def measure_objects(self, operand, workspace):
'''Performs the measurements on the requested objects'''
objects = workspace.get_objects(operand.operand_objects.value)
if objects.has_parent_image:
area_occupied = np.sum(objects.segmented[objects.parent_image.mask]>0)
perimeter = np.sum(outline(np.logical_and(objects.segmented != 0,objects.parent_image.mask)))
total_area = np.sum(objects.parent_image.mask)
else:
area_occupied = np.sum(objects.segmented > 0)
perimeter = np.sum(outline(objects.segmented) > 0)
total_area = np.product(objects.segmented.shape)
m = workspace.measurements
m.add_image_measurement(F_AREA_OCCUPIED%(operand.operand_objects.value),
np.array([area_occupied], dtype=float ))
m.add_image_measurement(F_PERIMETER%(operand.operand_objects.value),
np.array([perimeter], dtype=float ))
m.add_image_measurement(F_TOTAL_AREA%(operand.operand_objects.value),
np.array([total_area], dtype=float))
if operand.should_save_image.value:
binary_pixels = objects.segmented > 0
output_image = cpi.Image(binary_pixels,
parent_image = objects.parent_image)
workspace.image_set.add(operand.image_name.value,
output_image)
return[[operand.operand_objects.value,
str(area_occupied),str(perimeter),str(total_area)]]
def measure_objects(self, operand, workspace):
'''Performs the measurements on the requested objects'''
objects = workspace.get_objects(operand.operand_objects.value)
if objects.has_parent_image:
area_occupied = np.sum(
objects.segmented[objects.parent_image.mask] > 0)
perimeter = np.sum(
outline(
np.logical_and(objects.segmented != 0,
objects.parent_image.mask)))
total_area = np.sum(objects.parent_image.mask)
else:
area_occupied = np.sum(objects.segmented > 0)
perimeter = np.sum(outline(objects.segmented) > 0)
total_area = np.product(objects.segmented.shape)
m = workspace.measurements
m.add_image_measurement(
F_AREA_OCCUPIED % (operand.operand_objects.value),
np.array([area_occupied], dtype=float))
m.add_image_measurement(F_PERIMETER % (operand.operand_objects.value),
np.array([perimeter], dtype=float))
m.add_image_measurement(F_TOTAL_AREA % (operand.operand_objects.value),
np.array([total_area], dtype=float))
if operand.should_save_image.value:
binary_pixels = objects.segmented > 0
output_image = cpi.Image(binary_pixels,
parent_image=objects.parent_image)
workspace.image_set.add(operand.image_name.value, output_image)
return [[
operand.operand_objects.value,
str(area_occupied),
str(perimeter),
str(total_area)
]]
def display(self, workspace, figure):
"""Create an informative display for the module"""
import matplotlib
original_labels = workspace.display_data.original_labels
final_labels = workspace.display_data.final_labels
mask = workspace.display_data.mask
#
# Create a composition of the final labels and mask
#
outlines = outline(original_labels) > 0
cm = figure.return_cmap()
sm = matplotlib.cm.ScalarMappable(cmap=cm)
#
# Paint the labels in color
#
image = sm.to_rgba(final_labels, norm=False)[:, :, :3]
image[final_labels == 0, :] = 0
#
# Make the mask a dark gray
#
image[(final_labels == 0) & mask, :] = 0.25
#
# Make the outlines of the kept objects the primary color
# and the outlines of removed objects red.
#
final_outlines = outline(final_labels) > 0
original_color = (
np.array(cpprefs.get_secondary_outline_color()[0:3], float) / 255)
final_color = np.array(cpprefs.get_primary_outline_color()[0:3],
float) / 255
image[outlines, :] = original_color[np.newaxis, :]
image[final_outlines, :] = final_color[np.newaxis, :]
figure.set_subplots((2, 1))
figure.subplot_imshow_labels(
0,
0,
original_labels,
title=self.object_name.value,
colormap=sm,
)
figure.subplot_imshow_color(
1,
0,
image,
title=self.remaining_objects.value,
sharexy=figure.subplot(0, 0),
colormap=sm,
)
def test_07_01_make_ijv_outlines(self):
np.random.seed(70)
x = cpo.Objects()
ii, jj = np.mgrid[0:10, 0:20]
masks = [(ii - ic) ** 2 + (jj - jc) ** 2 < r ** 2
for ic, jc, r in ((4, 5, 5), (4, 12, 5), (6, 8, 5))]
i = np.hstack([ii[mask] for mask in masks])
j = np.hstack([jj[mask] for mask in masks])
v = np.hstack([[k + 1] * np.sum(mask) for k, mask in enumerate(masks)])
x.set_ijv(np.column_stack((i, j, v)), ii.shape)
x.parent_image = cpi.Image(np.zeros((10, 20)))
colors = np.random.uniform(size=(3, 3)).astype(np.float32)
image = x.make_ijv_outlines(colors)
i1 = [i for i, color in enumerate(colors) if np.all(color == image[0, 5, :])]
self.assertEqual(len(i1), 1)
i2 = [i for i, color in enumerate(colors) if np.all(color == image[0, 12, :])]
self.assertEqual(len(i2), 1)
i3 = [i for i, color in enumerate(colors) if np.all(color == image[-1, 8, :])]
self.assertEqual(len(i3), 1)
self.assertNotEqual(i1[0], i2[0])
self.assertNotEqual(i2[0], i3[0])
colors = colors[np.array([i1[0], i2[0], i3[0]])]
outlines = np.zeros((10, 20, 3), np.float32)
alpha = np.zeros((10, 20))
for i, (color, mask) in enumerate(zip(colors, masks)):
my_outline = outline(mask)
outlines[my_outline] += color
alpha[my_outline] += 1
alpha[alpha == 0] = 1
outlines /= alpha[:, :, np.newaxis]
np.testing.assert_almost_equal(outlines, image)
def test_01_02_object_with_cropping(self):
labels = np.zeros((10, 10), int)
labels[0:7, 3:8] = 1
mask = np.zeros((10, 10), bool)
mask[1:9, 1:9] = True
image = cpi.Image(np.zeros((10, 10)), mask=mask)
area_occupied = np.sum(labels[mask])
perimeter = np.sum(outline(np.logical_and(labels, mask)))
total_area = np.sum(mask)
workspace = self.make_workspace(labels, image)
module = workspace.module
module.operands[0].operand_choice.value = "Objects"
module.run(workspace)
m = workspace.measurements
def mn(x):
return "AreaOccupied_%s_%s" % (
x, module.operands[0].operand_objects.value)
self.assertEqual(
m.get_current_measurement("Image", mn("AreaOccupied")),
area_occupied)
self.assertEqual(m.get_current_measurement("Image", mn("Perimeter")),
perimeter)
self.assertEqual(m.get_current_measurement("Image", mn("TotalArea")),
total_area)
def test_04_02_masked_edge(self):
# Regression test of issue #1115
labels = np.zeros((20, 50), int)
labels[15:25, 15:25] = 1
image = np.random.uniform(size=labels.shape).astype(np.float32)
#
# Mask the edge of the object
#
mask = ~cpmo.outline(labels).astype(bool)
m = cpmeas.Measurements()
m.add(IMAGE_NAME, cpi.Image(image, mask=mask))
object_set = cpo.ObjectSet()
o = cpo.Objects()
o.segmented = labels
object_set.add_objects(o, OBJECT_NAME)
pipeline = P.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, P.RunExceptionEvent))
pipeline.add_listener(callback)
module = MOI.MeasureObjectIntensity()
module.module_num = 1
module.images[0].name.value = IMAGE_NAME
module.objects[0].name.value = OBJECT_NAME
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module, m, object_set, m, None)
module.run(workspace)
def outlines(self):
'''Get a mask of all the points on the border of objects'''
if self._outlines is None:
for i, labels in enumerate(self.labels):
if i == 0:
self._outlines = outline(labels) != 0
else:
self._outlines = self._outlines | (outline(labels) != 0)
if self.line_width > 1:
hw = float(self.line_width) / 2
d = distance_transform_edt(~self._outlines)
dti, dtj = np.where((d < hw + .5) & ~self._outlines)
self._outlines = self._outlines.astype(np.float32)
self._outlines[dti, dtj] = np.minimum(1, hw + .5 - d[dti, dtj])
return self._outlines.astype(np.float32)
def test_04_02_masked_edge(self):
# Regression test of issue #1115
labels = np.zeros((20, 50), int)
labels[15:25, 15:25] = 1
image = np.random.uniform(size=labels.shape).astype(np.float32)
#
# Mask the edge of the object
#
mask = ~ cpmo.outline(labels).astype(bool)
m = cpmeas.Measurements()
m.add(IMAGE_NAME, cpi.Image(image, mask=mask))
object_set = cpo.ObjectSet()
o = cpo.Objects()
o.segmented = labels
object_set.add_objects(o, OBJECT_NAME)
pipeline = P.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, P.RunExceptionEvent))
pipeline.add_listener(callback)
module = MOI.MeasureObjectIntensity()
module.module_num = 1
module.images[0].name.value = IMAGE_NAME
module.objects[0].name.value = OBJECT_NAME
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module, m, object_set,
m, None)
module.run(workspace)
def test_01_02_two_disjoint(self):
x = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
e = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 2, 2, 0, 0],
[0, 0, 2, 0, 2, 0, 0],
[0, 0, 2, 2, 2, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
result = OL.outline(x)
self.assertTrue(numpy.all(result == e))
def test_07_01_make_ijv_outlines(self):
np.random.seed(70)
x = cpo.Objects()
ii, jj = np.mgrid[0:10, 0:20]
masks = [(ii - ic) ** 2 + (jj - jc) ** 2 < r ** 2
for ic, jc, r in ((4, 5, 5), (4, 12, 5), (6, 8, 5))]
i = np.hstack([ii[mask] for mask in masks])
j = np.hstack([jj[mask] for mask in masks])
v = np.hstack([[k + 1] * np.sum(mask) for k, mask in enumerate(masks)])
x.set_ijv(np.column_stack((i, j, v)), ii.shape)
x.parent_image = cpi.Image(np.zeros((10, 20)))
colors = np.random.uniform(size=(3, 3)).astype(np.float32)
image = x.make_ijv_outlines(colors)
i1 = [i for i, color in enumerate(colors) if np.all(color == image[0, 5, :])]
self.assertEqual(len(i1), 1)
i2 = [i for i, color in enumerate(colors) if np.all(color == image[0, 12, :])]
self.assertEqual(len(i2), 1)
i3 = [i for i, color in enumerate(colors) if np.all(color == image[-1, 8, :])]
self.assertEqual(len(i3), 1)
self.assertNotEqual(i1[0], i2[0])
self.assertNotEqual(i2[0], i3[0])
colors = colors[np.array([i1[0], i2[0], i3[0]])]
outlines = np.zeros((10, 20, 3), np.float32)
alpha = np.zeros((10, 20))
for i, (color, mask) in enumerate(zip(colors, masks)):
my_outline = outline(mask)
outlines[my_outline] += color
alpha[my_outline] += 1
alpha[alpha == 0] = 1
outlines /= alpha[:, :, np.newaxis]
np.testing.assert_almost_equal(outlines, image)
def outlines(self):
'''Get a mask of all the points on the border of objects'''
if self._outlines is None:
for i, labels in enumerate(self.labels):
if i == 0:
self._outlines = outline(labels) != 0
else:
self._outlines = self._outlines | (outline(labels) != 0)
if self.line_width > 1:
hw = float(self.line_width) / 2
d = distance_transform_edt(~ self._outlines)
dti, dtj = np.where((d < hw+.5) & ~self._outlines)
self._outlines = self._outlines.astype(np.float32)
self._outlines[dti, dtj] = np.minimum(1, hw + .5 - d[dti, dtj])
return self._outlines.astype(np.float32)
def run(self, workspace):
input_objects = workspace.object_set.get_objects(self.object_name.value)
output_objects = cpo.Objects()
output_objects.segmented = self.do_labels(input_objects.segmented)
if (input_objects.has_small_removed_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.small_removed_segmented = \
self.do_labels(input_objects.small_removed_segmented)
if (input_objects.has_unedited_segmented and
self.operation not in (O_EXPAND, O_EXPAND_INF, O_DIVIDE)):
output_objects.unedited_segmented = \
self.do_labels(input_objects.unedited_segmented)
workspace.object_set.add_objects(output_objects,
self.output_object_name.value)
add_object_count_measurements(workspace.measurements,
self.output_object_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(workspace.measurements,
self.output_object_name.value,
output_objects.segmented)
if self.wants_outlines.value:
outline_image = cpi.Image(outline(output_objects.segmented) > 0,
parent_image=input_objects.parent_image)
workspace.image_set.add(self.outlines_name.value, outline_image)
if self.show_window:
workspace.display_data.input_objects_segmented = input_objects.segmented
workspace.display_data.output_objects_segmented = output_objects.segmented
def test_02_04_edge(self):
x = numpy.array([[0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]])
e = numpy.array([[0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]])
result = OL.outline(x)
self.assertTrue(numpy.all(result == e))
def display(self, workspace, figure):
'''Create an informative display for the module'''
import matplotlib
from cellprofiler.gui.tools import renumber_labels_for_display
original_labels = workspace.display_data.original_labels
final_labels = workspace.display_data.final_labels
mask = workspace.display_data.mask
#
# Create a composition of the final labels and mask
#
final_labels = renumber_labels_for_display(final_labels)
outlines = outline(original_labels) > 0
cm = matplotlib.cm.get_cmap(cpprefs.get_default_colormap())
sm = matplotlib.cm.ScalarMappable(cmap=cm)
#
# Paint the labels in color
#
image = sm.to_rgba(final_labels)[:, :, :3]
image[final_labels == 0, :] = 0
#
# Make the mask a dark gray
#
image[(final_labels == 0) & mask, :] = .25
#
# Make the outlines of the kept objects the primary color
# and the outlines of removed objects red.
#
final_outlines = outline(final_labels) > 0
original_color = np.array(cpprefs.get_secondary_outline_color(),
float) / 255
final_color = np.array(cpprefs.get_primary_outline_color(),
float) / 255
image[outlines, :] = original_color[np.newaxis, :]
image[final_outlines, :] = final_color[np.newaxis, :]
figure.set_subplots((2, 1))
figure.subplot_imshow_labels(0,
0,
original_labels,
title=self.object_name.value)
figure.subplot_imshow_color(1,
0,
image,
title=self.remaining_objects.value,
sharexy=figure.subplot(0, 0))
def test_02_04_edge(self):
x = numpy.array([[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,0,0,0,0,0]])
e = numpy.array([[ 0,0,1,1,1,0,0],
[ 0,0,1,0,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,0,0,0,0,0]])
result = OL.outline(x)
self.assertTrue(numpy.all(result==e))
def test_01_03_touching(self):
x = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 2, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0, 0],
[0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0]])
e = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 2, 2, 2, 0, 0], [0, 0, 2, 0, 2, 0, 0],
[0, 0, 2, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0]])
result = OL.outline(x)
self.assertTrue(numpy.all(result == e))
def display(self, workspace, figure):
'''Create an informative display for the module'''
import matplotlib
from cellprofiler.gui.cpfigure import renumber_labels_for_display
original_labels = workspace.display_data.original_labels
final_labels = workspace.display_data.final_labels
mask = workspace.display_data.mask
#
# Create a composition of the final labels and mask
#
final_labels = renumber_labels_for_display(final_labels)
outlines = outline(original_labels) > 0
cm = matplotlib.cm.get_cmap(cpprefs.get_default_colormap())
sm = matplotlib.cm.ScalarMappable(cmap = cm)
#
# Paint the labels in color
#
image = sm.to_rgba(final_labels)[:,:,:3]
image[final_labels == 0,:] = 0
#
# Make the mask a dark gray
#
image[(final_labels == 0) & mask,:] = .25
#
# Make the outlines of the kept objects the primary color
# and the outlines of removed objects red.
#
final_outlines = outline(final_labels) > 0
original_color = np.array(cpprefs.get_secondary_outline_color(), float) / 255
final_color = np.array(cpprefs.get_primary_outline_color(), float) / 255
image[outlines, :] = original_color[np.newaxis, :]
image[final_outlines, :] = final_color[np.newaxis, :]
figure.set_subplots((2, 1))
figure.subplot_imshow_labels(0, 0, original_labels,
title = self.object_name.value)
figure.subplot_imshow_color(1, 0, image,
title = self.remaining_objects.value,
sharexy = figure.subplot(0,0))
def measure_images(self, operand, workspace):
'''Performs measurements on the requested images'''
image = workspace.image_set.get_image(operand.binary_name.value, must_be_binary=True)
area_occupied = np.sum(image.pixel_data > 0)
perimeter = np.sum(outline(image.pixel_data) > 0)
total_area = np.prod(np.shape(image.pixel_data))
m = workspace.measurements
m.add_image_measurement(F_AREA_OCCUPIED % operand.binary_name.value,
np.array([area_occupied], dtype=float))
m.add_image_measurement(F_PERIMETER % operand.binary_name.value,
np.array([perimeter], dtype=float))
m.add_image_measurement(F_TOTAL_AREA % operand.binary_name.value,
np.array([total_area], dtype=float))
return [[operand.binary_name.value, str(area_occupied), str(perimeter), str(total_area)]]
def test_07_01_outlines(self):
"""Create an outline of the resulting objects"""
labels = np.zeros((10, 10), int)
labels[4, 4] = 1
i, j = np.mgrid[0:10, 0:10] - 4
expected = (i ** 2 + j ** 2 <= 4).astype(int)
expected_outlines = outline(expected)
workspace, module = self.make_workspace(labels, E.O_EXPAND, 2, wants_outlines=True)
module.run(workspace)
objects = workspace.object_set.get_objects(OUTPUT_NAME)
self.assertTrue(np.all(objects.segmented == expected))
self.assertTrue(OUTLINES_NAME in workspace.image_set.names)
outlines = workspace.image_set.get_image(OUTLINES_NAME).pixel_data
self.assertTrue(np.all(outlines == expected_outlines))
def measure_images(self,operand,workspace):
'''Performs measurements on the requested images'''
image = workspace.image_set.get_image(operand.binary_name.value, must_be_binary = True)
area_occupied = np.sum(image.pixel_data > 0)
perimeter = np.sum(outline(image.pixel_data) > 0)
total_area = np.prod(np.shape(image.pixel_data))
m = workspace.measurements
m.add_image_measurement(F_AREA_OCCUPIED%(operand.binary_name.value),
np.array([area_occupied], dtype=float))
m.add_image_measurement(F_PERIMETER%(operand.binary_name.value),
np.array([perimeter], dtype=float))
m.add_image_measurement(F_TOTAL_AREA%(operand.binary_name.value),
np.array([total_area], dtype=float))
return [[operand.binary_name.value, str(area_occupied), str(perimeter), str(total_area)]]
def test_07_01_outlines(self):
'''Create an outline of the resulting objects'''
labels = np.zeros((10,10), int)
labels[4,4] = 1
i,j = np.mgrid[0:10,0:10]-4
expected = (i**2 + j**2 <=4).astype(int)
expected_outlines = outline(expected)
workspace, module = self.make_workspace(labels, E.O_EXPAND, 2,
wants_outlines = True)
module.run(workspace)
objects = workspace.object_set.get_objects(OUTPUT_NAME)
self.assertTrue(np.all(objects.segmented == expected))
self.assertTrue(OUTLINES_NAME in workspace.image_set.get_names())
outlines = workspace.image_set.get_image(OUTLINES_NAME).pixel_data
self.assertTrue(np.all(outlines == expected_outlines))
def test_01_01_single(self):
x = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
e = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
result = OL.outline(x)
self.assertTrue(numpy.all(result == e))
def make_ijv_outlines(self, colors):
'''Make ijv-style color outlines
Make outlines, coloring each object differently to distinguish between
objects that might overlap.
colors: a N x 3 color map to be used to color the outlines
'''
#
# Get planes of non-overlapping objects. The idea here is to use
# the most similar colors in the color space for objects that
# don't overlap.
#
all_labels = [(outline(label), indexes)
for label, indexes in self.get_labels()]
image = np.zeros(list(all_labels[0][0].shape) + [3], np.float32)
#
# Find out how many unique labels in each
#
counts = [np.sum(np.unique(l) != 0) for l, _ in all_labels]
if len(counts) == 1 and counts[0] == 0:
return image
if len(colors) < len(all_labels):
# Have to color 2 planes using the same color!
# There's some chance that overlapping objects will get
# the same color. Give me more colors to work with please.
colors = np.vstack([colors] * (1 + len(all_labels) / len(colors)))
r = np.random.mtrand.RandomState()
alpha = np.zeros(all_labels[0][0].shape, np.float32)
order = np.lexsort([counts])
label_colors = []
for idx, i in enumerate(order):
max_available = len(colors) / (len(all_labels) - idx)
ncolors = min(counts[i], max_available)
my_colors = colors[:ncolors]
colors = colors[ncolors:]
my_colors = my_colors[r.permutation(np.arange(ncolors))]
my_labels, indexes = all_labels[i]
color_idx = np.zeros(np.max(indexes) + 1, int)
color_idx[indexes] = np.arange(len(indexes)) % ncolors
image[my_labels != 0,:] += \
my_colors[color_idx[my_labels[my_labels != 0]],:]
alpha[my_labels != 0] += 1
image[alpha > 0, :] /= alpha[alpha > 0][:, np.newaxis]
return image
def test_02_05_diagonal(self):
x = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 0, 0, 0],
])
e = numpy.array([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 0, 1, 0, 0],
[0, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 0, 0, 0],
])
result = OL.outline(x)
self.assertTrue(numpy.all(result == e))
def make_ijv_outlines(self, colors):
'''Make ijv-style color outlines
Make outlines, coloring each object differently to distinguish between
objects that might overlap.
colors: a N x 3 color map to be used to color the outlines
'''
#
# Get planes of non-overlapping objects. The idea here is to use
# the most similar colors in the color space for objects that
# don't overlap.
#
all_labels = [(outline(label), indexes) for label, indexes in self.get_labels()]
image = np.zeros(list(all_labels[0][0].shape) + [3], np.float32)
#
# Find out how many unique labels in each
#
counts = [np.sum(np.unique(l) != 0) for l, _ in all_labels]
if len(counts) == 1 and counts[0] == 0:
return image
if len(colors) < len(all_labels):
# Have to color 2 planes using the same color!
# There's some chance that overlapping objects will get
# the same color. Give me more colors to work with please.
colors = np.vstack([colors] * (1 + len(all_labels) / len(colors)))
r = np.random.mtrand.RandomState()
alpha = np.zeros(all_labels[0][0].shape, np.float32)
order = np.lexsort([counts])
label_colors = []
for idx,i in enumerate(order):
max_available = len(colors) / (len(all_labels) - idx)
ncolors = min(counts[i], max_available)
my_colors = colors[:ncolors]
colors = colors[ncolors:]
my_colors = my_colors[r.permutation(np.arange(ncolors))]
my_labels, indexes = all_labels[i]
color_idx = np.zeros(np.max(indexes) + 1, int)
color_idx[indexes] = np.arange(len(indexes)) % ncolors
image[my_labels != 0,:] += \
my_colors[color_idx[my_labels[my_labels != 0]],:]
alpha[my_labels != 0] += 1
image[alpha > 0, :] /= alpha[alpha > 0][:, np.newaxis]
return image
def test_01_03_touching(self):
x = numpy.array([[ 0,0,0,0,0,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,0,0,0,0,0]])
e = numpy.array([[ 0,0,0,0,0,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,0,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,0,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,0,0,0,0,0]])
result = OL.outline(x)
self.assertTrue(numpy.all(result==e))
def run(self, workspace):
image_name = self.image_name.value
objects_name = self.objects_name.value
outlines_name = self.outlines_name.value
image = workspace.image_set.get_image(image_name)
pixel_data = image.pixel_data
labels = workspace.interaction_request(
self, pixel_data, workspace.measurements.image_set_number)
if labels is None:
# User cancelled. Soldier on as best we can.
workspace.cancel_request()
labels = np.zeros(pixel_data.shape[:2], int)
objects = cpo.Objects()
objects.segmented = labels
workspace.object_set.add_objects(objects, objects_name)
##################
#
# Add measurements
#
m = workspace.measurements
#
# The object count
#
object_count = np.max(labels)
I.add_object_count_measurements(m, objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements(m, objects_name, labels)
#
# Outlines if we want them
#
if self.wants_outlines:
outlines_name = self.outlines_name.value
outlines = outline(labels)
outlines_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(outlines_name, outlines_image)
workspace.display_data.labels = labels
workspace.display_data.pixel_data = pixel_data
def run(self, workspace):
image_name = self.image_name.value
objects_name = self.objects_name.value
outlines_name = self.outlines_name.value
image = workspace.image_set.get_image(image_name)
pixel_data = image.pixel_data
labels = workspace.interaction_request(
self, pixel_data, workspace.measurements.image_set_number)
if labels is None:
# User cancelled. Soldier on as best we can.
workspace.cancel_request()
labels = np.zeros(pixel_data.shape[:2], int)
objects = cpo.Objects()
objects.segmented = labels
workspace.object_set.add_objects(objects, objects_name)
##################
#
# Add measurements
#
m = workspace.measurements
#
# The object count
#
object_count = np.max(labels)
I.add_object_count_measurements(m, objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements(m, objects_name, labels)
#
# Outlines if we want them
#
if self.wants_outlines:
outlines_name = self.outlines_name.value
outlines = outline(labels)
outlines_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(outlines_name, outlines_image)
workspace.display_data.labels = labels
workspace.display_data.pixel_data = pixel_data
def test_01_02_object_with_cropping(self):
labels = np.zeros((10,10),int)
labels[0:7,3:8] = 1
mask = np.zeros((10,10),bool)
mask[1:9,1:9] = True
image = cpi.Image(np.zeros((10,10)),mask=mask)
area_occupied = np.sum(labels[mask])
perimeter = np.sum(outline(np.logical_and(labels,mask)))
total_area = np.sum(mask)
workspace = self.make_workspace(labels, image)
module = workspace.module
module.operands[0].operand_choice.value = "Objects"
module.run(workspace)
m = workspace.measurements
def mn(x):
return "AreaOccupied_%s_%s"%(x, module.operands[0].operand_objects.value)
self.assertEqual(m.get_current_measurement("Image",mn("AreaOccupied")), area_occupied)
self.assertEqual(m.get_current_measurement("Image",mn("Perimeter")), perimeter)
self.assertEqual(m.get_current_measurement("Image",mn("TotalArea")), total_area)
def test_01_02_two_disjoint(self):
x = numpy.array([[ 0,0,0,0,0,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,0,0,0,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,0,0,0,0,0]])
e = numpy.array([[ 0,0,0,0,0,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,1,0,1,0,0],
[ 0,0,1,1,1,0,0],
[ 0,0,0,0,0,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,2,0,2,0,0],
[ 0,0,2,2,2,0,0],
[ 0,0,0,0,0,0,0]])
result = OL.outline(x)
self.assertTrue(numpy.all(result==e))
def run(self, workspace):
'''Find the outlines on the current image set
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
'''
gridding = workspace.get_grid(self.grid_name.value)
if self.shape_choice == SHAPE_RECTANGLE:
labels = self.run_rectangle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_FORCED:
labels = self.run_forced_circle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_NATURAL:
labels = self.run_natural_circle(workspace, gridding)
elif self.shape_choice == SHAPE_NATURAL:
labels = self.run_natural(workspace, gridding)
objects = cpo.Objects()
objects.segmented = labels
object_count = gridding.rows * gridding.columns
workspace.object_set.add_objects(objects,
self.output_objects_name.value)
add_object_location_measurements(workspace.measurements,
self.output_objects_name.value,
labels, object_count)
add_object_count_measurements(workspace.measurements,
self.output_objects_name.value,
object_count)
if self.wants_outlines:
outlines = outline(labels != 0)
outline_image = cpi.Image(outlines)
workspace.image_set.add(self.outlines_name.value, outline_image)
if self.show_window:
workspace.display_data.gridding = gridding
workspace.display_data.labels = labels
def run(self, workspace):
'''Find the outlines on the current image set
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
'''
gridding = workspace.get_grid(self.grid_name.value)
if self.shape_choice == SHAPE_RECTANGLE:
labels = self.run_rectangle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_FORCED:
labels = self.run_forced_circle(workspace, gridding)
elif self.shape_choice == SHAPE_CIRCLE_NATURAL:
labels = self.run_natural_circle(workspace, gridding)
elif self.shape_choice == SHAPE_NATURAL:
labels = self.run_natural(workspace, gridding)
objects = cpo.Objects()
objects.segmented = labels
object_count = gridding.rows * gridding.columns
workspace.object_set.add_objects(objects,
self.output_objects_name.value)
add_object_location_measurements(workspace.measurements,
self.output_objects_name.value,
labels, object_count)
add_object_count_measurements(workspace.measurements,
self.output_objects_name.value,
object_count)
if self.wants_outlines:
outlines = outline(labels != 0)
outline_image = cpi.Image(outlines)
workspace.image_set.add(self.outlines_name.value, outline_image)
if self.show_window:
workspace.display_data.gridding = gridding
workspace.display_data.labels = labels
def draw_outlines(self, pixel_data, labels):
'''Draw a color image that shows the objects
pixel_data - image, either b & w or color
labels - labels for image
returns - color image of same size as pixel_data
'''
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib
labels = renumber_labels_for_display(labels)
outlines = outline(labels)
if pixel_data.ndim == 3:
image = pixel_data.copy()
else:
image = np.dstack([pixel_data] * 3)
#
# make labeled pixels a grayscale times the label color
#
cm = matplotlib.cm.get_cmap(cpprefs.get_default_colormap())
sm = matplotlib.cm.ScalarMappable(cmap=cm)
labels_image = sm.to_rgba(labels)[:, :, :3]
lmask = labels > 0
gray = (image[lmask, 0] + image[lmask, 1] + image[lmask, 2]) / 3
for i in range(3):
image[lmask, i] = gray * labels_image[lmask, i]
#
# Make the outline pixels a solid color
#
outlines_image = sm.to_rgba(outlines)[:, :, :3]
image[outlines > 0, :] = outlines_image[outlines > 0, :]
return image
def draw_outlines(self, pixel_data, labels):
'''Draw a color image that shows the objects
pixel_data - image, either b & w or color
labels - labels for image
returns - color image of same size as pixel_data
'''
from cellprofiler.gui.tools import renumber_labels_for_display
import matplotlib
labels = renumber_labels_for_display(labels)
outlines = outline(labels)
if pixel_data.ndim == 3:
image = pixel_data.copy()
else:
image = np.dstack([pixel_data] * 3)
#
# make labeled pixels a grayscale times the label color
#
cm = matplotlib.cm.get_cmap(cpprefs.get_default_colormap())
sm = matplotlib.cm.ScalarMappable(cmap=cm)
labels_image = sm.to_rgba(labels)[:, :, :3]
lmask = labels > 0
gray = (image[lmask, 0] + image[lmask, 1] + image[lmask, 2]) / 3
for i in range(3):
image[lmask, i] = gray * labels_image[lmask, i]
#
# Make the outline pixels a solid color
#
outlines_image = sm.to_rgba(outlines)[:, :, :3]
image[outlines > 0, :] = outlines_image[outlines > 0, :]
return image
def test_00_00_zeros(self):
x = numpy.zeros((10, 10), int)
result = OL.outline(x)
self.assertTrue(numpy.all(x == result))
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,
I.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,
I.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):
if self.show_window:
workspace.display_data.col_labels = ("Image", "Object", "Feature", "Mean", "Median", "STD")
workspace.display_data.statistics = statistics = []
for image_name in [img.name for img in self.images]:
image = workspace.image_set.get_image(image_name.value, must_be_grayscale=True)
for object_name in [obj.name for obj in self.objects]:
# Need to refresh image after each iteration...
img = image.pixel_data
if image.has_mask:
masked_image = img.copy()
masked_image[~image.mask] = 0
else:
masked_image = img
objects = workspace.object_set.get_objects(object_name.value)
nobjects = objects.count
integrated_intensity = np.zeros((nobjects,))
integrated_intensity_edge = np.zeros((nobjects,))
mean_intensity = np.zeros((nobjects,))
mean_intensity_edge = np.zeros((nobjects,))
std_intensity = np.zeros((nobjects,))
std_intensity_edge = np.zeros((nobjects,))
min_intensity = np.zeros((nobjects,))
min_intensity_edge = np.zeros((nobjects,))
max_intensity = np.zeros((nobjects,))
max_intensity_edge = np.zeros((nobjects,))
mass_displacement = np.zeros((nobjects,))
lower_quartile_intensity = np.zeros((nobjects,))
median_intensity = np.zeros((nobjects,))
mad_intensity = np.zeros((nobjects,))
upper_quartile_intensity = np.zeros((nobjects,))
cmi_x = np.zeros((nobjects,))
cmi_y = np.zeros((nobjects,))
max_x = np.zeros((nobjects,))
max_y = np.zeros((nobjects,))
for labels, lindexes in objects.get_labels():
lindexes = lindexes[lindexes != 0]
labels, img = cpo.crop_labels_and_image(labels, img)
_, masked_image = cpo.crop_labels_and_image(labels, masked_image)
outlines = cpmo.outline(labels)
if image.has_mask:
_, mask = cpo.crop_labels_and_image(labels, image.mask)
masked_labels = labels.copy()
masked_labels[~mask] = 0
masked_outlines = outlines.copy()
masked_outlines[~mask] = 0
else:
masked_labels = labels
masked_outlines = outlines
lmask = masked_labels > 0 & np.isfinite(img) # Ignore NaNs, Infs
has_objects = np.any(lmask)
if has_objects:
limg = img[lmask]
llabels = labels[lmask]
mesh_y, mesh_x = np.mgrid[0 : masked_image.shape[0], 0 : masked_image.shape[1]]
mesh_x = mesh_x[lmask]
mesh_y = mesh_y[lmask]
lcount = fix(nd.sum(np.ones(len(limg)), llabels, lindexes))
integrated_intensity[lindexes - 1] = fix(nd.sum(limg, llabels, lindexes))
mean_intensity[lindexes - 1] = integrated_intensity[lindexes - 1] / lcount
std_intensity[lindexes - 1] = np.sqrt(
fix(nd.mean((limg - mean_intensity[llabels - 1]) ** 2, llabels, lindexes))
)
min_intensity[lindexes - 1] = fix(nd.minimum(limg, llabels, lindexes))
max_intensity[lindexes - 1] = fix(nd.maximum(limg, llabels, lindexes))
# Compute the position of the intensity maximum
max_position = np.array(fix(nd.maximum_position(limg, llabels, lindexes)), dtype=int)
max_position = np.reshape(max_position, (max_position.shape[0],))
max_x[lindexes - 1] = mesh_x[max_position]
max_y[lindexes - 1] = mesh_y[max_position]
# The mass displacement is the distance between the center
# of mass of the binary image and of the intensity image. The
# center of mass is the average X or Y for the binary image
# and the sum of X or Y * intensity / integrated intensity
cm_x = fix(nd.mean(mesh_x, llabels, lindexes))
cm_y = fix(nd.mean(mesh_y, llabels, lindexes))
i_x = fix(nd.sum(mesh_x * limg, llabels, lindexes))
i_y = fix(nd.sum(mesh_y * limg, llabels, lindexes))
cmi_x[lindexes - 1] = i_x / integrated_intensity[lindexes - 1]
cmi_y[lindexes - 1] = i_y / integrated_intensity[lindexes - 1]
diff_x = cm_x - cmi_x[lindexes - 1]
diff_y = cm_y - cmi_y[lindexes - 1]
mass_displacement[lindexes - 1] = np.sqrt(diff_x * diff_x + diff_y * diff_y)
#
# Sort the intensities by label, then intensity.
# For each label, find the index above and below
# the 25%, 50% and 75% mark and take the weighted
# average.
#
order = np.lexsort((limg, llabels))
areas = lcount.astype(int)
indices = np.cumsum(areas) - areas
for dest, fraction in (
(lower_quartile_intensity, 1.0 / 4.0),
(median_intensity, 1.0 / 2.0),
(upper_quartile_intensity, 3.0 / 4.0),
):
qindex = indices.astype(float) + areas * fraction
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
dest[lindexes[qmask] - 1] = limg[order[qi]] * (1 - qf) + limg[order[qi + 1]] * qf
#
# In some situations (e.g. only 3 points), there may
# not be an upper bound.
#
qmask = (~qmask) & (areas > 0)
dest[lindexes[qmask] - 1] = limg[order[qindex[qmask]]]
#
# Once again, for the MAD
#
madimg = np.abs(limg - median_intensity[llabels - 1])
order = np.lexsort((madimg, llabels))
qindex = indices.astype(float) + areas / 2.0
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
mad_intensity[lindexes[qmask] - 1] = madimg[order[qi]] * (1 - qf) + madimg[order[qi + 1]] * qf
qmask = (~qmask) & (areas > 0)
mad_intensity[lindexes[qmask] - 1] = madimg[order[qindex[qmask]]]
emask = masked_outlines > 0
eimg = img[emask]
elabels = labels[emask]
has_edge = len(eimg) > 0
if has_edge:
ecount = fix(nd.sum(np.ones(len(eimg)), elabels, lindexes))
integrated_intensity_edge[lindexes - 1] = fix(nd.sum(eimg, elabels, lindexes))
mean_intensity_edge[lindexes - 1] = integrated_intensity_edge[lindexes - 1] / ecount
std_intensity_edge[lindexes - 1] = np.sqrt(
fix(nd.mean((eimg - mean_intensity_edge[elabels - 1]) ** 2, elabels, lindexes))
)
min_intensity_edge[lindexes - 1] = fix(nd.minimum(eimg, elabels, lindexes))
max_intensity_edge[lindexes - 1] = fix(nd.maximum(eimg, elabels, lindexes))
m = workspace.measurements
for category, feature_name, measurement in (
(INTENSITY, INTEGRATED_INTENSITY, integrated_intensity),
(INTENSITY, MEAN_INTENSITY, mean_intensity),
(INTENSITY, STD_INTENSITY, std_intensity),
(INTENSITY, MIN_INTENSITY, min_intensity),
(INTENSITY, MAX_INTENSITY, max_intensity),
(INTENSITY, INTEGRATED_INTENSITY_EDGE, integrated_intensity_edge),
(INTENSITY, MEAN_INTENSITY_EDGE, mean_intensity_edge),
(INTENSITY, STD_INTENSITY_EDGE, std_intensity_edge),
(INTENSITY, MIN_INTENSITY_EDGE, min_intensity_edge),
(INTENSITY, MAX_INTENSITY_EDGE, max_intensity_edge),
(INTENSITY, MASS_DISPLACEMENT, mass_displacement),
(INTENSITY, LOWER_QUARTILE_INTENSITY, lower_quartile_intensity),
(INTENSITY, MEDIAN_INTENSITY, median_intensity),
(INTENSITY, MAD_INTENSITY, mad_intensity),
(INTENSITY, UPPER_QUARTILE_INTENSITY, upper_quartile_intensity),
(C_LOCATION, LOC_CMI_X, cmi_x),
(C_LOCATION, LOC_CMI_Y, cmi_y),
(C_LOCATION, LOC_MAX_X, max_x),
(C_LOCATION, LOC_MAX_Y, max_y),
):
measurement_name = "%s_%s_%s" % (category, feature_name, image_name.value)
m.add_measurement(object_name.value, measurement_name, measurement)
if self.show_window and len(measurement) > 0:
statistics.append(
(
image_name.value,
object_name.value,
feature_name,
np.round(np.mean(measurement), 3),
np.round(np.median(measurement), 3),
np.round(np.std(measurement), 3),
)
)
def test_02_01_forced_location(self):
d = D.DefineGrid()
d.ordering.value = D.NUM_BY_COLUMNS
#
# Grid with x spacing = 10, y spacing = 20
#
diameter = 6
gridding =d.build_grid_info(15,25,1,1,25,45,2,2)
expected = self.make_rectangular_grid(gridding)
i,j = np.mgrid[0:expected.shape[0],0:expected.shape[1]]
ispot, jspot = np.mgrid[0:gridding.rows, 0:gridding.columns]
y_locations = np.zeros(np.max(gridding.spot_table)+1,int)
y_locations[gridding.spot_table.flatten()] = \
gridding.y_locations[ispot.flatten()]
x_locations = np.zeros(np.max(gridding.spot_table)+1,int)
x_locations[gridding.spot_table.flatten()] =\
gridding.x_locations[jspot.flatten()]
idist = (i - y_locations[expected])
jdist = (j - x_locations[expected])
expected[idist**2 + jdist**2 > (float(diameter + 1)/2)**2] = 0
workspace, module = self.make_workspace(gridding)
self.assertTrue(isinstance(module, I.IdentifyObjectsInGrid))
module.diameter_choice.value = I.AM_MANUAL
module.diameter.value = diameter
module.shape_choice.value = I.SHAPE_CIRCLE_FORCED
module.wants_outlines.value = True
module.run(workspace)
labels = workspace.object_set.get_objects(OUTPUT_OBJECTS_NAME).segmented
self.assertTrue(np.all(labels == expected[0:labels.shape[0],0:labels.shape[1]]))
#
# Check measurements
#
m = workspace.measurements
self.assertTrue(isinstance(m, cpmeas.Measurements))
xm = m.get_current_measurement(OUTPUT_OBJECTS_NAME, 'Location_Center_X')
self.assertTrue(np.all(xm == x_locations[1:]))
ym = m.get_current_measurement(OUTPUT_OBJECTS_NAME, 'Location_Center_Y')
self.assertTrue(np.all(ym == y_locations[1:]))
count = m.get_current_image_measurement('Count_%s'%OUTPUT_OBJECTS_NAME)
self.assertEqual(count, gridding.rows * gridding.columns)
columns = module.get_measurement_columns(workspace.pipeline)
self.assertEqual(len(columns), 4)
count_feature = 'Count_%s'%OUTPUT_OBJECTS_NAME
self.assertTrue(all([column[0] == ("Image" if column[1] == count_feature
else OUTPUT_OBJECTS_NAME)
for column in columns]))
self.assertTrue(all([column[1] in ('Location_Center_X','Location_Center_Y', count_feature,'Number_Object_Number')
for column in columns]))
#
# Check the outlines
#
outlines = workspace.image_set.get_image(OUTLINES_NAME)
outlines = outlines.pixel_data
expected_outlines = outline(expected)
self.assertTrue(np.all(outlines == (expected_outlines[0:outlines.shape[0],0:outlines.shape[1]]>0)))
#
# Check the measurements
#
categories = list(module.get_categories(None, OUTPUT_OBJECTS_NAME))
self.assertEqual(len(categories), 2)
categories.sort()
self.assertEqual(categories[0], "Location")
self.assertEqual(categories[1], "Number")
categories = module.get_categories(None, cpmeas.IMAGE)
self.assertEqual(len(categories), 1)
self.assertEqual(categories[0], "Count")
measurements = module.get_measurements(None, cpmeas.IMAGE, "Count")
self.assertEqual(len(measurements), 1)
self.assertEqual(measurements[0], OUTPUT_OBJECTS_NAME)
measurements = module.get_measurements(None, OUTPUT_OBJECTS_NAME, "Location")
self.assertEqual(len(measurements), 2)
self.assertTrue(all(m in ('Center_X','Center_Y') for m in measurements))
self.assertTrue('Center_X' in measurements)
self.assertTrue('Center_Y' in measurements)
measurements = module.get_measurements(None, OUTPUT_OBJECTS_NAME, "Number")
self.assertEqual(len(measurements),1)
self.assertEqual(measurements[0], "Object_Number")
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):
"""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 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 test_02_01_forced_location(self):
d = D.DefineGrid()
d.ordering.value = D.NUM_BY_COLUMNS
#
# Grid with x spacing = 10, y spacing = 20
#
diameter = 6
gridding = d.build_grid_info(15, 25, 1, 1, 25, 45, 2, 2)
expected = self.make_rectangular_grid(gridding)
i, j = np.mgrid[0:expected.shape[0], 0:expected.shape[1]]
ispot, jspot = np.mgrid[0:gridding.rows, 0:gridding.columns]
y_locations = np.zeros(np.max(gridding.spot_table) + 1, int)
y_locations[gridding.spot_table.flatten()] = \
gridding.y_locations[ispot.flatten()]
x_locations = np.zeros(np.max(gridding.spot_table) + 1, int)
x_locations[gridding.spot_table.flatten()] = \
gridding.x_locations[jspot.flatten()]
idist = (i - y_locations[expected])
jdist = (j - x_locations[expected])
expected[idist**2 + jdist**2 > (float(diameter + 1) / 2)**2] = 0
workspace, module = self.make_workspace(gridding)
self.assertTrue(isinstance(module, I.IdentifyObjectsInGrid))
module.diameter_choice.value = I.AM_MANUAL
module.diameter.value = diameter
module.shape_choice.value = I.SHAPE_CIRCLE_FORCED
module.wants_outlines.value = True
module.run(workspace)
labels = workspace.object_set.get_objects(
OUTPUT_OBJECTS_NAME).segmented
self.assertTrue(
np.all(labels == expected[0:labels.shape[0], 0:labels.shape[1]]))
#
# Check measurements
#
m = workspace.measurements
self.assertTrue(isinstance(m, cpmeas.Measurements))
xm = m.get_current_measurement(OUTPUT_OBJECTS_NAME,
'Location_Center_X')
self.assertTrue(np.all(xm == x_locations[1:]))
ym = m.get_current_measurement(OUTPUT_OBJECTS_NAME,
'Location_Center_Y')
self.assertTrue(np.all(ym == y_locations[1:]))
count = m.get_current_image_measurement('Count_%s' %
OUTPUT_OBJECTS_NAME)
self.assertEqual(count, gridding.rows * gridding.columns)
columns = module.get_measurement_columns(workspace.pipeline)
self.assertEqual(len(columns), 4)
count_feature = 'Count_%s' % OUTPUT_OBJECTS_NAME
self.assertTrue(
all([
column[0] == ("Image" if column[1] == count_feature else
OUTPUT_OBJECTS_NAME) for column in columns
]))
self.assertTrue(
all([
column[1] in ('Location_Center_X', 'Location_Center_Y',
count_feature, 'Number_Object_Number')
for column in columns
]))
#
# Check the outlines
#
outlines = workspace.image_set.get_image(OUTLINES_NAME)
outlines = outlines.pixel_data
expected_outlines = outline(expected)
self.assertTrue(
np.all(outlines == (expected_outlines[0:outlines.shape[0],
0:outlines.shape[1]] > 0)))
#
# Check the measurements
#
categories = list(module.get_categories(None, OUTPUT_OBJECTS_NAME))
self.assertEqual(len(categories), 2)
categories.sort()
self.assertEqual(categories[0], "Location")
self.assertEqual(categories[1], "Number")
categories = module.get_categories(None, cpmeas.IMAGE)
self.assertEqual(len(categories), 1)
self.assertEqual(categories[0], "Count")
measurements = module.get_measurements(None, cpmeas.IMAGE, "Count")
self.assertEqual(len(measurements), 1)
self.assertEqual(measurements[0], OUTPUT_OBJECTS_NAME)
measurements = module.get_measurements(None, OUTPUT_OBJECTS_NAME,
"Location")
self.assertEqual(len(measurements), 2)
self.assertTrue(
all(m in ('Center_X', 'Center_Y') for m in measurements))
self.assertTrue('Center_X' in measurements)
self.assertTrue('Center_Y' in measurements)
measurements = module.get_measurements(None, OUTPUT_OBJECTS_NAME,
"Number")
self.assertEqual(len(measurements), 1)
self.assertEqual(measurements[0], "Object_Number")
def run(self, workspace):
"""Run the module
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
"""
orig_objects_name = self.object_name.value
filtered_objects_name = self.filtered_objects.value
orig_objects = workspace.object_set.get_objects(orig_objects_name)
assert isinstance(orig_objects, cpo.Objects)
orig_labels = [l for l, c in orig_objects.get_labels()]
if self.wants_image_display:
guide_image = workspace.image_set.get_image(self.image_name.value)
guide_image = guide_image.pixel_data
if np.any(guide_image != np.min(guide_image)):
guide_image = (guide_image - np.min(guide_image)) / (np.max(guide_image) - np.min(guide_image))
else:
guide_image = None
filtered_labels = workspace.interaction_request(
self, orig_labels, guide_image, workspace.measurements.image_set_number)
if filtered_labels is None:
# Ask whoever is listening to stop doing stuff
workspace.cancel_request()
# Have to soldier on until the cancel takes effect...
filtered_labels = orig_labels
#
# Renumber objects consecutively if asked to do so
#
unique_labels = np.unique(np.array(filtered_labels))
unique_labels = unique_labels[unique_labels != 0]
object_count = len(unique_labels)
if self.renumber_choice == R_RENUMBER:
mapping = np.zeros(1 if len(unique_labels) == 0 else np.max(unique_labels)+1, int)
mapping[unique_labels] = np.arange(1,object_count + 1)
filtered_labels = [mapping[l] for l in filtered_labels]
#
# Make the objects out of the labels
#
filtered_objects = cpo.Objects()
i, j = np.mgrid[0:filtered_labels[0].shape[0],
0:filtered_labels[0].shape[1]]
ijv = np.zeros((0, 3), filtered_labels[0].dtype)
for l in filtered_labels:
ijv = np.vstack((ijv,
np.column_stack((i[l != 0],
j[l != 0],
l[l != 0]))))
filtered_objects.set_ijv(ijv, orig_labels[0].shape)
if orig_objects.has_unedited_segmented():
filtered_objects.unedited_segmented = orig_objects.unedited_segmented
if orig_objects.parent_image is not None:
filtered_objects.parent_image = orig_objects.parent_image
workspace.object_set.add_objects(filtered_objects,
filtered_objects_name)
#
# Add parent/child & other measurements
#
m = workspace.measurements
child_count, parents = orig_objects.relate_children(filtered_objects)
m.add_measurement(filtered_objects_name,
I.FF_PARENT%(orig_objects_name),
parents)
m.add_measurement(orig_objects_name,
I.FF_CHILDREN_COUNT%(filtered_objects_name),
child_count)
#
# The object count
#
I.add_object_count_measurements(m, filtered_objects_name,
object_count)
#
# The object locations
#
I.add_object_location_measurements_ijv(m, filtered_objects_name, ijv)
#
# Outlines if we want them
#
if self.wants_outlines:
outlines_name = self.outlines_name.value
outlines = outline(filtered_labels[0]).astype(bool)
outlines_image = cpi.Image(outlines)
workspace.image_set.add(outlines_name, outlines_image)
workspace.display_data.orig_ijv = orig_objects.ijv
workspace.display_data.filtered_ijv = filtered_objects.ijv
workspace.display_data.shape = orig_labels[0].shape
def run(self, workspace):
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)
nkept_objects = objects.count
nneighbors = np.max(neighbor_labels)
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
neighbor_has_pixels = has_pixels
else:
_, neighbor_numbers = neighbor_objects.relate_labels(
neighbor_labels, neighbor_objects.segmented)
neighbor_has_pixels = np.bincount(neighbor_labels.ravel())[1:] > 0
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[:, ~neighbor_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 order.shape[1] > 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 run(self, workspace):
assert isinstance(workspace, cpw.Workspace)
image_name = self.image_name.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale = True)
workspace.display_data.statistics = []
img = image.pixel_data
mask = image.mask
objects = workspace.object_set.get_objects(self.primary_objects.value)
global_threshold = None
if self.method == M_DISTANCE_N:
has_threshold = False
else:
thresholded_image = self.threshold_image(image_name, workspace)
has_threshold = True
#
# Get the following labels:
# * all edited labels
# * labels touching the edge, including small removed
#
labels_in = objects.unedited_segmented.copy()
labels_touching_edge = np.hstack(
(labels_in[0,:], labels_in[-1,:], labels_in[:,0], labels_in[:,-1]))
labels_touching_edge = np.unique(labels_touching_edge)
is_touching = np.zeros(np.max(labels_in)+1, bool)
is_touching[labels_touching_edge] = True
is_touching = is_touching[labels_in]
labels_in[(~ is_touching) & (objects.segmented == 0)] = 0
#
# Stretch the input labels to match the image size. If there's no
# label matrix, then there's no label in that area.
#
if tuple(labels_in.shape) != tuple(img.shape):
tmp = np.zeros(img.shape, labels_in.dtype)
i_max = min(img.shape[0], labels_in.shape[0])
j_max = min(img.shape[1], labels_in.shape[1])
tmp[:i_max, :j_max] = labels_in[:i_max, :j_max]
labels_in = tmp
if self.method in (M_DISTANCE_B, M_DISTANCE_N):
if self.method == M_DISTANCE_N:
distances,(i,j) = scind.distance_transform_edt(labels_in == 0,
return_indices = True)
labels_out = np.zeros(labels_in.shape,int)
dilate_mask = distances <= self.distance_to_dilate.value
labels_out[dilate_mask] =\
labels_in[i[dilate_mask],j[dilate_mask]]
else:
labels_out, distances = propagate(img, labels_in,
thresholded_image,
1.0)
labels_out[distances>self.distance_to_dilate.value] = 0
labels_out[labels_in > 0] = labels_in[labels_in>0]
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
#
# Create the final output labels by removing labels in the
# output matrix that are missing from the segmented image
#
segmented_labels = objects.segmented
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_PROPAGATION:
labels_out, distance = propagate(img, labels_in,
thresholded_image,
self.regularization_factor.value)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_G:
#
# First, apply the sobel filter to the image (both horizontal
# and vertical). The filter measures gradient.
#
sobel_image = np.abs(scind.sobel(img))
#
# Combine the image mask and threshold to mask the watershed
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the first watershed
#
labels_out = watershed(sobel_image,
labels_in,
np.ones((3,3),bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_I:
#
# invert the image so that the maxima are filled first
# and the cells compete over what's close to the threshold
#
inverted_img = 1-img
#
# Same as above, but perform the watershed on the original image
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the watershed
#
labels_out = watershed(inverted_img,
labels_in,
np.ones((3,3),bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
if self.wants_discard_edge and self.wants_discard_primary:
#
# Make a new primary object
#
lookup = scind.maximum(segmented_out,
objects.segmented,
range(np.max(objects.segmented)+1))
lookup = fix(lookup)
lookup[0] = 0
lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1
segmented_labels = lookup[objects.segmented]
segmented_out = lookup[segmented_out]
new_objects = cpo.Objects()
new_objects.segmented = segmented_labels
if objects.has_unedited_segmented:
new_objects.unedited_segmented = objects.unedited_segmented
if objects.has_small_removed_segmented:
new_objects.small_removed_segmented = objects.small_removed_segmented
new_objects.parent_image = objects.parent_image
primary_outline = outline(segmented_labels)
if self.wants_primary_outlines:
out_img = cpi.Image(primary_outline.astype(bool),
parent_image = image)
workspace.image_set.add(self.new_primary_outlines_name.value,
out_img)
else:
primary_outline = outline(objects.segmented)
secondary_outline = outline(segmented_out)
#
# Add the objects to the object set
#
objects_out = cpo.Objects()
objects_out.unedited_segmented = small_removed_segmented_out
objects_out.small_removed_segmented = small_removed_segmented_out
objects_out.segmented = segmented_out
objects_out.parent_image = image
objname = self.objects_name.value
workspace.object_set.add_objects(objects_out, objname)
if self.use_outlines.value:
out_img = cpi.Image(secondary_outline.astype(bool),
parent_image = image)
workspace.image_set.add(self.outlines_name.value, out_img)
object_count = np.max(segmented_out)
#
# Add measurements
#
measurements = workspace.measurements
cpmi.add_object_count_measurements(measurements, objname, object_count)
cpmi.add_object_location_measurements(measurements, objname,
segmented_out)
#
# Relate the secondary objects to the primary ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(objects_out)
measurements.add_measurement(self.primary_objects.value,
cpmi.FF_CHILDREN_COUNT%objname,
children_per_parent)
measurements.add_measurement(objname,
cpmi.FF_PARENT%self.primary_objects.value,
parents_of_children)
image_numbers = np.ones(len(parents_of_children), int) *\
measurements.image_set_number
mask = parents_of_children > 0
measurements.add_relate_measurement(
self.module_num, R_PARENT,
self.primary_objects.value, self.objects_name.value,
image_numbers[mask], parents_of_children[mask],
image_numbers[mask],
np.arange(1, len(parents_of_children) + 1)[mask])
#
# If primary objects were created, add them
#
if self.wants_discard_edge and self.wants_discard_primary:
workspace.object_set.add_objects(new_objects,
self.new_primary_objects_name.value)
cpmi.add_object_count_measurements(measurements,
self.new_primary_objects_name.value,
np.max(new_objects.segmented))
cpmi.add_object_location_measurements(measurements,
self.new_primary_objects_name.value,
new_objects.segmented)
for parent_objects, parent_name, child_objects, child_name in (
(objects, self.primary_objects.value,
new_objects, self.new_primary_objects_name.value),
(new_objects, self.new_primary_objects_name.value,
objects_out, objname)):
children_per_parent, parents_of_children = \
parent_objects.relate_children(child_objects)
measurements.add_measurement(parent_name,
cpmi.FF_CHILDREN_COUNT%child_name,
children_per_parent)
measurements.add_measurement(child_name,
cpmi.FF_PARENT%parent_name,
parents_of_children)
if self.show_window:
object_area = np.sum(segmented_out > 0)
workspace.display_data.object_pct = \
100 * object_area / np.product(segmented_out.shape)
workspace.display_data.img = img
workspace.display_data.segmented_out = segmented_out
workspace.display_data.primary_labels = objects.segmented
workspace.display_data.global_threshold = global_threshold
workspace.display_data.object_count = object_count
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)
nkept_objects = objects.count
nneighbors = np.max(neighbor_labels)
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]]
neighbor_has_pixels = np.bincount(neighbor_labels.ravel())[1:] > 0
_, 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[:, ~neighbor_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 order.shape[1] > 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 run(self, workspace):
assert isinstance(workspace, cpw.Workspace)
image_name = self.image_name.value
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
workspace.display_data.statistics = []
img = image.pixel_data
mask = image.mask
objects = workspace.object_set.get_objects(self.primary_objects.value)
global_threshold = None
if self.method == M_DISTANCE_N:
has_threshold = False
else:
thresholded_image = self.threshold_image(image_name, workspace)
has_threshold = True
#
# Get the following labels:
# * all edited labels
# * labels touching the edge, including small removed
#
labels_in = objects.unedited_segmented.copy()
labels_touching_edge = np.hstack(
(labels_in[0, :], labels_in[-1, :], labels_in[:,
0], labels_in[:,
-1]))
labels_touching_edge = np.unique(labels_touching_edge)
is_touching = np.zeros(np.max(labels_in) + 1, bool)
is_touching[labels_touching_edge] = True
is_touching = is_touching[labels_in]
labels_in[(~is_touching) & (objects.segmented == 0)] = 0
#
# Stretch the input labels to match the image size. If there's no
# label matrix, then there's no label in that area.
#
if tuple(labels_in.shape) != tuple(img.shape):
tmp = np.zeros(img.shape, labels_in.dtype)
i_max = min(img.shape[0], labels_in.shape[0])
j_max = min(img.shape[1], labels_in.shape[1])
tmp[:i_max, :j_max] = labels_in[:i_max, :j_max]
labels_in = tmp
if self.method in (M_DISTANCE_B, M_DISTANCE_N):
if self.method == M_DISTANCE_N:
distances, (i, j) = scind.distance_transform_edt(
labels_in == 0, return_indices=True)
labels_out = np.zeros(labels_in.shape, int)
dilate_mask = distances <= self.distance_to_dilate.value
labels_out[dilate_mask] =\
labels_in[i[dilate_mask],j[dilate_mask]]
else:
labels_out, distances = propagate(img, labels_in,
thresholded_image, 1.0)
labels_out[distances > self.distance_to_dilate.value] = 0
labels_out[labels_in > 0] = labels_in[labels_in > 0]
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
#
# Create the final output labels by removing labels in the
# output matrix that are missing from the segmented image
#
segmented_labels = objects.segmented
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_PROPAGATION:
labels_out, distance = propagate(img, labels_in, thresholded_image,
self.regularization_factor.value)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_G:
#
# First, apply the sobel filter to the image (both horizontal
# and vertical). The filter measures gradient.
#
sobel_image = np.abs(scind.sobel(img))
#
# Combine the image mask and threshold to mask the watershed
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the first watershed
#
labels_out = watershed(sobel_image,
labels_in,
np.ones((3, 3), bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out.copy()
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
elif self.method == M_WATERSHED_I:
#
# invert the image so that the maxima are filled first
# and the cells compete over what's close to the threshold
#
inverted_img = 1 - img
#
# Same as above, but perform the watershed on the original image
#
watershed_mask = np.logical_or(thresholded_image, labels_in > 0)
watershed_mask = np.logical_and(watershed_mask, mask)
#
# Perform the watershed
#
labels_out = watershed(inverted_img,
labels_in,
np.ones((3, 3), bool),
mask=watershed_mask)
if self.fill_holes:
small_removed_segmented_out = fill_labeled_holes(labels_out)
else:
small_removed_segmented_out = labels_out
segmented_out = self.filter_labels(small_removed_segmented_out,
objects, workspace)
if self.wants_discard_edge and self.wants_discard_primary:
#
# Make a new primary object
#
lookup = scind.maximum(segmented_out, objects.segmented,
range(np.max(objects.segmented) + 1))
lookup = fix(lookup)
lookup[0] = 0
lookup[lookup != 0] = np.arange(np.sum(lookup != 0)) + 1
segmented_labels = lookup[objects.segmented]
segmented_out = lookup[segmented_out]
new_objects = cpo.Objects()
new_objects.segmented = segmented_labels
if objects.has_unedited_segmented:
new_objects.unedited_segmented = objects.unedited_segmented
if objects.has_small_removed_segmented:
new_objects.small_removed_segmented = objects.small_removed_segmented
new_objects.parent_image = objects.parent_image
primary_outline = outline(segmented_labels)
if self.wants_primary_outlines:
out_img = cpi.Image(primary_outline.astype(bool),
parent_image=image)
workspace.image_set.add(self.new_primary_outlines_name.value,
out_img)
else:
primary_outline = outline(objects.segmented)
secondary_outline = outline(segmented_out)
#
# Add the objects to the object set
#
objects_out = cpo.Objects()
objects_out.unedited_segmented = small_removed_segmented_out
objects_out.small_removed_segmented = small_removed_segmented_out
objects_out.segmented = segmented_out
objects_out.parent_image = image
objname = self.objects_name.value
workspace.object_set.add_objects(objects_out, objname)
if self.use_outlines.value:
out_img = cpi.Image(secondary_outline.astype(bool),
parent_image=image)
workspace.image_set.add(self.outlines_name.value, out_img)
object_count = np.max(segmented_out)
#
# Add measurements
#
measurements = workspace.measurements
cpmi.add_object_count_measurements(measurements, objname, object_count)
cpmi.add_object_location_measurements(measurements, objname,
segmented_out)
#
# Relate the secondary objects to the primary ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(objects_out)
measurements.add_measurement(self.primary_objects.value,
cpmi.FF_CHILDREN_COUNT % objname,
children_per_parent)
measurements.add_measurement(
objname, cpmi.FF_PARENT % self.primary_objects.value,
parents_of_children)
image_numbers = np.ones(len(parents_of_children), int) *\
measurements.image_set_number
mask = parents_of_children > 0
measurements.add_relate_measurement(
self.module_num, R_PARENT, self.primary_objects.value,
self.objects_name.value, image_numbers[mask],
parents_of_children[mask], image_numbers[mask],
np.arange(1,
len(parents_of_children) + 1)[mask])
#
# If primary objects were created, add them
#
if self.wants_discard_edge and self.wants_discard_primary:
workspace.object_set.add_objects(
new_objects, self.new_primary_objects_name.value)
cpmi.add_object_count_measurements(
measurements, self.new_primary_objects_name.value,
np.max(new_objects.segmented))
cpmi.add_object_location_measurements(
measurements, self.new_primary_objects_name.value,
new_objects.segmented)
for parent_objects, parent_name, child_objects, child_name in (
(objects, self.primary_objects.value, new_objects,
self.new_primary_objects_name.value),
(new_objects, self.new_primary_objects_name.value, objects_out,
objname)):
children_per_parent, parents_of_children = \
parent_objects.relate_children(child_objects)
measurements.add_measurement(
parent_name, cpmi.FF_CHILDREN_COUNT % child_name,
children_per_parent)
measurements.add_measurement(child_name,
cpmi.FF_PARENT % parent_name,
parents_of_children)
if self.show_window:
object_area = np.sum(segmented_out > 0)
workspace.display_data.object_pct = \
100 * object_area / np.product(segmented_out.shape)
workspace.display_data.img = img
workspace.display_data.segmented_out = segmented_out
workspace.display_data.primary_labels = objects.segmented
workspace.display_data.global_threshold = global_threshold
workspace.display_data.object_count = object_count
def run(self, workspace):
"""Run the module
workspace - The workspace contains
pipeline - instance of cpp for this run
image_set - the images in the image set being processed
object_set - the objects (labeled masks) in this image set
measurements - the measurements for this run
frame - the parent frame to whatever frame is created. None means don't draw.
"""
orig_objects_name = self.object_name.value
filtered_objects_name = self.filtered_objects.value
orig_objects = workspace.object_set.get_objects(orig_objects_name)
assert isinstance(orig_objects, cpo.Objects)
orig_labels = [l for l, c in orig_objects.get_labels()]
if self.wants_image_display:
guide_image = workspace.image_set.get_image(self.image_name.value)
guide_image = guide_image.pixel_data
if np.any(guide_image != np.min(guide_image)):
guide_image = (guide_image - np.min(guide_image)) / (
np.max(guide_image) - np.min(guide_image))
else:
guide_image = None
filtered_labels = workspace.interaction_request(
self, orig_labels, guide_image,
workspace.measurements.image_set_number)
if filtered_labels is None:
# Ask whoever is listening to stop doing stuff
workspace.cancel_request()
# Have to soldier on until the cancel takes effect...
filtered_labels = orig_labels
#
# Renumber objects consecutively if asked to do so
#
unique_labels = np.unique(np.array(filtered_labels))
unique_labels = unique_labels[unique_labels != 0]
object_count = len(unique_labels)
if self.renumber_choice == R_RENUMBER:
mapping = np.zeros(
1 if len(unique_labels) == 0 else np.max(unique_labels) + 1,
int)
mapping[unique_labels] = np.arange(1, object_count + 1)
filtered_labels = [mapping[l] for l in filtered_labels]
#
# Make the objects out of the labels
#
filtered_objects = cpo.Objects()
i, j = np.mgrid[0:filtered_labels[0].shape[0],
0:filtered_labels[0].shape[1]]
ijv = np.zeros((0, 3), filtered_labels[0].dtype)
for l in filtered_labels:
ijv = np.vstack(
(ijv, np.column_stack((i[l != 0], j[l != 0], l[l != 0]))))
filtered_objects.set_ijv(ijv, orig_labels[0].shape)
if orig_objects.has_unedited_segmented():
filtered_objects.unedited_segmented = orig_objects.unedited_segmented
if orig_objects.parent_image is not None:
filtered_objects.parent_image = orig_objects.parent_image
workspace.object_set.add_objects(filtered_objects,
filtered_objects_name)
#
# Add parent/child & other measurements
#
m = workspace.measurements
child_count, parents = orig_objects.relate_children(filtered_objects)
m.add_measurement(filtered_objects_name,
I.FF_PARENT % orig_objects_name, parents)
m.add_measurement(orig_objects_name,
I.FF_CHILDREN_COUNT % filtered_objects_name,
child_count)
#
# The object count
#
I.add_object_count_measurements(m, filtered_objects_name, object_count)
#
# The object locations
#
I.add_object_location_measurements_ijv(m, filtered_objects_name, ijv)
#
# Outlines if we want them
#
if self.wants_outlines:
outlines_name = self.outlines_name.value
outlines = outline(filtered_labels[0]).astype(bool)
outlines_image = cpi.Image(outlines)
workspace.image_set.add(outlines_name, outlines_image)
workspace.display_data.orig_ijv = orig_objects.ijv
workspace.display_data.filtered_ijv = filtered_objects.ijv
workspace.display_data.shape = orig_labels[0].shape
def run(self, workspace):
if self.show_window:
workspace.display_data.col_labels = ("Image", "channel", "Object",
"Feature", "Mean", "Median",
"STD")
workspace.display_data.statistics = statistics = []
for im in self.images:
image_name = im.name
image = workspace.image_set.get_image(image_name.value,
must_be_grayscale=False)
nchan = im.nchannels.value
for channel in range(nchan):
for object_name in [obj.name for obj in self.objects]:
# Need to refresh image after each iteration...
if nchan == 1:
img = image.pixel_data.copy()
else:
img = image.pixel_data[:, :, channel].squeeze().copy()
if image.has_mask:
masked_image = img.copy()
masked_image[~image.mask] = 0
else:
masked_image = img
objects = workspace.object_set.get_objects(
object_name.value)
nobjects = objects.count
integrated_intensity = np.zeros((nobjects, ))
integrated_intensity_edge = np.zeros((nobjects, ))
mean_intensity = np.zeros((nobjects, ))
mean_intensity_edge = np.zeros((nobjects, ))
std_intensity = np.zeros((nobjects, ))
std_intensity_edge = np.zeros((nobjects, ))
min_intensity = np.zeros((nobjects, ))
min_intensity_edge = np.zeros((nobjects, ))
max_intensity = np.zeros((nobjects, ))
max_intensity_edge = np.zeros((nobjects, ))
mass_displacement = np.zeros((nobjects, ))
lower_quartile_intensity = np.zeros((nobjects, ))
median_intensity = np.zeros((nobjects, ))
mad_intensity = np.zeros((nobjects, ))
upper_quartile_intensity = np.zeros((nobjects, ))
cmi_x = np.zeros((nobjects, ))
cmi_y = np.zeros((nobjects, ))
max_x = np.zeros((nobjects, ))
max_y = np.zeros((nobjects, ))
for labels, lindexes in objects.get_labels():
lindexes = lindexes[lindexes != 0]
labels, img = cpo.crop_labels_and_image(labels, img)
_, masked_image = cpo.crop_labels_and_image(
labels, masked_image)
outlines = cpmo.outline(labels)
if image.has_mask:
_, mask = cpo.crop_labels_and_image(
labels, image.mask)
masked_labels = labels.copy()
masked_labels[~mask] = 0
masked_outlines = outlines.copy()
masked_outlines[~mask] = 0
else:
masked_labels = labels
masked_outlines = outlines
lmask = masked_labels > 0 & np.isfinite(
img) # Ignore NaNs, Infs
has_objects = np.any(lmask)
if has_objects:
limg = img[lmask]
llabels = labels[lmask]
mesh_y, mesh_x = np.mgrid[0:masked_image.shape[0],
0:masked_image.shape[1]]
mesh_x = mesh_x[lmask]
mesh_y = mesh_y[lmask]
lcount = fix(
nd.sum(np.ones(len(limg)), llabels, lindexes))
integrated_intensity[lindexes - 1] = \
fix(nd.sum(limg, llabels, lindexes))
mean_intensity[lindexes - 1] = \
integrated_intensity[lindexes - 1] / lcount
std_intensity[lindexes - 1] = np.sqrt(
fix(
nd.mean((limg -
mean_intensity[llabels - 1])**2,
llabels, lindexes)))
min_intensity[lindexes - 1] = fix(
nd.minimum(limg, llabels, lindexes))
max_intensity[lindexes - 1] = fix(
nd.maximum(limg, llabels, lindexes))
# Compute the position of the intensity maximum
max_position = np.array(fix(
nd.maximum_position(limg, llabels, lindexes)),
dtype=int)
max_position = np.reshape(
max_position, (max_position.shape[0], ))
max_x[lindexes - 1] = mesh_x[max_position]
max_y[lindexes - 1] = mesh_y[max_position]
# The mass displacement is the distance between the center
# of mass of the binary image and of the intensity image. The
# center of mass is the average X or Y for the binary image
# and the sum of X or Y * intensity / integrated intensity
cm_x = fix(nd.mean(mesh_x, llabels, lindexes))
cm_y = fix(nd.mean(mesh_y, llabels, lindexes))
i_x = fix(nd.sum(mesh_x * limg, llabels, lindexes))
i_y = fix(nd.sum(mesh_y * limg, llabels, lindexes))
cmi_x[lindexes -
1] = i_x / integrated_intensity[lindexes - 1]
cmi_y[lindexes -
1] = i_y / integrated_intensity[lindexes - 1]
diff_x = cm_x - cmi_x[lindexes - 1]
diff_y = cm_y - cmi_y[lindexes - 1]
mass_displacement[lindexes - 1] = \
np.sqrt(diff_x * diff_x + diff_y * diff_y)
#
# Sort the intensities by label, then intensity.
# For each label, find the index above and below
# the 25%, 50% and 75% mark and take the weighted
# average.
#
order = np.lexsort((limg, llabels))
areas = lcount.astype(int)
indices = np.cumsum(areas) - areas
for dest, fraction in ((lower_quartile_intensity,
1.0 / 4.0),
(median_intensity,
1.0 / 2.0),
(upper_quartile_intensity,
3.0 / 4.0)):
qindex = indices.astype(
float) + areas * fraction
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
dest[lindexes[qmask] -
1] = (limg[order[qi]] * (1 - qf) +
limg[order[qi + 1]] * qf)
#
# In some situations (e.g. only 3 points), there may
# not be an upper bound.
#
qmask = (~qmask) & (areas > 0)
dest[lindexes[qmask] -
1] = limg[order[qindex[qmask]]]
#
# Once again, for the MAD
#
madimg = np.abs(limg -
median_intensity[llabels - 1])
order = np.lexsort((madimg, llabels))
qindex = indices.astype(float) + areas / 2.0
qfraction = qindex - np.floor(qindex)
qindex = qindex.astype(int)
qmask = qindex < indices + areas - 1
qi = qindex[qmask]
qf = qfraction[qmask]
mad_intensity[lindexes[qmask] -
1] = (madimg[order[qi]] * (1 - qf) +
madimg[order[qi + 1]] * qf)
qmask = (~qmask) & (areas > 0)
mad_intensity[lindexes[qmask] -
1] = madimg[order[qindex[qmask]]]
emask = masked_outlines > 0
eimg = img[emask]
elabels = labels[emask]
has_edge = len(eimg) > 0
if has_edge:
ecount = fix(
nd.sum(np.ones(len(eimg)), elabels, lindexes))
integrated_intensity_edge[lindexes - 1] = \
fix(nd.sum(eimg, elabels, lindexes))
mean_intensity_edge[lindexes - 1] = \
integrated_intensity_edge[lindexes - 1] / ecount
std_intensity_edge[lindexes - 1] = \
np.sqrt(fix(nd.mean(
(eimg - mean_intensity_edge[elabels - 1]) ** 2,
elabels, lindexes)))
min_intensity_edge[lindexes - 1] = fix(
nd.minimum(eimg, elabels, lindexes))
max_intensity_edge[lindexes - 1] = fix(
nd.maximum(eimg, elabels, lindexes))
m = workspace.measurements
for category, feature_name, measurement in \
((INTENSITY, INTEGRATED_INTENSITY, integrated_intensity),
(INTENSITY, MEAN_INTENSITY, mean_intensity),
(INTENSITY, STD_INTENSITY, std_intensity),
(INTENSITY, MIN_INTENSITY, min_intensity),
(INTENSITY, MAX_INTENSITY, max_intensity),
(INTENSITY, INTEGRATED_INTENSITY_EDGE, integrated_intensity_edge),
(INTENSITY, MEAN_INTENSITY_EDGE, mean_intensity_edge),
(INTENSITY, STD_INTENSITY_EDGE, std_intensity_edge),
(INTENSITY, MIN_INTENSITY_EDGE, min_intensity_edge),
(INTENSITY, MAX_INTENSITY_EDGE, max_intensity_edge),
(INTENSITY, MASS_DISPLACEMENT, mass_displacement),
(INTENSITY, LOWER_QUARTILE_INTENSITY, lower_quartile_intensity),
(INTENSITY, MEDIAN_INTENSITY, median_intensity),
(INTENSITY, MAD_INTENSITY, mad_intensity),
(INTENSITY, UPPER_QUARTILE_INTENSITY, upper_quartile_intensity),
(C_LOCATION, LOC_CMI_X, cmi_x),
(C_LOCATION, LOC_CMI_Y, cmi_y),
(C_LOCATION, LOC_MAX_X, max_x),
(C_LOCATION, LOC_MAX_Y, max_y)):
measurement_name = "%s_%s_%s_c%s" % (
category, feature_name, image_name.value,
str(channel + 1))
m.add_measurement(object_name.value, measurement_name,
measurement)
if self.show_window and len(measurement) > 0:
statistics.append(
(image_name.value, 'c' + str(channel + 1),
object_name.value, feature_name,
np.round(np.mean(measurement), 3),
np.round(np.median(measurement),
3), np.round(np.std(measurement),
3)))
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 test_00_00_zeros(self):
x = numpy.zeros((10,10),int)
result = OL.outline(x)
self.assertTrue(numpy.all(x==result))
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, I.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,
I.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):
objects = workspace.object_set.get_objects(self.object_name.value)
dimensions = len(objects.shape)
assert isinstance(objects, 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)
neighbor_labels = neighbor_objects.small_removed_segmented
neighbor_kept_labels = neighbor_objects.segmented
assert isinstance(neighbor_objects, Objects)
if not self.wants_excluded_objects.value:
# Remove labels not present in kept segmentation while preserving object IDs.
mask = neighbor_kept_labels > 0
neighbor_labels[~mask] = 0
nobjects = numpy.max(labels)
nkept_objects = len(objects.indices)
nneighbors = numpy.max(neighbor_labels)
_, object_numbers = objects.relate_labels(labels, kept_labels)
if self.neighbors_are_objects:
neighbor_numbers = object_numbers
neighbor_has_pixels = has_pixels
else:
_, neighbor_numbers = neighbor_objects.relate_labels(
neighbor_labels, neighbor_objects.small_removed_segmented)
neighbor_has_pixels = numpy.bincount(
neighbor_labels.ravel())[1:] > 0
neighbor_count = numpy.zeros((nobjects, ))
pixel_count = numpy.zeros((nobjects, ))
first_object_number = numpy.zeros((nobjects, ), int)
second_object_number = numpy.zeros((nobjects, ), int)
first_x_vector = numpy.zeros((nobjects, ))
second_x_vector = numpy.zeros((nobjects, ))
first_y_vector = numpy.zeros((nobjects, ))
second_y_vector = numpy.zeros((nobjects, ))
angle = numpy.zeros((nobjects, ))
percent_touching = numpy.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
if dimensions == 2:
i, j = scipy.ndimage.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]
else:
k, i, j = scipy.ndimage.distance_transform_edt(
labels == 0, return_distances=False, return_indices=True)
labels = labels[k, 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 = numpy.arange(nobjects, dtype=numpy.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(
scipy.ndimage.sum(numpy.ones(labels.shape), labels,
object_indexes))
perimeter_outlines = outline(labels)
perimeters = fix(
scipy.ndimage.sum(numpy.ones(labels.shape), perimeter_outlines,
object_indexes))
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
order = numpy.zeros((nobjects, min(nneighbors, 3)),
dtype=numpy.uint32)
j = numpy.arange(nneighbors)
# (0, 1, 2) unless there are less than 3 neighbors
partition_keys = tuple(range(min(nneighbors, 3)))
for i in range(nobjects):
dr = numpy.sqrt((ocenters[i, 0] - ncenters[j, 0])**2 +
(ocenters[i, 1] - ncenters[j, 1])**2)
order[i, :] = numpy.argpartition(dr, partition_keys)[:3]
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 = numpy.array((first_x_vector, first_y_vector))
v2 = numpy.array((second_x_vector, second_y_vector))
#
# Project the unit vector v1 against the unit vector v2
#
dot = numpy.sum(v1 * v2, 0) / numpy.sqrt(
numpy.sum(v1**2, 0) * numpy.sum(v2**2, 0))
angle = numpy.arccos(dot) * 180.0 / numpy.pi
# Make the structuring element for dilation
if dimensions == 2:
strel = strel_disk(distance)
else:
strel = skimage.morphology.ball(distance)
#
# A little bigger one to enter into the border with a structure
# that mimics the one used to create the outline
#
if dimensions == 2:
strel_touching = strel_disk(distance + 0.5)
else:
strel_touching = skimage.morphology.ball(distance + 0.5)
#
# Get the extents for each object and calculate the patch
# that excises the part of the image that is "distance"
# away
if dimensions == 2:
i, j = numpy.mgrid[0:labels.shape[0], 0:labels.shape[1]]
minimums_i, maximums_i, _, _ = scipy.ndimage.extrema(
i, labels, object_indexes)
minimums_j, maximums_j, _, _ = scipy.ndimage.extrema(
j, labels, object_indexes)
minimums_i = numpy.maximum(fix(minimums_i) - distance,
0).astype(int)
maximums_i = numpy.minimum(
fix(maximums_i) + distance + 1,
labels.shape[0]).astype(int)
minimums_j = numpy.maximum(fix(minimums_j) - distance,
0).astype(int)
maximums_j = numpy.minimum(
fix(maximums_j) + distance + 1,
labels.shape[1]).astype(int)
else:
k, i, j = numpy.mgrid[0:labels.shape[0], 0:labels.shape[1],
0:labels.shape[2]]
minimums_k, maximums_k, _, _ = scipy.ndimage.extrema(
k, labels, object_indexes)
minimums_i, maximums_i, _, _ = scipy.ndimage.extrema(
i, labels, object_indexes)
minimums_j, maximums_j, _, _ = scipy.ndimage.extrema(
j, labels, object_indexes)
minimums_k = numpy.maximum(fix(minimums_k) - distance,
0).astype(int)
maximums_k = numpy.minimum(
fix(maximums_k) + distance + 1,
labels.shape[0]).astype(int)
minimums_i = numpy.maximum(fix(minimums_i) - distance,
0).astype(int)
maximums_i = numpy.minimum(
fix(maximums_i) + distance + 1,
labels.shape[1]).astype(int)
minimums_j = numpy.maximum(fix(minimums_j) - distance,
0).astype(int)
maximums_j = numpy.minimum(
fix(maximums_j) + distance + 1,
labels.shape[2]).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
if dimensions == 2:
patch = labels[minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ]
npatch = neighbor_labels[
minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ]
else:
patch = labels[minimums_k[index]:maximums_k[index],
minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ]
npatch = neighbor_labels[
minimums_k[index]:maximums_k[index],
minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ]
#
# Find the neighbors
#
patch_mask = patch == (index + 1)
if distance <= 5:
extended = scipy.ndimage.binary_dilation(patch_mask, strel)
else:
extended = (scipy.signal.fftconvolve(
patch_mask, strel, mode="same") > 0.5)
neighbors = numpy.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(numpy.ones(nc, int) * object_number)
second_objects.append(neighbors)
#
# 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.
#
if dimensions == 2:
outline_patch = (perimeter_outlines[
minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ] == object_number
)
else:
outline_patch = (perimeter_outlines[
minimums_k[index]:maximums_k[index],
minimums_i[index]:maximums_i[index],
minimums_j[index]:maximums_j[index], ] == object_number
)
if self.neighbors_are_objects:
extendme = (patch != 0) & (patch != object_number)
if distance <= 5:
extended = scipy.ndimage.binary_dilation(
extendme, strel_touching)
else:
extended = (scipy.signal.fftconvolve(
extendme, strel_touching, mode="same") > 0.5)
else:
if distance <= 5:
extended = scipy.ndimage.binary_dilation(
(npatch != 0), strel_touching)
else:
extended = (scipy.signal.fftconvolve(
(npatch != 0), strel_touching, mode="same") > 0.5)
overlap = numpy.sum(outline_patch & extended)
pixel_count[index] = overlap
if sum([len(x) for x in first_objects]) > 0:
first_objects = numpy.hstack(first_objects)
reverse_object_numbers = numpy.zeros(
max(numpy.max(object_numbers), numpy.max(first_objects)) +
1, int)
reverse_object_numbers[object_numbers] = (
numpy.arange(len(object_numbers)) + 1)
first_objects = reverse_object_numbers[first_objects]
second_objects = numpy.hstack(second_objects)
reverse_neighbor_numbers = numpy.zeros(
max(numpy.max(neighbor_numbers), numpy.max(second_objects))
+ 1, int)
reverse_neighbor_numbers[neighbor_numbers] = (
numpy.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 = numpy.zeros(0, int)
second_objects = numpy.zeros(0, int)
percent_touching = pixel_count * 100 / perimeters
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
#
# Have to recompute nearest
#
first_object_number = numpy.zeros(nkept_objects, int)
second_object_number = numpy.zeros(nkept_objects, int)
if nkept_objects > (1 if self.neighbors_are_objects else 0):
di = (ocenters[object_indexes[:, numpy.newaxis], 0] -
ncenters[neighbor_indexes[numpy.newaxis, :], 0])
dj = (ocenters[object_indexes[:, numpy.newaxis], 1] -
ncenters[neighbor_indexes[numpy.newaxis, :], 1])
distance_matrix = numpy.sqrt(di * di + dj * dj)
distance_matrix[~has_pixels, :] = numpy.inf
distance_matrix[:, ~neighbor_has_pixels] = numpy.inf
#
# order[:,0] should be arange(nobjects)
# order[:,1] should be the nearest neighbor
# order[:,2] should be the next nearest neighbor
#
order = numpy.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 order.shape[1] > 1:
second_object_number[has_pixels] = order[has_pixels,
1] + 1
else:
object_indexes = object_numbers - 1
neighbor_indexes = neighbor_numbers - 1
first_objects = numpy.zeros(0, int)
second_objects = numpy.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, Workspace)
m = workspace.measurements
assert isinstance(m, 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,
numpy.sqrt(first_x_vector**2 + first_y_vector**2),
),
(M_SECOND_CLOSEST_OBJECT_NUMBER, second_object_number),
(
M_SECOND_CLOSEST_DISTANCE,
numpy.sqrt(second_x_vector**2 + second_y_vector**2),
),
(M_ANGLE_BETWEEN_NEIGHBORS, angle),
(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,
NEIGHBORS,
self.object_name.value,
self.object_name.value
if self.neighbors_are_objects else self.neighbors_name.value,
m.image_set_number * numpy.ones(first_objects.shape, int),
first_objects,
m.image_set_number * numpy.ones(second_objects.shape, int),
second_objects,
)
labels = kept_labels
neighbor_count_image = numpy.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
percent_touching_image = numpy.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 = Image(img, masking_objects=objects)
image_set.add(self.count_image_name.value, count_image)
else:
neighbor_cm_name = "Blues"
neighbor_cm = matplotlib.cm.get_cmap(neighbor_cm_name)
if 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 = Image(img, masking_objects=objects)
image_set.add(self.touching_image_name.value, touching_image)
else:
percent_touching_cm_name = "Oranges"
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.neighbor_labels = neighbor_labels
workspace.display_data.expanded_labels = expanded_labels
workspace.display_data.object_mask = object_mask
workspace.display_data.dimensions = dimensions
