def run_object(self, image_name, object_name, workspace):
statistics = []
input_image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixels = input_image.pixel_data
if input_image.has_mask:
mask = input_image.mask
else:
mask = None
labels = objects.segmented
try:
pixels = objects.crop_image_similarly(pixels)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixels, m1 = size_similarly(labels, pixels)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = size_similarly(labels, mask)
mask[~m2] = False
if mask is not None:
labels = labels.copy()
labels[~mask] = 0
for name in self.moms.value.split(','):
fn = MOM_TO_F[name]
value = get_object_moment(pixels, fn)
statistics += self.record_measurement(workspace, image_name,
object_name, name, value)
return statistics
def run_one_gabor(self, image_name, object_name, scale, workspace):
objects = workspace.get_objects(object_name)
labels = objects.segmented
object_count = np.max(labels)
if object_count > 0:
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = objects.segmented
if image.has_mask:
mask = image.mask
else:
mask = None
try:
pixel_data = objects.crop_image_similarly(pixel_data)
if mask is not None:
mask = objects.crop_image_similarly(mask)
labels[~mask] = 0
except ValueError:
pixel_data, m1 = size_similarly(labels, pixel_data)
labels[~m1] = 0
if mask is not None:
mask, m2 = size_similarly(labels, mask)
labels[~m2] = 0
labels[~mask] = 0
pixel_data = normalized_per_object(pixel_data, labels)
best_score = np.zeros((object_count,))
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = fix(scind.sum(g.real, labels,
np.arange(object_count, dtype=np.int32)+ 1))
score_i = fix(scind.sum(g.imag, labels,
np.arange(object_count, dtype=np.int32)+ 1))
score = np.sqrt(score_r**2+score_i**2)
best_score = np.maximum(best_score, score)
else:
best_score = np.zeros((0,))
statistics = self.record_measurement(workspace,
image_name,
object_name,
scale,
F_GABOR,
best_score)
return statistics
def run_object(self, image_name, object_name, bins, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = size_similarly(labels, mask)
mask[~m2] = False
if mask is not None:
labels = labels.copy()
labels[~mask] = 0
hist = get_objects_histogram(pixel_data, labels, bins)
if np.all(labels == 0):
for b in range(0, bins):
statistics += self.record_measurement(
workspace, image_name, object_name,
str(bins) + BINS + str(b), np.zeros((0, )))
else:
for b in range(0, bins):
value = hist[:, b]
statistics += self.record_measurement(
workspace, image_name, object_name,
str(bins) + BINS + str(b), value)
return statistics
def run_one(self, image_name, object_name, scale, angle, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_HARALICK:
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, np.zeros((0,)))
else:
scale_i, scale_j = self.get_angle_ij(angle, scale)
for name, value in zip(F_HARALICK, Haralick(pixel_data,
labels,
scale_i,
scale_j,
mask=mask).all()):
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, value)
return statistics
def filter_labels(self, labels_out, objects, workspace):
"""Filter labels out of the output
Filter labels that are not in the segmented input labels. Optionally
filter labels that are touching the edge.
labels_out - the unfiltered output labels
objects - the objects thing, containing both segmented and
small_removed labels
"""
segmented_labels = objects.segmented
max_out = numpy.max(labels_out)
if max_out > 0:
segmented_labels, m1 = size_similarly(labels_out, segmented_labels)
segmented_labels[~m1] = 0
lookup = scipy.ndimage.maximum(
segmented_labels, labels_out, list(range(max_out + 1))
)
lookup = numpy.array(lookup, int)
lookup[0] = 0
segmented_labels_out = lookup[labels_out]
else:
segmented_labels_out = labels_out.copy()
if self.wants_discard_edge:
image = workspace.image_set.get_image(self.image_name.value)
if image.has_mask:
mask_border = image.mask & ~scipy.ndimage.binary_erosion(image.mask)
edge_labels = segmented_labels_out[mask_border]
else:
edge_labels = numpy.hstack(
(
segmented_labels_out[0, :],
segmented_labels_out[-1, :],
segmented_labels_out[:, 0],
segmented_labels_out[:, -1],
)
)
edge_labels = numpy.unique(edge_labels)
#
# Make a lookup table that translates edge labels to zero
# but translates everything else to itself
#
lookup = numpy.arange(max(max_out, numpy.max(segmented_labels)) + 1)
lookup[edge_labels] = 0
#
# Run the segmented labels through this to filter out edge
# labels
segmented_labels_out = lookup[segmented_labels_out]
return segmented_labels_out
def run_one(self, image_name, object_name, scale, workspace):
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
labels = objects.segmented
gray_levels = int(self.gray_levels.value)
unique_labels = numpy.unique(labels)
if unique_labels[0] == 0:
unique_labels = unique_labels[1:]
n_directions = 13 if objects.volumetric else 4
if len(unique_labels) == 0:
for direction in range(n_directions):
for feature_name in F_HARALICK:
statistics += self.record_measurement(
image=image_name,
feature=feature_name,
obj=object_name,
result=numpy.zeros((0, )),
scale="{:d}_{:02d}".format(scale, direction),
workspace=workspace,
gray_levels="{:d}".format(gray_levels),
)
return statistics
# IMG-961: Ensure image and objects have the same shape.
try:
mask = (image.mask if image.has_mask else numpy.ones_like(
image.pixel_data, dtype=bool))
pixel_data = objects.crop_image_similarly(image.pixel_data)
except ValueError:
pixel_data, m1 = size_similarly(labels, image.pixel_data)
if numpy.any(~m1):
if image.has_mask:
mask, m2 = size_similarly(labels, image.mask)
mask[~m2] = False
else:
mask = m1
pixel_data[~mask] = 0
# mahotas.features.haralick bricks itself when provided a dtype larger than uint8 (version 1.4.3)
pixel_data = skimage.util.img_as_ubyte(pixel_data)
if gray_levels != 256:
pixel_data = skimage.exposure.rescale_intensity(
pixel_data, in_range=(0, 255),
out_range=(0, gray_levels - 1)).astype(numpy.uint8)
props = skimage.measure.regionprops(labels, pixel_data)
features = numpy.empty((n_directions, 13, len(unique_labels)))
for index, prop in enumerate(props):
label_data = prop["intensity_image"]
try:
features[:, :,
index] = mahotas.features.haralick(label_data,
distance=scale,
ignore_zeros=True)
except ValueError:
features[:, :, index] = numpy.nan
for direction, direction_features in enumerate(features):
for feature_name, feature in zip(F_HARALICK, direction_features):
statistics += self.record_measurement(
image=image_name,
feature=feature_name,
obj=object_name,
result=feature,
scale="{:d}_{:02d}".format(scale, direction),
workspace=workspace,
gray_levels="{:d}".format(gray_levels),
)
return statistics
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_one_tamura(self, image_name, object_name, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_ALL:
statistics += self.record_measurement(workspace, image_name,
object_name, "", "%s_%s" % (F_TAMURA, name),
np.zeros((0,)))
else:
labs=np.unique(labels)
values=np.zeros([np.max(labs)+1,2])
for l in labs:
if l!=0:
px = pixel_data
px[np.where(labels != l)] = 0.0
values[l,0]=self.contrast(px)
values[l,1]=self.directionality(px)
statistics += self.record_measurement(
workspace, image_name, object_name,
"-", "%s_%s" % (F_TAMURA, F_2), values[:,0])
statistics += self.record_measurement(
workspace, image_name, object_name,
"-", "%s_%s" % (F_TAMURA, F_3), values[:,1])
coars = np.zeros([np.max(labs)+1])
coars_hist=np.zeros([np.max(labs)+1,HIST_COARS_BINS])
for l in labs:
if l!=0:
px = pixel_data
px[np.where(labels != l)] = 0.0
coars[l], coars_hist[l,:] = self.coarseness(px)
statistics += self.record_measurement(
workspace, image_name, object_name,
"-", "%s_%s" % (F_TAMURA, F_1), coars)
for b in range(0,HIST_COARS_BINS):
value = coars_hist[1:,b]
name = "CoarsenessHist_%dBinsHist_Bin%d" % (HIST_COARS_BINS, b)
statistics += self.record_measurement(
workspace, image_name, object_name,
"-", "%s_%s" % (F_TAMURA, name) , value)
return statistics
def run(self, workspace):
"""Run the module on the image set"""
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = numpy.max(labels)
label_range = numpy.arange(labels_count, dtype=numpy.int32) + 1
skeleton_image = workspace.image_set.get_image(
skeleton_name, must_be_binary=True
)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = centrosome.cpmorphology.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels, footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~is_object) and (area <= self.maximum_hole_size.value)
combined_skel = centrosome.cpmorphology.fill_labeled_holes(
combined_skel, ~seed_center, size_fn
)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = centrosome.cpmorphology.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(
numpy.zeros(labels.shape), dilated_labels, combined_skel, 1
)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = centrosome.cpmorphology.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (
combined_skel[:-1, :-1]
& combined_skel[1:, :-1]
& combined_skel[:-1, 1:]
& combined_skel[1, 1]
)
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# emanating from a point other than the line it might be on).
#
branching_counts = centrosome.cpmorphology.branchings(combined_skel)
branching_counts = numpy.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scipy.ndimage.binary_dilation(
outside_skel, centrosome.cpmorphology.eight_connect
)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = centrosome.cpmorphology.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(
scipy.ndimage.sum(branching_counts, nearby_labels, label_range)
).astype(int)
else:
trunk_counts = numpy.zeros((0,), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scipy.ndimage.sum(branch_points, outside_labels, label_range)
)
else:
branch_counts = numpy.zeros((0,), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scipy.ndimage.sum(end_points, outside_labels, label_range))
else:
end_counts = numpy.zeros((0,), int)
#
# Calculate the distances
#
total_distance = centrosome.cpmorphology.skeleton_length(
dlabels * outside_skel, label_range
)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, Measurements)
feature = "_".join((C_OBJSKELETON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_OBJSKELETON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_OBJSKELETON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
feature = "_".join((C_OBJSKELETON, F_TOTAL_OBJSKELETON_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# Collect the graph information
#
if self.wants_objskeleton_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value
)
edge_graph, vertex_graph = self.make_objskeleton_graph(
combined_skel,
dlabels,
trunk_mask,
branch_points & ~trunk_mask,
end_points,
intensity_image.pixel_data,
)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(m, m.image_number)
workspace.interaction_request(
self,
m.image_number,
edge_path,
edge_graph,
vertex_path,
vertex_graph,
headless_ok=True,
)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = numpy.zeros((skeleton.shape[0], skeleton.shape[1], 3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= 0.875
branchpoint_image[dilated_labels != 0, :] += 0.1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
