def display(self, workspace):
'''Display the results of relabeling
workspace - workspace containing saved display data
'''
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib.cm as cm
figure = workspace.create_or_find_figure(title="UnifyObjectsByShape, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(1,2))
figure.subplot_imshow_labels(0,0, workspace.display_data.orig_labels,
title = self.objects_name.value)
output_labels = renumber_labels_for_display(
workspace.display_data.output_labels)
objects = workspace.object_set.get_objects(self.objects_name.value)
labels = objects.segmented
image = labels.astype(float) / (1.0 if np.max(labels) == 0 else np.max(labels))
image = (stretch(image) * 255).astype(np.uint8)
image = np.dstack((image,image,image))
my_cm = cm.get_cmap(cpprefs.get_default_colormap())
my_cm.set_bad((0,0,0))
sm = cm.ScalarMappable(cmap=my_cm)
m_output_labels = np.ma.array(output_labels,
mask = output_labels == 0)
output_image = sm.to_rgba(m_output_labels, bytes=True)[:,:,:3]
image[output_labels > 0 ] = (
image[output_labels > 0] / 4 * 3 +
output_image[output_labels > 0,:] / 4)
figure.subplot_imshow(0,1, image,
title = self.output_objects_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
def display(self, workspace, figure):
'''Display the results of relabeling
workspace - workspace containing saved display data
'''
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib.cm as cm
figure.set_subplots((1, 2))
figure.subplot_imshow_labels(0,
0,
workspace.display_data.orig_labels,
title=self.objects_name.value)
output_labels = renumber_labels_for_display(
workspace.display_data.output_labels)
if self.relabel_option == OPTION_UNIFY and (
(self.unify_option == UNIFY_DISTANCE and self.wants_image) or
(self.unify_option == UNIFY_PARENT)):
if self.unify_option == UNIFY_DISTANCE and self.wants_image:
#
# Make a nice picture which superimposes the labels on the
# guiding image
#
image = (stretch(workspace.display_data.image) * 255).astype(
np.uint8)
elif self.unify_option == UNIFY_PARENT:
parent_objects = workspace.object_set.get_objects(
self.parent_object.value)
labels = parent_objects.segmented
image = labels.astype(float) / (1.0 if np.max(labels) == 0 else
np.max(labels))
image = (stretch(image) * 255).astype(np.uint8)
image = np.dstack((image, image, image))
my_cm = cm.get_cmap(cpprefs.get_default_colormap())
my_cm.set_bad((0, 0, 0))
sm = cm.ScalarMappable(cmap=my_cm)
m_output_labels = np.ma.array(output_labels,
mask=output_labels == 0)
output_image = sm.to_rgba(m_output_labels, bytes=True)[:, :, :3]
image[output_labels > 0] = (image[output_labels > 0] / 4 * 3 +
output_image[output_labels > 0, :] / 4)
figure.subplot_imshow(0,
1,
image,
title=self.output_objects_name.value,
sharexy=figure.subplot(0, 0))
else:
figure.subplot_imshow_labels(0,
1,
workspace.display_data.output_labels,
title=self.output_objects_name.value,
sharexy=figure.subplot(0, 0))
def display(self, workspace):
"""Display the results of relabeling
workspace - workspace containing saved display data
"""
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib.cm as cm
figure = workspace.create_or_find_figure(
title="ReassignObjectNumbers, image cycle #%d" % (workspace.measurements.image_set_number), subplots=(1, 2)
)
figure.subplot_imshow_labels(0, 0, workspace.display_data.orig_labels, title=self.objects_name.value)
output_labels = renumber_labels_for_display(workspace.display_data.output_labels)
if self.relabel_option == OPTION_UNIFY:
if self.unify_option == UNIFY_DISTANCE and self.wants_image:
#
# Make a nice picture which superimposes the labels on the
# guiding image
#
image = (stretch(workspace.display_data.image) * 255).astype(np.uint8)
elif self.unify_option == UNIFY_PARENT:
parent_objects = workspace.object_set.get_objects(self.parent_object.value)
labels = parent_objects.segmented
image = labels.astype(float) / (1.0 if np.max(labels) == 0 else np.max(labels))
image = (stretch(image) * 255).astype(np.uint8)
image = np.dstack((image, image, image))
my_cm = cm.get_cmap(cpprefs.get_default_colormap())
my_cm.set_bad((0, 0, 0))
sm = cm.ScalarMappable(cmap=my_cm)
m_output_labels = np.ma.array(output_labels, mask=output_labels == 0)
output_image = sm.to_rgba(m_output_labels, bytes=True)[:, :, :3]
image[output_labels > 0] = image[output_labels > 0] / 4 * 3 + output_image[output_labels > 0, :] / 4
figure.subplot_imshow(
0,
1,
image,
title=self.output_objects_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0),
)
else:
figure.subplot_imshow_labels(
0,
1,
workspace.display_data.output_labels,
title=self.output_objects_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0),
)
def display():
if len(orig_axes.images) > 0:
# Save zoom and scale if coming through here a second time
x0, x1 = orig_axes.get_xlim()
y0, y1 = orig_axes.get_ylim()
set_lim = True
else:
set_lim = False
for axes, labels, title in ((orig_axes, orig_labels,
"Original: %s" % orig_objects_name),
(keep_axes, orig_labels * mask,
"Objects to keep"),
(remove_axes, orig_labels * (~mask),
"Objects to remove")):
assert isinstance(axes, matplotlib.axes.Axes)
labels = renumber_labels_for_display(labels)
axes.clear()
if np.all(labels == 0):
use_cm = matplotlib.cm.gray
is_blank = True
else:
use_cm = cm
is_blank = False
if wants_image_display[0]:
outlines = outline(labels)
image = workspace.image_set.get_image(
self.image_name.value)
image = image.pixel_data.astype(np.float)
image, _ = cpo.size_similarly(labels, image)
if image.ndim == 2:
image = np.dstack((image, image, image))
if not is_blank:
mappable = matplotlib.cm.ScalarMappable(cmap=use_cm)
mappable.set_clim(1, labels.max())
limage = mappable.to_rgba(labels)[:, :, :3]
image[outlines != 0, :] = limage[outlines != 0, :]
axes.imshow(image)
else:
axes.imshow(labels, cmap=use_cm)
axes.set_title(title,
fontname=cpprefs.get_title_font_name(),
fontsize=cpprefs.get_title_font_size())
if set_lim:
orig_axes.set_xlim((x0, x1))
orig_axes.set_ylim((y0, y1))
figure.canvas.draw()
panel.Refresh()
def display(self, workspace):
'''Display the results of relabeling
workspace - workspace containing saved display data
'''
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib.cm as cm
figure = workspace.create_or_find_figure(
title="ReassignObjectNumbers, image cycle #%d" %
(workspace.measurements.image_set_number),
subplots=(1, 2))
figure.subplot_imshow_labels(0,
0,
workspace.display_data.orig_labels,
title=self.objects_name.value)
if self.wants_image:
#
# Make a nice picture which superimposes the labels on the
# guiding image
#
output_labels = renumber_labels_for_display(
workspace.display_data.output_labels)
image = (stretch(workspace.display_data.image) * 255).astype(
np.uint8)
image = np.dstack((image, image, image))
my_cm = cm.get_cmap(cpprefs.get_default_colormap())
my_cm.set_bad((0, 0, 0))
sm = cm.ScalarMappable(cmap=my_cm)
m_output_labels = np.ma.array(output_labels,
mask=output_labels == 0)
output_image = sm.to_rgba(m_output_labels, bytes=True)[:, :, :3]
image[output_labels > 0] = (image[output_labels > 0] / 4 * 3 +
output_image[output_labels > 0, :] / 4)
figure.subplot_imshow(0,
1,
image,
title=self.output_objects_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
else:
figure.subplot_imshow_labels(0,
1,
workspace.display_data.output_labels,
title=self.output_objects_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
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 display():
if len(orig_axes.images) > 0:
# Save zoom and scale if coming through here a second time
x0, x1 = orig_axes.get_xlim()
y0, y1 = orig_axes.get_ylim()
set_lim = True
else:
set_lim = False
for axes, labels, title in (
(orig_axes, orig_labels, "Original: %s"%orig_objects_name),
(keep_axes, orig_labels * mask,"Objects to keep"),
(remove_axes, orig_labels * (~ mask), "Objects to remove")):
assert isinstance(axes, matplotlib.axes.Axes)
labels = renumber_labels_for_display(labels)
axes.clear()
if np.all(labels == 0):
use_cm = matplotlib.cm.gray
is_blank = True
else:
use_cm = cm
is_blank = False
if wants_image_display[0]:
outlines = outline(labels)
image = workspace.image_set.get_image(self.image_name.value)
image = image.pixel_data.astype(np.float)
image, _ = cpo.size_similarly(labels, image)
if image.ndim == 2:
image = np.dstack((image, image, image))
if not is_blank:
mappable = matplotlib.cm.ScalarMappable(cmap=use_cm)
mappable.set_clim(1,labels.max())
limage = mappable.to_rgba(labels)[:,:,:3]
image[outlines != 0,:] = limage[outlines != 0, :]
axes.imshow(image)
else:
axes.imshow(labels, cmap = use_cm)
axes.set_title(title,
fontname=cpprefs.get_title_font_name(),
fontsize=cpprefs.get_title_font_size())
if set_lim:
orig_axes.set_xlim((x0, x1))
orig_axes.set_ylim((y0, y1))
figure.canvas.draw()
panel.Refresh()
def display(self, workspace):
'''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 = workspace.create_or_find_figure(title="MaskObjects, image cycle #%d"%(
workspace.measurements.image_set_number),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,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
def display(self, workspace):
'''Display the results of relabeling
workspace - workspace containing saved display data
'''
from cellprofiler.gui.cpfigure import renumber_labels_for_display
import matplotlib.cm as cm
figure = workspace.create_or_find_figure(title="ReassignObjectNumbers, image cycle #%d"%(
workspace.measurements.image_set_number),subplots=(1,2))
figure.subplot_imshow_labels(0,0, workspace.display_data.orig_labels,
title = self.objects_name.value)
if self.wants_image:
#
# Make a nice picture which superimposes the labels on the
# guiding image
#
output_labels = renumber_labels_for_display(
workspace.display_data.output_labels)
image = (stretch(workspace.display_data.image) * 255).astype(np.uint8)
image = np.dstack((image,image,image))
my_cm = cm.get_cmap(cpprefs.get_default_colormap())
my_cm.set_bad((0,0,0))
sm = cm.ScalarMappable(cmap=my_cm)
m_output_labels = np.ma.array(output_labels,
mask = output_labels == 0)
output_image = sm.to_rgba(m_output_labels, bytes=True)[:,:,:3]
image[output_labels > 0 ] = (
image[output_labels > 0] / 4 * 3 +
output_image[output_labels > 0,:] / 4)
figure.subplot_imshow(0,1, image,
title = self.output_objects_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
else:
figure.subplot_imshow_labels(0,1,
workspace.display_data.output_labels,
title = self.output_objects_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
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.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 run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
labels = objects.segmented
convert = True
if not workspace.frame is None:
figure = workspace.create_or_find_figure(
title="ConvertObjectsToImage, image cycle #%d" % (workspace.measurements.image_set_number),
subplots=(2, 1),
)
figure.subplot_imshow_labels(0, 0, labels, "Original: %s" % self.object_name.value)
if self.image_mode == IM_BINARY:
pixel_data = labels != 0
if not workspace.frame is None:
figure.subplot_imshow_bw(
1, 0, pixel_data, self.image_name.value, sharex=figure.subplot(0, 0), sharey=figure.subplot(0, 0)
)
elif self.image_mode == IM_GRAYSCALE:
pixel_data = labels.astype(float) / np.max(labels)
if not workspace.frame is None:
figure.subplot_imshow_grayscale(
1, 0, pixel_data, self.image_name.value, sharex=figure.subplot(0, 0), sharey=figure.subplot(0, 0)
)
elif self.image_mode == IM_COLOR:
import matplotlib.cm
from cellprofiler.gui.cpfigure import renumber_labels_for_display
if self.colormap.value == DEFAULT_COLORMAP:
cm_name = cpprefs.get_default_colormap()
elif self.colormap.value == COLORCUBE:
# Colorcube missing from matplotlib
cm_name = "gist_rainbow"
elif self.colormap.value == LINES:
# Lines missing from matplotlib and not much like it,
# Pretty boring palette anyway, hence
cm_name = "Pastel1"
elif self.colormap.value == WHITE:
# White missing from matplotlib, it's just a colormap
# of all completely white... not even different kinds of
# white. And, isn't white just a uniform sampling of
# frequencies from the spectrum?
cm_name = "Spectral"
else:
cm_name = self.colormap.value
cm = matplotlib.cm.get_cmap(cm_name)
mapper = matplotlib.cm.ScalarMappable(cmap=cm)
pixel_data = mapper.to_rgba(renumber_labels_for_display(labels))
pixel_data = pixel_data[:, :, :3]
pixel_data[labels == 0, :] = 0
if not workspace.frame is None:
figure.subplot_imshow(
1, 0, pixel_data, self.image_name.value, sharex=figure.subplot(0, 0), sharey=figure.subplot(0, 0)
)
elif self.image_mode == IM_UINT16:
pixel_data = labels.copy()
if not workspace.frame is None:
figure.subplot_imshow_grayscale(
1, 0, pixel_data, self.image_name.value, sharex=figure.subplot(0, 0), sharey=figure.subplot(0, 0)
)
convert = False
image = cpi.Image(pixel_data, parent_image=objects.parent_image, convert=convert)
workspace.image_set.add(self.image_name.value, image)
def run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
labels = objects.segmented
convert = True
if not workspace.frame is None:
figure = workspace.create_or_find_figure(
title="ConvertObjectsToImage, image cycle #%d" %
(workspace.measurements.image_set_number),
subplots=(2, 1))
figure.subplot_imshow_labels(
0, 0, labels, "Original: %s" % self.object_name.value)
if self.image_mode == IM_BINARY:
pixel_data = labels != 0
if not workspace.frame is None:
figure.subplot_imshow_bw(1,
0,
pixel_data,
self.image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
elif self.image_mode == IM_GRAYSCALE:
pixel_data = labels.astype(float) / (1.0 if np.max(labels) == 0
else np.max(labels))
if not workspace.frame is None:
figure.subplot_imshow_grayscale(1,
0,
pixel_data,
self.image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
elif self.image_mode == IM_COLOR:
import matplotlib.cm
from cellprofiler.gui.cpfigure import renumber_labels_for_display
if self.colormap.value == DEFAULT_COLORMAP:
cm_name = cpprefs.get_default_colormap()
elif self.colormap.value == COLORCUBE:
# Colorcube missing from matplotlib
cm_name = "gist_rainbow"
elif self.colormap.value == LINES:
# Lines missing from matplotlib and not much like it,
# Pretty boring palette anyway, hence
cm_name = "Pastel1"
elif self.colormap.value == WHITE:
# White missing from matplotlib, it's just a colormap
# of all completely white... not even different kinds of
# white. And, isn't white just a uniform sampling of
# frequencies from the spectrum?
cm_name = "Spectral"
else:
cm_name = self.colormap.value
cm = matplotlib.cm.get_cmap(cm_name)
mapper = matplotlib.cm.ScalarMappable(cmap=cm)
pixel_data = mapper.to_rgba(renumber_labels_for_display(labels))
pixel_data = pixel_data[:, :, :3]
pixel_data[labels == 0, :] = 0
if not workspace.frame is None:
figure.subplot_imshow(1,
0,
pixel_data,
self.image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
elif self.image_mode == IM_UINT16:
pixel_data = labels.copy()
if not workspace.frame is None:
figure.subplot_imshow_grayscale(1,
0,
pixel_data,
self.image_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
convert = False
image = cpi.Image(pixel_data,
parent_image=objects.parent_image,
convert=convert)
workspace.image_set.add(self.image_name.value, image)