def filtered_labels(self, workspace, gridding):
'''Filter labels by proximity to edges of grid'''
#
# A label might slightly graze a grid other than its own or
# a label might be something small in a corner of the grid.
# This function filters out those parts of the guide labels matrix
#
assert isinstance(gridding, cpg.CPGridInfo)
guide_labels = self.get_guide_labels(workspace)
labels = self.fill_grid(workspace, gridding)
centers = np.zeros((2, np.max(guide_labels) + 1))
centers[:, 1:] = centers_of_labels(guide_labels)
bad_centers = ((~np.isfinite(centers[0, :])) |
(~np.isfinite(centers[1, :])) |
(centers[0, :] >= labels.shape[0]) |
(centers[1, :] >= labels.shape[1]))
centers = np.round(centers).astype(int)
masked_labels = labels.copy()
x_border = int(np.ceil(gridding.x_spacing / 10))
y_border = int(np.ceil(gridding.y_spacing / 10))
#
# erase anything that's not like what's next to it
#
ymask = labels[y_border:, :] != labels[:-y_border, :]
masked_labels[y_border:, :][ymask] = 0
masked_labels[:-y_border, :][ymask] = 0
xmask = labels[:, x_border:] != labels[:, :-x_border]
masked_labels[:, x_border:][xmask] = 0
masked_labels[:, :-x_border][xmask] = 0
#
# Find out the grid that each center falls into. If a center falls
# into the border region, it will get a grid number of 0 and be
# erased. The guide objects may fall below or to the right of the
# grid or there may be gaps in numbering, so we set the center label
# of bad centers to 0.
#
centers[:, bad_centers] = 0
lcenters = masked_labels[centers[0, :], centers[1, :]]
lcenters[bad_centers] = 0
#
# Use the guide labels to look up the corresponding center for
# each guide object pixel. Mask out guide labels that don't match
# centers.
#
mask = np.zeros(guide_labels.shape, bool)
ii_labels = np.index_exp[0:labels.shape[0], 0:labels.shape[1]]
mask[ii_labels] = lcenters[guide_labels[ii_labels]] != labels
mask[guide_labels == 0] = True
mask[lcenters[guide_labels] == 0] = True
filtered_guide_labels = guide_labels.copy()
filtered_guide_labels[mask] = 0
return filtered_guide_labels
def filtered_labels(self, workspace, gridding):
'''Filter labels by proximity to edges of grid'''
#
# A label might slightly graze a grid other than its own or
# a label might be something small in a corner of the grid.
# This function filters out those parts of the guide labels matrix
#
assert isinstance(gridding, cpg.CPGridInfo)
guide_labels = self.get_guide_labels(workspace)
labels = self.fill_grid(workspace,gridding)
centers = np.zeros((2,np.max(guide_labels)+1))
centers[:,1:] = centers_of_labels(guide_labels)
bad_centers = ((~ np.isfinite(centers[0,:])) |
(~ np.isfinite(centers[1,:])) |
(centers[0,:] >= labels.shape[0]) |
(centers[1,:] >= labels.shape[1]))
centers = np.round(centers).astype(int)
masked_labels = labels.copy()
x_border = int(np.ceil(gridding.x_spacing /10))
y_border = int(np.ceil(gridding.y_spacing / 10))
#
# erase anything that's not like what's next to it
#
ymask = labels[y_border:,:] != labels[:-y_border,:]
masked_labels[y_border:,:][ymask] = 0
masked_labels[:-y_border,:][ymask] = 0
xmask = labels[:,x_border:] != labels[:,:-x_border]
masked_labels[:,x_border:][xmask] = 0
masked_labels[:,:-x_border][xmask] = 0
#
# Find out the grid that each center falls into. If a center falls
# into the border region, it will get a grid number of 0 and be
# erased. The guide objects may fall below or to the right of the
# grid or there may be gaps in numbering, so we set the center label
# of bad centers to 0.
#
centers[:,bad_centers] = 0
lcenters = masked_labels[centers[0,:],centers[1,:]]
lcenters[bad_centers] = 0
#
# Use the guide labels to look up the corresponding center for
# each guide object pixel. Mask out guide labels that don't match
# centers.
#
mask = np.zeros(guide_labels.shape, bool)
ii_labels = np.index_exp[0:labels.shape[0],0:labels.shape[1]]
mask[ii_labels] = lcenters[guide_labels[ii_labels]] != labels
mask[guide_labels == 0] = True
mask[lcenters[guide_labels] == 0] = True
filtered_guide_labels = guide_labels.copy()
filtered_guide_labels[mask] = 0
return filtered_guide_labels
def calculate_centroid_distances(self, workspace, parent_name):
'''Calculate the centroid-centroid distance between parent & child'''
meas = workspace.measurements
assert isinstance(meas,cpmeas.Measurements)
sub_object_name = self.sub_object_name.value
parents = workspace.object_set.get_objects(parent_name)
children = workspace.object_set.get_objects(sub_object_name)
parents_of = self.get_parents_of(workspace, parent_name)
pcenters = centers_of_labels(parents.segmented).transpose()
ccenters = centers_of_labels(children.segmented).transpose()
if pcenters.shape[0] == 0 or ccenters.shape[0] == 0:
dist = np.array([np.NaN] * len(parents_of))
else:
#
# Make indexing of parents_of be same as pcenters
#
parents_of = parents_of - 1
mask = (parents_of != -1) | (parents_of > pcenters.shape[0])
dist = np.array([np.NaN] * ccenters.shape[0])
dist[mask] = np.sqrt(np.sum((ccenters[mask,:] -
pcenters[parents_of[mask],:])**2,1))
meas.add_measurement(sub_object_name, FF_CENTROID % parent_name, dist)
def calculate_centroid_distances(self, workspace, parent_name):
'''Calculate the centroid-centroid distance between parent & child'''
meas = workspace.measurements
assert isinstance(meas, cpmeas.Measurements)
sub_object_name = self.sub_object_name.value
parents = workspace.object_set.get_objects(parent_name)
children = workspace.object_set.get_objects(sub_object_name)
parents_of = self.get_parents_of(workspace, parent_name)
pcenters = centers_of_labels(parents.segmented).transpose()
ccenters = centers_of_labels(children.segmented).transpose()
if pcenters.shape[0] == 0 or ccenters.shape[0] == 0:
dist = np.array([np.NaN] * len(parents_of))
else:
#
# Make indexing of parents_of be same as pcenters
#
parents_of = parents_of - 1
mask = (parents_of != -1) | (parents_of > pcenters.shape[0])
dist = np.array([np.NaN] * ccenters.shape[0])
dist[mask] = np.sqrt(
np.sum((ccenters[mask, :] - pcenters[parents_of[mask], :])**2,
1))
meas.add_measurement(sub_object_name, FF_CENTROID % parent_name, dist)
def run_automatic(self, workspace):
"""Automatically define a grid based on objects
Returns a CPGridInfo object
"""
objects = workspace.object_set.get_objects(self.object_name.value)
centroids = centers_of_labels(objects.segmented)
try:
if centroids.shape[1] < 2:
#
# Failed if too few objects
#
raise RuntimeError("%s has too few grid cells" % self.object_name.value)
#
# Artificially swap these to match the user's orientation
#
first_row, second_row = (1, self.grid_rows.value)
if self.origin in (NUM_BOTTOM_LEFT, NUM_BOTTOM_RIGHT):
first_row, second_row = (second_row, first_row)
first_column, second_column = (1, self.grid_columns.value)
if self.origin in (NUM_TOP_RIGHT, NUM_BOTTOM_RIGHT):
first_column, second_column = (second_column, first_column)
first_x = np.min(centroids[1, :])
first_y = np.min(centroids[0, :])
second_x = np.max(centroids[1, :])
second_y = np.max(centroids[0, :])
result = self.build_grid_info(
first_x,
first_y,
first_row,
first_column,
second_x,
second_y,
second_row,
second_column,
objects.segmented.shape,
)
except Exception:
if self.failed_grid_choice != FAIL_NO:
result = self.get_good_gridding(workspace)
if result is None:
raise RuntimeError(
"%s has too few grid cells and there is no previous successful grid" % self.object_name.value
)
raise
return result
def run_natural_circle(self, workspace, gridding):
'''Return a labels matrix composed of circles found from objects'''
#
# Find the centroid of any guide label in a grid
#
guide_label = self.filtered_labels(workspace, gridding)
labels = self.fill_grid(workspace,gridding)
labels[guide_label[0:labels.shape[0],0:labels.shape[1]] == 0] = 0
centers_i, centers_j = centers_of_labels(labels)
nmissing = np.max(gridding.spot_table) - len(centers_i)
if nmissing > 0:
centers_i = np.hstack((centers_i, [np.NaN] * nmissing))
centers_j = np.hstack((centers_j, [np.NaN] * nmissing))
#
# Broadcast these using the spot table
#
centers_i = centers_i[gridding.spot_table-1]
centers_j = centers_j[gridding.spot_table-1]
return self.run_circle(workspace, gridding, centers_i, centers_j)
def run_natural_circle(self, workspace, gridding):
'''Return a labels matrix composed of circles found from objects'''
#
# Find the centroid of any guide label in a grid
#
guide_label = self.filtered_labels(workspace, gridding)
labels = self.fill_grid(workspace, gridding)
labels[guide_label[0:labels.shape[0], 0:labels.shape[1]] == 0] = 0
centers_i, centers_j = centers_of_labels(labels)
nmissing = np.max(gridding.spot_table) - len(centers_i)
if nmissing > 0:
centers_i = np.hstack((centers_i, [np.NaN] * nmissing))
centers_j = np.hstack((centers_j, [np.NaN] * nmissing))
#
# Broadcast these using the spot table
#
centers_i = centers_i[gridding.spot_table - 1]
centers_j = centers_j[gridding.spot_table - 1]
return self.run_circle(workspace, gridding, centers_i, centers_j)
def make_workspace(self, measurement, labels=None, image=None):
object_set = cpo.ObjectSet()
module = D.DisplayDataOnImage()
module.module_num = 1
module.image_name.value = INPUT_IMAGE_NAME
module.display_image.value = OUTPUT_IMAGE_NAME
module.objects_name.value = OBJECTS_NAME
m = cpmeas.Measurements()
if labels is None:
module.objects_or_image.value = D.OI_IMAGE
m.add_image_measurement(MEASUREMENT_NAME, measurement)
if image is None:
image = np.zeros((50, 120))
else:
module.objects_or_image.value = D.OI_OBJECTS
o = cpo.Objects()
o.segmented = labels
object_set.add_objects(o, OBJECTS_NAME)
m.add_measurement(OBJECTS_NAME, MEASUREMENT_NAME,
np.array(measurement))
y, x = centers_of_labels(labels)
m.add_measurement(OBJECTS_NAME, "Location_Center_X", x)
m.add_measurement(OBJECTS_NAME, "Location_Center_Y", y)
if image is None:
image = np.zeros(labels.shape)
module.measurement.value = MEASUREMENT_NAME
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(INPUT_IMAGE_NAME, cpi.Image(image))
workspace = cpw.Workspace(pipeline, module, image_set, object_set, m,
image_set_list)
return workspace, module
def run_automatic(self, workspace):
'''Automatically define a grid based on objects
Returns a CPGridInfo object
'''
objects = workspace.object_set.get_objects(self.object_name.value)
centroids = centers_of_labels(objects.segmented)
try:
if centroids.shape[1] < 2:
#
# Failed if too few objects
#
raise RuntimeError("%s has too few grid cells" %
self.object_name.value)
#
# Artificially swap these to match the user's orientation
#
first_row, second_row = (1, self.grid_rows.value)
if self.origin in (NUM_BOTTOM_LEFT, NUM_BOTTOM_RIGHT):
first_row, second_row = (second_row, first_row)
first_column, second_column = (1, self.grid_columns.value)
if self.origin in (NUM_TOP_RIGHT, NUM_BOTTOM_RIGHT):
first_column, second_column = (second_column, first_column)
first_x = np.min(centroids[1, :])
first_y = np.min(centroids[0, :])
second_x = np.max(centroids[1, :])
second_y = np.max(centroids[0, :])
result = self.build_grid_info(first_x, first_y, first_row,
first_column, second_x, second_y,
second_row, second_column,
objects.segmented.shape)
except Exception:
if self.failed_grid_choice != FAIL_NO:
result = self.get_good_gridding(workspace)
if result is None:
raise RuntimeError(
"%s has too few grid cells and there is no previous successful grid"
% self.object_name.value)
raise
return result
def make_workspace(self, measurement, labels = None, image = None):
object_set = cpo.ObjectSet()
module = D.DisplayDataOnImage()
module.module_num = 1
module.image_name.value = INPUT_IMAGE_NAME
module.display_image.value = OUTPUT_IMAGE_NAME
module.objects_name.value = OBJECTS_NAME
m = cpmeas.Measurements()
if labels is None:
module.objects_or_image.value = D.OI_IMAGE
m.add_image_measurement(MEASUREMENT_NAME, measurement)
if image is None:
image = np.zeros((50,120))
else:
module.objects_or_image.value = D.OI_OBJECTS
o = cpo.Objects()
o.segmented = labels
object_set.add_objects(o, OBJECTS_NAME)
m.add_measurement(OBJECTS_NAME, MEASUREMENT_NAME, np.array(measurement))
y,x = centers_of_labels(labels)
m.add_measurement(OBJECTS_NAME, "Location_Center_X",x)
m.add_measurement(OBJECTS_NAME, "Location_Center_Y",y)
if image is None:
image = np.zeros(labels.shape)
module.measurement.value = MEASUREMENT_NAME
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(INPUT_IMAGE_NAME,cpi.Image(image))
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
m, image_set_list)
return workspace, module
def do_measurements(self, workspace, image_name, object_name,
center_object_name, center_choice,
bin_count_settings, dd):
'''Perform the radial measurements on the image set
workspace - workspace that holds images / objects
image_name - make measurements on this image
object_name - make measurements on these objects
center_object_name - use the centers of these related objects as
the centers for radial measurements. None to use the
objects themselves.
center_choice - the user's center choice for this object:
C_SELF, C_CENTERS_OF_OBJECTS or C_EDGES_OF_OBJECTS.
bin_count_settings - the bin count settings group
d - a dictionary for saving reusable partial results
returns one statistics tuple per ring.
'''
assert isinstance(workspace, cpw.Workspace)
assert isinstance(workspace.object_set, cpo.ObjectSet)
bin_count = bin_count_settings.bin_count.value
wants_scaled = bin_count_settings.wants_scaled.value
maximum_radius = bin_count_settings.maximum_radius.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
labels, pixel_data = cpo.crop_labels_and_image(objects.segmented,
image.pixel_data)
nobjects = np.max(objects.segmented)
measurements = workspace.measurements
assert isinstance(measurements, cpmeas.Measurements)
if nobjects == 0:
for bin in range(1, bin_count+1):
for feature in (F_FRAC_AT_D, F_MEAN_FRAC, F_RADIAL_CV):
feature_name = (
(feature + FF_GENERIC) % (image_name, bin, bin_count))
measurements.add_measurement(
object_name, "_".join([M_CATEGORY, feature_name]),
np.zeros(0))
if not wants_scaled:
measurement_name = "_".join([M_CATEGORY, feature,
image_name, FF_OVERFLOW])
measurements.add_measurement(
object_name, measurement_name, np.zeros(0))
return [(image_name, object_name, "no objects","-","-","-","-")]
name = (object_name if center_object_name is None
else "%s_%s"%(object_name, center_object_name))
if dd.has_key(name):
normalized_distance, i_center, j_center, good_mask = dd[name]
else:
d_to_edge = distance_to_edge(labels)
if center_object_name is not None:
#
# Use the center of the centering objects to assign a center
# to each labeled pixel using propagation
#
center_objects=workspace.object_set.get_objects(center_object_name)
center_labels, cmask = cpo.size_similarly(
labels, center_objects.segmented)
pixel_counts = fix(scind.sum(
np.ones(center_labels.shape),
center_labels,
np.arange(1, np.max(center_labels)+1,dtype=np.int32)))
good = pixel_counts > 0
i,j = (centers_of_labels(center_labels) + .5).astype(int)
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i)+1)[good]
center_labels = np.zeros(center_labels.shape, int)
center_labels[ig,jg] = lg
cl,d_from_center = propagate(np.zeros(center_labels.shape),
center_labels,
labels != 0, 1)
#
# Erase the centers that fall outside of labels
#
cl[labels == 0] = 0
#
# If objects are hollow or crescent-shaped, there may be
# objects without center labels. As a backup, find the
# center that is the closest to the center of mass.
#
missing_mask = (labels != 0) & (cl == 0)
missing_labels = np.unique(labels[missing_mask])
if len(missing_labels):
all_centers = centers_of_labels(labels)
missing_i_centers, missing_j_centers = \
all_centers[:, missing_labels-1]
di = missing_i_centers[:, np.newaxis] - ig[np.newaxis, :]
dj = missing_j_centers[:, np.newaxis] - jg[np.newaxis, :]
missing_best = lg[np.lexsort((di*di + dj*dj, ))[:, 0]]
best = np.zeros(np.max(labels) + 1, int)
best[missing_labels] = missing_best
cl[missing_mask] = best[labels[missing_mask]]
#
# Now compute the crow-flies distance to the centers
# of these pixels from whatever center was assigned to
# the object.
#
iii, jjj = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
di = iii[missing_mask] - i[cl[missing_mask] - 1]
dj = jjj[missing_mask] - j[cl[missing_mask] - 1]
d_from_center[missing_mask] = np.sqrt(di*di + dj*dj)
else:
# Find the point in each object farthest away from the edge.
# This does better than the centroid:
# * The center is within the object
# * The center tends to be an interesting point, like the
# center of the nucleus or the center of one or the other
# of two touching cells.
#
i,j = maximum_position_of_labels(d_to_edge, labels, objects.indices)
center_labels = np.zeros(labels.shape, int)
center_labels[i,j] = labels[i,j]
#
# Use the coloring trick here to process touching objects
# in separate operations
#
colors = color_labels(labels)
ncolors = np.max(colors)
d_from_center = np.zeros(labels.shape)
cl = np.zeros(labels.shape, int)
for color in range(1,ncolors+1):
mask = colors == color
l,d = propagate(np.zeros(center_labels.shape),
center_labels,
mask, 1)
d_from_center[mask] = d[mask]
cl[mask] = l[mask]
good_mask = cl > 0
if center_choice == C_EDGES_OF_OTHER:
# Exclude pixels within the centering objects
# when performing calculations from the centers
good_mask = good_mask & (center_labels == 0)
i_center = np.zeros(cl.shape)
i_center[good_mask] = i[cl[good_mask]-1]
j_center = np.zeros(cl.shape)
j_center[good_mask] = j[cl[good_mask]-1]
normalized_distance = np.zeros(labels.shape)
if wants_scaled:
total_distance = d_from_center + d_to_edge
normalized_distance[good_mask] = (d_from_center[good_mask] /
(total_distance[good_mask] + .001))
else:
normalized_distance[good_mask] = \
d_from_center[good_mask] / maximum_radius
dd[name] = [normalized_distance, i_center, j_center, good_mask]
ngood_pixels = np.sum(good_mask)
good_labels = labels[good_mask]
bin_indexes = (normalized_distance * bin_count).astype(int)
bin_indexes[bin_indexes > bin_count] = bin_count
labels_and_bins = (good_labels-1,bin_indexes[good_mask])
histogram = coo_matrix((pixel_data[good_mask], labels_and_bins),
(nobjects, bin_count+1)).toarray()
sum_by_object = np.sum(histogram, 1)
sum_by_object_per_bin = np.dstack([sum_by_object]*(bin_count + 1))[0]
fraction_at_distance = histogram / sum_by_object_per_bin
number_at_distance = coo_matrix((np.ones(ngood_pixels),labels_and_bins),
(nobjects, bin_count+1)).toarray()
object_mask = number_at_distance > 0
sum_by_object = np.sum(number_at_distance, 1)
sum_by_object_per_bin = np.dstack([sum_by_object]*(bin_count+1))[0]
fraction_at_bin = number_at_distance / sum_by_object_per_bin
mean_pixel_fraction = fraction_at_distance / (fraction_at_bin +
np.finfo(float).eps)
masked_fraction_at_distance = masked_array(fraction_at_distance,
~object_mask)
masked_mean_pixel_fraction = masked_array(mean_pixel_fraction,
~object_mask)
# Anisotropy calculation. Split each cell into eight wedges, then
# compute coefficient of variation of the wedges' mean intensities
# in each ring.
#
# Compute each pixel's delta from the center object's centroid
i,j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
imask = i[good_mask] > i_center[good_mask]
jmask = j[good_mask] > j_center[good_mask]
absmask = (abs(i[good_mask] - i_center[good_mask]) >
abs(j[good_mask] - j_center[good_mask]))
radial_index = (imask.astype(int) + jmask.astype(int)*2 +
absmask.astype(int)*4)
statistics = []
for bin in range(bin_count + (0 if wants_scaled else 1)):
bin_mask = (good_mask & (bin_indexes == bin))
bin_pixels = np.sum(bin_mask)
bin_labels = labels[bin_mask]
bin_radial_index = radial_index[bin_indexes[good_mask] == bin]
labels_and_radii = (bin_labels-1, bin_radial_index)
radial_values = coo_matrix((pixel_data[bin_mask],
labels_and_radii),
(nobjects, 8)).toarray()
pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii),
(nobjects, 8)).toarray()
mask = pixel_count==0
radial_means = masked_array(radial_values / pixel_count, mask)
radial_cv = np.std(radial_means,1) / np.mean(radial_means, 1)
radial_cv[np.sum(~mask,1)==0] = 0
for measurement, feature, overflow_feature in (
(fraction_at_distance[:,bin], MF_FRAC_AT_D, OF_FRAC_AT_D),
(mean_pixel_fraction[:,bin], MF_MEAN_FRAC, OF_MEAN_FRAC),
(np.array(radial_cv), MF_RADIAL_CV, OF_RADIAL_CV)):
if bin == bin_count:
measurement_name = overflow_feature % image_name
else:
measurement_name = feature % (image_name, bin+1, bin_count)
measurements.add_measurement(object_name,
measurement_name,
measurement)
radial_cv.mask = np.sum(~mask,1)==0
bin_name = str(bin+1) if bin < bin_count else "Overflow"
statistics += [(image_name, object_name, bin_name, str(bin_count),
round(np.mean(masked_fraction_at_distance[:,bin]),4),
round(np.mean(masked_mean_pixel_fraction[:, bin]),4),
round(np.mean(radial_cv),4))]
return statistics
def filter_using_image(self, workspace, mask):
'''Filter out connections using local intensity minima between objects
workspace - the workspace for the image set
mask - mask of background points within the minimum distance
'''
#
# NOTE: This is an efficient implementation and an improvement
# in accuracy over the Matlab version. It would be faster and
# more accurate to eliminate the line-connecting and instead
# do the following:
# * Distance transform to get the coordinates of the closest
# point in an object for points in the background that are
# at most 1/2 of the max distance between objects.
# * Take the intensity at this closest point and similarly
# label the background point if the background intensity
# is at least the minimum intensity fraction
# * Assume there is a connection between objects if, after this
# labeling, there are adjacent points in each object.
#
# As it is, the algorithm duplicates the Matlab version but suffers
# for cells whose intensity isn't high in the centroid and clearly
# suffers when two cells touch at some point that's off of the line
# between the two.
#
objects = workspace.object_set.get_objects(self.objects_name.value)
labels = objects.segmented
image = self.get_image(workspace)
if self.show_window:
# Save the image for display
workspace.display_data.image = image
#
# Do a distance transform into the background to label points
# in the background with their closest foreground object
#
i, j = scind.distance_transform_edt(labels==0,
return_indices=True,
return_distances=False)
confluent_labels = labels[i,j]
confluent_labels[~mask] = 0
if self.where_algorithm == CA_CLOSEST_POINT:
#
# For the closest point method, find the intensity at
# the closest point in the object (which will be the point itself
# for points in the object).
#
object_intensity = image[i,j] * self.minimum_intensity_fraction.value
confluent_labels[object_intensity > image] = 0
count, index, c_j = morph.find_neighbors(confluent_labels)
if len(c_j) == 0:
# Nobody touches - return the labels matrix
return labels
#
# Make a row of i matching the touching j
#
c_i = np.zeros(len(c_j))
#
# Eliminate labels without matches
#
label_numbers = np.arange(1,len(count)+1)[count > 0]
index = index[count > 0]
count = count[count > 0]
#
# Get the differences between labels so we can use a cumsum trick
# to increment to the next label when they change
#
label_numbers[1:] = label_numbers[1:] - label_numbers[:-1]
c_i[index] = label_numbers
c_i = np.cumsum(c_i).astype(int)
if self.where_algorithm == CA_CENTROIDS:
#
# Only connect points > minimum intensity fraction
#
center_i, center_j = morph.centers_of_labels(labels)
indexes, counts, i, j = morph.get_line_pts(
center_i[c_i-1], center_j[c_i-1],
center_i[c_j-1], center_j[c_j-1])
#
# The indexes of the centroids at pt1
#
last_indexes = indexes+counts-1
#
# The minimum of the intensities at pt0 and pt1
#
centroid_intensities = np.minimum(
image[i[indexes],j[indexes]],
image[i[last_indexes], j[last_indexes]])
#
# Assign label numbers to each point so we can use
# scipy.ndimage.minimum. The label numbers are indexes into
# "connections" above.
#
pt_labels = np.zeros(len(i), int)
pt_labels[indexes[1:]] = 1
pt_labels = np.cumsum(pt_labels)
minima = scind.minimum(image[i,j], pt_labels, np.arange(len(indexes)))
minima = morph.fixup_scipy_ndimage_result(minima)
#
# Filter the connections using the image
#
mif = self.minimum_intensity_fraction.value
i = c_i[centroid_intensities * mif <= minima]
j = c_j[centroid_intensities * mif <= minima]
else:
i = c_i
j = c_j
#
# Add in connections from self to self
#
unique_labels = np.unique(labels)
i = np.hstack((i, unique_labels))
j = np.hstack((j, unique_labels))
#
# Run "all_connected_components" to get a component # for
# objects identified as same.
#
new_indexes = morph.all_connected_components(i, j)
new_labels = np.zeros(labels.shape, int)
new_labels[labels != 0] = new_indexes[labels[labels != 0]]
return new_labels
def filter_using_image(self, workspace, mask):
'''Filter out connections using local intensity minima between objects
workspace - the workspace for the image set
mask - mask of background points within the minimum distance
'''
#
# NOTE: This is an efficient implementation and an improvement
# in accuracy over the Matlab version. It would be faster and
# more accurate to eliminate the line-connecting and instead
# do the following:
# * Distance transform to get the coordinates of the closest
# point in an object for points in the background that are
# at most 1/2 of the max distance between objects.
# * Take the intensity at this closest point and similarly
# label the background point if the background intensity
# is at least the minimum intensity fraction
# * Assume there is a connection between objects if, after this
# labeling, there are adjacent points in each object.
#
# As it is, the algorithm duplicates the Matlab version but suffers
# for cells whose intensity isn't high in the centroid and clearly
# suffers when two cells touch at some point that's off of the line
# between the two.
#
objects = workspace.object_set.get_objects(self.objects_name.value)
labels = objects.segmented
image = self.get_image(workspace)
if self.show_window:
# Save the image for display
workspace.display_data.image = image
#
# Do a distance transform into the background to label points
# in the background with their closest foreground object
#
i, j = scind.distance_transform_edt(labels == 0,
return_indices=True,
return_distances=False)
confluent_labels = labels[i, j]
confluent_labels[~mask] = 0
if self.where_algorithm == CA_CLOSEST_POINT:
#
# For the closest point method, find the intensity at
# the closest point in the object (which will be the point itself
# for points in the object).
#
object_intensity = image[i,
j] * self.minimum_intensity_fraction.value
confluent_labels[object_intensity > image] = 0
count, index, c_j = morph.find_neighbors(confluent_labels)
if len(c_j) == 0:
# Nobody touches - return the labels matrix
return labels
#
# Make a row of i matching the touching j
#
c_i = np.zeros(len(c_j))
#
# Eliminate labels without matches
#
label_numbers = np.arange(1, len(count) + 1)[count > 0]
index = index[count > 0]
count = count[count > 0]
#
# Get the differences between labels so we can use a cumsum trick
# to increment to the next label when they change
#
label_numbers[1:] = label_numbers[1:] - label_numbers[:-1]
c_i[index] = label_numbers
c_i = np.cumsum(c_i).astype(int)
if self.where_algorithm == CA_CENTROIDS:
#
# Only connect points > minimum intensity fraction
#
center_i, center_j = morph.centers_of_labels(labels)
indexes, counts, i, j = morph.get_line_pts(center_i[c_i - 1],
center_j[c_i - 1],
center_i[c_j - 1],
center_j[c_j - 1])
#
# The indexes of the centroids at pt1
#
last_indexes = indexes + counts - 1
#
# The minimum of the intensities at pt0 and pt1
#
centroid_intensities = np.minimum(
image[i[indexes], j[indexes]], image[i[last_indexes],
j[last_indexes]])
#
# Assign label numbers to each point so we can use
# scipy.ndimage.minimum. The label numbers are indexes into
# "connections" above.
#
pt_labels = np.zeros(len(i), int)
pt_labels[indexes[1:]] = 1
pt_labels = np.cumsum(pt_labels)
minima = scind.minimum(image[i, j], pt_labels,
np.arange(len(indexes)))
minima = morph.fixup_scipy_ndimage_result(minima)
#
# Filter the connections using the image
#
mif = self.minimum_intensity_fraction.value
i = c_i[centroid_intensities * mif <= minima]
j = c_j[centroid_intensities * mif <= minima]
else:
i = c_i
j = c_j
#
# Add in connections from self to self
#
unique_labels = np.unique(labels)
i = np.hstack((i, unique_labels))
j = np.hstack((j, unique_labels))
#
# Run "all_connected_components" to get a component # for
# objects identified as same.
#
new_indexes = morph.all_connected_components(i, j)
new_labels = np.zeros(labels.shape, int)
new_labels[labels != 0] = new_indexes[labels[labels != 0]]
return new_labels
def run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
assert isinstance(objects, cpo.Objects)
has_pixels = objects.areas > 0
labels = objects.small_removed_segmented
kept_labels = objects.segmented
neighbor_objects = workspace.object_set.get_objects(self.neighbors_name.value)
assert isinstance(neighbor_objects, cpo.Objects)
neighbor_labels = neighbor_objects.small_removed_segmented
#
# Need to add in labels touching border.
#
unedited_segmented = neighbor_objects.unedited_segmented
touching_border = np.zeros(np.max(unedited_segmented) + 1, bool)
touching_border[unedited_segmented[0, :]] = True
touching_border[unedited_segmented[-1, :]] = True
touching_border[unedited_segmented[:, 0]] = True
touching_border[unedited_segmented[:, -1]] = True
touching_border[0] = False
touching_border_mask = touching_border[unedited_segmented]
nobjects = np.max(labels)
nneighbors = np.max(neighbor_labels)
nkept_objects = objects.count
if np.any(touching_border) and \
np.all(~ touching_border_mask[neighbor_labels!=0]):
# Add the border labels if any were excluded
touching_border_object_number = np.cumsum(touching_border) + \
np.max(neighbor_labels)
touching_border_mask = touching_border_mask & neighbor_labels == 0
neighbor_labels = neighbor_labels.copy().astype(np.int32)
neighbor_labels[touching_border_mask] = touching_border_object_number[
unedited_segmented[touching_border_mask]]
_, object_numbers = objects.relate_labels(labels, kept_labels)
if self.neighbors_are_objects:
neighbor_numbers = object_numbers
else:
_, neighbor_numbers = neighbor_objects.relate_labels(
neighbor_labels, neighbor_objects.segmented)
neighbor_count = np.zeros((nobjects,))
pixel_count = np.zeros((nobjects,))
first_object_number = np.zeros((nobjects,),int)
second_object_number = np.zeros((nobjects,),int)
first_x_vector = np.zeros((nobjects,))
second_x_vector = np.zeros((nobjects,))
first_y_vector = np.zeros((nobjects,))
second_y_vector = np.zeros((nobjects,))
angle = np.zeros((nobjects,))
percent_touching = np.zeros((nobjects,))
expanded_labels = None
if self.distance_method == D_EXPAND:
# Find the i,j coordinates of the nearest foreground point
# to every background point
i,j = scind.distance_transform_edt(labels==0,
return_distances=False,
return_indices=True)
# Assign each background pixel to the label of its nearest
# foreground pixel. Assign label to label for foreground.
labels = labels[i,j]
expanded_labels = labels # for display
distance = 1 # dilate once to make touching edges overlap
scale = S_EXPANDED
if self.neighbors_are_objects:
neighbor_labels = labels.copy()
elif self.distance_method == D_WITHIN:
distance = self.distance.value
scale = str(distance)
elif self.distance_method == D_ADJACENT:
distance = 1
scale = S_ADJACENT
else:
raise ValueError("Unknown distance method: %s" %
self.distance_method.value)
if nneighbors > (1 if self.neighbors_are_objects else 0):
first_objects = []
second_objects = []
object_indexes = np.arange(nobjects, dtype=np.int32)+1
#
# First, compute the first and second nearest neighbors,
# and the angles between self and the first and second
# nearest neighbors
#
ocenters = centers_of_labels(
objects.small_removed_segmented).transpose()
ncenters = centers_of_labels(
neighbor_objects.small_removed_segmented).transpose()
areas = fix(scind.sum(np.ones(labels.shape),labels, object_indexes))
perimeter_outlines = outline(labels)
perimeters = fix(scind.sum(
np.ones(labels.shape), perimeter_outlines, object_indexes))
i,j = np.mgrid[0:nobjects,0:nneighbors]
distance_matrix = np.sqrt((ocenters[i,0] - ncenters[j,0])**2 +
(ocenters[i,1] - ncenters[j,1])**2)
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
if distance_matrix.shape[1] == 1:
# a little buggy, lexsort assumes that a 2-d array of
# second dimension = 1 is a 1-d array
order = np.zeros(distance_matrix.shape, int)
else:
order = np.lexsort([distance_matrix])
first_neighbor = 1 if self.neighbors_are_objects else 0
first_object_index = order[:, first_neighbor]
first_x_vector = ncenters[first_object_index,1] - ocenters[:,1]
first_y_vector = ncenters[first_object_index,0] - ocenters[:,0]
if nneighbors > first_neighbor+1:
second_object_index = order[:, first_neighbor + 1]
second_x_vector = ncenters[second_object_index,1] - ocenters[:,1]
second_y_vector = ncenters[second_object_index,0] - ocenters[:,0]
v1 = np.array((first_x_vector,first_y_vector))
v2 = np.array((second_x_vector,second_y_vector))
#
# Project the unit vector v1 against the unit vector v2
#
dot = (np.sum(v1*v2,0) /
np.sqrt(np.sum(v1**2,0)*np.sum(v2**2,0)))
angle = np.arccos(dot) * 180. / np.pi
# Make the structuring element for dilation
strel = strel_disk(distance)
#
# A little bigger one to enter into the border with a structure
# that mimics the one used to create the outline
#
strel_touching = strel_disk(distance + .5)
#
# Get the extents for each object and calculate the patch
# that excises the part of the image that is "distance"
# away
i,j = np.mgrid[0:labels.shape[0],0:labels.shape[1]]
min_i, max_i, min_i_pos, max_i_pos =\
scind.extrema(i,labels,object_indexes)
min_j, max_j, min_j_pos, max_j_pos =\
scind.extrema(j,labels,object_indexes)
min_i = np.maximum(fix(min_i)-distance,0).astype(int)
max_i = np.minimum(fix(max_i)+distance+1,labels.shape[0]).astype(int)
min_j = np.maximum(fix(min_j)-distance,0).astype(int)
max_j = np.minimum(fix(max_j)+distance+1,labels.shape[1]).astype(int)
#
# Loop over all objects
# Calculate which ones overlap "index"
# Calculate how much overlap there is of others to "index"
#
for object_number in object_numbers:
if object_number == 0:
#
# No corresponding object in small-removed. This means
# that the object has no pixels, e.g. not renumbered.
#
continue
index = object_number - 1
patch = labels[min_i[index]:max_i[index],
min_j[index]:max_j[index]]
npatch = neighbor_labels[min_i[index]:max_i[index],
min_j[index]:max_j[index]]
#
# Find the neighbors
#
patch_mask = patch==(index+1)
extended = scind.binary_dilation(patch_mask,strel)
neighbors = np.unique(npatch[extended])
neighbors = neighbors[neighbors != 0]
if self.neighbors_are_objects:
neighbors = neighbors[neighbors != object_number]
nc = len(neighbors)
neighbor_count[index] = nc
if nc > 0:
first_objects.append(np.ones(nc,int) * object_number)
second_objects.append(neighbors)
if self.neighbors_are_objects:
#
# Find the # of overlapping pixels. Dilate the neighbors
# and see how many pixels overlap our image. Use a 3x3
# structuring element to expand the overlapping edge
# into the perimeter.
#
outline_patch = perimeter_outlines[
min_i[index]:max_i[index],
min_j[index]:max_j[index]] == object_number
extended = scind.binary_dilation(
(patch != 0) & (patch != object_number), strel_touching)
overlap = np.sum(outline_patch & extended)
pixel_count[index] = overlap
if sum([len(x) for x in first_objects]) > 0:
first_objects = np.hstack(first_objects)
reverse_object_numbers = np.zeros(
max(np.max(object_numbers), np.max(first_objects)) + 1, int)
reverse_object_numbers[object_numbers] = np.arange(len(object_numbers)) + 1
first_objects = reverse_object_numbers[first_objects]
second_objects = np.hstack(second_objects)
reverse_neighbor_numbers = np.zeros(
max(np.max(neighbor_numbers), np.max(second_objects)) + 1, int)
reverse_neighbor_numbers[neighbor_numbers] = np.arange(len(neighbor_numbers)) + 1
second_objects= reverse_neighbor_numbers[second_objects]
to_keep = (first_objects > 0) & (second_objects > 0)
first_objects = first_objects[to_keep]
second_objects = second_objects[to_keep]
else:
first_objects = np.zeros(0, int)
second_objects = np.zeros(0, int)
if self.neighbors_are_objects:
percent_touching = pixel_count * 100 / perimeters
else:
percent_touching = pixel_count * 100.0 / areas
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
#
# Have to recompute nearest
#
first_object_number = np.zeros(nkept_objects, int)
second_object_number = np.zeros(nkept_objects, int)
if nkept_objects > (1 if self.neighbors_are_objects else 0):
di = (ocenters[object_indexes[:, np.newaxis], 0] -
ncenters[neighbor_indexes[np.newaxis, :], 0])
dj = (ocenters[object_indexes[:, np.newaxis], 1] -
ncenters[neighbor_indexes[np.newaxis, :], 1])
distance_matrix = np.sqrt(di*di + dj*dj)
distance_matrix[~ has_pixels, :] = np.inf
distance_matrix[:, ~has_pixels] = np.inf
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
order = np.lexsort([distance_matrix]).astype(
first_object_number.dtype)
if self.neighbors_are_objects:
first_object_number[has_pixels] = order[has_pixels,1] + 1
if nkept_objects > 2:
second_object_number[has_pixels] = order[has_pixels,2] + 1
else:
first_object_number[has_pixels] = order[has_pixels,0] + 1
if nneighbors > 1:
second_object_number[has_pixels] = order[has_pixels,1] + 1
else:
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
first_objects = np.zeros(0, int)
second_objects = np.zeros(0, int)
#
# Now convert all measurements from the small-removed to
# the final number set.
#
neighbor_count = neighbor_count[object_indexes]
neighbor_count[~ has_pixels] = 0
percent_touching = percent_touching[object_indexes]
percent_touching[~ has_pixels] = 0
first_x_vector = first_x_vector[object_indexes]
second_x_vector = second_x_vector[object_indexes]
first_y_vector = first_y_vector[object_indexes]
second_y_vector = second_y_vector[object_indexes]
angle = angle[object_indexes]
#
# Record the measurements
#
assert(isinstance(workspace, cpw.Workspace))
m = workspace.measurements
assert(isinstance(m, cpmeas.Measurements))
image_set = workspace.image_set
features_and_data = [
(M_NUMBER_OF_NEIGHBORS, neighbor_count),
(M_FIRST_CLOSEST_OBJECT_NUMBER, first_object_number),
(M_FIRST_CLOSEST_DISTANCE, np.sqrt(first_x_vector**2+first_y_vector**2)),
(M_SECOND_CLOSEST_OBJECT_NUMBER, second_object_number),
(M_SECOND_CLOSEST_DISTANCE, np.sqrt(second_x_vector**2+second_y_vector**2)),
(M_ANGLE_BETWEEN_NEIGHBORS, angle)]
if self.neighbors_are_objects:
features_and_data.append((M_PERCENT_TOUCHING, percent_touching))
for feature_name, data in features_and_data:
m.add_measurement(self.object_name.value,
self.get_measurement_name(feature_name),
data)
if len(first_objects) > 0:
m.add_relate_measurement(
self.module_num,
cpmeas.NEIGHBORS,
self.object_name.value,
self.object_name.value if self.neighbors_are_objects
else self.neighbors_name.value,
m.image_set_number * np.ones(first_objects.shape, int),
first_objects,
m.image_set_number * np.ones(second_objects.shape, int),
second_objects)
labels = kept_labels
neighbor_count_image = np.zeros(labels.shape,int)
object_mask = objects.segmented != 0
object_indexes = objects.segmented[object_mask]-1
neighbor_count_image[object_mask] = neighbor_count[object_indexes]
workspace.display_data.neighbor_count_image = neighbor_count_image
if self.neighbors_are_objects:
percent_touching_image = np.zeros(labels.shape)
percent_touching_image[object_mask] = percent_touching[object_indexes]
workspace.display_data.percent_touching_image = percent_touching_image
image_set = workspace.image_set
if self.wants_count_image.value:
neighbor_cm_name = self.count_colormap.value
neighbor_cm = get_colormap(neighbor_cm_name)
sm = matplotlib.cm.ScalarMappable(cmap = neighbor_cm)
img = sm.to_rgba(neighbor_count_image)[:,:,:3]
img[:,:,0][~ object_mask] = 0
img[:,:,1][~ object_mask] = 0
img[:,:,2][~ object_mask] = 0
count_image = cpi.Image(img, masking_objects = objects)
image_set.add(self.count_image_name.value, count_image)
else:
neighbor_cm_name = cpprefs.get_default_colormap()
neighbor_cm = matplotlib.cm.get_cmap(neighbor_cm_name)
if self.neighbors_are_objects and self.wants_percent_touching_image:
percent_touching_cm_name = self.touching_colormap.value
percent_touching_cm = get_colormap(percent_touching_cm_name)
sm = matplotlib.cm.ScalarMappable(cmap = percent_touching_cm)
img = sm.to_rgba(percent_touching_image)[:,:,:3]
img[:,:,0][~ object_mask] = 0
img[:,:,1][~ object_mask] = 0
img[:,:,2][~ object_mask] = 0
touching_image = cpi.Image(img, masking_objects = objects)
image_set.add(self.touching_image_name.value,
touching_image)
else:
percent_touching_cm_name = cpprefs.get_default_colormap()
percent_touching_cm = matplotlib.cm.get_cmap(percent_touching_cm_name)
if self.show_window:
workspace.display_data.neighbor_cm_name = neighbor_cm_name
workspace.display_data.percent_touching_cm_name = percent_touching_cm_name
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.expanded_labels = expanded_labels
workspace.display_data.object_mask = object_mask
def do_measurements(self, workspace, image_name, object_name,
center_object_name, bin_count,
dd):
'''Perform the radial measurements on the image set
workspace - workspace that holds images / objects
image_name - make measurements on this image
object_name - make measurements on these objects
center_object_name - use the centers of these related objects as
the centers for radial measurements. None to use the
objects themselves.
bin_count - bin the object into this many concentric rings
d - a dictionary for saving reusable partial results
returns one statistics tuple per ring.
'''
assert isinstance(workspace, cpw.Workspace)
assert isinstance(workspace.object_set, cpo.ObjectSet)
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
labels, pixel_data = cpo.crop_labels_and_image(objects.segmented,
image.pixel_data)
nobjects = np.max(objects.segmented)
measurements = workspace.measurements
assert isinstance(measurements, cpmeas.Measurements)
if nobjects == 0:
for bin in range(1, bin_count+1):
for feature in (FF_FRAC_AT_D, FF_MEAN_FRAC, FF_RADIAL_CV):
measurements.add_measurement(object_name,
M_CATEGORY + "_" + feature %
(image_name, bin, bin_count),
np.zeros(0))
return [(image_name, object_name, "no objects","-","-","-","-")]
name = (object_name if center_object_name is None
else "%s_%s"%(object_name, center_object_name))
if dd.has_key(name):
normalized_distance, i_center, j_center, good_mask = dd[name]
else:
d_to_edge = distance_to_edge(labels)
if center_object_name is not None:
center_objects=workspace.object_set.get_objects(center_object_name)
center_labels, cmask = cpo.size_similarly(
labels, center_objects.segmented)
pixel_counts = fix(scind.sum(np.ones(center_labels.shape),
center_labels,
np.arange(1, np.max(center_labels)+1,dtype=np.int32)))
good = pixel_counts > 0
i,j = (centers_of_labels(center_labels) + .5).astype(int)
ig = i[good]
jg = j[good]
center_labels = np.zeros(center_labels.shape, int)
center_labels[ig,jg] = labels[ig,jg] ## TODO: This is incorrect when objects are annular. Retrieves label# = 0
cl,d_from_center = propagate(np.zeros(center_labels.shape),
center_labels,
labels != 0, 1)
else:
# Find the point in each object farthest away from the edge.
# This does better than the centroid:
# * The center is within the object
# * The center tends to be an interesting point, like the
# center of the nucleus or the center of one or the other
# of two touching cells.
#
i,j = maximum_position_of_labels(d_to_edge, labels, objects.indices)
center_labels = np.zeros(labels.shape, int)
center_labels[i,j] = labels[i,j]
#
# Use the coloring trick here to process touching objects
# in separate operations
#
colors = color_labels(labels)
ncolors = np.max(colors)
d_from_center = np.zeros(labels.shape)
cl = np.zeros(labels.shape, int)
for color in range(1,ncolors+1):
mask = colors == color
l,d = propagate(np.zeros(center_labels.shape),
center_labels,
mask, 1)
d_from_center[mask] = d[mask]
cl[mask] = l[mask]
good_mask = cl > 0
i_center = np.zeros(cl.shape)
i_center[good_mask] = i[cl[good_mask]-1]
j_center = np.zeros(cl.shape)
j_center[good_mask] = j[cl[good_mask]-1]
normalized_distance = np.zeros(labels.shape)
total_distance = d_from_center + d_to_edge
normalized_distance[good_mask] = (d_from_center[good_mask] /
(total_distance[good_mask] + .001))
dd[name] = [normalized_distance, i_center, j_center, good_mask]
ngood_pixels = np.sum(good_mask)
good_labels = objects.segmented[good_mask]
bin_indexes = (normalized_distance * bin_count).astype(int)
labels_and_bins = (good_labels-1,bin_indexes[good_mask])
histogram = coo_matrix((image.pixel_data[good_mask], labels_and_bins),
(nobjects, bin_count)).toarray()
sum_by_object = np.sum(histogram, 1)
sum_by_object_per_bin = np.dstack([sum_by_object]*bin_count)[0]
fraction_at_distance = histogram / sum_by_object_per_bin
number_at_distance = coo_matrix((np.ones(ngood_pixels),labels_and_bins),
(nobjects, bin_count)).toarray()
object_mask = number_at_distance > 0
sum_by_object = np.sum(number_at_distance, 1)
sum_by_object_per_bin = np.dstack([sum_by_object]*bin_count)[0]
fraction_at_bin = number_at_distance / sum_by_object_per_bin
mean_pixel_fraction = fraction_at_distance / (fraction_at_bin +
np.finfo(float).eps)
masked_fraction_at_distance = masked_array(fraction_at_distance,
~object_mask)
masked_mean_pixel_fraction = masked_array(mean_pixel_fraction,
~object_mask)
# Anisotropy calculation. Split each cell into eight wedges, then
# compute coefficient of variation of the wedges' mean intensities
# in each ring.
#
# Compute each pixel's delta from the center object's centroid
i,j = np.mgrid[0:labels.shape[0], 0:labels.shape[1]]
imask = i[good_mask] > i_center[good_mask]
jmask = j[good_mask] > j_center[good_mask]
absmask = (abs(i[good_mask] - i_center[good_mask]) >
abs(j[good_mask] - j_center[good_mask]))
radial_index = (imask.astype(int) + jmask.astype(int)*2 +
absmask.astype(int)*4)
statistics = []
for bin in range(bin_count):
bin_mask = (good_mask & (bin_indexes == bin))
bin_pixels = np.sum(bin_mask)
bin_labels = labels[bin_mask]
bin_radial_index = radial_index[bin_indexes[good_mask] == bin]
labels_and_radii = (bin_labels-1, bin_radial_index)
radial_values = coo_matrix((pixel_data[bin_mask],
labels_and_radii),
(nobjects, 8)).toarray()
pixel_count = coo_matrix((np.ones(bin_pixels), labels_and_radii),
(nobjects, 8)).toarray()
mask = pixel_count==0
radial_means = masked_array(radial_values / pixel_count, mask)
radial_cv = np.std(radial_means,1) / np.mean(radial_means, 1)
radial_cv[np.sum(~mask,1)==0] = 0
for measurement, feature in ((fraction_at_distance[:,bin], MF_FRAC_AT_D),
(mean_pixel_fraction[:,bin], MF_MEAN_FRAC),
(np.array(radial_cv), MF_RADIAL_CV)):
measurements.add_measurement(object_name,
feature %
(image_name, bin+1, bin_count),
measurement)
radial_cv.mask = np.sum(~mask,1)==0
statistics += [(image_name, object_name, str(bin+1), str(bin_count),
round(np.mean(masked_fraction_at_distance[:,bin]),4),
round(np.mean(masked_mean_pixel_fraction[:, bin]),4),
round(np.mean(radial_cv),4))]
return statistics
def calculate_minimum_distances(self, workspace, parent_name):
'''Calculate the distance from child center to parent perimeter'''
meas = workspace.measurements
assert isinstance(meas, cpmeas.Measurements)
sub_object_name = self.sub_object_name.value
parents = workspace.object_set.get_objects(parent_name)
children = workspace.object_set.get_objects(sub_object_name)
parents_of = self.get_parents_of(workspace, parent_name)
if len(parents_of) == 0:
dist = np.zeros((0, ))
elif np.all(parents_of == 0):
dist = np.array([np.NaN] * len(parents_of))
else:
mask = parents_of > 0
ccenters = centers_of_labels(children.segmented).transpose()
ccenters = ccenters[mask, :]
parents_of_masked = parents_of[mask] - 1
pperim = outline(parents.segmented)
#
# Get a list of all points on the perimeter
#
perim_loc = np.argwhere(pperim != 0)
#
# Get the label # for each point
#
perim_idx = pperim[perim_loc[:, 0], perim_loc[:, 1]]
#
# Sort the points by label #
#
idx = np.lexsort((perim_loc[:, 1], perim_loc[:, 0], perim_idx))
perim_loc = perim_loc[idx, :]
perim_idx = perim_idx[idx]
#
# Get counts and indexes to each run of perimeter points
#
counts = fix(
scind.sum(np.ones(len(perim_idx)), perim_idx,
np.arange(1, perim_idx[-1] + 1))).astype(np.int32)
indexes = np.cumsum(counts) - counts
#
# For the children, get the index and count of the parent
#
ccounts = counts[parents_of_masked]
cindexes = indexes[parents_of_masked]
#
# Now make an array that has an element for each of that child's
# perimeter points
#
clabel = np.zeros(np.sum(ccounts), int)
#
# cfirst is the eventual first index of each child in the
# clabel array
#
cfirst = np.cumsum(ccounts) - ccounts
clabel[cfirst[1:]] += 1
clabel = np.cumsum(clabel)
#
# Make an index that runs from 0 to ccounts for each
# child label.
#
cp_index = np.arange(len(clabel)) - cfirst[clabel]
#
# then add cindexes to get an index to the perimeter point
#
cp_index += cindexes[clabel]
#
# Now, calculate the distance from the centroid of each label
# to each perimeter point in the parent.
#
dist = np.sqrt(
np.sum((perim_loc[cp_index, :] - ccenters[clabel, :])**2, 1))
#
# Finally, find the minimum distance per child
#
min_dist = fix(scind.minimum(dist, clabel,
np.arange(len(ccounts))))
#
# Account for unparented children
#
dist = np.array([np.NaN] * len(mask))
dist[mask] = min_dist
meas.add_measurement(sub_object_name, FF_MINIMUM % parent_name, dist)
def run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
assert isinstance(objects, cpo.Objects)
has_pixels = objects.areas > 0
labels = objects.small_removed_segmented
kept_labels = objects.segmented
neighbor_objects = workspace.object_set.get_objects(self.neighbors_name.value)
assert isinstance(neighbor_objects, cpo.Objects)
neighbor_labels = neighbor_objects.small_removed_segmented
#
# Need to add in labels touching border.
#
unedited_segmented = neighbor_objects.unedited_segmented
touching_border = np.zeros(np.max(unedited_segmented) + 1, bool)
touching_border[unedited_segmented[0, :]] = True
touching_border[unedited_segmented[-1, :]] = True
touching_border[unedited_segmented[:, 0]] = True
touching_border[unedited_segmented[:, -1]] = True
touching_border[0] = False
touching_border_mask = touching_border[unedited_segmented]
if np.any(touching_border) and \
np.all(~ touching_border_mask[neighbor_labels]):
# Add the border labels if any were excluded
touching_border_object_number = np.cumsum(touching_border) + \
np.max(neighbor_labels)
touching_border_mask = touching_border_mask & neighbor_labels == 0
neighbor_labels[touching_border_mask] = touching_border_object_number[
unedited_segmented[touching_border_mask]]
nobjects = np.max(labels)
nneighbors = np.max(neighbor_labels)
nkept_objects = objects.count
_, object_numbers = objects.relate_labels(labels, kept_labels)
if self.neighbors_are_objects:
neighbor_numbers = object_numbers
else:
_, neighbor_numbers = neighbor_objects.relate_labels(
neighbor_labels, neighbor_objects.segmented)
neighbor_count = np.zeros((nobjects,))
pixel_count = np.zeros((nobjects,))
first_object_number = np.zeros((nobjects,),int)
second_object_number = np.zeros((nobjects,),int)
first_x_vector = np.zeros((nobjects,))
second_x_vector = np.zeros((nobjects,))
first_y_vector = np.zeros((nobjects,))
second_y_vector = np.zeros((nobjects,))
angle = np.zeros((nobjects,))
percent_touching = np.zeros((nobjects,))
expanded_labels = None
if self.distance_method == D_EXPAND:
# Find the i,j coordinates of the nearest foreground point
# to every background point
i,j = scind.distance_transform_edt(labels==0,
return_distances=False,
return_indices=True)
# Assign each background pixel to the label of its nearest
# foreground pixel. Assign label to label for foreground.
labels = labels[i,j]
expanded_labels = labels # for display
distance = 1 # dilate once to make touching edges overlap
scale = S_EXPANDED
if self.neighbors_are_objects:
neighbor_labels = labels.copy()
elif self.distance_method == D_WITHIN:
distance = self.distance.value
scale = str(distance)
elif self.distance_method == D_ADJACENT:
distance = 1
scale = S_ADJACENT
else:
raise ValueError("Unknown distance method: %s" %
self.distance_method.value)
if nneighbors > (1 if self.neighbors_are_objects else 0):
first_objects = []
second_objects = []
object_indexes = np.arange(nobjects, dtype=np.int32)+1
#
# First, compute the first and second nearest neighbors,
# and the angles between self and the first and second
# nearest neighbors
#
ocenters = centers_of_labels(
objects.small_removed_segmented).transpose()
ncenters = centers_of_labels(
neighbor_objects.small_removed_segmented).transpose()
areas = fix(scind.sum(np.ones(labels.shape),labels, object_indexes))
perimeter_outlines = outline(labels)
perimeters = fix(scind.sum(
np.ones(labels.shape), perimeter_outlines, object_indexes))
i,j = np.mgrid[0:nobjects,0:nneighbors]
distance_matrix = np.sqrt((ocenters[i,0] - ncenters[j,0])**2 +
(ocenters[i,1] - ncenters[j,1])**2)
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
if distance_matrix.shape[1] == 1:
# a little buggy, lexsort assumes that a 2-d array of
# second dimension = 1 is a 1-d array
order = np.zeros(distance_matrix.shape, int)
else:
order = np.lexsort([distance_matrix])
first_neighbor = 1 if self.neighbors_are_objects else 0
first_object_index = order[:, first_neighbor]
first_x_vector = ncenters[first_object_index,1] - ocenters[:,1]
first_y_vector = ncenters[first_object_index,0] - ocenters[:,0]
if nneighbors > first_neighbor+1:
second_object_index = order[:, first_neighbor + 1]
second_x_vector = ncenters[second_object_index,1] - ocenters[:,1]
second_y_vector = ncenters[second_object_index,0] - ocenters[:,0]
v1 = np.array((first_x_vector,first_y_vector))
v2 = np.array((second_x_vector,second_y_vector))
#
# Project the unit vector v1 against the unit vector v2
#
dot = (np.sum(v1*v2,0) /
np.sqrt(np.sum(v1**2,0)*np.sum(v2**2,0)))
angle = np.arccos(dot) * 180. / np.pi
# Make the structuring element for dilation
strel = strel_disk(distance)
#
# A little bigger one to enter into the border with a structure
# that mimics the one used to create the outline
#
strel_touching = strel_disk(distance + .5)
#
# Get the extents for each object and calculate the patch
# that excises the part of the image that is "distance"
# away
i,j = np.mgrid[0:labels.shape[0],0:labels.shape[1]]
min_i, max_i, min_i_pos, max_i_pos =\
scind.extrema(i,labels,object_indexes)
min_j, max_j, min_j_pos, max_j_pos =\
scind.extrema(j,labels,object_indexes)
min_i = np.maximum(fix(min_i)-distance,0).astype(int)
max_i = np.minimum(fix(max_i)+distance+1,labels.shape[0]).astype(int)
min_j = np.maximum(fix(min_j)-distance,0).astype(int)
max_j = np.minimum(fix(max_j)+distance+1,labels.shape[1]).astype(int)
#
# Loop over all objects
# Calculate which ones overlap "index"
# Calculate how much overlap there is of others to "index"
#
for object_number in object_numbers:
if object_number == 0:
#
# No corresponding object in small-removed. This means
# that the object has no pixels, e.g. not renumbered.
#
continue
index = object_number - 1
patch = labels[min_i[index]:max_i[index],
min_j[index]:max_j[index]]
npatch = neighbor_labels[min_i[index]:max_i[index],
min_j[index]:max_j[index]]
#
# Find the neighbors
#
patch_mask = patch==(index+1)
extended = scind.binary_dilation(patch_mask,strel)
neighbors = np.unique(npatch[extended])
neighbors = neighbors[neighbors != 0]
if self.neighbors_are_objects:
neighbors = neighbors[neighbors != object_number]
nc = len(neighbors)
neighbor_count[index] = nc
if nc > 0:
first_objects.append(np.ones(nc,int) * object_number)
second_objects.append(neighbors)
if self.neighbors_are_objects:
#
# Find the # of overlapping pixels. Dilate the neighbors
# and see how many pixels overlap our image. Use a 3x3
# structuring element to expand the overlapping edge
# into the perimeter.
#
outline_patch = perimeter_outlines[
min_i[index]:max_i[index],
min_j[index]:max_j[index]] == object_number
extended = scind.binary_dilation(
(patch != 0) & (patch != object_number), strel_touching)
overlap = np.sum(outline_patch & extended)
pixel_count[index] = overlap
if sum([len(x) for x in first_objects]) > 0:
first_objects = np.hstack(first_objects)
reverse_object_numbers = np.zeros(
max(np.max(object_numbers), np.max(first_objects)) + 1, int)
reverse_object_numbers[object_numbers] = np.arange(len(object_numbers)) + 1
first_objects = reverse_object_numbers[first_objects]
second_objects = np.hstack(second_objects)
reverse_neighbor_numbers = np.zeros(
max(np.max(neighbor_numbers), np.max(second_objects)) + 1, int)
reverse_neighbor_numbers[neighbor_numbers] = np.arange(len(neighbor_numbers)) + 1
second_objects= reverse_neighbor_numbers[second_objects]
to_keep = (first_objects > 0) & (second_objects > 0)
first_objects = first_objects[to_keep]
second_objects = second_objects[to_keep]
else:
first_objects = np.zeros(0, int)
second_objects = np.zeros(0, int)
if self.neighbors_are_objects:
percent_touching = pixel_count * 100 / perimeters
else:
percent_touching = pixel_count * 100.0 / areas
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
#
# Have to recompute nearest
#
first_object_number = np.zeros(nkept_objects, int)
second_object_number = np.zeros(nkept_objects, int)
if nkept_objects > (1 if self.neighbors_are_objects else 0):
di = (ocenters[object_indexes[:, np.newaxis], 0] -
ncenters[neighbor_indexes[np.newaxis, :], 0])
dj = (ocenters[object_indexes[:, np.newaxis], 1] -
ncenters[neighbor_indexes[np.newaxis, :], 1])
distance_matrix = np.sqrt(di*di + dj*dj)
distance_matrix[~ has_pixels, :] = np.inf
distance_matrix[:, ~has_pixels] = np.inf
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
order = np.lexsort([distance_matrix]).astype(
first_object_number.dtype)
if self.neighbors_are_objects:
first_object_number[has_pixels] = order[has_pixels,1] + 1
if nkept_objects > 2:
second_object_number[has_pixels] = order[has_pixels,2] + 1
else:
first_object_number[has_pixels] = order[has_pixels,0] + 1
if nneighbors > 1:
second_object_number[has_pixels] = order[has_pixels,1] + 1
else:
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
first_objects = np.zeros(0, int)
second_objects = np.zeros(0, int)
#
# Now convert all measurements from the small-removed to
# the final number set.
#
neighbor_count = neighbor_count[object_indexes]
neighbor_count[~ has_pixels] = 0
percent_touching = percent_touching[object_indexes]
percent_touching[~ has_pixels] = 0
first_x_vector = first_x_vector[object_indexes]
second_x_vector = second_x_vector[object_indexes]
first_y_vector = first_y_vector[object_indexes]
second_y_vector = second_y_vector[object_indexes]
angle = angle[object_indexes]
#
# Record the measurements
#
assert(isinstance(workspace, cpw.Workspace))
m = workspace.measurements
assert(isinstance(m, cpmeas.Measurements))
image_set = workspace.image_set
features_and_data = [
(M_NUMBER_OF_NEIGHBORS, neighbor_count),
(M_FIRST_CLOSEST_OBJECT_NUMBER, first_object_number),
(M_FIRST_CLOSEST_DISTANCE, np.sqrt(first_x_vector**2+first_y_vector**2)),
(M_SECOND_CLOSEST_OBJECT_NUMBER, second_object_number),
(M_SECOND_CLOSEST_DISTANCE, np.sqrt(second_x_vector**2+second_y_vector**2)),
(M_ANGLE_BETWEEN_NEIGHBORS, angle)]
if self.neighbors_are_objects:
features_and_data.append((M_PERCENT_TOUCHING, percent_touching))
for feature_name, data in features_and_data:
m.add_measurement(self.object_name.value,
self.get_measurement_name(feature_name),
data)
if len(first_objects) > 0:
m.add_relate_measurement(
self.module_num,
cpmeas.NEIGHBORS,
self.object_name.value,
self.object_name.value if self.neighbors_are_objects
else self.neighbors_name.value,
m.image_set_number * np.ones(first_objects.shape, int),
first_objects,
m.image_set_number * np.ones(second_objects.shape, int),
second_objects)
labels = kept_labels
neighbor_count_image = np.zeros(labels.shape,int)
object_mask = objects.segmented != 0
object_indexes = objects.segmented[object_mask]-1
neighbor_count_image[object_mask] = neighbor_count[object_indexes]
workspace.display_data.neighbor_count_image = neighbor_count_image
if self.neighbors_are_objects:
percent_touching_image = np.zeros(labels.shape)
percent_touching_image[object_mask] = percent_touching[object_indexes]
workspace.display_data.percent_touching_image = percent_touching_image
image_set = workspace.image_set
if self.wants_count_image.value:
neighbor_cm_name = self.count_colormap.value
neighbor_cm = get_colormap(neighbor_cm_name)
sm = matplotlib.cm.ScalarMappable(cmap = neighbor_cm)
img = sm.to_rgba(neighbor_count_image)[:,:,:3]
img[:,:,0][~ object_mask] = 0
img[:,:,1][~ object_mask] = 0
img[:,:,2][~ object_mask] = 0
count_image = cpi.Image(img, masking_objects = objects)
image_set.add(self.count_image_name.value, count_image)
else:
neighbor_cm_name = cpprefs.get_default_colormap()
neighbor_cm = matplotlib.cm.get_cmap(neighbor_cm_name)
if self.neighbors_are_objects and self.wants_percent_touching_image:
percent_touching_cm_name = self.touching_colormap.value
percent_touching_cm = get_colormap(percent_touching_cm_name)
sm = matplotlib.cm.ScalarMappable(cmap = percent_touching_cm)
img = sm.to_rgba(percent_touching_image)[:,:,:3]
img[:,:,0][~ object_mask] = 0
img[:,:,1][~ object_mask] = 0
img[:,:,2][~ object_mask] = 0
touching_image = cpi.Image(img, masking_objects = objects)
image_set.add(self.touching_image_name.value,
touching_image)
else:
percent_touching_cm_name = cpprefs.get_default_colormap()
percent_touching_cm = matplotlib.cm.get_cmap(percent_touching_cm_name)
if self.show_window:
workspace.display_data.neighbor_cm_name = neighbor_cm_name
workspace.display_data.percent_touching_cm_name = percent_touching_cm_name
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.expanded_labels = expanded_labels
workspace.display_data.object_mask = object_mask
def calculate_minimum_distances(self, workspace, parent_name):
'''Calculate the distance from child center to parent perimeter'''
meas = workspace.measurements
assert isinstance(meas,cpmeas.Measurements)
sub_object_name = self.sub_object_name.value
parents = workspace.object_set.get_objects(parent_name)
children = workspace.object_set.get_objects(sub_object_name)
parents_of = self.get_parents_of(workspace, parent_name)
if len(parents_of) == 0:
dist = np.zeros((0,))
elif np.all(parents_of == 0):
dist = np.array([np.NaN] * len(parents_of))
else:
mask = parents_of > 0
ccenters = centers_of_labels(children.segmented).transpose()
ccenters = ccenters[mask,:]
parents_of_masked = parents_of[mask] - 1
pperim = outline(parents.segmented)
#
# Get a list of all points on the perimeter
#
perim_loc = np.argwhere(pperim != 0)
#
# Get the label # for each point
#
perim_idx = pperim[perim_loc[:,0],perim_loc[:,1]]
#
# Sort the points by label #
#
idx = np.lexsort((perim_loc[:,1],perim_loc[:,0],perim_idx))
perim_loc = perim_loc[idx,:]
perim_idx = perim_idx[idx]
#
# Get counts and indexes to each run of perimeter points
#
counts = fix(scind.sum(np.ones(len(perim_idx)),perim_idx,
np.arange(1,perim_idx[-1]+1))).astype(np.int32)
indexes = np.cumsum(counts) - counts
#
# For the children, get the index and count of the parent
#
ccounts = counts[parents_of_masked]
cindexes = indexes[parents_of_masked]
#
# Now make an array that has an element for each of that child's
# perimeter points
#
clabel = np.zeros(np.sum(ccounts), int)
#
# cfirst is the eventual first index of each child in the
# clabel array
#
cfirst = np.cumsum(ccounts) - ccounts
clabel[cfirst[1:]] += 1
clabel = np.cumsum(clabel)
#
# Make an index that runs from 0 to ccounts for each
# child label.
#
cp_index = np.arange(len(clabel)) - cfirst[clabel]
#
# then add cindexes to get an index to the perimeter point
#
cp_index += cindexes[clabel]
#
# Now, calculate the distance from the centroid of each label
# to each perimeter point in the parent.
#
dist = np.sqrt(np.sum((perim_loc[cp_index,:] -
ccenters[clabel,:])**2,1))
#
# Finally, find the minimum distance per child
#
min_dist = fix(scind.minimum(dist, clabel, np.arange(len(ccounts))))
#
# Account for unparented children
#
dist = np.array([np.NaN] * len(mask))
dist[mask] = min_dist
meas.add_measurement(sub_object_name, FF_MINIMUM % parent_name, dist)
