def display(self, workspace, figure):
'''Show an informative display'''
import matplotlib
import cellprofiler.gui.figure
figure.set_subplots((2, 1))
assert isinstance(figure, cellprofiler.gui.figure.Figure)
i = workspace.display_data.i
j = workspace.display_data.j
angles = workspace.display_data.angle
mask = workspace.display_data.mask
labels = workspace.display_data.labels
count = workspace.display_data.count
color_image = np.zeros((mask.shape[0], mask.shape[1], 4))
#
# We do the coloring using alpha values to let the different
# things we draw meld together.
#
# The binary mask is white.
#
color_image[mask, :] = MASK_ALPHA
if count > 0:
mappable = matplotlib.cm.ScalarMappable(
cmap=matplotlib.cm.get_cmap(cpprefs.get_default_colormap()))
np.random.seed(0)
colors = mappable.to_rgba(np.random.permutation(np.arange(count)))
#
# The labels
#
color_image[labels > 0, :] += colors[labels[labels > 0] -
1, :] * LABEL_ALPHA
#
# Do each diamond individually (because the angles are almost certainly
# different for each
#
lcolors = colors * .5 + .5 # Wash the colors out a little
for ii in range(count):
diamond = self.get_diamond(angles[ii])
hshape = ((np.array(diamond.shape) - 1) / 2).astype(int)
iii = i[ii]
jjj = j[ii]
color_image[iii - hshape[0]:iii + hshape[0] + 1,
jjj - hshape[1]:jjj + hshape[1] + 1, :][diamond, :] += \
lcolors[ii, :] * WORM_ALPHA
#
# Do our own alpha-normalization
#
color_image[:, :, -1][color_image[:, :, -1] == 0] = 1
color_image[:, :, :-1] = (color_image[:, :, :-1] /
color_image[:, :, -1][:, :, np.newaxis])
plot00 = figure.subplot_imshow_bw(0, 0, mask, self.image_name.value)
figure.subplot_imshow_color(1,
0,
color_image[:, :, :-1],
title=self.object_name.value,
normalize=False,
sharexy=plot00)
def display(self, workspace, figure):
'''Show an informative display'''
import matplotlib
import cellprofiler.gui.figure
figure.set_subplots((2, 1))
assert isinstance(figure, cellprofiler.gui.figure.Figure)
i = workspace.display_data.i
j = workspace.display_data.j
angles = workspace.display_data.angle
mask = workspace.display_data.mask
labels = workspace.display_data.labels
count = workspace.display_data.count
color_image = np.zeros((mask.shape[0], mask.shape[1], 4))
#
# We do the coloring using alpha values to let the different
# things we draw meld together.
#
# The binary mask is white.
#
color_image[mask, :] = MASK_ALPHA
if count > 0:
mappable = matplotlib.cm.ScalarMappable(
cmap=matplotlib.cm.get_cmap(cpprefs.get_default_colormap()))
np.random.seed(0)
colors = mappable.to_rgba(np.random.permutation(np.arange(count)))
#
# The labels
#
color_image[labels > 0, :] += colors[labels[labels > 0] - 1, :] * LABEL_ALPHA
#
# Do each diamond individually (because the angles are almost certainly
# different for each
#
lcolors = colors * .5 + .5 # Wash the colors out a little
for ii in range(count):
diamond = self.get_diamond(angles[ii])
hshape = ((np.array(diamond.shape) - 1) / 2).astype(int)
iii = i[ii]
jjj = j[ii]
color_image[iii - hshape[0]:iii + hshape[0] + 1,
jjj - hshape[1]:jjj + hshape[1] + 1, :][diamond, :] += \
lcolors[ii, :] * WORM_ALPHA
#
# Do our own alpha-normalization
#
color_image[:, :, -1][color_image[:, :, -1] == 0] = 1
color_image[:, :, :-1] = (color_image[:, :, :-1] /
color_image[:, :, -1][:, :, np.newaxis])
plot00 = figure.subplot_imshow_bw(0, 0, mask, self.image_name.value)
figure.subplot_imshow_color(1, 0, color_image[:, :, :-1],
title=self.object_name.value,
normalize=False,
sharexy=plot00)
