo.Objects()
objects.segmented = labels
workspace.object_set.add_objects(objects, objects_name)
##################
#
# Add measurements
#
m = workspace.measurements
#
# The object count
#
object_count = np.max(labels)
I.add_object_count_measurements(m, objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements(m, objects_name, labels)
workspace.display_data.labels = labels
workspace.display_data.pixel_data = pixel_data
def run(self, workspace):
'''Find the outlines on the current image set
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
'''
gridding = workspace.get_grid(self.grid_name.value)
if self.shape_choice == SHAPE_RECTANGLE:
labels = self.run_rectangle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_FORCED:
labels = self.run_forced_circle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_NATURAL:
labels = self.run_natural_circle(workspace, gridding)
elif self.shape_choice == SHAPE_NATURAL:
labels = self.run_natural(workspace, gridding)
objects = cpo.Objects()
objects.segmented = labels
object_count = gridding.rows * gridding.columns
workspace.object_set.add_objects(objects,
self.output_objects_name.value)
add_object_location_measurements(workspace.measurements,
self.output_objects_name.value,
labels, object_count)
add_object_count_measurements(workspace.measurements,
self.output_objects_name.value,
object_count)
if self.wants_outlines:
outlines = outline(labels!=0)
outline_image = cpi.Image(outlines)
workspace.image_set.add(self.outlines_name.value, outline_image)
def run(self, workspace):
input_objects = workspace.object_set.get_objects(self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.wants_outlines.value:
outline_image = cpi.Image(outline(output_objects.segmented) > 0,
parent_image=input_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
if self.show_window:
workspace.display_data.input_objects_segmented = input_objects.segmented
workspace.display_data.output_objects_segmented = output_objects.segmented
def run(self, workspace):
image_name = self.image_name.value
objects_name = self.objects_name.value
image = workspace.image_set.get_image(image_name)
pixel_data = image.pixel_data
labels = workspace.interaction_request(
self, pixel_data, workspace.measurements.image_set_number)
if labels is None:
# User cancelled. Soldier on as best we can.
workspace.cancel_request()
labels = np.zeros(pixel_data.shape[:2], int)
objects = cpo.Objects()
objects.segmented = labels
workspace.object_set.add_objects(objects, objects_name)
##################
#
# Add measurements
#
m = workspace.measurements
#
# The object count
#
object_count = np.max(labels)
I.add_object_count_measurements(m, objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements(m, objects_name, labels)
workspace.display_data.labels = labels
workspace.display_data.pixel_data = pixel_data
def run(self, workspace):
input_objects = workspace.object_set.get_objects(self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.wants_outlines.value:
outline_image = cpi.Image(outline(output_objects.segmented) > 0,
parent_image = input_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
if self.show_window:
workspace.display_data.input_objects_segmented = input_objects.segmented
workspace.display_data.output_objects_segmented = output_objects.segmented
def run(self, workspace):
'''Find the outlines on the current image set
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
'''
gridding = workspace.get_grid(self.grid_name.value)
if self.shape_choice == SHAPE_RECTANGLE:
labels = self.run_rectangle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_FORCED:
labels = self.run_forced_circle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_NATURAL:
labels = self.run_natural_circle(workspace, gridding)
elif self.shape_choice == SHAPE_NATURAL:
labels = self.run_natural(workspace, gridding)
objects = cpo.Objects()
objects.segmented = labels
object_count = gridding.rows * gridding.columns
workspace.object_set.add_objects(objects,
self.output_objects_name.value)
add_object_location_measurements(workspace.measurements,
self.output_objects_name.value,
labels, object_count)
add_object_count_measurements(workspace.measurements,
self.output_objects_name.value,
object_count)
if self.show_window:
workspace.display_data.gridding = gridding
workspace.display_data.labels = labels
def run(self, workspace):
input_objects = workspace.object_set.get_objects(self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.wants_outlines.value:
outline_image = cpi.Image(outline(output_objects.segmented) > 0,
parent_image = input_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
if workspace.frame is not None:
figure = workspace.create_or_find_figure(title="ExpandOrShrinkObjects, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(2,1))
figure.subplot_imshow_labels(0,0,input_objects.segmented,
self.object_name.value)
figure.subplot_imshow_labels(1,0,output_objects.segmented,
self.output_object_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
def run(self, workspace):
objects = workspace.object_set.get_objects(self.objects_name.value)
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:
parent_objects = workspace.object_set.get_objects(self.parent_object.value)
output_labels = parent_objects.segmented.copy()
output_labels[labels == 0] = 0
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 = cellprofiler.cpmath.outline.outline(output_labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(self.outlines_name.value, outline_image)
if workspace.frame is not None:
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.output_labels = output_objects.segmented
def run(self, workspace):
input_objects = workspace.object_set.get_objects(
self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented
and self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented
and self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.wants_outlines.value:
outline_image = cpi.Image(outline(output_objects.segmented) > 0,
parent_image=input_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
if workspace.frame is not None:
figure = workspace.create_or_find_figure(
title="ExpandOrShrinkObjects, image cycle #%d" %
(workspace.measurements.image_set_number),
subplots=(2, 1))
figure.subplot_imshow_labels(0, 0, input_objects.segmented,
self.object_name.value)
figure.subplot_imshow_labels(1,
0,
output_objects.segmented,
self.output_object_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
def run(self, workspace):
input_objects = workspace.object_set.get_objects(
self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.show_window:
workspace.display_data.input_objects_segmented = input_objects.segmented
workspace.display_data.output_objects_segmented = output_objects.segmented
def run(self, workspace):
objects = workspace.object_set.get_objects(self.objects_name.value)
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:
parent_objects = workspace.object_set.get_objects(
self.parent_object.value)
output_labels = parent_objects.segmented.copy()
output_labels[labels == 0] = 0
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 = cellprofiler.cpmath.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
def run(self, workspace):
"""Run the module on the current data set
workspace - has the current image set, object set, measurements
and the parent frame for the application if the module
is allowed to display. If the module should not display,
workspace.frame is None.
"""
#
# The object set holds "objects". Each of these is a container
# for holding up to three kinds of image labels.
#
object_set = workspace.object_set
#
# Get the primary objects (the centers to be removed).
# Get the string value out of primary_object_name.
#
primary_objects = object_set.get_objects(
self.primary_objects_name.value)
#
# Get the cleaned-up labels image
#
primary_labels = primary_objects.segmented
#
# Do the same with the secondary object
secondary_objects = object_set.get_objects(
self.secondary_objects_name.value)
secondary_labels = secondary_objects.segmented
#
# If one of the two label images is smaller than the other, we
# try to find the cropping mask and we apply that mask to the larger
#
try:
if any([
p_size < s_size for p_size, s_size in zip(
primary_labels.shape, secondary_labels.shape)
]):
#
# Look for a cropping mask associated with the primary_labels
# and apply that mask to resize the secondary labels
#
secondary_labels = primary_objects.crop_image_similarly(
secondary_labels)
tertiary_image = primary_objects.parent_image
elif any([
p_size > s_size for p_size, s_size in zip(
primary_labels.shape, secondary_labels.shape)
]):
primary_labels = secondary_objects.crop_image_similarly(
primary_labels)
tertiary_image = secondary_objects.parent_image
elif secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
except ValueError:
# No suitable cropping - resize all to fit the secondary
# labels which are the most critical.
#
primary_labels, _ = cpo.size_similarly(secondary_labels,
primary_labels)
if secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
if tertiary_image is not None:
tertiary_image, _ = cpo.size_similarly(
secondary_labels, tertiary_image)
#
# Find the outlines of the primary image and use this to shrink the
# primary image by one. This guarantees that there is something left
# of the secondary image after subtraction
#
primary_outline = outline(primary_labels)
tertiary_labels = secondary_labels.copy()
if self.shrink_primary:
primary_mask = np.logical_or(primary_labels == 0, primary_outline)
else:
primary_mask = primary_labels == 0
tertiary_labels[primary_mask == False] = 0
#
# Get the outlines of the tertiary image
#
tertiary_outlines = outline(tertiary_labels) != 0
#
# Make the tertiary objects container
#
tertiary_objects = cpo.Objects()
tertiary_objects.segmented = tertiary_labels
tertiary_objects.parent_image = tertiary_image
#
# Relate tertiary objects to their parents & record
#
child_count_of_secondary, secondary_parents = \
secondary_objects.relate_children(tertiary_objects)
if self.shrink_primary:
child_count_of_primary, primary_parents = \
primary_objects.relate_children(tertiary_objects)
else:
# Primary and tertiary don't overlap.
# Establish overlap between primary and secondary and commute
_, secondary_of_primary = \
secondary_objects.relate_children(primary_objects)
mask = secondary_of_primary != 0
child_count_of_primary = np.zeros(mask.shape, int)
child_count_of_primary[mask] = child_count_of_secondary[
secondary_of_primary[mask] - 1]
primary_parents = np.zeros(secondary_parents.shape,
secondary_parents.dtype)
primary_of_secondary = np.zeros(secondary_objects.count + 1, int)
primary_of_secondary[secondary_of_primary] = \
np.arange(1, len(secondary_of_primary) + 1)
primary_of_secondary[0] = 0
primary_parents = primary_of_secondary[secondary_parents]
#
# Write out the objects
#
workspace.object_set.add_objects(tertiary_objects,
self.subregion_objects_name.value)
#
# Write out the measurements
#
m = workspace.measurements
#
# The parent/child associations
#
for parent_objects_name, parents_of, child_count, relationship in (
(self.primary_objects_name, primary_parents,
child_count_of_primary, R_REMOVED),
(self.secondary_objects_name, secondary_parents,
child_count_of_secondary, R_PARENT)):
m.add_measurement(
self.subregion_objects_name.value,
cellprofiler.measurement.FF_PARENT % parent_objects_name.value,
parents_of)
m.add_measurement(
parent_objects_name.value,
cellprofiler.measurement.FF_CHILDREN_COUNT %
self.subregion_objects_name.value, child_count)
mask = parents_of != 0
image_number = np.ones(np.sum(mask), int) * m.image_set_number
child_object_number = np.argwhere(mask).flatten() + 1
parent_object_number = parents_of[mask]
m.add_relate_measurement(self.module_num, relationship,
parent_objects_name.value,
self.subregion_objects_name.value,
image_number, parent_object_number,
image_number, child_object_number)
object_count = tertiary_objects.count
#
# The object count
#
cpmi.add_object_count_measurements(workspace.measurements,
self.subregion_objects_name.value,
object_count)
#
# The object locations
#
cpmi.add_object_location_measurements(
workspace.measurements, self.subregion_objects_name.value,
tertiary_labels)
if self.show_window:
workspace.display_data.primary_labels = primary_labels
workspace.display_data.secondary_labels = secondary_labels
workspace.display_data.tertiary_labels = tertiary_labels
workspace.display_data.tertiary_outlines = tertiary_outlines
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
def run(self, workspace):
"""Run the module
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
"""
orig_objects_name = self.object_name.value
filtered_objects_name = self.filtered_objects.value
orig_objects = workspace.object_set.get_objects(orig_objects_name)
assert isinstance(orig_objects, cpo.Objects)
orig_labels = [l for l, c in orig_objects.get_labels()]
if self.wants_image_display:
guide_image = workspace.image_set.get_image(self.image_name.value)
guide_image = guide_image.pixel_data
if np.any(guide_image != np.min(guide_image)):
guide_image = (guide_image - np.min(guide_image)) / (
np.max(guide_image) - np.min(guide_image))
else:
guide_image = None
filtered_labels = workspace.interaction_request(
self, orig_labels, guide_image,
workspace.measurements.image_set_number)
if filtered_labels is None:
# Ask whoever is listening to stop doing stuff
workspace.cancel_request()
# Have to soldier on until the cancel takes effect...
filtered_labels = orig_labels
#
# Renumber objects consecutively if asked to do so
#
unique_labels = np.unique(np.array(filtered_labels))
unique_labels = unique_labels[unique_labels != 0]
object_count = len(unique_labels)
if self.renumber_choice == R_RENUMBER:
mapping = np.zeros(
1 if len(unique_labels) == 0 else np.max(unique_labels) + 1,
int)
mapping[unique_labels] = np.arange(1, object_count + 1)
filtered_labels = [mapping[l] for l in filtered_labels]
#
# Make the objects out of the labels
#
filtered_objects = cpo.Objects()
i, j = np.mgrid[0:filtered_labels[0].shape[0],
0:filtered_labels[0].shape[1]]
ijv = np.zeros((0, 3), filtered_labels[0].dtype)
for l in filtered_labels:
ijv = np.vstack(
(ijv, np.column_stack((i[l != 0], j[l != 0], l[l != 0]))))
filtered_objects.set_ijv(ijv, orig_labels[0].shape)
if orig_objects.has_unedited_segmented():
filtered_objects.unedited_segmented = orig_objects.unedited_segmented
if orig_objects.parent_image is not None:
filtered_objects.parent_image = orig_objects.parent_image
workspace.object_set.add_objects(filtered_objects,
filtered_objects_name)
#
# Add parent/child & other measurements
#
m = workspace.measurements
child_count, parents = orig_objects.relate_children(filtered_objects)
m.add_measurement(
filtered_objects_name,
cellprofiler.measurement.FF_PARENT % orig_objects_name,
parents,
)
m.add_measurement(
orig_objects_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % filtered_objects_name,
child_count,
)
#
# The object count
#
I.add_object_count_measurements(m, filtered_objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements_ijv(m, filtered_objects_name, ijv)
workspace.display_data.orig_ijv = orig_objects.ijv
workspace.display_data.filtered_ijv = filtered_objects.ijv
workspace.display_data.shape = orig_labels[0].shape
def run(self, workspace):
"""Run the module on the current data set
workspace - has the current image set, object set, measurements
and the parent frame for the application if the module
is allowed to display. If the module should not display,
workspace.frame is None.
"""
#
# The object set holds "objects". Each of these is a container
# for holding up to three kinds of image labels.
#
object_set = workspace.object_set
#
# Get the primary objects (the centers to be removed).
# Get the string value out of primary_object_name.
#
primary_objects = object_set.get_objects(self.primary_objects_name.value)
#
# Get the cleaned-up labels image
#
primary_labels = primary_objects.segmented
#
# Do the same with the secondary object
secondary_objects = object_set.get_objects(self.secondary_objects_name.value)
secondary_labels = secondary_objects.segmented
#
# If one of the two label images is smaller than the other, we
# try to find the cropping mask and we apply that mask to the larger
#
try:
if any([p_size < s_size
for p_size, s_size
in zip(primary_labels.shape, secondary_labels.shape)]):
#
# Look for a cropping mask associated with the primary_labels
# and apply that mask to resize the secondary labels
#
secondary_labels = primary_objects.crop_image_similarly(secondary_labels)
tertiary_image = primary_objects.parent_image
elif any([p_size > s_size
for p_size, s_size
in zip(primary_labels.shape, secondary_labels.shape)]):
primary_labels = secondary_objects.crop_image_similarly(primary_labels)
tertiary_image = secondary_objects.parent_image
elif secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
except ValueError:
# No suitable cropping - resize all to fit the secondary
# labels which are the most critical.
#
primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels)
if secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
if tertiary_image is not None:
tertiary_image, _ = cpo.size_similarly(secondary_labels, tertiary_image)
#
# Find the outlines of the primary image and use this to shrink the
# primary image by one. This guarantees that there is something left
# of the secondary image after subtraction
#
primary_outline = outline(primary_labels)
tertiary_labels = secondary_labels.copy()
if self.shrink_primary:
primary_mask = np.logical_or(primary_labels == 0,
primary_outline)
else:
primary_mask = primary_labels == 0
tertiary_labels[primary_mask == False] = 0
#
# Get the outlines of the tertiary image
#
tertiary_outlines = outline(tertiary_labels) != 0
#
# Make the tertiary objects container
#
tertiary_objects = cpo.Objects()
tertiary_objects.segmented = tertiary_labels
tertiary_objects.parent_image = tertiary_image
#
# Relate tertiary objects to their parents & record
#
child_count_of_secondary, secondary_parents = \
secondary_objects.relate_children(tertiary_objects)
if self.shrink_primary:
child_count_of_primary, primary_parents = \
primary_objects.relate_children(tertiary_objects)
else:
# Primary and tertiary don't overlap.
# Establish overlap between primary and secondary and commute
_, secondary_of_primary = \
secondary_objects.relate_children(primary_objects)
mask = secondary_of_primary != 0
child_count_of_primary = np.zeros(mask.shape, int)
child_count_of_primary[mask] = child_count_of_secondary[
secondary_of_primary[mask] - 1]
primary_parents = np.zeros(secondary_parents.shape,
secondary_parents.dtype)
primary_of_secondary = np.zeros(secondary_objects.count + 1, int)
primary_of_secondary[secondary_of_primary] = \
np.arange(1, len(secondary_of_primary) + 1)
primary_of_secondary[0] = 0
primary_parents = primary_of_secondary[secondary_parents]
#
# Write out the objects
#
workspace.object_set.add_objects(tertiary_objects,
self.subregion_objects_name.value)
#
# Write out the measurements
#
m = workspace.measurements
#
# The parent/child associations
#
for parent_objects_name, parents_of, child_count, relationship in (
(self.primary_objects_name, primary_parents,
child_count_of_primary, R_REMOVED),
(self.secondary_objects_name, secondary_parents,
child_count_of_secondary, R_PARENT)):
m.add_measurement(self.subregion_objects_name.value,
cellprofiler.measurement.FF_PARENT % parent_objects_name.value,
parents_of)
m.add_measurement(parent_objects_name.value,
cellprofiler.measurement.FF_CHILDREN_COUNT % self.subregion_objects_name.value,
child_count)
mask = parents_of != 0
image_number = np.ones(np.sum(mask), int) * m.image_set_number
child_object_number = np.argwhere(mask).flatten() + 1
parent_object_number = parents_of[mask]
m.add_relate_measurement(
self.module_num, relationship,
parent_objects_name.value, self.subregion_objects_name.value,
image_number, parent_object_number,
image_number, child_object_number)
object_count = tertiary_objects.count
#
# The object count
#
cpmi.add_object_count_measurements(workspace.measurements,
self.subregion_objects_name.value,
object_count)
#
# The object locations
#
cpmi.add_object_location_measurements(workspace.measurements,
self.subregion_objects_name.value,
tertiary_labels)
if self.show_window:
workspace.display_data.primary_labels = primary_labels
workspace.display_data.secondary_labels = secondary_labels
workspace.display_data.tertiary_labels = tertiary_labels
workspace.display_data.tertiary_outlines = tertiary_outlines
def run(self, workspace):
statistics = []
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
#
# Hack: if LoadSingleImage is first, no paths are populated
#
if self.file_wants_images(self.file_settings[0]):
m_path = "_".join(
(C_PATH_NAME, self.file_settings[0].image_name.value))
else:
m_path = "_".join((C_OBJECTS_PATH_NAME,
self.file_settings[0].objects_name.value))
if m.get_current_image_measurement(m_path) is None:
self.prepare_run(workspace)
image_set = workspace.image_set
for file_setting in self.file_settings:
wants_images = self.file_wants_images(file_setting)
image_name = (file_setting.image_name.value
if wants_images else file_setting.objects_name.value)
m_path, m_file, m_md5_digest, m_scaling, m_height, m_width = [
"_".join((c, image_name)) for c in (
C_PATH_NAME if wants_images else C_OBJECTS_PATH_NAME,
C_FILE_NAME if wants_images else C_OBJECTS_FILE_NAME,
C_MD5_DIGEST,
C_SCALING,
C_HEIGHT,
C_WIDTH,
)
]
pathname = m.get_current_image_measurement(m_path)
filename = m.get_current_image_measurement(m_file)
rescale = wants_images and file_setting.rescale.value
provider = LoadImagesImageProvider(image_name, pathname, filename,
rescale)
image = provider.provide_image(image_set)
pixel_data = image.pixel_data
if wants_images:
md5 = provider.get_md5_hash(m)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
md5)
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
image_set.providers.append(provider)
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
#
# Add outlines if appropriate
#
if file_setting.wants_outlines:
outlines = centrosome.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_setting.outlines_name.value,
outline_image)
statistics += [(image_name, filename)]
workspace.display_data.col_labels = ("Image name", "File")
workspace.display_data.statistics = statistics
def run(self, workspace):
'''Filter objects for this image set, display results'''
src_objects = workspace.get_objects(self.object_name.value)
if self.rules_or_measurement == ROM_RULES:
indexes = self.keep_by_rules(workspace, src_objects)
elif self.filter_choice in (FI_MINIMAL, FI_MAXIMAL):
indexes = self.keep_one(workspace, src_objects)
elif self.filter_choice in (FI_MINIMAL_PER_OBJECT,
FI_MAXIMAL_PER_OBJECT):
indexes = self.keep_per_object(workspace, src_objects)
elif self.filter_choice == FI_LIMITS:
indexes = self.keep_within_limits(workspace, src_objects)
else:
raise ValueError("Unknown filter choice: %s" %
self.filter_choice.value)
#
# Create an array that maps label indexes to their new values
# All labels to be deleted have a value in this array of zero
#
new_object_count = len(indexes)
max_label = np.max(src_objects.segmented)
label_indexes = np.zeros((max_label + 1, ), int)
label_indexes[indexes] = np.arange(1, new_object_count + 1)
#
# Loop over both the primary and additional objects
#
object_list = (
[(self.object_name.value, self.target_name.value,
self.wants_outlines.value, self.outlines_name.value)] +
[(x.object_name.value, x.target_name.value, x.wants_outlines.value,
x.outlines_name.value) for x in self.additional_objects])
m = workspace.measurements
for src_name, target_name, wants_outlines, outlines_name in object_list:
src_objects = workspace.get_objects(src_name)
target_labels = src_objects.segmented.copy()
#
# Reindex the labels of the old source image
#
target_labels[target_labels > max_label] = 0
target_labels = label_indexes[target_labels]
#
# Make a new set of objects - retain the old set's unedited
# segmentation for the new and generally try to copy stuff
# from the old to the new.
#
target_objects = cpo.Objects()
target_objects.segmented = target_labels
target_objects.unedited_segmented = src_objects.unedited_segmented
if src_objects.has_parent_image:
target_objects.parent_image = src_objects.parent_image
workspace.object_set.add_objects(target_objects, target_name)
#
# Add measurements for the new objects
add_object_count_measurements(m, target_name, new_object_count)
add_object_location_measurements(m, target_name, target_labels)
#
# Relate the old numbering to the new numbering
#
m.add_measurement(target_name, FF_PARENT % (src_name),
np.array(indexes))
#
# Count the children (0 / 1)
#
child_count = (label_indexes[1:] > 0).astype(int)
m.add_measurement(src_name, FF_CHILDREN_COUNT % target_name,
child_count)
#
# Add an outline if asked to do so
#
if wants_outlines:
outline_image = cpi.Image(
outline(target_labels) > 0,
parent_image=target_objects.parent_image)
workspace.image_set.add(outlines_name, outline_image)
def run(self, workspace):
"""Run the module
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
"""
orig_objects_name = self.object_name.value
filtered_objects_name = self.filtered_objects.value
orig_objects = workspace.object_set.get_objects(orig_objects_name)
assert isinstance(orig_objects, cpo.Objects)
orig_labels = [l for l, c in orig_objects.get_labels()]
if self.wants_image_display:
guide_image = workspace.image_set.get_image(self.image_name.value)
guide_image = guide_image.pixel_data
if np.any(guide_image != np.min(guide_image)):
guide_image = (guide_image - np.min(guide_image)) / (np.max(guide_image) - np.min(guide_image))
else:
guide_image = None
filtered_labels = workspace.interaction_request(
self, orig_labels, guide_image, workspace.measurements.image_set_number)
if filtered_labels is None:
# Ask whoever is listening to stop doing stuff
workspace.cancel_request()
# Have to soldier on until the cancel takes effect...
filtered_labels = orig_labels
#
# Renumber objects consecutively if asked to do so
#
unique_labels = np.unique(np.array(filtered_labels))
unique_labels = unique_labels[unique_labels != 0]
object_count = len(unique_labels)
if self.renumber_choice == R_RENUMBER:
mapping = np.zeros(1 if len(unique_labels) == 0 else np.max(unique_labels) + 1, int)
mapping[unique_labels] = np.arange(1, object_count + 1)
filtered_labels = [mapping[l] for l in filtered_labels]
#
# Make the objects out of the labels
#
filtered_objects = cpo.Objects()
i, j = np.mgrid[0:filtered_labels[0].shape[0],
0:filtered_labels[0].shape[1]]
ijv = np.zeros((0, 3), filtered_labels[0].dtype)
for l in filtered_labels:
ijv = np.vstack((ijv,
np.column_stack((i[l != 0],
j[l != 0],
l[l != 0]))))
filtered_objects.set_ijv(ijv, orig_labels[0].shape)
if orig_objects.has_unedited_segmented():
filtered_objects.unedited_segmented = orig_objects.unedited_segmented
if orig_objects.parent_image is not None:
filtered_objects.parent_image = orig_objects.parent_image
workspace.object_set.add_objects(filtered_objects,
filtered_objects_name)
#
# Add parent/child & other measurements
#
m = workspace.measurements
child_count, parents = orig_objects.relate_children(filtered_objects)
m.add_measurement(filtered_objects_name,
cellprofiler.measurement.FF_PARENT % orig_objects_name,
parents)
m.add_measurement(orig_objects_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % filtered_objects_name,
child_count)
#
# The object count
#
I.add_object_count_measurements(m, filtered_objects_name,
object_count)
#
# The object locations
#
I.add_object_location_measurements_ijv(m, filtered_objects_name, ijv)
workspace.display_data.orig_ijv = orig_objects.ijv
workspace.display_data.filtered_ijv = filtered_objects.ijv
workspace.display_data.shape = orig_labels[0].shape
def run(self, workspace):
statistics = []
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
#
# Hack: if LoadSingleImage is first, no paths are populated
#
if self.file_wants_images(self.file_settings[0]):
m_path = "_".join((C_PATH_NAME,
self.file_settings[0].image_name.value))
else:
m_path = "_".join((C_OBJECTS_PATH_NAME,
self.file_settings[0].objects_name.value))
if m.get_current_image_measurement(m_path) is None:
self.prepare_run(workspace)
image_set = workspace.image_set
for file_setting in self.file_settings:
wants_images = self.file_wants_images(file_setting)
image_name = file_setting.image_name.value if wants_images else \
file_setting.objects_name.value
m_path, m_file, m_md5_digest, m_scaling, m_height, m_width = [
"_".join((c, image_name)) for c in (
C_PATH_NAME if wants_images else C_OBJECTS_PATH_NAME,
C_FILE_NAME if wants_images else C_OBJECTS_FILE_NAME,
C_MD5_DIGEST, C_SCALING, C_HEIGHT, C_WIDTH)]
pathname = m.get_current_image_measurement(m_path)
filename = m.get_current_image_measurement(m_file)
rescale = (wants_images and file_setting.rescale.value)
provider = LoadImagesImageProvider(
image_name, pathname, filename, rescale)
image = provider.provide_image(image_set)
pixel_data = image.pixel_data
if wants_images:
md5 = provider.get_md5_hash(m)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
md5)
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
image_set.providers.append(provider)
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
#
# Add outlines if appropriate
#
if file_setting.wants_outlines:
outlines = centrosome.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_setting.outlines_name.value,
outline_image)
statistics += [(image_name, filename)]
workspace.display_data.col_labels = ("Image name", "File")
workspace.display_data.statistics = statistics
def run(self, workspace):
'''Run the module on an image set'''
object_name = self.object_name.value
remaining_object_name = self.remaining_objects.value
original_objects = workspace.object_set.get_objects(object_name)
if self.mask_choice == MC_IMAGE:
mask = workspace.image_set.get_image(self.masking_image.value,
must_be_binary=True)
mask = mask.pixel_data
else:
masking_objects = workspace.object_set.get_objects(
self.masking_objects.value)
mask = masking_objects.segmented > 0
if self.wants_inverted_mask:
mask = ~mask
#
# Load the labels
#
labels = original_objects.segmented.copy()
nobjects = np.max(labels)
#
# Resize the mask to cover the objects
#
mask, m1 = cpo.size_similarly(labels, mask)
mask[~m1] = False
#
# Apply the mask according to the overlap choice.
#
if nobjects == 0:
pass
elif self.overlap_choice == P_MASK:
labels = labels * mask
else:
pixel_counts = fix(scind.sum(mask, labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_KEEP:
keep = pixel_counts > 0
else:
total_pixels = fix(scind.sum(np.ones(labels.shape), labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_REMOVE:
keep = pixel_counts == total_pixels
elif self.overlap_choice == P_REMOVE_PERCENTAGE:
fraction = self.overlap_fraction.value
keep = pixel_counts / total_pixels >= fraction
else:
raise NotImplementedError("Unknown overlap-handling choice: %s",
self.overlap_choice.value)
keep = np.hstack(([False], keep))
labels[~ keep[labels]] = 0
#
# Renumber the labels matrix if requested
#
if self.retain_or_renumber == R_RENUMBER:
unique_labels = np.unique(labels[labels != 0])
indexer = np.zeros(nobjects + 1, int)
indexer[unique_labels] = np.arange(1, len(unique_labels) + 1)
labels = indexer[labels]
parent_objects = unique_labels
else:
parent_objects = np.arange(1, nobjects + 1)
#
# Add the objects
#
remaining_objects = cpo.Objects()
remaining_objects.segmented = labels
remaining_objects.unedited_segmented = original_objects.unedited_segmented
workspace.object_set.add_objects(remaining_objects,
remaining_object_name)
#
# Add measurements
#
m = workspace.measurements
m.add_measurement(remaining_object_name,
cellprofiler.measurement.FF_PARENT % object_name,
parent_objects)
if np.max(original_objects.segmented) == 0:
child_count = np.array([], int)
else:
child_count = fix(scind.sum(labels, original_objects.segmented,
np.arange(1, nobjects + 1, dtype=np.int32)))
child_count = (child_count > 0).astype(int)
m.add_measurement(object_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % remaining_object_name,
child_count)
if self.retain_or_renumber == R_RETAIN:
remaining_object_count = nobjects
else:
remaining_object_count = len(unique_labels)
I.add_object_count_measurements(m, remaining_object_name,
remaining_object_count)
I.add_object_location_measurements(m, remaining_object_name, labels)
#
# Save the input, mask and output images for display
#
if self.show_window:
workspace.display_data.original_labels = original_objects.segmented
workspace.display_data.final_labels = labels
workspace.display_data.mask = mask
def run(self, workspace):
'''Filter objects for this image set, display results'''
src_objects = workspace.get_objects(self.object_name.value)
if self.mode == MODE_RULES:
indexes = self.keep_by_rules(workspace, src_objects)
elif self.mode == MODE_MEASUREMENTS:
if self.filter_choice in (FI_MINIMAL, FI_MAXIMAL):
indexes = self.keep_one(workspace, src_objects)
if self.filter_choice in (FI_MINIMAL_PER_OBJECT,
FI_MAXIMAL_PER_OBJECT):
indexes = self.keep_per_object(workspace, src_objects)
if self.filter_choice == FI_LIMITS:
indexes = self.keep_within_limits(workspace, src_objects)
elif self.mode == MODE_BORDER:
indexes = self.discard_border_objects(workspace, src_objects)
else:
raise ValueError("Unknown filter choice: %s"%
self.filter_choice.value)
#
# Create an array that maps label indexes to their new values
# All labels to be deleted have a value in this array of zero
#
new_object_count = len(indexes)
max_label = np.max(src_objects.segmented)
label_indexes = np.zeros((max_label+1,),int)
label_indexes[indexes] = np.arange(1,new_object_count+1)
#
# Loop over both the primary and additional objects
#
object_list = ([(self.object_name.value, self.target_name.value,
self.wants_outlines.value, self.outlines_name.value)] +
[(x.object_name.value, x.target_name.value,
x.wants_outlines.value, x.outlines_name.value)
for x in self.additional_objects])
m = workspace.measurements
for src_name, target_name, wants_outlines, outlines_name in object_list:
src_objects = workspace.get_objects(src_name)
target_labels = src_objects.segmented.copy()
#
# Reindex the labels of the old source image
#
target_labels[target_labels > max_label] = 0
target_labels = label_indexes[target_labels]
#
# Make a new set of objects - retain the old set's unedited
# segmentation for the new and generally try to copy stuff
# from the old to the new.
#
target_objects = cpo.Objects()
target_objects.segmented = target_labels
target_objects.unedited_segmented = src_objects.unedited_segmented
if src_objects.has_parent_image:
target_objects.parent_image = src_objects.parent_image
workspace.object_set.add_objects(target_objects, target_name)
#
# Add measurements for the new objects
add_object_count_measurements(m, target_name, new_object_count)
add_object_location_measurements(m, target_name, target_labels)
#
# Relate the old numbering to the new numbering
#
m.add_measurement(target_name,
FF_PARENT%(src_name),
np.array(indexes))
#
# Count the children (0 / 1)
#
child_count = (label_indexes[1:] > 0).astype(int)
m.add_measurement(src_name,
FF_CHILDREN_COUNT % target_name,
child_count)
#
# Add an outline if asked to do so
#
if wants_outlines:
outline_image = cpi.Image(outline(target_labels) > 0,
parent_image = target_objects.parent_image)
workspace.image_set.add(outlines_name, outline_image)
if self.show_window:
src_objects = workspace.get_objects(src_name)
image_names = \
[image for image in
[m.measurement.get_image_name(workspace.pipeline)
for m in self.measurements]
if image is not None
and image in workspace.image_set.get_names()]
if len(image_names) == 0:
# Measurement isn't image-based
if src_objects.has_parent_image:
image = src_objects.parent_image.pixel_data
else:
image = None
else:
image = workspace.image_set.get_image(image_names[0]).pixel_data
workspace.display_data.src_objects_segmented = \
src_objects.segmented
workspace.display_data.image_names = image_names
workspace.display_data.image = image
workspace.display_data.target_objects_segmented = target_objects.segmented
def run(self, workspace):
'''Run the module on an image set'''
object_name = self.object_name.value
remaining_object_name = self.remaining_objects.value
original_objects = workspace.object_set.get_objects(object_name)
if self.mask_choice == MC_IMAGE:
mask = workspace.image_set.get_image(self.masking_image.value,
must_be_binary=True)
mask = mask.pixel_data
else:
masking_objects = workspace.object_set.get_objects(
self.masking_objects.value)
mask = masking_objects.segmented > 0
if self.wants_inverted_mask:
mask = ~mask
#
# Load the labels
#
labels = original_objects.segmented.copy()
nobjects = np.max(labels)
#
# Resize the mask to cover the objects
#
mask, m1 = cpo.size_similarly(labels, mask)
mask[~m1] = False
#
# Apply the mask according to the overlap choice.
#
if nobjects == 0:
pass
elif self.overlap_choice == P_MASK:
labels = labels * mask
else:
pixel_counts = fix(
scind.sum(mask, labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_KEEP:
keep = pixel_counts > 0
else:
total_pixels = fix(
scind.sum(np.ones(labels.shape), labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_REMOVE:
keep = pixel_counts == total_pixels
elif self.overlap_choice == P_REMOVE_PERCENTAGE:
fraction = self.overlap_fraction.value
keep = pixel_counts / total_pixels >= fraction
else:
raise NotImplementedError(
"Unknown overlap-handling choice: %s",
self.overlap_choice.value)
keep = np.hstack(([False], keep))
labels[~keep[labels]] = 0
#
# Renumber the labels matrix if requested
#
if self.retain_or_renumber == R_RENUMBER:
unique_labels = np.unique(labels[labels != 0])
indexer = np.zeros(nobjects + 1, int)
indexer[unique_labels] = np.arange(1, len(unique_labels) + 1)
labels = indexer[labels]
parent_objects = unique_labels
else:
parent_objects = np.arange(1, nobjects + 1)
#
# Add the objects
#
remaining_objects = cpo.Objects()
remaining_objects.segmented = labels
remaining_objects.unedited_segmented = original_objects.unedited_segmented
workspace.object_set.add_objects(remaining_objects,
remaining_object_name)
#
# Add measurements
#
m = workspace.measurements
m.add_measurement(remaining_object_name,
cellprofiler.measurement.FF_PARENT % object_name,
parent_objects)
if np.max(original_objects.segmented) == 0:
child_count = np.array([], int)
else:
child_count = fix(
scind.sum(labels, original_objects.segmented,
np.arange(1, nobjects + 1, dtype=np.int32)))
child_count = (child_count > 0).astype(int)
m.add_measurement(
object_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % remaining_object_name,
child_count)
if self.retain_or_renumber == R_RETAIN:
remaining_object_count = nobjects
else:
remaining_object_count = len(unique_labels)
I.add_object_count_measurements(m, remaining_object_name,
remaining_object_count)
I.add_object_location_measurements(m, remaining_object_name, labels)
#
# Save the input, mask and output images for display
#
if self.show_window:
workspace.display_data.original_labels = original_objects.segmented
workspace.display_data.final_labels = labels
workspace.display_data.mask = mask
def run(self, workspace):
objects = workspace.object_set.get_objects(self.objects_name.value)
assert isinstance(objects, cpo.Objects)
labels = objects.segmented
half_window_size = self.window_size.value
threshold = self.threshold.value
output_labels = objects.segmented.copy()
max_objects = np.max(output_labels)
indices = np.arange(max_objects+1)
# Get object slices
object_slices = morph.fixup_scipy_ndimage_result(scind.measurements.find_objects(output_labels))
# Calculate perimeters
perimeters = morph.calculate_perimeters(output_labels, indices)
# Find the neighbors
neighbors = np.zeros((max_objects+1, max_objects+1), bool) # neighbors[i,j] = True when object j is a neighbor of object i.
lmax = scind.grey_dilation(output_labels, footprint=np.ones((3,3), bool)) # lower pixel values will be replaced by adjacent larger pixel values
lbig = output_labels.copy()
lbig[lbig == 0] = np.iinfo(output_labels.dtype).max # set the background to be large so that it is ignored next
lmin = scind.grey_erosion(lbig, footprint=np.ones((3,3), bool)) # larger pixel values will be replaced by adjacent smaller pixel values
for i in range(1, max_objects+1):
object_bounds = (object_slices[i-1][0],object_slices[i-1][1])
object_map = output_labels[object_bounds] == i # The part of the slice that contains the object
lower_neighbors = np.unique(lmin[object_bounds][object_map])
higher_neighbors = np.unique(lmax[object_bounds][object_map])
for neighbor_list in [lower_neighbors, higher_neighbors]:
for j in range(0, len(neighbor_list)):
neighbors[i,neighbor_list[j]] = True
neighbors[i,i] = False
# Generate window dimensions for each location (necessary for the edges)
dim1_window = np.ones(output_labels.shape, int) * half_window_size
dim2_window = np.ones(output_labels.shape, int) * half_window_size
for i in range(0, half_window_size):
dim1_window[i:output_labels.shape[0]-i,0:output_labels.shape[1]] += 1
dim2_window[0:output_labels.shape[0],i:output_labels.shape[1]-i] += 1
# Loop over all objects
for k in range(1, max_objects+1):
if (perimeters[k] == 0) or not np.max(neighbors[k]): # Has been removed by a merge or has no neighbors
continue
k_bounds_array = object_slices[k-1]
k_dim1_bounds = k_bounds_array[0].indices(output_labels.shape[0])
k_dim2_bounds = k_bounds_array[1].indices(output_labels.shape[1])
# Loop over all neighbors of object k
l = 0
while l < max_objects:
l += 1
if k == l or (perimeters[l] == 0) or not neighbors[k,l]: # Has been removed by a merge or is not a neighbor of object k
continue
l_bounds_array = object_slices[l-1]
l_dim1_bounds = l_bounds_array[0].indices(output_labels.shape[0])
l_dim2_bounds = l_bounds_array[1].indices(output_labels.shape[1])
kl_dim1_min_bound = min(k_dim1_bounds[0], l_dim1_bounds[0])
kl_dim1_max_bound = max(k_dim1_bounds[1], l_dim1_bounds[1])
kl_dim2_min_bound = min(k_dim2_bounds[0], l_dim2_bounds[0])
kl_dim2_max_bound = max(k_dim2_bounds[1], l_dim2_bounds[1])
kl_bounds = (slice(kl_dim1_min_bound, kl_dim1_max_bound), slice(kl_dim2_min_bound, kl_dim2_max_bound))
kl_shape = (kl_dim1_max_bound - kl_dim1_min_bound, kl_dim2_max_bound - kl_dim2_min_bound)
#
# Find the vertices of object pair k, l
#
vertices = np.zeros(kl_shape, bool)
isAdjacent = np.zeros(output_labels.shape, bool)
isBorder = np.zeros(output_labels.shape, bool)
for i in range(-1,2):
ki_min = 0
ki_max = 0
li_min = 0
li_max = 0
if k_dim1_bounds[0] + i < 0:
ki_min = -i
else:
ki_min = k_dim1_bounds[0]
if l_dim1_bounds[0] + i < 0:
li_min = -i
else:
li_min = l_dim1_bounds[0]
if k_dim1_bounds[1] + i > output_labels.shape[0]:
ki_max = output_labels.shape[0] - i
else:
ki_max = k_dim1_bounds[1]
if l_dim1_bounds[1] + i > output_labels.shape[0]:
li_max = output_labels.shape[0] - i
else:
li_max = l_dim1_bounds[1]
ki_slice = slice(ki_min, ki_max)
li_slice = slice(li_min, li_max)
ki_test_slice = slice(ki_min+i, ki_max+i)
li_test_slice = slice(li_min+i, li_max+i)
for j in range(-1,2):
if i == j == 0:
continue
kj_min = 0
kj_max = 0
lj_min = 0
lj_max = 0
if k_dim2_bounds[0] + j < 0:
kj_min = -j
else:
kj_min = k_dim2_bounds[0]
if l_dim2_bounds[0] + j < 0:
lj_min = -j
else:
lj_min = l_dim2_bounds[0]
if k_dim2_bounds[1] + j > output_labels.shape[0]:
kj_max = output_labels.shape[0] - j
else:
kj_max = k_dim2_bounds[1]
if l_dim2_bounds[1] + j > output_labels.shape[0]:
lj_max = output_labels.shape[0] - j
else:
lj_max = l_dim2_bounds[1]
if (kj_min == kj_max or ki_min == ki_max) and (lj_min == lj_max or li_min == li_max):
continue
kj_slice = slice(kj_min, kj_max)
lj_slice = slice(lj_min, lj_max)
kj_test_slice = slice(kj_min+j, kj_max+j)
lj_test_slice = slice(lj_min+j, lj_max+j)
k_bounds = (ki_slice, kj_slice)
l_bounds = (li_slice, lj_slice)
k_test_bounds = (ki_test_slice, kj_test_slice)
l_test_bounds = (li_test_slice, lj_test_slice)
kl_mod_bounds = (slice(min(ki_min, li_min), max(ki_max, li_max)), slice(min(kj_min, lj_min), max(kj_max, lj_max)))
isAdjacentSlice = np.zeros(output_labels.shape, bool)
isAdjacentSlice[k_bounds] = np.logical_and(output_labels[k_bounds] == k,
output_labels[k_test_bounds] == l)
isAdjacentSlice[l_bounds] = np.logical_or(isAdjacentSlice[l_bounds],
np.logical_and(output_labels[l_bounds] == l,
output_labels[l_test_bounds] == k))
isAdjacent[kl_mod_bounds] = np.logical_or(isAdjacent[kl_mod_bounds],
isAdjacentSlice[kl_mod_bounds])
isBorderSlice = np.zeros(output_labels.shape, bool)
isBorderSlice[k_bounds] = np.logical_and(output_labels[k_bounds] == k,
np.logical_and(output_labels[k_test_bounds] != k,
output_labels[k_test_bounds] != l))
isBorderSlice[l_bounds] = np.logical_or(isBorderSlice[l_bounds],
np.logical_and(output_labels[l_bounds] == l,
np.logical_and(output_labels[l_test_bounds] != k,
output_labels[l_test_bounds] != l)))
isBorder[kl_mod_bounds] = np.logical_or(isBorder[kl_mod_bounds],
isBorderSlice[kl_mod_bounds])
vertices = np.logical_and(isAdjacent[kl_bounds], isBorder[kl_bounds])
#
# Calculate the maximum vertex score for the pair
#
'''+1 for every non-I/J labeled pixel in the window
+1 more for every non-I/J labeled pixel adjacent to it
Divided by the score that would result if the objects were flat
e.g. (vertex is starred)
0 0 0 0 0 0 0
0 0 0 0 0 0 0
1 1 0 0 0 0 0
1 1 1 *1* 2 2 0
1 1 1 1 2 2 2
1 1 1 1 2 2 2
1 1 1 2 2 2 2
If it were flat, it would be 7 * 3 = 21, so the divisor 7 * 6 = 42.
In the case that the window is rectangular (such as at the image edge),
we split the difference.
Generalized:
Vertex Score = (Sum of NotIJ) / [A * B - 0.5 * (A + B)]
'''
sum_not_IJ = np.zeros(kl_shape, int)
for i in range(0, kl_shape[0]):
for j in range(0,kl_shape[1]):
if not vertices[i,j]:
continue
window_bounds = (slice(i - half_window_size + kl_dim1_min_bound, i + half_window_size + kl_dim1_min_bound),
slice(j - half_window_size + kl_dim2_min_bound, j + half_window_size + kl_dim2_min_bound))
window_slice = output_labels[window_bounds]
sum_not_IJ[i,j] = count_nonzero(np.logical_and(window_slice != k,
window_slice != l))
# Should be np.count_nonzero, but it doesn't exist in the version that comes with
# CellProfiler 2.0 r11710
vertex_scores = sum_not_IJ / (dim1_window[kl_bounds] * dim2_window[kl_bounds] - 0.5 * (dim1_window[kl_bounds] + dim2_window[kl_bounds]))
merged = np.zeros(kl_shape, int)
merged[output_labels[kl_bounds] == k] = 1
merged[output_labels[kl_bounds] == l] = 1
merged_perimeter = morph.calculate_perimeters(merged, [1])
seam_length = (perimeters[k] + perimeters[l] - merged_perimeter) / 2
min_external_perimeter = min(perimeters[k], perimeters[l]) - seam_length
adjusted_min_external_perimeter = min_external_perimeter / np.pi
seam_fraction = seam_length / (adjusted_min_external_perimeter + seam_length)
max_vertex_score = np.max(vertex_scores)
score = (max_vertex_score + seam_fraction) / 2
#
# Join object pair with score above the threshold
#
if score >= threshold:
output_labels[output_labels == l] = k
# Calculate new perimeters
perimeters[k] = merged_perimeter
perimeters[l] = 0
# Reconfigure neighbors
neighbors[k] = np.logical_or(neighbors[k], neighbors[l])
for x in range(1, max_objects+1): # Is there an array method to do this?
if neighbors[x,l]:
neighbors[x,k] = True
neighbors[0:max_objects,l] = False
# Recalculate bounds
k_dim1_bounds = kl_bounds[0].indices(output_labels.shape[0])
k_dim2_bounds = kl_bounds[1].indices(output_labels.shape[1])
# Reset l to 0 so that we check all the neighbors against this new object
l = 0
else:
pass
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 = cellprofiler.cpmath.outline.outline(output_labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(self.outlines_name.value,
outline_image)
if workspace.frame is not None:
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.output_labels = output_objects.segmented
def run(self, workspace):
import cellprofiler.modules.identify as I
#
# Get the input and output image names. You need to get the .value
# because otherwise you'll get the setting object instead of
# the string name.
#
input_image_name = self.input_image_name.value
prediction_image_name = self.prediction_image_name.value
input_objects_name = self.input_objects.value
output_objects_name = self.output_objects.value
#
# Get the image set. The image set has all of the images in it.
# The assert statement makes sure that it really is an image set,
# but, more importantly, it lets my editor do context-sensitive
# completion for the image set.
#
image_set = workspace.image_set
assert isinstance(image_set, cpi.ImageSet)
#
# Get the input image object. We want a grayscale image here.
# The image set will convert a color image to a grayscale one
# and warn the user.
#
input_image = image_set.get_image(input_image_name,
must_be_grayscale = True).pixel_data
#
# I do something a little odd here and elsewhere. I normalize the
# brightness by ordering the image pixels by brightness. I notice that
# the samples vary in intensity (why?) and EM is a scanning technology
# (right?) so there should be uniform illumination across an image.
#
r = np.random.RandomState()
r.seed(np.sum((input_image * 65535).astype(np.uint16)))
npixels = np.prod(input_image.shape)
shape = input_image.shape
order = np.lexsort([r.uniform(size=npixels),
input_image.flatten()])
input_image = np.zeros(npixels)
input_image[order] = np.arange(npixels).astype(float) / npixels
input_image.shape = shape
prediction_image = image_set.get_image(prediction_image_name,
must_be_grayscale=True).pixel_data
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
input_objects = object_set.get_objects(input_objects_name)
assert isinstance(input_objects, cpo.Objects)
input_labeling = input_objects.segmented
#
# Find the border pixels - 4 connected
#
# There will be some repeats in here - I'm being lazy and I'm not
# removing them. The inaccuracies will be random.
#
touch = ((input_labeling[1:, :] != input_labeling[:-1, :]) &
(input_labeling[1:, :] != 0) &
(input_labeling[:-1, :] != 0))
touchpair = np.argwhere(touch)
touchpair = np.column_stack([
# touchpair[:,0:2] are the left coords
touchpair,
# touchpair[:,2:4] are the right coords
touchpair + np.array([1,0], int)[np.newaxis,:],
# touchpair[:,4] is the identity of the left object
input_labeling[touchpair[:,0], touchpair[:, 1]],
# touchpair[:,5] is the identity of the right object
input_labeling[touchpair[:,0]+1, touchpair[:, 1]]])
TP_I0 = 0
TP_J0 = 1
TP_I1 = 2
TP_J1 = 3
TP_L0 = 4
TP_L1 = 5
touch = ((input_labeling[:, 1:] != input_labeling[:, :-1]) &
(input_labeling[:, 1:] != 0) &
(input_labeling[:, :-1] != 0))
tp2 = np.argwhere(touch)
touchpair = np.vstack([touchpair, np.column_stack([
tp2,
tp2 + np.array([0, 1], int)[np.newaxis,:],
input_labeling[tp2[:, 0], tp2[:, 1]],
input_labeling[tp2[:, 0], tp2[:, 1]+1]])])
if np.any(touch):
#
# Broadcast the touchpair counts and scores into sparse arrays.
# The sparse array convention is to sum duplicates
#
counts = coo_matrix(
(np.ones(touchpair.shape[0], int),
(touchpair[:,TP_L0], touchpair[:,TP_L1])),
shape=[input_objects.count+1]*2).toarray()
scores = coo_matrix(
(prediction_image[touchpair[:, TP_I0],
touchpair[:, TP_J0]] +
prediction_image[touchpair[:, TP_I1],
touchpair[:, TP_J1]],
(touchpair[:,TP_L0], touchpair[:,TP_L1])),
shape=[input_objects.count+1]*2).toarray() / 2.0
scores = scores / counts
to_remove = ((counts > self.min_border.value) &
(scores > 1 - self.min_support.value))
else:
to_remove = np.zeros((0,2), bool)
#
# For all_connected_components, do forward and backward links and
# self-to-self links
#
remove_pairs = np.vstack([
np.argwhere(to_remove),
np.argwhere(to_remove.transpose()),
np.column_stack([np.arange(np.max(input_labeling)+1)] * 2)])
#
# Find small objects and dark objects
#
areas = np.bincount(input_labeling.flatten())
brightness = np.bincount(input_labeling.flatten(), input_image.flatten())
brightness = brightness / areas
#
to_remove = ((areas < self.min_area.value) |
(brightness < self.darkness.value))
to_remove[0] = False
#
# Find the biggest neighbor to all. If no neighbors, label = 0
#
largest = np.zeros(areas.shape[0], np.uint32)
if np.any(touch):
largest[:counts.shape[1]] = \
np.argmax(np.maximum(counts, counts.transpose()), 0)
remove_pairs = np.vstack([
remove_pairs,
np.column_stack([np.arange(len(to_remove))[to_remove],
largest[to_remove]])])
lnumbers = all_connected_components(remove_pairs[:, 0],
remove_pairs[:, 1]).astype(np.uint32)
#
# Renumber.
#
output_labeling = lnumbers[input_labeling]
#
# Fill holes.
#
output_labeling = fill_labeled_holes(output_labeling)
#
# Remove the border pixels. This is for the challenge which requires
# a mask.
#
can_remove = lnumbers[touchpair[:, TP_L0]] != lnumbers[touchpair[:, TP_L1]]
output_labeling[touchpair[can_remove, TP_I0],
touchpair[can_remove, TP_J0]] = 0
output_labeling[touchpair[can_remove, TP_I1],
touchpair[can_remove, TP_J1]] = 0
output_objects = cpo.Objects()
output_objects.segmented = output_labeling
object_set.add_objects(output_objects, output_objects_name)
nobjects = np.max(output_labeling)
I.add_object_count_measurements(workspace.measurements,
output_objects_name,
nobjects)
I.add_object_location_measurements(workspace.measurements,
output_objects_name,
output_labeling, nobjects)
# Make an outline image
outline_image = cellprofiler.cpmath.outline.outline(output_labeling).astype(bool)
out_img = cpi.Image(outline_image,
parent_image = image_set.get_image(input_image_name))
workspace.image_set.add(self.output_outlines_name.value, out_img)
workspace.display_data.input_pixels = input_image
workspace.display_data.input_labels = input_labeling
workspace.display_data.output_labels = output_labeling
workspace.display_data.outlines = outline_image
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):
"""
Run the module
workspace - contains
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
"""
image_name = self.image_name.value
cpimage = workspace.image_set.get_image(image_name)
image = cpimage.pixel_data
mask = cpimage.mask
workspace.display_data.statistics = []
level = int(self.atrous_level.value)
wavelet = self.a_trous(1.0 * image, level + 1)
wlevprod = wavelet[:, :, level - 1] * 3.0
spotthresh = wlevprod.mean() + float(
self.noise_removal_factor.value) * wlevprod.std()
tidx = wlevprod < spotthresh
wlevprod[tidx] = 0
wlevprod = self.circular_average_filter(
wlevprod, int(self.smoothing_filter_size.value))
wlevprod = self.smooth_image(wlevprod, mask)
max_wlevprod = scipy.ndimage.filters.maximum_filter(wlevprod, 3)
maxloc = (wlevprod == max_wlevprod)
twlevprod = max_wlevprod > float(self.final_spot_threshold.value)
maxloc[twlevprod == 0] = 0
labeled_image, object_count = scipy.ndimage.label(
maxloc, np.ones((3, 3), bool))
unedited_labels = labeled_image.copy()
# Filter out objects touching the border or mask
border_excluded_labeled_image = labeled_image.copy()
labeled_image = self.filter_on_border(image, labeled_image)
border_excluded_labeled_image[labeled_image > 0] = 0
# Relabel the image
labeled_image, object_count = relabel(labeled_image)
new_labeled_image, new_object_count = self.limit_object_count(
labeled_image, object_count)
if new_object_count < object_count:
# Add the labels that were filtered out into the border
# image.
border_excluded_mask = ((border_excluded_labeled_image > 0) |
((labeled_image > 0) &
(new_labeled_image == 0)))
border_excluded_labeled_image = scipy.ndimage.label(
border_excluded_mask, np.ones((3, 3), bool))[0]
object_count = new_object_count
labeled_image = new_labeled_image
# Make an outline image
outline_image = cellprofiler.cpmath.outline.outline(labeled_image)
outline_border_excluded_image = cellprofiler.cpmath.outline.outline(
border_excluded_labeled_image)
if self.show_window:
statistics = workspace.display_data.statistics
statistics.append(["# of accepted objects", "%d" % (object_count)])
workspace.display_data.image = image
workspace.display_data.labeled_image = labeled_image
workspace.display_data.border_excluded_labels = border_excluded_labeled_image
# Add image measurements
objname = self.object_name.value
measurements = workspace.measurements
cpmi.add_object_count_measurements(measurements, objname, object_count)
# Add label matrices to the object set
objects = cellprofiler.objects.Objects()
objects.segmented = labeled_image
objects.unedited_segmented = unedited_labels
objects.parent_image = image
workspace.object_set.add_objects(objects, self.object_name.value)
cpmi.add_object_location_measurements(workspace.measurements,
self.object_name.value,
labeled_image)
if self.should_save_outlines.value:
out_img = cpi.Image(outline_image.astype(bool), parent_image=image)
workspace.image_set.add(self.save_outlines.value, out_img)