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,
I.FF_PARENT%(orig_objects_name),
parents)
m.add_measurement(orig_objects_name,
I.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)
#
# Outlines if we want them
#
if self.wants_outlines:
outlines_name = self.outlines_name.value
outlines = outline(filtered_labels[0]).astype(bool)
outlines_image = cpi.Image(outlines)
workspace.image_set.add(outlines_name, outlines_image)
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
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