def display(self, workspace):
from cellprofiler.gui.cpfigure_tools import renumber_labels_for_display
figure = workspace.create_or_find_figure(subplots=(2,2))
renumbered_parent_labels = renumber_labels_for_display(
workspace.display_data.parent_labels)
child_labels = workspace.display_data.child_labels
parents_of = workspace.display_data.parents_of
#
# discover the mapping so that we can apply it to the children
#
mapping = np.arange(workspace.display_data.parent_count+1)
mapping[workspace.display_data.parent_labels.flatten()] = \
renumbered_parent_labels.flatten()
parent_labeled_children = np.zeros(child_labels.shape, int)
mask = child_labels > 0
parent_labeled_children[mask] = \
mapping[parents_of[child_labels[mask] - 1]]
figure.subplot_imshow_labels(
0, 0, renumbered_parent_labels,
title = self.parent_name.value,
renumber=False)
figure.subplot_imshow_labels(
1, 0, child_labels,
title = self.sub_object_name.value,
sharex = figure.subplot(0,0),
sharey = figure.subplot(0,0))
figure.subplot_imshow_labels(
0, 1, parent_labeled_children,
"%s labeled by %s"%
(self.sub_object_name.value,
self.parent_name.value),
renumber=False,
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_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 display(self, workspace, figure):
if not self.show_window:
return
from cellprofiler.gui.cpfigure_tools import renumber_labels_for_display
figure.set_subplots((2, 2))
renumbered_parent_labels = renumber_labels_for_display(
workspace.display_data.parent_labels)
child_labels = workspace.display_data.child_labels
parents_of = workspace.display_data.parents_of
#
# discover the mapping so that we can apply it to the children
#
mapping = np.arange(workspace.display_data.parent_count + 1)
mapping[workspace.display_data.parent_labels.flatten()] = \
renumbered_parent_labels.flatten()
parent_labeled_children = np.zeros(child_labels.shape, int)
mask = child_labels > 0
parent_labeled_children[mask] = \
mapping[parents_of[child_labels[mask] - 1]]
figure.subplot_imshow_labels(0,
0,
renumbered_parent_labels,
title=self.parent_name.value,
renumber=False)
figure.subplot_imshow_labels(1,
0,
child_labels,
title=self.sub_object_name.value,
sharex=figure.subplot(0, 0),
sharey=figure.subplot(0, 0))
figure.subplot_imshow_labels(
0,
1,
parent_labeled_children,
"%s labeled by %s" %
(self.sub_object_name.value, self.parent_name.value),
renumber=False,
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_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 overlay(self):
'''Return a color image of the segmentation as an overlay
'''
if self.__overlay is not None:
return self.__overlay
sm = matplotlib.cm.ScalarMappable(cmap = self.colormap)
sm.set_clim(vmin=1, vmax=np.max([np.max(l) for l in self.labels])+1)
img = None
lmin = 0
for l in self.labels:
if self.scramble:
lmin = np.min(l[l!=0])
l[l!=0] = renumber_labels_for_display(l)[l!=0]+lmin
lmin = np.max(l)
if img is None:
img = sm.to_rgba(l)
img[l==0, :] = 0
else:
img[l!=0, :] = sm.to_rgba(l[l!=0])
self.__overlay = img
return img
def overlay(self):
'''Return a color image of the segmentation as an overlay
'''
if self.__overlay is not None:
return self.__overlay
sm = matplotlib.cm.ScalarMappable(cmap=self.colormap)
sm.set_clim(vmin=1, vmax=np.max([np.max(l) for l in self.labels]) + 1)
img = None
lmin = 0
for l in self.labels:
if self.scramble:
lmin = np.min(l[l != 0])
l[l != 0] = renumber_labels_for_display(l)[l != 0] + lmin
lmin = np.max(l)
if img is None:
img = sm.to_rgba(l)
img[l == 0, :] = 0
else:
img[l != 0, :] = sm.to_rgba(l[l != 0])
self.__overlay = img
return img
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_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 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_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 run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
alpha = np.zeros(objects.shape)
if self.image_mode == IM_BINARY:
pixel_data = np.zeros(objects.shape, bool)
elif self.image_mode == IM_GRAYSCALE:
pixel_data = np.zeros(objects.shape)
elif self.image_mode == IM_UINT16:
pixel_data = np.zeros(objects.shape, np.int32)
else:
pixel_data = np.zeros((objects.shape[0], objects.shape[1], 3))
convert = True
for labels, indices in objects.get_labels():
mask = labels != 0
if np.all(~ mask):
continue
if self.image_mode == IM_BINARY:
pixel_data[mask] = True
alpha[mask] = 1
elif self.image_mode == IM_GRAYSCALE:
pixel_data[mask] = labels[mask].astype(float) / np.max(labels)
alpha[mask] = 1
elif self.image_mode == IM_COLOR:
import matplotlib.cm
from cellprofiler.gui.cpfigure_tools 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[mask, :] += \
mapper.to_rgba(renumber_labels_for_display(labels))[mask, :3]
alpha[mask] += 1
elif self.image_mode == IM_UINT16:
pixel_data[mask] = labels[mask]
alpha[mask] = 1
convert = False
mask = alpha > 0
if self.image_mode == IM_BINARY:
pass
elif self.image_mode == IM_COLOR:
pixel_data[mask, :] = pixel_data[mask, :] / alpha[mask][:, np.newaxis]
else:
pixel_data[mask] = pixel_data[mask] / alpha[mask]
image = cpi.Image(pixel_data, parent_image = objects.parent_image,
convert = convert)
workspace.image_set.add(self.image_name.value, image)
if self.show_window:
workspace.display_data.ijv = objects.ijv
workspace.display_data.pixel_data = pixel_data
def run(self, workspace):
objects = workspace.object_set.get_objects(self.object_name.value)
alpha = np.zeros(objects.shape)
if self.image_mode == IM_BINARY:
pixel_data = np.zeros(objects.shape, bool)
elif self.image_mode == IM_GRAYSCALE:
pixel_data = np.zeros(objects.shape)
elif self.image_mode == IM_UINT16:
pixel_data = np.zeros(objects.shape, np.int32)
else:
pixel_data = np.zeros((objects.shape[0], objects.shape[1], 3))
convert = True
for labels, indices in objects.get_labels():
mask = labels != 0
if np.all(~mask):
continue
if self.image_mode == IM_BINARY:
pixel_data[mask] = True
alpha[mask] = 1
elif self.image_mode == IM_GRAYSCALE:
pixel_data[mask] = labels[mask].astype(float) / np.max(labels)
alpha[mask] = 1
elif self.image_mode == IM_COLOR:
import matplotlib.cm
from cellprofiler.gui.cpfigure_tools 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[mask, :] += \
mapper.to_rgba(renumber_labels_for_display(labels))[mask, :3]
alpha[mask] += 1
elif self.image_mode == IM_UINT16:
pixel_data[mask] = labels[mask]
alpha[mask] = 1
convert = False
mask = alpha > 0
if self.image_mode == IM_BINARY:
pass
elif self.image_mode == IM_COLOR:
pixel_data[mask, :] = pixel_data[mask, :] / alpha[mask][:,
np.newaxis]
else:
pixel_data[mask] = pixel_data[mask] / alpha[mask]
image = cpi.Image(pixel_data,
parent_image=objects.parent_image,
convert=convert)
workspace.image_set.add(self.image_name.value, image)
if self.show_window:
workspace.display_data.ijv = objects.ijv
workspace.display_data.pixel_data = pixel_data
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_tools 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_tools 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)
