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:
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):
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):
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 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)
# 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 = 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_core.measurement.FF_PARENT %
parent_objects_name.value,
parents_of,
)
m.add_measurement(
parent_objects_name.value,
cellprofiler_core.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):
"""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_core.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_core.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):
"""Run the algorithm on one image set"""
#
# Get the image as a binary image
#
image_set = workspace.image_set
image = image_set.get_image(self.image_name.value, must_be_binary=True)
mask = image.pixel_data
if image.has_mask:
mask = mask & image.mask
angle_count = self.angle_count.value
#
# We collect the i,j and angle of pairs of points that
# are 3-d adjacent after erosion.
#
# i - the i coordinate of each point found after erosion
# j - the j coordinate of each point found after erosion
# a - the angle of the structuring element for each point found
#
i = np.zeros(0, int)
j = np.zeros(0, int)
a = np.zeros(0, int)
ig, jg = np.mgrid[0:mask.shape[0], 0:mask.shape[1]]
this_idx = 0
for angle_number in range(angle_count):
angle = float(angle_number) * np.pi / float(angle_count)
strel = self.get_diamond(angle)
erosion = binary_erosion(mask, strel)
#
# Accumulate the count, i, j and angle for all foreground points
# in the erosion
#
this_count = np.sum(erosion)
i = np.hstack((i, ig[erosion]))
j = np.hstack((j, jg[erosion]))
a = np.hstack((a, np.ones(this_count, float) * angle))
#
# Find connections based on distances, not adjacency
#
first, second = self.find_adjacent_by_distance(i, j, a)
#
# Do all connected components.
#
if len(first) > 0:
ij_labels = all_connected_components(first, second) + 1
nlabels = np.max(ij_labels)
label_indexes = np.arange(1, nlabels + 1)
#
# Compute the measurements
#
center_x = fix(mean_of_labels(j, ij_labels, label_indexes))
center_y = fix(mean_of_labels(i, ij_labels, label_indexes))
#
# The angles are wierdly complicated because of the wrap-around.
# You can imagine some horrible cases, like a circular patch of
# "worm" in which all angles are represented or a gentle "U"
# curve.
#
# For now, I'm going to use the following heuristic:
#
# Compute two different "angles". The angles of one go
# from 0 to 180 and the angles of the other go from -90 to 90.
# Take the variance of these from the mean and
# choose the representation with the lowest variance.
#
# An alternative would be to compute the variance at each possible
# dividing point. Another alternative would be to actually trace through
# the connected components - both overkill for such an inconsequential
# measurement I hope.
#
angles = fix(mean_of_labels(a, ij_labels, label_indexes))
vangles = fix(
mean_of_labels((a - angles[ij_labels - 1])**2, ij_labels,
label_indexes))
aa = a.copy()
aa[a > np.pi / 2] -= np.pi
aangles = fix(mean_of_labels(aa, ij_labels, label_indexes))
vaangles = fix(
mean_of_labels((aa - aangles[ij_labels - 1])**2, ij_labels,
label_indexes))
aangles[aangles < 0] += np.pi
angles[vaangles < vangles] = aangles[vaangles < vangles]
#
# Squish the labels to 2-d. The labels for overlaps are arbitrary.
#
labels = np.zeros(mask.shape, int)
labels[i, j] = ij_labels
else:
center_x = np.zeros(0, int)
center_y = np.zeros(0, int)
angles = np.zeros(0)
nlabels = 0
label_indexes = np.zeros(0, int)
labels = np.zeros(mask.shape, int)
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
object_name = self.object_name.value
m.add_measurement(object_name,
cellprofiler_core.measurement.M_LOCATION_CENTER_X,
center_x)
m.add_measurement(object_name,
cellprofiler_core.measurement.M_LOCATION_CENTER_Y,
center_y)
m.add_measurement(object_name, M_ANGLE, angles * 180 / np.pi)
m.add_measurement(object_name,
cellprofiler_core.measurement.M_NUMBER_OBJECT_NUMBER,
label_indexes)
m.add_image_measurement(
cellprofiler_core.measurement.FF_COUNT % object_name, nlabels)
#
# Make the objects
#
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
objects = cpo.Objects()
objects.segmented = labels
objects.parent_image = image
object_set.add_objects(objects, object_name)
if self.show_window:
workspace.display_data.i = center_y
workspace.display_data.j = center_x
workspace.display_data.angle = angles
workspace.display_data.mask = mask
workspace.display_data.labels = labels
workspace.display_data.count = nlabels
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_core.measurement.FF_PARENT % orig_objects_name,
parents,
)
m.add_measurement(
orig_objects_name,
cellprofiler_core.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 algorithm on one image set'''
#
# Get the image as a binary image
#
image_set = workspace.image_set
image = image_set.get_image(self.image_name.value,
must_be_binary = True)
mask = image.pixel_data
if image.has_mask:
mask = mask & image.mask
angle_count = self.angle_count.value
#
# We collect the i,j and angle of pairs of points that
# are 3-d adjacent after erosion.
#
# i - the i coordinate of each point found after erosion
# j - the j coordinate of each point found after erosion
# a - the angle of the structuring element for each point found
#
i = np.zeros(0, int)
j = np.zeros(0, int)
a = np.zeros(0, int)
ig, jg = np.mgrid[0:mask.shape[0], 0:mask.shape[1]]
#this_idx = 0
for angle_number in range(angle_count):
angle = float(angle_number) * np.pi / float(angle_count)
strel = self.get_diamond(angle)
erosion = binary_erosion(mask, strel)
#
# Accumulate the count, i, j and angle for all foreground points
# in the erosion
#
this_count = np.sum(erosion)
i = np.hstack((i, ig[erosion]))
j = np.hstack((j, jg[erosion]))
a = np.hstack((a, np.ones(this_count, float) * angle))
#
# Find connections based on distances, not adjacency
#
first, second = self.find_adjacent_by_distance(i,j,a)
#
# Do all connected components.
#
if len(first) > 0:
ij_labels = all_connected_components(first, second) + 1
nlabels = np.max(ij_labels)
label_indexes = np.arange(1, nlabels + 1)
#
# Compute the measurements
#
center_x = fix(mean_of_labels(j, ij_labels, label_indexes))
center_y = fix(mean_of_labels(i, ij_labels, label_indexes))
#
# The angles are wierdly complicated because of the wrap-around.
# You can imagine some horrible cases, like a circular patch of
# "linear object" in which all angles are represented or a gentle "U"
# curve.
#
# For now, I'm going to use the following heuristic:
#
# Compute two different "angles". The angles of one go
# from 0 to 180 and the angles of the other go from -90 to 90.
# Take the variance of these from the mean and
# choose the representation with the lowest variance.
#
# An alternative would be to compute the variance at each possible
# dividing point. Another alternative would be to actually trace through
# the connected components - both overkill for such an inconsequential
# measurement I hope.
#
angles = fix(mean_of_labels(a, ij_labels, label_indexes))
vangles = fix(mean_of_labels((a - angles[ij_labels-1])**2,
ij_labels, label_indexes))
aa = a.copy()
aa[a > np.pi / 2] -= np.pi
aangles = fix(mean_of_labels(aa, ij_labels, label_indexes))
vaangles = fix(mean_of_labels((aa-aangles[ij_labels-1])**2,
ij_labels, label_indexes))
aangles[aangles < 0] += np.pi
angles[vaangles < vangles] = aangles[vaangles < vangles]
else:
center_x = np.zeros(0, int)
center_y = np.zeros(0, int)
angles = np.zeros(0)
nlabels = 0
label_indexes = np.zeros(0, int)
labels = np.zeros(mask.shape, int)
ifull=[]
jfull=[]
ij_labelsfull=[]
labeldict={}
for label_id in np.unique(label_indexes):
r = np.array(i)*(ij_labels==label_id)
r=r[r!=0]
c = np.array(j)*(ij_labels==label_id)
c=c[c!=0]
rect_strel=self.get_rectangle(angles[label_id-1])
seedmask = np.zeros_like(mask, int)
seedmask[r,c]=label_id
reconstructedlinearobject=skimage.filters.rank.maximum(seedmask,rect_strel)
reconstructedlinearobject=reconstructedlinearobject*mask
if self.overlap_within_angle==False:
itemp,jtemp=np.where(reconstructedlinearobject==label_id)
ifull+=list(itemp)
jfull+=list(jtemp)
ij_labelsfull+=[label_id]*len(itemp)
else:
itemp,jtemp=np.where(reconstructedlinearobject==label_id)
labeldict[label_id]=zip(itemp,jtemp)
if self.overlap_within_angle==True:
angledict={}
for eachangle in range(len(angles)):
angledict[eachangle+1]=[angles[eachangle]]
nmerges=1
while nmerges!=0:
nmerges=sum([self.mergeduplicates(firstlabel,secondlabel,labeldict,angledict) for firstlabel in label_indexes for secondlabel in label_indexes if firstlabel!=secondlabel])
newlabels = sorted(labeldict.keys())
newangles = sorted(angledict.keys())
angles=[]
for eachnewlabel in range(len(newlabels)):
ifull+=[int(eachloc[0]) for eachloc in labeldict[newlabels[eachnewlabel]]]
jfull+=[int(eachloc[1]) for eachloc in labeldict[newlabels[eachnewlabel]]]
ij_labelsfull+=[eachnewlabel+1]*len(labeldict[newlabels[eachnewlabel]])
angles.append(np.mean(angledict[newlabels[eachnewlabel]]))
angles=np.array(angles)
ijv = np.zeros([len(ifull),3],dtype=int)
ijv[:,0]=ifull
ijv[:,1]=jfull
ijv[:,2]=ij_labelsfull
#
# Make the objects
#
object_set = workspace.object_set
object_name = self.object_name.value
assert isinstance(object_set, cpo.ObjectSet)
objects = cpo.Objects()
objects.ijv = ijv
objects.parent_image = image
object_set.add_objects(objects, object_name)
if self.show_window:
workspace.display_data.mask = mask
workspace.display_data.overlapping_labels = [
l for l, idx in objects.get_labels()]
if self.overlap_within_angle==True:
center_x = np.bincount(ijv[:, 2], ijv[:, 1])[objects.indices] / objects.areas
center_y = np.bincount(ijv[:, 2], ijv[:, 0])[objects.indices] / objects.areas
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
m.add_measurement(object_name, "Location_Center_X", center_x)
m.add_measurement(object_name, "Location_Center_Y", center_y)
m.add_measurement(object_name, M_ANGLE, angles * 180 / np.pi)
m.add_measurement(object_name, "Number_Object_Number", label_indexes)
m.add_image_measurement("Count_%s" % object_name, nlabels)
