def test_01_02_larger_secondary(self):
secondary, mask = cpo.size_similarly(np.zeros((10, 20)),
np.zeros((10, 30)))
self.assertEqual(tuple(secondary.shape), (10, 20))
self.assertTrue(np.all(mask))
secondary, mask = cpo.size_similarly(np.zeros((10, 20)),
np.zeros((20, 20)))
self.assertEqual(tuple(secondary.shape), (10, 20))
self.assertTrue(np.all(mask))
def test_01_04_size_color(self):
secondary, mask = cpo.size_similarly(np.zeros((10, 20), int),
np.zeros((10, 15, 3), np.float32))
self.assertEqual(tuple(secondary.shape), (10, 20, 3))
self.assertTrue(np.all(mask[:10, :15]))
self.assertTrue(np.all(~mask[:10, 15:]))
self.assertEqual(secondary.dtype, np.dtype(np.float32))
def run_one_gabor(self, image_name, object_name, scale, workspace):
objects = workspace.get_objects(object_name)
labels = objects.segmented
object_count = np.max(labels)
if object_count > 0:
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
labels = objects.segmented
if image.has_mask:
mask = image.mask
else:
mask = None
try:
pixel_data = objects.crop_image_similarly(pixel_data)
if mask is not None:
mask = objects.crop_image_similarly(mask)
labels[~mask] = 0
except ValueError:
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
labels[~m1] = 0
if mask is not None:
mask, m2 = cpo.size_similarly(labels, mask)
labels[~m2] = 0
labels[~mask] = 0
pixel_data = normalized_per_object(pixel_data, labels)
best_score = np.zeros((object_count,))
for angle in range(self.gabor_angles.value):
theta = np.pi * angle / self.gabor_angles.value
g = gabor(pixel_data, labels, scale, theta)
score_r = fix(scind.sum(g.real, labels,
np.arange(object_count, dtype=np.int32) + 1))
score_i = fix(scind.sum(g.imag, labels,
np.arange(object_count, dtype=np.int32) + 1))
score = np.sqrt(score_r ** 2 + score_i ** 2)
best_score = np.maximum(best_score, score)
else:
best_score = np.zeros((0,))
statistics = self.record_measurement(workspace,
image_name,
object_name,
scale,
F_GABOR,
best_score)
return statistics
def run_one(self, image_name, object_name, scale, angle, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = cpo.size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_HARALICK:
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, np.zeros((0,)))
else:
scale_i, scale_j = self.get_angle_ij(angle, scale)
for name, value in zip(F_HARALICK, Haralick(pixel_data,
labels,
scale_i,
scale_j,
mask=mask).all()):
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, value)
return statistics
def run_one(self, image_name, object_name, scale, angle, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = cpo.size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_HARALICK:
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, np.zeros((0, )))
else:
scale_i, scale_j = self.get_angle_ij(angle, scale)
for name, value in zip(
F_HARALICK,
Haralick(pixel_data, labels, scale_i, scale_j,
mask=mask).all()):
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, value)
return statistics
def filter_labels(self, labels_out, objects, workspace):
"""Filter labels out of the output
Filter labels that are not in the segmented input labels. Optionally
filter labels that are touching the edge.
labels_out - the unfiltered output labels
objects - the objects thing, containing both segmented and
small_removed labels
"""
segmented_labels = objects.segmented
max_out = np.max(labels_out)
if max_out > 0:
segmented_labels, m1 = cpo.size_similarly(labels_out, segmented_labels)
segmented_labels[~m1] = 0
lookup = scind.maximum(segmented_labels, labels_out, range(max_out + 1))
lookup = np.array(lookup, int)
lookup[0] = 0
segmented_labels_out = lookup[labels_out]
else:
segmented_labels_out = labels_out.copy()
if self.wants_discard_edge:
image = workspace.image_set.get_image(self.image_name.value)
if image.has_mask:
mask_border = image.mask & ~scind.binary_erosion(image.mask)
edge_labels = segmented_labels_out[mask_border]
else:
edge_labels = np.hstack(
(
segmented_labels_out[0, :],
segmented_labels_out[-1, :],
segmented_labels_out[:, 0],
segmented_labels_out[:, -1],
)
)
edge_labels = np.unique(edge_labels)
#
# Make a lookup table that translates edge labels to zero
# but translates everything else to itself
#
lookup = np.arange(max(max_out, np.max(segmented_labels)) + 1)
lookup[edge_labels] = 0
#
# Run the segmented labels through this to filter out edge
# labels
segmented_labels_out = lookup[segmented_labels_out]
return segmented_labels_out
def filter_labels(self, labels_out, objects, workspace):
"""Filter labels out of the output
Filter labels that are not in the segmented input labels. Optionally
filter labels that are touching the edge.
labels_out - the unfiltered output labels
objects - the objects thing, containing both segmented and
small_removed labels
"""
segmented_labels = objects.segmented
max_out = np.max(labels_out)
if max_out > 0:
segmented_labels, m1 = cpo.size_similarly(labels_out,
segmented_labels)
segmented_labels[~m1] = 0
lookup = scind.maximum(segmented_labels, labels_out,
range(max_out + 1))
lookup = np.array(lookup, int)
lookup[0] = 0
segmented_labels_out = lookup[labels_out]
else:
segmented_labels_out = labels_out.copy()
if self.wants_discard_edge:
image = workspace.image_set.get_image(self.image_name.value)
if image.has_mask:
mask_border = (image.mask & ~scind.binary_erosion(image.mask))
edge_labels = segmented_labels_out[mask_border]
else:
edge_labels = np.hstack(
(segmented_labels_out[0, :], segmented_labels_out[-1, :],
segmented_labels_out[:, 0], segmented_labels_out[:, -1]))
edge_labels = np.unique(edge_labels)
#
# Make a lookup table that translates edge labels to zero
# but translates everything else to itself
#
lookup = np.arange(max(max_out, np.max(segmented_labels)) + 1)
lookup[edge_labels] = 0
#
# Run the segmented labels through this to filter out edge
# labels
segmented_labels_out = lookup[segmented_labels_out]
return segmented_labels_out
def run(self, workspace):
'''Run the module on an image set'''
object_name = self.object_name.value
remaining_object_name = self.remaining_objects.value
original_objects = workspace.object_set.get_objects(object_name)
if self.mask_choice == MC_IMAGE:
mask = workspace.image_set.get_image(self.masking_image.value,
must_be_binary=True)
mask = mask.pixel_data
else:
masking_objects = workspace.object_set.get_objects(
self.masking_objects.value)
mask = masking_objects.segmented > 0
if self.wants_inverted_mask:
mask = ~mask
#
# Load the labels
#
labels = original_objects.segmented.copy()
nobjects = np.max(labels)
#
# Resize the mask to cover the objects
#
mask, m1 = cpo.size_similarly(labels, mask)
mask[~m1] = False
#
# Apply the mask according to the overlap choice.
#
if nobjects == 0:
pass
elif self.overlap_choice == P_MASK:
labels = labels * mask
else:
pixel_counts = fix(scind.sum(mask, labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_KEEP:
keep = pixel_counts > 0
else:
total_pixels = fix(scind.sum(np.ones(labels.shape), labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_REMOVE:
keep = pixel_counts == total_pixels
elif self.overlap_choice == P_REMOVE_PERCENTAGE:
fraction = self.overlap_fraction.value
keep = pixel_counts / total_pixels >= fraction
else:
raise NotImplementedError("Unknown overlap-handling choice: %s",
self.overlap_choice.value)
keep = np.hstack(([False], keep))
labels[~ keep[labels]] = 0
#
# Renumber the labels matrix if requested
#
if self.retain_or_renumber == R_RENUMBER:
unique_labels = np.unique(labels[labels != 0])
indexer = np.zeros(nobjects + 1, int)
indexer[unique_labels] = np.arange(1, len(unique_labels) + 1)
labels = indexer[labels]
parent_objects = unique_labels
else:
parent_objects = np.arange(1, nobjects + 1)
#
# Add the objects
#
remaining_objects = cpo.Objects()
remaining_objects.segmented = labels
remaining_objects.unedited_segmented = original_objects.unedited_segmented
workspace.object_set.add_objects(remaining_objects,
remaining_object_name)
#
# Add measurements
#
m = workspace.measurements
m.add_measurement(remaining_object_name,
cellprofiler.measurement.FF_PARENT % object_name,
parent_objects)
if np.max(original_objects.segmented) == 0:
child_count = np.array([], int)
else:
child_count = fix(scind.sum(labels, original_objects.segmented,
np.arange(1, nobjects + 1, dtype=np.int32)))
child_count = (child_count > 0).astype(int)
m.add_measurement(object_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % remaining_object_name,
child_count)
if self.retain_or_renumber == R_RETAIN:
remaining_object_count = nobjects
else:
remaining_object_count = len(unique_labels)
I.add_object_count_measurements(m, remaining_object_name,
remaining_object_count)
I.add_object_location_measurements(m, remaining_object_name, labels)
#
# Add an outline if asked to do so
#
if self.wants_outlines.value:
outline_image = cpi.Image(outline(labels) > 0,
parent_image=original_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
#
# Save the input, mask and output images for display
#
if self.show_window:
workspace.display_data.original_labels = original_objects.segmented
workspace.display_data.final_labels = labels
workspace.display_data.mask = mask
def run(self, workspace):
"""Run the module on the current data set
workspace - has the current image set, object set, measurements
and the parent frame for the application if the module
is allowed to display. If the module should not display,
workspace.frame is None.
"""
#
# The object set holds "objects". Each of these is a container
# for holding up to three kinds of image labels.
#
object_set = workspace.object_set
#
# Get the primary objects (the centers to be removed).
# Get the string value out of primary_object_name.
#
primary_objects = object_set.get_objects(
self.primary_objects_name.value)
#
# Get the cleaned-up labels image
#
primary_labels = primary_objects.segmented
#
# Do the same with the secondary object
secondary_objects = object_set.get_objects(
self.secondary_objects_name.value)
secondary_labels = secondary_objects.segmented
#
# If one of the two label images is smaller than the other, we
# try to find the cropping mask and we apply that mask to the larger
#
try:
if any([
p_size < s_size for p_size, s_size in zip(
primary_labels.shape, secondary_labels.shape)
]):
#
# Look for a cropping mask associated with the primary_labels
# and apply that mask to resize the secondary labels
#
secondary_labels = primary_objects.crop_image_similarly(
secondary_labels)
tertiary_image = primary_objects.parent_image
elif any([
p_size > s_size for p_size, s_size in zip(
primary_labels.shape, secondary_labels.shape)
]):
primary_labels = secondary_objects.crop_image_similarly(
primary_labels)
tertiary_image = secondary_objects.parent_image
elif secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
except ValueError:
# No suitable cropping - resize all to fit the secondary
# labels which are the most critical.
#
primary_labels, _ = cpo.size_similarly(secondary_labels,
primary_labels)
if secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
if tertiary_image is not None:
tertiary_image, _ = cpo.size_similarly(
secondary_labels, tertiary_image)
#
# Find the outlines of the primary image and use this to shrink the
# primary image by one. This guarantees that there is something left
# of the secondary image after subtraction
#
primary_outline = outline(primary_labels)
tertiary_labels = secondary_labels.copy()
if self.shrink_primary:
primary_mask = np.logical_or(primary_labels == 0, primary_outline)
else:
primary_mask = primary_labels == 0
tertiary_labels[primary_mask == False] = 0
#
# Get the outlines of the tertiary image
#
tertiary_outlines = outline(tertiary_labels) != 0
#
# Make the tertiary objects container
#
tertiary_objects = cpo.Objects()
tertiary_objects.segmented = tertiary_labels
tertiary_objects.parent_image = tertiary_image
#
# Relate tertiary objects to their parents & record
#
child_count_of_secondary, secondary_parents = \
secondary_objects.relate_children(tertiary_objects)
if self.shrink_primary:
child_count_of_primary, primary_parents = \
primary_objects.relate_children(tertiary_objects)
else:
# Primary and tertiary don't overlap.
# Establish overlap between primary and secondary and commute
_, secondary_of_primary = \
secondary_objects.relate_children(primary_objects)
mask = secondary_of_primary != 0
child_count_of_primary = np.zeros(mask.shape, int)
child_count_of_primary[mask] = child_count_of_secondary[
secondary_of_primary[mask] - 1]
primary_parents = np.zeros(secondary_parents.shape,
secondary_parents.dtype)
primary_of_secondary = np.zeros(secondary_objects.count + 1, int)
primary_of_secondary[secondary_of_primary] = \
np.arange(1, len(secondary_of_primary) + 1)
primary_of_secondary[0] = 0
primary_parents = primary_of_secondary[secondary_parents]
#
# Write out the objects
#
workspace.object_set.add_objects(tertiary_objects,
self.subregion_objects_name.value)
#
# Write out the measurements
#
m = workspace.measurements
#
# The parent/child associations
#
for parent_objects_name, parents_of, child_count, relationship in (
(self.primary_objects_name, primary_parents,
child_count_of_primary, R_REMOVED),
(self.secondary_objects_name, secondary_parents,
child_count_of_secondary, R_PARENT)):
m.add_measurement(
self.subregion_objects_name.value,
cellprofiler.measurement.FF_PARENT % parent_objects_name.value,
parents_of)
m.add_measurement(
parent_objects_name.value,
cellprofiler.measurement.FF_CHILDREN_COUNT %
self.subregion_objects_name.value, child_count)
mask = parents_of != 0
image_number = np.ones(np.sum(mask), int) * m.image_set_number
child_object_number = np.argwhere(mask).flatten() + 1
parent_object_number = parents_of[mask]
m.add_relate_measurement(self.module_num, relationship,
parent_objects_name.value,
self.subregion_objects_name.value,
image_number, parent_object_number,
image_number, child_object_number)
object_count = tertiary_objects.count
#
# The object count
#
cpmi.add_object_count_measurements(workspace.measurements,
self.subregion_objects_name.value,
object_count)
#
# The object locations
#
cpmi.add_object_location_measurements(
workspace.measurements, self.subregion_objects_name.value,
tertiary_labels)
if self.show_window:
workspace.display_data.primary_labels = primary_labels
workspace.display_data.secondary_labels = secondary_labels
workspace.display_data.tertiary_labels = tertiary_labels
workspace.display_data.tertiary_outlines = tertiary_outlines
def run_image_pair_objects(self, workspace, first_image_name,
second_image_name, object_name):
'''Calculate per-object correlations between intensities in two images'''
first_image = workspace.image_set.get_image(first_image_name,
must_be_grayscale=True)
second_image = workspace.image_set.get_image(second_image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
#
# Crop both images to the size of the labels matrix
#
labels = objects.segmented
try:
first_pixels = objects.crop_image_similarly(first_image.pixel_data)
first_mask = objects.crop_image_similarly(first_image.mask)
except ValueError:
first_pixels, m1 = cpo.size_similarly(labels, first_image.pixel_data)
first_mask, m1 = cpo.size_similarly(labels, first_image.mask)
first_mask[~m1] = False
try:
second_pixels = objects.crop_image_similarly(second_image.pixel_data)
second_mask = objects.crop_image_similarly(second_image.mask)
except ValueError:
second_pixels, m1 = cpo.size_similarly(labels, second_image.pixel_data)
second_mask, m1 = cpo.size_similarly(labels, second_image.mask)
second_mask[~m1] = False
mask = ((labels > 0) & first_mask & second_mask)
first_pixels = first_pixels[mask]
second_pixels = second_pixels[mask]
labels = labels[mask]
result = []
first_pixel_data = first_image.pixel_data
first_mask = first_image.mask
first_pixel_count = np.product(first_pixel_data.shape)
second_pixel_data = second_image.pixel_data
second_mask = second_image.mask
second_pixel_count = np.product(second_pixel_data.shape)
#
# Crop the larger image similarly to the smaller one
#
if first_pixel_count < second_pixel_count:
second_pixel_data = first_image.crop_image_similarly(second_pixel_data)
second_mask = first_image.crop_image_similarly(second_mask)
elif second_pixel_count < first_pixel_count:
first_pixel_data = second_image.crop_image_similarly(first_pixel_data)
first_mask = second_image.crop_image_similarly(first_mask)
mask = (first_mask & second_mask &
(~ np.isnan(first_pixel_data)) &
(~ np.isnan(second_pixel_data)))
if np.any(mask):
#
# Perform the correlation, which returns:
# [ [ii, ij],
# [ji, jj] ]
#
fi = first_pixel_data[mask]
si = second_pixel_data[mask]
n_objects = objects.count
# Handle case when both images for the correlation are completely masked out
if n_objects == 0:
corr = np.zeros((0,))
overlap = np.zeros((0,))
K1 = np.zeros((0,))
K2 = np.zeros((0,))
M1 = np.zeros((0,))
M2 = np.zeros((0,))
RWC1 = np.zeros((0,))
RWC2 = np.zeros((0,))
C1 = np.zeros((0,))
C2 = np.zeros((0,))
elif np.where(mask)[0].__len__() == 0:
corr = np.zeros((n_objects,))
corr[:] = np.NaN
overlap = K1 = K2 = M1 = M2 = RWC1 = RWC2 = C1 = C2 = corr
else:
#
# The correlation is sum((x-mean(x))(y-mean(y)) /
# ((n-1) * std(x) *std(y)))
#
lrange = np.arange(n_objects, dtype=np.int32) + 1
area = fix(scind.sum(np.ones_like(labels), labels, lrange))
mean1 = fix(scind.mean(first_pixels, labels, lrange))
mean2 = fix(scind.mean(second_pixels, labels, lrange))
#
# Calculate the standard deviation times the population.
#
std1 = np.sqrt(fix(scind.sum((first_pixels - mean1[labels - 1]) ** 2,
labels, lrange)))
std2 = np.sqrt(fix(scind.sum((second_pixels - mean2[labels - 1]) ** 2,
labels, lrange)))
x = first_pixels - mean1[labels - 1] # x - mean(x)
y = second_pixels - mean2[labels - 1] # y - mean(y)
corr = fix(scind.sum(x * y / (std1[labels - 1] * std2[labels - 1]),
labels, lrange))
# Explicitly set the correlation to NaN for masked objects
corr[scind.sum(1, labels, lrange) == 0] = np.NaN
result += [
[first_image_name, second_image_name, object_name, "Mean Correlation coeff", "%.3f" % np.mean(corr)],
[first_image_name, second_image_name, object_name, "Median Correlation coeff",
"%.3f" % np.median(corr)],
[first_image_name, second_image_name, object_name, "Min Correlation coeff", "%.3f" % np.min(corr)],
[first_image_name, second_image_name, object_name, "Max Correlation coeff", "%.3f" % np.max(corr)]]
# Threshold as percentage of maximum intensity of objects in each channel
tff = (self.thr.value / 100) * fix(scind.maximum(first_pixels, labels, lrange))
tss = (self.thr.value / 100) * fix(scind.maximum(second_pixels, labels, lrange))
combined_thresh = (first_pixels >= tff[labels - 1]) & (second_pixels >= tss[labels - 1])
fi_thresh = first_pixels[combined_thresh]
si_thresh = second_pixels[combined_thresh]
tot_fi_thr = scind.sum(first_pixels[first_pixels >= tff[labels - 1]], labels[first_pixels >= tff[labels - 1]],
lrange)
tot_si_thr = scind.sum(second_pixels[second_pixels >= tss[labels - 1]],
labels[second_pixels >= tss[labels - 1]], lrange)
nonZero = (fi > 0) | (si > 0)
xvar = np.var(fi[nonZero], axis=0, ddof=1)
yvar = np.var(si[nonZero], axis=0, ddof=1)
xmean = np.mean(fi[nonZero], axis=0)
ymean = np.mean(si[nonZero], axis=0)
z = fi[nonZero] + si[nonZero]
zvar = np.var(z, axis=0, ddof=1)
covar = 0.5 * (zvar - (xvar + yvar))
denom = 2 * covar
num = (yvar - xvar) + np.sqrt((yvar - xvar) * (yvar - xvar) + 4 * (covar * covar))
a = (num / denom)
b = (ymean - a * xmean)
i = 1
while i > 0.003921568627:
thr_fi_c = i
thr_si_c = (a * i) + b
combt = (fi < thr_fi_c) | (si < thr_si_c)
costReg = scistat.pearsonr(fi[combt], si[combt])
if costReg[0] <= 0:
break
i = i - 0.003921568627
# Costes' thershold for entire image is applied to each object
fi_above_thr = first_pixels > thr_fi_c
si_above_thr = second_pixels > thr_si_c
combined_thresh_c = fi_above_thr & si_above_thr
fi_thresh_c = first_pixels[combined_thresh_c]
si_thresh_c = second_pixels[combined_thresh_c]
if np.any(fi_above_thr):
tot_fi_thr_c = scind.sum(first_pixels[first_pixels >= thr_fi_c], labels[first_pixels >= thr_fi_c], lrange)
else:
tot_fi_thr_c = np.zeros(len(lrange))
if np.any(si_above_thr):
tot_si_thr_c = scind.sum(second_pixels[second_pixels >= thr_si_c], labels[second_pixels >= thr_si_c],
lrange)
else:
tot_si_thr_c = np.zeros(len(lrange))
# Manders Coefficient
M1 = np.zeros(len(lrange))
M2 = np.zeros(len(lrange))
if np.any(combined_thresh):
M1 = np.array(scind.sum(fi_thresh,labels[combined_thresh],lrange)) / np.array(tot_fi_thr)
M2 = np.array(scind.sum(si_thresh,labels[combined_thresh],lrange)) / np.array(tot_si_thr)
result += [[first_image_name, second_image_name, object_name,"Mean Manders coeff","%.3f"%np.mean(M1)],
[first_image_name, second_image_name, object_name,"Median Manders coeff","%.3f"%np.median(M1)],
[first_image_name, second_image_name, object_name,"Min Manders coeff","%.3f"%np.min(M1)],
[first_image_name, second_image_name, object_name,"Max Manders coeff","%.3f"%np.max(M1)]]
result += [[second_image_name, first_image_name, object_name,"Mean Manders coeff","%.3f"%np.mean(M2)],
[second_image_name, first_image_name, object_name,"Median Manders coeff","%.3f"%np.median(M2)],
[second_image_name, first_image_name, object_name,"Min Manders coeff","%.3f"%np.min(M2)],
[second_image_name, first_image_name, object_name,"Max Manders coeff","%.3f"%np.max(M2)]]
# RWC Coefficient
RWC1 = np.zeros(len(lrange))
RWC2 = np.zeros(len(lrange))
[Rank1] = np.lexsort(([labels], [first_pixels]))
[Rank2] = np.lexsort(([labels], [second_pixels]))
Rank1_U = np.hstack([[False], first_pixels[Rank1[:-1]] != first_pixels[Rank1[1:]]])
Rank2_U = np.hstack([[False], second_pixels[Rank2[:-1]] != second_pixels[Rank2[1:]]])
Rank1_S = np.cumsum(Rank1_U)
Rank2_S = np.cumsum(Rank2_U)
Rank_im1 = np.zeros(first_pixels.shape, dtype=int)
Rank_im2 = np.zeros(second_pixels.shape, dtype=int)
Rank_im1[Rank1] = Rank1_S
Rank_im2[Rank2] = Rank2_S
R = max(Rank_im1.max(), Rank_im2.max()) + 1
Di = abs(Rank_im1 - Rank_im2)
weight = (R - Di) * 1.0 / R
weight_thresh = weight[combined_thresh]
if np.any(combined_thresh):
RWC1 = np.array(scind.sum(fi_thresh * weight_thresh, labels[combined_thresh], lrange)) / np.array(
tot_fi_thr)
RWC2 = np.array(scind.sum(si_thresh * weight_thresh, labels[combined_thresh], lrange)) / np.array(
tot_si_thr)
result += [[first_image_name, second_image_name, object_name, "Mean RWC coeff", "%.3f" % np.mean(RWC1)],
[first_image_name, second_image_name, object_name, "Median RWC coeff", "%.3f" % np.median(RWC1)],
[first_image_name, second_image_name, object_name, "Min RWC coeff", "%.3f" % np.min(RWC1)],
[first_image_name, second_image_name, object_name, "Max RWC coeff", "%.3f" % np.max(RWC1)]]
result += [[second_image_name, first_image_name, object_name, "Mean RWC coeff", "%.3f" % np.mean(RWC2)],
[second_image_name, first_image_name, object_name, "Median RWC coeff", "%.3f" % np.median(RWC2)],
[second_image_name, first_image_name, object_name, "Min RWC coeff", "%.3f" % np.min(RWC2)],
[second_image_name, first_image_name, object_name, "Max RWC coeff", "%.3f" % np.max(RWC2)]]
# Costes Automated Threshold
C1 = np.zeros(len(lrange))
C2 = np.zeros(len(lrange))
if np.any(combined_thresh_c):
C1 = np.array(scind.sum(fi_thresh_c,labels[combined_thresh_c],lrange)) / np.array(tot_fi_thr_c)
C2 = np.array(scind.sum(si_thresh_c,labels[combined_thresh_c],lrange)) / np.array(tot_si_thr_c)
result += [[first_image_name, second_image_name, object_name,"Mean Manders coeff (Costes)","%.3f"%np.mean(C1)],
[first_image_name, second_image_name, object_name,"Median Manders coeff (Costes)","%.3f"%np.median(C1)],
[first_image_name, second_image_name, object_name,"Min Manders coeff (Costes)","%.3f"%np.min(C1)],
[first_image_name, second_image_name, object_name,"Max Manders coeff (Costes)","%.3f"%np.max(C1)]
]
result += [[second_image_name, first_image_name, object_name,"Mean Manders coeff (Costes)","%.3f"%np.mean(C2)],
[second_image_name, first_image_name, object_name,"Median Manders coeff (Costes)","%.3f"%np.median(C2)],
[second_image_name, first_image_name, object_name,"Min Manders coeff (Costes)","%.3f"%np.min(C2)],
[second_image_name, first_image_name, object_name,"Max Manders coeff (Costes)","%.3f"%np.max(C2)]
]
# Overlap Coefficient
if np.any(combined_thresh):
fpsq = scind.sum(first_pixels[combined_thresh] ** 2, labels[combined_thresh], lrange)
spsq = scind.sum(second_pixels[combined_thresh] ** 2, labels[combined_thresh], lrange)
pdt = np.sqrt(np.array(fpsq) * np.array(spsq))
overlap = fix(
scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh],
labels[combined_thresh],
lrange) / pdt)
K1 = fix((scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh],
labels[combined_thresh], lrange)) / (np.array(fpsq)))
K2 = fix(
scind.sum(first_pixels[combined_thresh] * second_pixels[combined_thresh],
labels[combined_thresh],
lrange) / np.array(spsq))
else:
overlap = K1 = K2 = np.zeros(len(lrange))
result += [
[first_image_name, second_image_name, object_name, "Mean Overlap coeff", "%.3f" % np.mean(overlap)],
[first_image_name, second_image_name, object_name, "Median Overlap coeff", "%.3f" % np.median(overlap)],
[first_image_name, second_image_name, object_name, "Min Overlap coeff", "%.3f" % np.min(overlap)],
[first_image_name, second_image_name, object_name, "Max Overlap coeff", "%.3f" % np.max(overlap)]]
measurement = ("Correlation_Correlation_%s_%s" %
(first_image_name, second_image_name))
overlap_measurement = (F_OVERLAP_FORMAT % (first_image_name,
second_image_name))
k_measurement_1 = (F_K_FORMAT % (first_image_name,
second_image_name))
k_measurement_2 = (F_K_FORMAT % (second_image_name,
first_image_name))
manders_measurement_1 = (F_MANDERS_FORMAT % (first_image_name,
second_image_name))
manders_measurement_2 = (F_MANDERS_FORMAT % (second_image_name,
first_image_name))
rwc_measurement_1 = (F_RWC_FORMAT % (first_image_name,
second_image_name))
rwc_measurement_2 = (F_RWC_FORMAT % (second_image_name,
first_image_name))
costes_measurement_1 = (F_COSTES_FORMAT % (first_image_name,
second_image_name))
costes_measurement_2 = (F_COSTES_FORMAT % (second_image_name,
first_image_name))
workspace.measurements.add_measurement(object_name, measurement, corr)
workspace.measurements.add_measurement(object_name, overlap_measurement, overlap)
workspace.measurements.add_measurement(object_name, k_measurement_1, K1)
workspace.measurements.add_measurement(object_name, k_measurement_2, K2)
workspace.measurements.add_measurement(object_name, manders_measurement_1, M1)
workspace.measurements.add_measurement(object_name, manders_measurement_2, M2)
workspace.measurements.add_measurement(object_name, rwc_measurement_1, RWC1)
workspace.measurements.add_measurement(object_name, rwc_measurement_2, RWC2)
workspace.measurements.add_measurement(object_name, costes_measurement_1, C1)
workspace.measurements.add_measurement(object_name, costes_measurement_2, C2)
if n_objects == 0:
return [[first_image_name, second_image_name, object_name,
"Mean correlation", "-"],
[first_image_name, second_image_name, object_name,
"Median correlation", "-"],
[first_image_name, second_image_name, object_name,
"Min correlation", "-"],
[first_image_name, second_image_name, object_name,
"Max correlation", "-"]]
else:
return result
def run(self, workspace):
"""Run the module on the current data set
workspace - has the current image set, object set, measurements
and the parent frame for the application if the module
is allowed to display. If the module should not display,
workspace.frame is None.
"""
#
# The object set holds "objects". Each of these is a container
# for holding up to three kinds of image labels.
#
object_set = workspace.object_set
#
# Get the primary objects (the centers to be removed).
# Get the string value out of primary_object_name.
#
primary_objects = object_set.get_objects(self.primary_objects_name.value)
#
# Get the cleaned-up labels image
#
primary_labels = primary_objects.segmented
#
# Do the same with the secondary object
secondary_objects = object_set.get_objects(self.secondary_objects_name.value)
secondary_labels = secondary_objects.segmented
#
# If one of the two label images is smaller than the other, we
# try to find the cropping mask and we apply that mask to the larger
#
try:
if any([p_size < s_size for p_size, s_size in zip(primary_labels.shape, secondary_labels.shape)]):
#
# Look for a cropping mask associated with the primary_labels
# and apply that mask to resize the secondary labels
#
secondary_labels = primary_objects.crop_image_similarly(secondary_labels)
tertiary_image = primary_objects.parent_image
elif any([p_size > s_size for p_size, s_size in zip(primary_labels.shape, secondary_labels.shape)]):
primary_labels = secondary_objects.crop_image_similarly(primary_labels)
tertiary_image = secondary_objects.parent_image
elif secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
except ValueError:
# No suitable cropping - resize all to fit the secondary
# labels which are the most critical.
#
primary_labels, _ = cpo.size_similarly(secondary_labels, primary_labels)
if secondary_objects.parent_image is not None:
tertiary_image = secondary_objects.parent_image
else:
tertiary_image = primary_objects.parent_image
if tertiary_image is not None:
tertiary_image, _ = cpo.size_similarly(secondary_labels, tertiary_image)
#
# Find the outlines of the primary image and use this to shrink the
# primary image by one. This guarantees that there is something left
# of the secondary image after subtraction
#
primary_outline = outline(primary_labels)
tertiary_labels = secondary_labels.copy()
if self.shrink_primary:
primary_mask = np.logical_or(primary_labels == 0, primary_outline)
else:
primary_mask = primary_labels == 0
tertiary_labels[primary_mask == False] = 0
#
# Get the outlines of the tertiary image
#
tertiary_outlines = outline(tertiary_labels) != 0
#
# Make the tertiary objects container
#
tertiary_objects = cpo.Objects()
tertiary_objects.segmented = tertiary_labels
tertiary_objects.parent_image = tertiary_image
#
# Relate tertiary objects to their parents & record
#
child_count_of_secondary, secondary_parents = secondary_objects.relate_children(tertiary_objects)
if self.shrink_primary:
child_count_of_primary, primary_parents = primary_objects.relate_children(tertiary_objects)
else:
# Primary and tertiary don't overlap.
# Establish overlap between primary and secondary and commute
_, secondary_of_primary = secondary_objects.relate_children(primary_objects)
mask = secondary_of_primary != 0
child_count_of_primary = np.zeros(mask.shape, int)
child_count_of_primary[mask] = child_count_of_secondary[secondary_of_primary[mask] - 1]
primary_parents = np.zeros(secondary_parents.shape, secondary_parents.dtype)
primary_of_secondary = np.zeros(secondary_objects.count + 1, int)
primary_of_secondary[secondary_of_primary] = np.arange(1, len(secondary_of_primary) + 1)
primary_of_secondary[0] = 0
primary_parents = primary_of_secondary[secondary_parents]
#
# Write out the objects
#
workspace.object_set.add_objects(tertiary_objects, self.subregion_objects_name.value)
#
# Write out the measurements
#
m = workspace.measurements
#
# The parent/child associations
#
for parent_objects_name, parents_of, child_count, relationship in (
(self.primary_objects_name, primary_parents, child_count_of_primary, R_REMOVED),
(self.secondary_objects_name, secondary_parents, child_count_of_secondary, R_PARENT),
):
m.add_measurement(self.subregion_objects_name.value, cpmi.FF_PARENT % parent_objects_name.value, parents_of)
m.add_measurement(
parent_objects_name.value, cpmi.FF_CHILDREN_COUNT % self.subregion_objects_name.value, child_count
)
mask = parents_of != 0
image_number = np.ones(np.sum(mask), int) * m.image_set_number
child_object_number = np.argwhere(mask).flatten() + 1
parent_object_number = parents_of[mask]
m.add_relate_measurement(
self.module_num,
relationship,
parent_objects_name.value,
self.subregion_objects_name.value,
image_number,
parent_object_number,
image_number,
child_object_number,
)
object_count = tertiary_objects.count
#
# The object count
#
cpmi.add_object_count_measurements(workspace.measurements, self.subregion_objects_name.value, object_count)
#
# The object locations
#
cpmi.add_object_location_measurements(
workspace.measurements, self.subregion_objects_name.value, tertiary_labels
)
#
# The outlines
#
if self.use_outlines.value:
out_img = cpi.Image(tertiary_outlines.astype(bool), parent_image=tertiary_image)
workspace.image_set.add(self.outlines_name.value, out_img)
if self.show_window:
workspace.display_data.primary_labels = primary_labels
workspace.display_data.secondary_labels = secondary_labels
workspace.display_data.tertiary_labels = tertiary_labels
workspace.display_data.tertiary_outlines = tertiary_outlines
def run(self, workspace):
'''Run the module on an image set'''
object_name = self.object_name.value
remaining_object_name = self.remaining_objects.value
original_objects = workspace.object_set.get_objects(object_name)
if self.mask_choice == MC_IMAGE:
mask = workspace.image_set.get_image(self.masking_image.value,
must_be_binary=True)
mask = mask.pixel_data
else:
masking_objects = workspace.object_set.get_objects(
self.masking_objects.value)
mask = masking_objects.segmented > 0
if self.wants_inverted_mask:
mask = ~mask
#
# Load the labels
#
labels = original_objects.segmented.copy()
nobjects = np.max(labels)
#
# Resize the mask to cover the objects
#
mask, m1 = cpo.size_similarly(labels, mask)
mask[~m1] = False
#
# Apply the mask according to the overlap choice.
#
if nobjects == 0:
pass
elif self.overlap_choice == P_MASK:
labels = labels * mask
else:
pixel_counts = fix(
scind.sum(mask, labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_KEEP:
keep = pixel_counts > 0
else:
total_pixels = fix(
scind.sum(np.ones(labels.shape), labels,
np.arange(1, nobjects + 1, dtype=np.int32)))
if self.overlap_choice == P_REMOVE:
keep = pixel_counts == total_pixels
elif self.overlap_choice == P_REMOVE_PERCENTAGE:
fraction = self.overlap_fraction.value
keep = pixel_counts / total_pixels >= fraction
else:
raise NotImplementedError(
"Unknown overlap-handling choice: %s",
self.overlap_choice.value)
keep = np.hstack(([False], keep))
labels[~keep[labels]] = 0
#
# Renumber the labels matrix if requested
#
if self.retain_or_renumber == R_RENUMBER:
unique_labels = np.unique(labels[labels != 0])
indexer = np.zeros(nobjects + 1, int)
indexer[unique_labels] = np.arange(1, len(unique_labels) + 1)
labels = indexer[labels]
parent_objects = unique_labels
else:
parent_objects = np.arange(1, nobjects + 1)
#
# Add the objects
#
remaining_objects = cpo.Objects()
remaining_objects.segmented = labels
remaining_objects.unedited_segmented = original_objects.unedited_segmented
workspace.object_set.add_objects(remaining_objects,
remaining_object_name)
#
# Add measurements
#
m = workspace.measurements
m.add_measurement(remaining_object_name,
cellprofiler.measurement.FF_PARENT % object_name,
parent_objects)
if np.max(original_objects.segmented) == 0:
child_count = np.array([], int)
else:
child_count = fix(
scind.sum(labels, original_objects.segmented,
np.arange(1, nobjects + 1, dtype=np.int32)))
child_count = (child_count > 0).astype(int)
m.add_measurement(
object_name,
cellprofiler.measurement.FF_CHILDREN_COUNT % remaining_object_name,
child_count)
if self.retain_or_renumber == R_RETAIN:
remaining_object_count = nobjects
else:
remaining_object_count = len(unique_labels)
I.add_object_count_measurements(m, remaining_object_name,
remaining_object_count)
I.add_object_location_measurements(m, remaining_object_name, labels)
#
# Save the input, mask and output images for display
#
if self.show_window:
workspace.display_data.original_labels = original_objects.segmented
workspace.display_data.final_labels = labels
workspace.display_data.mask = mask
def run_image_pair_objects(self, workspace, first_image_name,
second_image_name, object_name):
'''Calculate per-object correlations between intensities in two images'''
first_image = workspace.image_set.get_image(first_image_name,
must_be_grayscale=True)
second_image = workspace.image_set.get_image(second_image_name,
must_be_grayscale=True)
objects = workspace.object_set.get_objects(object_name)
#
# Crop both images to the size of the labels matrix
#
labels = objects.segmented
try:
first_pixels = objects.crop_image_similarly(first_image.pixel_data)
first_mask = objects.crop_image_similarly(first_image.mask)
except ValueError:
first_pixels, m1 = cpo.size_similarly(labels,
first_image.pixel_data)
first_mask, m1 = cpo.size_similarly(labels, first_image.mask)
first_mask[~m1] = False
try:
second_pixels = objects.crop_image_similarly(
second_image.pixel_data)
second_mask = objects.crop_image_similarly(second_image.mask)
except ValueError:
second_pixels, m1 = cpo.size_similarly(labels,
second_image.pixel_data)
second_mask, m1 = cpo.size_similarly(labels, second_image.mask)
second_mask[~m1] = False
mask = ((labels > 0) & first_mask & second_mask)
first_pixels = first_pixels[mask]
second_pixels = second_pixels[mask]
labels = labels[mask]
result = []
first_pixel_data = first_image.pixel_data
first_mask = first_image.mask
first_pixel_count = np.product(first_pixel_data.shape)
second_pixel_data = second_image.pixel_data
second_mask = second_image.mask
second_pixel_count = np.product(second_pixel_data.shape)
#
# Crop the larger image similarly to the smaller one
#
if first_pixel_count < second_pixel_count:
second_pixel_data = first_image.crop_image_similarly(
second_pixel_data)
second_mask = first_image.crop_image_similarly(second_mask)
elif second_pixel_count < first_pixel_count:
first_pixel_data = second_image.crop_image_similarly(
first_pixel_data)
first_mask = second_image.crop_image_similarly(first_mask)
mask = (first_mask & second_mask & (~np.isnan(first_pixel_data)) &
(~np.isnan(second_pixel_data)))
if np.any(mask):
#
# Perform the correlation, which returns:
# [ [ii, ij],
# [ji, jj] ]
#
fi = first_pixel_data[mask]
si = second_pixel_data[mask]
n_objects = objects.count
# Handle case when both images for the correlation are completely masked out
if n_objects == 0:
corr = np.zeros((0, ))
overlap = np.zeros((0, ))
K1 = np.zeros((0, ))
K2 = np.zeros((0, ))
M1 = np.zeros((0, ))
M2 = np.zeros((0, ))
RWC1 = np.zeros((0, ))
RWC2 = np.zeros((0, ))
C1 = np.zeros((0, ))
C2 = np.zeros((0, ))
elif np.where(mask)[0].__len__() == 0:
corr = np.zeros((n_objects, ))
corr[:] = np.NaN
overlap = K1 = K2 = M1 = M2 = RWC1 = RWC2 = C1 = C2 = corr
else:
#
# The correlation is sum((x-mean(x))(y-mean(y)) /
# ((n-1) * std(x) *std(y)))
#
lrange = np.arange(n_objects, dtype=np.int32) + 1
area = fix(scind.sum(np.ones_like(labels), labels, lrange))
mean1 = fix(scind.mean(first_pixels, labels, lrange))
mean2 = fix(scind.mean(second_pixels, labels, lrange))
#
# Calculate the standard deviation times the population.
#
std1 = np.sqrt(
fix(
scind.sum((first_pixels - mean1[labels - 1])**2, labels,
lrange)))
std2 = np.sqrt(
fix(
scind.sum((second_pixels - mean2[labels - 1])**2, labels,
lrange)))
x = first_pixels - mean1[labels - 1] # x - mean(x)
y = second_pixels - mean2[labels - 1] # y - mean(y)
corr = fix(
scind.sum(x * y / (std1[labels - 1] * std2[labels - 1]),
labels, lrange))
# Explicitly set the correlation to NaN for masked objects
corr[scind.sum(1, labels, lrange) == 0] = np.NaN
result += [[
first_image_name, second_image_name, object_name,
"Mean Correlation coeff",
"%.3f" % np.mean(corr)
],
[
first_image_name, second_image_name, object_name,
"Median Correlation coeff",
"%.3f" % np.median(corr)
],
[
first_image_name, second_image_name, object_name,
"Min Correlation coeff",
"%.3f" % np.min(corr)
],
[
first_image_name, second_image_name, object_name,
"Max Correlation coeff",
"%.3f" % np.max(corr)
]]
# Threshold as percentage of maximum intensity of objects in each channel
tff = (self.thr.value / 100) * fix(
scind.maximum(first_pixels, labels, lrange))
tss = (self.thr.value / 100) * fix(
scind.maximum(second_pixels, labels, lrange))
combined_thresh = (first_pixels >= tff[labels - 1]) & (
second_pixels >= tss[labels - 1])
fi_thresh = first_pixels[combined_thresh]
si_thresh = second_pixels[combined_thresh]
tot_fi_thr = scind.sum(
first_pixels[first_pixels >= tff[labels - 1]],
labels[first_pixels >= tff[labels - 1]], lrange)
tot_si_thr = scind.sum(
second_pixels[second_pixels >= tss[labels - 1]],
labels[second_pixels >= tss[labels - 1]], lrange)
nonZero = (fi > 0) | (si > 0)
xvar = np.var(fi[nonZero], axis=0, ddof=1)
yvar = np.var(si[nonZero], axis=0, ddof=1)
xmean = np.mean(fi[nonZero], axis=0)
ymean = np.mean(si[nonZero], axis=0)
z = fi[nonZero] + si[nonZero]
zvar = np.var(z, axis=0, ddof=1)
covar = 0.5 * (zvar - (xvar + yvar))
denom = 2 * covar
num = (yvar - xvar) + np.sqrt((yvar - xvar) * (yvar - xvar) + 4 *
(covar * covar))
a = (num / denom)
b = (ymean - a * xmean)
i = 1
while i > 0.003921568627:
thr_fi_c = i
thr_si_c = (a * i) + b
combt = (fi < thr_fi_c) | (si < thr_si_c)
costReg = scistat.pearsonr(fi[combt], si[combt])
if costReg[0] <= 0:
break
i = i - 0.003921568627
# Costes' thershold for entire image is applied to each object
fi_above_thr = first_pixels > thr_fi_c
si_above_thr = second_pixels > thr_si_c
combined_thresh_c = fi_above_thr & si_above_thr
fi_thresh_c = first_pixels[combined_thresh_c]
si_thresh_c = second_pixels[combined_thresh_c]
if np.any(fi_above_thr):
tot_fi_thr_c = scind.sum(
first_pixels[first_pixels >= thr_fi_c],
labels[first_pixels >= thr_fi_c], lrange)
else:
tot_fi_thr_c = np.zeros(len(lrange))
if np.any(si_above_thr):
tot_si_thr_c = scind.sum(
second_pixels[second_pixels >= thr_si_c],
labels[second_pixels >= thr_si_c], lrange)
else:
tot_si_thr_c = np.zeros(len(lrange))
# Manders Coefficient
M1 = np.zeros(len(lrange))
M2 = np.zeros(len(lrange))
if np.any(combined_thresh):
M1 = np.array(
scind.sum(fi_thresh, labels[combined_thresh],
lrange)) / np.array(tot_fi_thr)
M2 = np.array(
scind.sum(si_thresh, labels[combined_thresh],
lrange)) / np.array(tot_si_thr)
result += [[
first_image_name, second_image_name, object_name,
"Mean Manders coeff",
"%.3f" % np.mean(M1)
],
[
first_image_name, second_image_name, object_name,
"Median Manders coeff",
"%.3f" % np.median(M1)
],
[
first_image_name, second_image_name, object_name,
"Min Manders coeff",
"%.3f" % np.min(M1)
],
[
first_image_name, second_image_name, object_name,
"Max Manders coeff",
"%.3f" % np.max(M1)
]]
result += [[
second_image_name, first_image_name, object_name,
"Mean Manders coeff",
"%.3f" % np.mean(M2)
],
[
second_image_name, first_image_name, object_name,
"Median Manders coeff",
"%.3f" % np.median(M2)
],
[
second_image_name, first_image_name, object_name,
"Min Manders coeff",
"%.3f" % np.min(M2)
],
[
second_image_name, first_image_name, object_name,
"Max Manders coeff",
"%.3f" % np.max(M2)
]]
# RWC Coefficient
RWC1 = np.zeros(len(lrange))
RWC2 = np.zeros(len(lrange))
[Rank1] = np.lexsort(([labels], [first_pixels]))
[Rank2] = np.lexsort(([labels], [second_pixels]))
Rank1_U = np.hstack(
[[False], first_pixels[Rank1[:-1]] != first_pixels[Rank1[1:]]])
Rank2_U = np.hstack(
[[False],
second_pixels[Rank2[:-1]] != second_pixels[Rank2[1:]]])
Rank1_S = np.cumsum(Rank1_U)
Rank2_S = np.cumsum(Rank2_U)
Rank_im1 = np.zeros(first_pixels.shape, dtype=int)
Rank_im2 = np.zeros(second_pixels.shape, dtype=int)
Rank_im1[Rank1] = Rank1_S
Rank_im2[Rank2] = Rank2_S
R = max(Rank_im1.max(), Rank_im2.max()) + 1
Di = abs(Rank_im1 - Rank_im2)
weight = (R - Di) * 1.0 / R
weight_thresh = weight[combined_thresh]
if np.any(combined_thresh):
RWC1 = np.array(
scind.sum(fi_thresh * weight_thresh,
labels[combined_thresh],
lrange)) / np.array(tot_fi_thr)
RWC2 = np.array(
scind.sum(si_thresh * weight_thresh,
labels[combined_thresh],
lrange)) / np.array(tot_si_thr)
result += [[
first_image_name, second_image_name, object_name,
"Mean RWC coeff",
"%.3f" % np.mean(RWC1)
],
[
first_image_name, second_image_name, object_name,
"Median RWC coeff",
"%.3f" % np.median(RWC1)
],
[
first_image_name, second_image_name, object_name,
"Min RWC coeff",
"%.3f" % np.min(RWC1)
],
[
first_image_name, second_image_name, object_name,
"Max RWC coeff",
"%.3f" % np.max(RWC1)
]]
result += [[
second_image_name, first_image_name, object_name,
"Mean RWC coeff",
"%.3f" % np.mean(RWC2)
],
[
second_image_name, first_image_name, object_name,
"Median RWC coeff",
"%.3f" % np.median(RWC2)
],
[
second_image_name, first_image_name, object_name,
"Min RWC coeff",
"%.3f" % np.min(RWC2)
],
[
second_image_name, first_image_name, object_name,
"Max RWC coeff",
"%.3f" % np.max(RWC2)
]]
# Costes Automated Threshold
C1 = np.zeros(len(lrange))
C2 = np.zeros(len(lrange))
if np.any(combined_thresh_c):
C1 = np.array(
scind.sum(fi_thresh_c, labels[combined_thresh_c],
lrange)) / np.array(tot_fi_thr_c)
C2 = np.array(
scind.sum(si_thresh_c, labels[combined_thresh_c],
lrange)) / np.array(tot_si_thr_c)
result += [[
first_image_name, second_image_name, object_name,
"Mean Manders coeff (Costes)",
"%.3f" % np.mean(C1)
],
[
first_image_name, second_image_name, object_name,
"Median Manders coeff (Costes)",
"%.3f" % np.median(C1)
],
[
first_image_name, second_image_name, object_name,
"Min Manders coeff (Costes)",
"%.3f" % np.min(C1)
],
[
first_image_name, second_image_name, object_name,
"Max Manders coeff (Costes)",
"%.3f" % np.max(C1)
]]
result += [[
second_image_name, first_image_name, object_name,
"Mean Manders coeff (Costes)",
"%.3f" % np.mean(C2)
],
[
second_image_name, first_image_name, object_name,
"Median Manders coeff (Costes)",
"%.3f" % np.median(C2)
],
[
second_image_name, first_image_name, object_name,
"Min Manders coeff (Costes)",
"%.3f" % np.min(C2)
],
[
second_image_name, first_image_name, object_name,
"Max Manders coeff (Costes)",
"%.3f" % np.max(C2)
]]
# Overlap Coefficient
if np.any(combined_thresh):
fpsq = scind.sum(first_pixels[combined_thresh]**2,
labels[combined_thresh], lrange)
spsq = scind.sum(second_pixels[combined_thresh]**2,
labels[combined_thresh], lrange)
pdt = np.sqrt(np.array(fpsq) * np.array(spsq))
overlap = fix(
scind.sum(
first_pixels[combined_thresh] *
second_pixels[combined_thresh],
labels[combined_thresh], lrange) / pdt)
K1 = fix((scind.sum(
first_pixels[combined_thresh] *
second_pixels[combined_thresh], labels[combined_thresh],
lrange)) / (np.array(fpsq)))
K2 = fix(
scind.sum(
first_pixels[combined_thresh] *
second_pixels[combined_thresh],
labels[combined_thresh], lrange) / np.array(spsq))
else:
overlap = K1 = K2 = np.zeros(len(lrange))
result += [[
first_image_name, second_image_name, object_name,
"Mean Overlap coeff",
"%.3f" % np.mean(overlap)
],
[
first_image_name, second_image_name, object_name,
"Median Overlap coeff",
"%.3f" % np.median(overlap)
],
[
first_image_name, second_image_name, object_name,
"Min Overlap coeff",
"%.3f" % np.min(overlap)
],
[
first_image_name, second_image_name, object_name,
"Max Overlap coeff",
"%.3f" % np.max(overlap)
]]
measurement = ("Correlation_Correlation_%s_%s" %
(first_image_name, second_image_name))
overlap_measurement = (F_OVERLAP_FORMAT %
(first_image_name, second_image_name))
k_measurement_1 = (F_K_FORMAT % (first_image_name, second_image_name))
k_measurement_2 = (F_K_FORMAT % (second_image_name, first_image_name))
manders_measurement_1 = (F_MANDERS_FORMAT %
(first_image_name, second_image_name))
manders_measurement_2 = (F_MANDERS_FORMAT %
(second_image_name, first_image_name))
rwc_measurement_1 = (F_RWC_FORMAT %
(first_image_name, second_image_name))
rwc_measurement_2 = (F_RWC_FORMAT %
(second_image_name, first_image_name))
costes_measurement_1 = (F_COSTES_FORMAT %
(first_image_name, second_image_name))
costes_measurement_2 = (F_COSTES_FORMAT %
(second_image_name, first_image_name))
workspace.measurements.add_measurement(object_name, measurement, corr)
workspace.measurements.add_measurement(object_name,
overlap_measurement, overlap)
workspace.measurements.add_measurement(object_name, k_measurement_1,
K1)
workspace.measurements.add_measurement(object_name, k_measurement_2,
K2)
workspace.measurements.add_measurement(object_name,
manders_measurement_1, M1)
workspace.measurements.add_measurement(object_name,
manders_measurement_2, M2)
workspace.measurements.add_measurement(object_name, rwc_measurement_1,
RWC1)
workspace.measurements.add_measurement(object_name, rwc_measurement_2,
RWC2)
workspace.measurements.add_measurement(object_name,
costes_measurement_1, C1)
workspace.measurements.add_measurement(object_name,
costes_measurement_2, C2)
if n_objects == 0:
return [[
first_image_name, second_image_name, object_name,
"Mean correlation", "-"
],
[
first_image_name, second_image_name, object_name,
"Median correlation", "-"
],
[
first_image_name, second_image_name, object_name,
"Min correlation", "-"
],
[
first_image_name, second_image_name, object_name,
"Max correlation", "-"
]]
else:
return result
def test_01_01_size_same(self):
secondary, mask = cpo.size_similarly(np.zeros((10, 20)),
np.zeros((10, 20)))
self.assertEqual(tuple(secondary.shape), (10, 20))
self.assertTrue(np.all(mask))
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)
heatmaps = {}
for heatmap in self.heatmaps:
if heatmap.object_name.get_objects_name() == object_name and \
image_name == heatmap.image_name.get_image_name() and \
heatmap.get_number_of_bins() == bin_count:
dd[id(heatmap)] = \
heatmaps[MEASUREMENT_ALIASES[heatmap.measurement.value]] = \
np.zeros(labels.shape)
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)
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i) + 1)[good]
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
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.argsort((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)
if feature in heatmaps:
heatmaps[feature][bin_mask] = measurement[bin_labels - 1]
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 run_one_tamura(self, image_name, object_name, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = cpo.size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_ALL:
statistics += self.record_measurement(
workspace, image_name, object_name, "",
"%s_%s" % (F_TAMURA, name), np.zeros((0, )))
else:
labs = np.unique(labels)
values = np.zeros([np.max(labs) + 1, 2])
for l in labs:
if l != 0:
px = pixel_data
px[np.where(labels != l)] = 0.0
values[l, 0] = self.contrast(px)
values[l, 1] = self.directionality(px)
statistics += self.record_measurement(
workspace, image_name, object_name, "-",
"%s_%s" % (F_TAMURA, F_2), values[:, 0])
statistics += self.record_measurement(
workspace, image_name, object_name, "-",
"%s_%s" % (F_TAMURA, F_3), values[:, 1])
coars = np.zeros([np.max(labs) + 1])
coars_hist = np.zeros([np.max(labs) + 1, HIST_COARS_BINS])
for l in labs:
if l != 0:
px = pixel_data
px[np.where(labels != l)] = 0.0
coars[l], coars_hist[l, :] = self.coarseness(px)
statistics += self.record_measurement(
workspace, image_name, object_name, "-",
"%s_%s" % (F_TAMURA, F_1), coars)
for b in range(0, HIST_COARS_BINS):
value = coars_hist[1:, b]
name = "CoarsenessHist_%dBinsHist_Bin%d" % (HIST_COARS_BINS, b)
statistics += self.record_measurement(
workspace, image_name, object_name, "-",
"%s_%s" % (F_TAMURA, name), value)
return statistics
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = 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
# emanating 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)
#
# Calculate the distances
#
total_distance = morph.skeleton_length(dlabels * outside_skel,
label_range)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_OBJSKELETON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_OBJSKELETON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join(
(C_OBJSKELETON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
feature = "_".join(
(C_OBJSKELETON, F_TOTAL_OBJSKELETON_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# Collect the graph information
#
if self.wants_objskeleton_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_objskeleton_graph(
combined_skel, dlabels, trunk_mask,
branch_points & ~trunk_mask, end_points,
intensity_image.pixel_data)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(
m, m.image_number)
workspace.interaction_request(self,
m.image_number,
edge_path,
edge_graph,
vertex_path,
vertex_graph,
headless_ok=True)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = 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 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)
heatmaps = {}
for heatmap in self.heatmaps:
if heatmap.object_name.get_objects_name() == object_name and \
image_name == heatmap.image_name.get_image_name() and \
heatmap.get_number_of_bins() == bin_count:
dd[id(heatmap)] = \
heatmaps[MEASUREMENT_ALIASES[heatmap.measurement.value]] = \
np.zeros(labels.shape)
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)
ig = i[good]
jg = j[good]
lg = np.arange(1, len(i) + 1)[good]
if center_choice == C_CENTERS_OF_OTHER:
#
# Reduce the propagation labels to the centers of
# the centering objects
#
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.argsort((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)
if feature in heatmaps:
heatmaps[feature][bin_mask] = measurement[bin_labels - 1]
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 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)
#
# Calculate the distances
#
total_distance = morph.skeleton_length(
dlabels * outside_skel, label_range)
#
# 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)
feature = "_".join((C_NEURON, F_TOTAL_NEURITE_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# 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)
