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 = numpy.zeros(pixel_data.shape[:2], int)
objects = Objects()
objects.segmented = labels
workspace.object_set.add_objects(objects, objects_name)
##################
#
# Add measurements
#
m = workspace.measurements
#
# The object count
#
object_count = numpy.max(labels)
add_object_count_measurements(m, objects_name, object_count)
#
# The object locations
#
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 = 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:
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):
input_objects = workspace.object_set.get_objects(
self.object_name.value)
output_objects = cellprofiler_core.object.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
# If we're shrinking objects we treat objects from the final segmentation as truth when generating
# the unedited segmentations. This prevents edited/hole-filled objects from ending up with slightly
# different centers (which would impact other modules).
if input_objects.has_small_removed_segmented and self.operation not in (
O_EXPAND,
O_EXPAND_INF,
O_DIVIDE,
):
shrunk_objects = self.do_labels(
input_objects.small_removed_segmented)
output_objects.small_removed_segmented = numpy.where(
input_objects.segmented > 0, output_objects.segmented,
shrunk_objects)
if input_objects.has_unedited_segmented and self.operation not in (
O_EXPAND,
O_EXPAND_INF,
O_DIVIDE,
):
shrunk_objects = self.do_labels(input_objects.unedited_segmented)
output_objects.unedited_segmented = numpy.where(
input_objects.segmented > 0, output_objects.segmented,
shrunk_objects)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(
workspace.measurements,
self.output_object_name.value,
numpy.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):
for object_name in (self.objects_x.value, self.objects_y.value):
if object_name not in workspace.object_set.object_names:
raise ValueError(
"The %s objects are missing from the pipeline." %
object_name)
objects_x = workspace.object_set.get_objects(self.objects_x.value)
objects_y = workspace.object_set.get_objects(self.objects_y.value)
dimensions = objects_x.dimensions
assert (objects_x.shape == objects_y.shape
), "Objects sets must have the same dimensions"
labels_x = objects_x.segmented.copy().astype("uint16")
labels_y = objects_y.segmented.copy().astype("uint16")
output = combineobjects(self.merge_method.value, labels_x, labels_y,
dimensions)
output_labels = skimage.morphology.label(output)
output_objects = Objects()
output_objects.segmented = output_labels
workspace.object_set.add_objects(output_objects,
self.output_object.value)
m = workspace.measurements
object_count = numpy.max(output_labels)
add_object_count_measurements(m, self.output_object.value,
object_count)
add_object_location_measurements(m, self.output_object.value,
output_labels)
if self.show_window:
workspace.display_data.input_object_x_name = self.objects_x.value
workspace.display_data.input_object_x = objects_x.segmented
workspace.display_data.input_object_y_name = self.objects_y.value
workspace.display_data.input_object_y = objects_y.segmented
workspace.display_data.output_object_name = self.output_object.value
workspace.display_data.output_object = output_objects.segmented
workspace.display_data.dimensions = dimensions
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, _ = 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, _ = size_similarly(secondary_labels,
tertiary_image)
# If size/shape differences were too extreme, raise an error.
if primary_labels.shape != secondary_labels.shape:
raise ValueError(
"This module requires that the object sets have matching widths and matching heights.\n"
"The %s and %s objects do not (%s vs %s).\n"
"If they are paired correctly you may want to use the ResizeObjects module "
"to make them the same size." % (
self.secondary_objects_name,
self.primary_objects_name,
secondary_labels.shape,
primary_labels.shape,
))
#
# 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 = numpy.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 = 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 = numpy.zeros(mask.shape, int)
child_count_of_primary[mask] = child_count_of_secondary[
secondary_of_primary[mask] - 1]
primary_parents = numpy.zeros(secondary_parents.shape,
secondary_parents.dtype)
primary_of_secondary = numpy.zeros(secondary_objects.count + 1,
int)
primary_of_secondary[secondary_of_primary] = numpy.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,
FF_PARENT % parent_objects_name.value,
parents_of,
)
m.add_measurement(
parent_objects_name.value,
FF_CHILDREN_COUNT % self.subregion_objects_name.value,
child_count,
)
mask = parents_of != 0
image_number = numpy.ones(numpy.sum(mask),
int) * m.image_set_number
child_object_number = numpy.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
#
add_object_count_measurements(workspace.measurements,
self.subregion_objects_name.value,
object_count)
#
# The object locations
#
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):
"""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 = numpy.max(labels)
#
# Resize the mask to cover the objects
#
mask, m1 = 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 = fixup_scipy_ndimage_result(
scipy.ndimage.sum(
mask, labels, numpy.arange(1, nobjects + 1, dtype=numpy.int32)
)
)
if self.overlap_choice == P_KEEP:
keep = pixel_counts > 0
else:
total_pixels = fixup_scipy_ndimage_result(
scipy.ndimage.sum(
numpy.ones(labels.shape),
labels,
numpy.arange(1, nobjects + 1, dtype=numpy.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 = numpy.hstack(([False], keep))
labels[~keep[labels]] = 0
#
# Renumber the labels matrix if requested
#
if self.retain_or_renumber == R_RENUMBER:
unique_labels = numpy.unique(labels[labels != 0])
indexer = numpy.zeros(nobjects + 1, int)
indexer[unique_labels] = numpy.arange(1, len(unique_labels) + 1)
labels = indexer[labels]
parent_objects = unique_labels
else:
parent_objects = numpy.arange(1, nobjects + 1)
#
# Add the objects
#
remaining_objects = 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, FF_PARENT % object_name, parent_objects,
)
if numpy.max(original_objects.segmented) == 0:
child_count = numpy.array([], int)
else:
child_count = fixup_scipy_ndimage_result(
scipy.ndimage.sum(
labels,
original_objects.segmented,
numpy.arange(1, nobjects + 1, dtype=numpy.int32),
)
)
child_count = (child_count > 0).astype(int)
m.add_measurement(
object_name, 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)
add_object_count_measurements(m, remaining_object_name, remaining_object_count)
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_name = self.objects_name.value
objects = workspace.object_set.get_objects(objects_name)
assert isinstance(objects, Objects)
labels = objects.segmented
if self.relabel_option == OPTION_SPLIT:
output_labels, count = scipy.ndimage.label(
labels > 0, numpy.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 = scipy.ndimage.distance_transform_edt(~mask)
mask = d < self.distance_threshold.value / 2 + 1
output_labels, count = scipy.ndimage.label(
mask, numpy.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(numpy.uint32)
output_labels[labels > 0] = parents_of[labels[labels > 0] - 1]
if self.merging_method == UM_CONVEX_HULL:
ch_pts, n_pts = centrosome.cpmorphology.convex_hull(
output_labels)
ijv = centrosome.cpmorphology.fill_convex_hulls(
ch_pts, n_pts)
output_labels[ijv[:, 0], ijv[:, 1]] = ijv[:, 2]
output_objects = 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,
numpy.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
