def get_skeleton_points(self, obj):
'''Get points by skeletonizing the objects and decimating'''
ii = []
jj = []
total_skel = np.zeros(obj.shape, bool)
for labels, indexes in obj.get_labels():
colors = morph.color_labels(labels)
for color in range(1, np.max(colors) + 1):
labels_mask = colors == color
skel = morph.skeletonize(
labels_mask,
ordering = distance_transform_edt(labels_mask) *
poisson_equation(labels_mask))
total_skel = total_skel | skel
n_pts = np.sum(total_skel)
if n_pts == 0:
return np.zeros(0, np.int32), np.zeros(0, np.int32)
i, j = np.where(total_skel)
if n_pts > self.max_points.value:
#
# Decimate the skeleton by finding the branchpoints in the
# skeleton and propagating from those.
#
markers = np.zeros(total_skel.shape, np.int32)
branchpoints = \
morph.branchpoints(total_skel) | morph.endpoints(total_skel)
markers[branchpoints] = np.arange(np.sum(branchpoints))+1
#
# We compute the propagation distance to that point, then impose
# a slightly arbitarary order to get an unambiguous ordering
# which should number the pixels in a skeleton branch monotonically
#
ts_labels, distances = propagate(np.zeros(markers.shape),
markers, total_skel, 1)
order = np.lexsort((j, i, distances[i, j], ts_labels[i, j]))
#
# Get a linear space of self.max_points elements with bounds at
# 0 and len(order)-1 and use that to select the points.
#
order = order[
np.linspace(0, len(order)-1, self.max_points.value).astype(int)]
return i[order], j[order]
return i, j
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 = np.max(labels)
label_range = np.arange(labels_count, dtype=np.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 = cpo.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 = morph.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 = morph.fill_labeled_holes(combined_skel,
~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.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(np.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 = morph.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
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.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(
scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0, ), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scind.sum(branch_points, outside_labels, label_range))
else:
branch_counts = np.zeros((0, ), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels,
label_range))
else:
end_counts = np.zeros((0, ), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_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_neuron_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 = np.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, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run_function(self, function_name, pixel_data, mask, count, scale,
custom_repeats):
'''Apply the function once to the image, returning the result'''
is_binary = pixel_data.dtype.kind == 'b'
strel = morph.strel_disk(scale / 2.0)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SPUR, F_THICKEN,
F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SPUR, F_THICKEN, F_THIN, F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
img_max = np.max(image)
if img_max > 0:
image = image / img_max
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
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 = np.max(labels)
label_range = np.arange(labels_count,dtype=np.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 = cpo.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 = morph.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 = morph.fill_labeled_holes(
combined_skel, ~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.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(np.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 = morph.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
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0,0,0,1,2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.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(scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0,),int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(scind.sum(branch_points, outside_labels,
label_range))
else:
branch_counts = np.zeros((0,),int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels, label_range))
else:
end_counts = np.zeros((0,), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES,
skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_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_neuron_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 = np.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,:] *= .875
branchpoint_image[dilated_labels != 0,:] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image,
parent_image = skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
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 = np.max(labels)
label_range = np.arange(labels_count,dtype=np.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 = cpo.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 = morph.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 = morph.fill_labeled_holes(
combined_skel, ~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.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(np.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 = morph.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
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0,0,0,1,2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.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(scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0,),int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(scind.sum(branch_points, outside_labels,
label_range))
else:
branch_counts = np.zeros((0,),int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels, label_range))
else:
end_counts = np.zeros((0,), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES,
skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_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_neuron_graph(
combined_skel, dlabels,
trunk_mask,
branch_points & ~trunk_mask,
end_points,
intensity_image.pixel_data)
#
# Add an image number column to both and change vertex index
# to vertex number (one-based)
#
image_number = workspace.measurements.image_set_number
vertex_graph = np.rec.fromarrays(
(np.ones(len(vertex_graph)) * image_number,
np.arange(1, len(vertex_graph) + 1),
vertex_graph['i'],
vertex_graph['j'],
vertex_graph['labels'],
vertex_graph['kind']),
names = ("image_number", "vertex_number", "i", "j",
"labels", "kind"))
edge_graph = np.rec.fromarrays(
(np.ones(len(edge_graph)) * image_number,
edge_graph["v1"],
edge_graph["v2"],
edge_graph["length"],
edge_graph["total_intensity"]),
names = ("image_number", "v1", "v2", "length",
"total_intensity"))
path = self.directory.get_absolute_path(m)
edge_file = m.apply_metadata(self.edge_file_name.value)
edge_path = os.path.abspath(os.path.join(path, edge_file))
vertex_file = m.apply_metadata(self.vertex_file_name.value)
vertex_path = os.path.abspath(os.path.join(path, vertex_file))
d = self.get_dictionary(workspace.image_set_list)
for file_path, table, fmt in (
(edge_path, edge_graph, "%d,%d,%d,%d,%.4f"),
(vertex_path, vertex_graph, "%d,%d,%d,%d,%d,%s")):
#
# Delete files first time through / otherwise append
#
if not d.has_key(file_path):
d[file_path] = True
if os.path.exists(file_path):
if workspace.frame is not None:
import wx
if wx.MessageBox(
"%s already exists. Do you want to overwrite it?" %
file_path, "Warning: overwriting file",
style = wx.YES_NO,
parent = workspace.frame) != wx.YES:
raise ValueError("Can't overwrite %s" % file_path)
os.remove(file_path)
fd = open(file_path, 'wt')
header = ','.join(table.dtype.names)
fd.write(header + '\n')
else:
fd = open(file_path, 'at')
np.savetxt(fd, table, fmt)
fd.close()
if workspace.frame is not None:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
#
# Make the display image
#
if workspace.frame is not None or self.wants_branchpoint_image:
branchpoint_image = np.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,:] *= .875
branchpoint_image[dilated_labels != 0,:] += .1
if workspace.frame:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image,
parent_image = skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run_function(self, function, pixel_data, mask):
'''Apply the function once to the image, returning the result'''
count = function.repeat_count
function_name = function.function.value
scale = function.scale.value
custom_repeats = function.custom_repeats.value
is_binary = pixel_data.dtype.kind == 'b'
if function.structuring_element == SE_ARBITRARY:
strel = np.array(function.strel.get_matrix())
elif function.structuring_element == SE_DISK:
strel = morph.strel_disk(scale / 2.0)
elif function.structuring_element == SE_DIAMOND:
strel = morph.strel_diamond(scale / 2.0)
elif function.structuring_element == SE_LINE:
strel = morph.strel_line(scale, function.angle.value)
elif function.structuring_element == SE_OCTAGON:
strel = morph.strel_octagon(scale / 2.0)
elif function.structuring_element == SE_PAIR:
strel = morph.strel_pair(function.x_offset.value,
function.y_offset.value)
elif function.structuring_element == SE_PERIODIC_LINE:
xoff = function.x_offset.value
yoff = function.y_offset.value
n = max(scale / 2.0 / np.sqrt(float(xoff*xoff+yoff*yoff)), 1)
strel = morph.strel_periodicline(
xoff, yoff, n)
elif function.structuring_element == SE_RECTANGLE:
strel = morph.strel_rectangle(
function.width.value, function.height.value)
else:
strel = morph.strel_square(scale)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SKELPE, F_SPUR,
F_THICKEN, F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SKELPE, F_SPUR, F_THICKEN, F_THIN,
F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
if function.rescale_values.value:
image = image / np.max(image)
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SKELPE:
return morph.skeletonize(
pixel_data, mask,
scind.distance_transform_edt(pixel_data) *
poisson_equation(pixel_data))
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
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 = np.max(labels)
label_range = np.arange(labels_count, dtype=np.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 = cpo.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 = morph.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 = morph.fill_labeled_holes(combined_skel,
~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.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(np.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 = morph.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
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.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(
scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0, ), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scind.sum(branch_points, outside_labels, label_range))
else:
branch_counts = np.zeros((0, ), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels,
label_range))
else:
end_counts = np.zeros((0, ), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_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_neuron_graph(
combined_skel, dlabels, trunk_mask,
branch_points & ~trunk_mask, end_points,
intensity_image.pixel_data)
#
# Add an image number column to both and change vertex index
# to vertex number (one-based)
#
image_number = workspace.measurements.image_set_number
vertex_graph = np.rec.fromarrays(
(np.ones(len(vertex_graph)) * image_number,
np.arange(1,
len(vertex_graph) + 1), vertex_graph['i'],
vertex_graph['j'], vertex_graph['labels'],
vertex_graph['kind']),
names=("image_number", "vertex_number", "i", "j", "labels",
"kind"))
edge_graph = np.rec.fromarrays(
(np.ones(len(edge_graph)) * image_number, edge_graph["v1"],
edge_graph["v2"], edge_graph["length"],
edge_graph["total_intensity"]),
names=("image_number", "v1", "v2", "length",
"total_intensity"))
path = self.directory.get_absolute_path(m)
edge_file = m.apply_metadata(self.edge_file_name.value)
edge_path = os.path.abspath(os.path.join(path, edge_file))
vertex_file = m.apply_metadata(self.vertex_file_name.value)
vertex_path = os.path.abspath(os.path.join(path, vertex_file))
d = self.get_dictionary(workspace.image_set_list)
for file_path, table, fmt in ((edge_path, edge_graph,
"%d,%d,%d,%d,%.4f"),
(vertex_path, vertex_graph,
"%d,%d,%d,%d,%d,%s")):
#
# Delete files first time through / otherwise append
#
if not d.has_key(file_path):
d[file_path] = True
if os.path.exists(file_path):
if workspace.frame is not None:
import wx
if wx.MessageBox(
"%s already exists. Do you want to overwrite it?"
% file_path,
"Warning: overwriting file",
style=wx.YES_NO,
parent=workspace.frame) != wx.YES:
raise ValueError("Can't overwrite %s" %
file_path)
os.remove(file_path)
fd = open(file_path, 'wt')
header = ','.join(table.dtype.names)
fd.write(header + '\n')
else:
fd = open(file_path, 'at')
np.savetxt(fd, table, fmt)
fd.close()
if workspace.frame is not None:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
#
# Make the display image
#
if workspace.frame is not None or self.wants_branchpoint_image:
branchpoint_image = np.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, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if workspace.frame:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run_function(self, function_name, pixel_data, mask, count, scale,
custom_repeats):
'''Apply the function once to the image, returning the result'''
is_binary = pixel_data.dtype.kind == 'b'
strel = morph.strel_disk(scale / 2.0)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SPUR, F_THICKEN,
F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SPUR, F_THICKEN, F_THIN, F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
img_max = np.max(image)
if img_max > 0:
image = image / img_max
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
def run_function(self, function, pixel_data, mask):
'''Apply the function once to the image, returning the result'''
count = function.repeat_count
function_name = function.function.value
scale = function.scale.value
custom_repeats = function.custom_repeats.value
is_binary = pixel_data.dtype.kind == 'b'
if function.structuring_element == SE_ARBITRARY:
strel = np.array(function.strel.get_matrix())
elif function.structuring_element == SE_DISK:
strel = morph.strel_disk(scale / 2.0)
elif function.structuring_element == SE_DIAMOND:
strel = morph.strel_diamond(scale / 2.0)
elif function.structuring_element == SE_LINE:
strel = morph.strel_line(scale, function.angle.value)
elif function.structuring_element == SE_OCTAGON:
strel = morph.strel_octagon(scale / 2.0)
elif function.structuring_element == SE_PAIR:
strel = morph.strel_pair(function.x_offset.value,
function.y_offset.value)
elif function.structuring_element == SE_PERIODIC_LINE:
xoff = function.x_offset.value
yoff = function.y_offset.value
n = max(scale / 2.0 / np.sqrt(float(xoff*xoff+yoff*yoff)), 1)
strel = morph.strel_periodicline(
xoff, yoff, n)
elif function.structuring_element == SE_RECTANGLE:
strel = morph.strel_rectangle(
function.width.value, function.height.value)
else:
strel = morph.strel_square(scale)
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL, F_HBREAK, F_LIFE, F_MAJORITY,
F_REMOVE, F_SHRINK, F_SKEL, F_SKELPE, F_SPUR,
F_THICKEN, F_THIN, F_VBREAK)
and not is_binary):
# Apply a very crude threshold to the image for binary algorithms
logger.warning("Warning: converting image to binary for %s\n"%
function_name)
pixel_data = pixel_data != 0
if (function_name in (F_BRANCHPOINTS, F_BRIDGE, F_CLEAN, F_DIAG,
F_CONVEX_HULL, F_DISTANCE, F_ENDPOINTS, F_FILL,
F_FILL_SMALL,
F_HBREAK, F_INVERT, F_LIFE, F_MAJORITY, F_REMOVE,
F_SHRINK,
F_SKEL, F_SKELPE, F_SPUR, F_THICKEN, F_THIN,
F_VBREAK) or
(is_binary and
function_name in (F_CLOSE, F_DILATE, F_ERODE, F_OPEN))):
# All of these have an iterations argument or it makes no
# sense to iterate
if function_name == F_BRANCHPOINTS:
return morph.branchpoints(pixel_data, mask)
elif function_name == F_BRIDGE:
return morph.bridge(pixel_data, mask, count)
elif function_name == F_CLEAN:
return morph.clean(pixel_data, mask, count)
elif function_name == F_CLOSE:
if mask is None:
return scind.binary_closing(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_closing(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_CONVEX_HULL:
if mask is None:
return morph.convex_hull_image(pixel_data)
else:
return morph.convex_hull_image(pixel_data & mask)
elif function_name == F_DIAG:
return morph.diag(pixel_data, mask, count)
elif function_name == F_DILATE:
return scind.binary_dilation(pixel_data,
strel,
iterations=count,
mask=mask)
elif function_name == F_DISTANCE:
image = scind.distance_transform_edt(pixel_data)
if function.rescale_values.value:
image = image / np.max(image)
return image
elif function_name == F_ENDPOINTS:
return morph.endpoints(pixel_data, mask)
elif function_name == F_ERODE:
return scind.binary_erosion(pixel_data, strel,
iterations = count,
mask = mask)
elif function_name == F_FILL:
return morph.fill(pixel_data, mask, count)
elif function_name == F_FILL_SMALL:
def small_fn(area, foreground):
return (not foreground) and (area <= custom_repeats)
return morph.fill_labeled_holes(pixel_data, mask, small_fn)
elif function_name == F_HBREAK:
return morph.hbreak(pixel_data, mask, count)
elif function_name == F_INVERT:
if is_binary:
if mask is None:
return ~ pixel_data
result = pixel_data.copy()
result[mask] = ~result[mask]
return result
elif mask is None:
return 1-pixel_data
else:
result = pixel_data.copy()
result[mask] = 1-result[mask]
return result
elif function_name == F_LIFE:
return morph.life(pixel_data, count)
elif function_name == F_MAJORITY:
return morph.majority(pixel_data, mask, count)
elif function_name == F_OPEN:
if mask is None:
return scind.binary_opening(pixel_data,
strel,
iterations = count)
else:
return (scind.binary_opening(pixel_data & mask,
strel,
iterations = count) |
(pixel_data & ~ mask))
elif function_name == F_REMOVE:
return morph.remove(pixel_data, mask, count)
elif function_name == F_SHRINK:
return morph.binary_shrink(pixel_data, count)
elif function_name == F_SKEL:
return morph.skeletonize(pixel_data, mask)
elif function_name == F_SKELPE:
return morph.skeletonize(
pixel_data, mask,
scind.distance_transform_edt(pixel_data) *
poisson_equation(pixel_data))
elif function_name == F_SPUR:
return morph.spur(pixel_data, mask, count)
elif function_name == F_THICKEN:
return morph.thicken(pixel_data, mask, count)
elif function_name == F_THIN:
return morph.thin(pixel_data, mask, count)
elif function_name == F_VBREAK:
return morph.vbreak(pixel_data, mask)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
else:
for i in range(count):
if function_name == F_BOTHAT:
new_pixel_data = morph.black_tophat(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_CLOSE:
new_pixel_data = morph.closing(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_DILATE:
new_pixel_data = morph.grey_dilation(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_ERODE:
new_pixel_data = morph.grey_erosion(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_OPEN:
new_pixel_data = morph.opening(pixel_data, mask=mask,
footprint=strel)
elif function_name == F_TOPHAT:
new_pixel_data = morph.white_tophat(pixel_data, mask=mask,
footprint=strel)
else:
raise NotImplementedError("Unimplemented morphological function: %s" %
function_name)
if np.all(new_pixel_data == pixel_data):
break;
pixel_data = new_pixel_data
return pixel_data
