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)
def display(self, workspace, figure):
dimensions = workspace.display_data.dimensions
if self.blank_image.value:
figure.set_subplots((1, 1), dimensions=dimensions)
if self.wants_color.value == WANTS_COLOR:
figure.subplot_imshow(
0,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions
)
else:
figure.subplot_imshow_bw(
0,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions
)
else:
figure.set_subplots((2, 1), dimensions=dimensions)
figure.subplot_imshow_bw(
0,
0,
workspace.display_data.image_pixel_data,
self.image_name.value,
dimensions=dimensions
)
if self.wants_color.value == WANTS_COLOR:
figure.subplot_imshow(
1,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions
)
else:
figure.subplot_imshow_bw(
1,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions
)
def display(self, workspace, figure):
'''Show an informative display'''
#import matplotlib
import cellprofiler.gui.figure
cplabels=[]
figure.set_subplots((1, 1))
assert isinstance(figure, cellprofiler.gui.figure.Figure)
title = self.object_name.value
cplabels.append(
dict(name = self.object_name.value,
labels = workspace.display_data.overlapping_labels,
mode = CPLDM_ALPHA))
mask = workspace.display_data.mask
if mask.ndim == 2:
figure.subplot_imshow_grayscale(
0, 0, mask, title = title, cplabels = cplabels)
def display(self, workspace, figure):
dimensions = workspace.display_data.dimensions
if self.blank_image.value:
figure.set_subplots((1, 1), dimensions=dimensions)
if self.wants_color.value == WANTS_COLOR:
figure.subplot_imshow(0,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions)
else:
figure.subplot_imshow_bw(0,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions)
else:
figure.set_subplots((2, 1), dimensions=dimensions)
figure.subplot_imshow_bw(0,
0,
workspace.display_data.image_pixel_data,
self.output_image_name.value,
dimensions=dimensions)
if self.wants_color.value == WANTS_COLOR:
figure.subplot_imshow(1,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions)
else:
figure.subplot_imshow_bw(1,
0,
workspace.display_data.pixel_data,
self.output_image_name.value,
dimensions=dimensions)
frame=frm,
create_new_window=False)
frm.Show(False)
## generate identify primary objects' output
x.show_window = True
x.run(workspace) #ON CHERCHE A RECUP NOYAUX SEGMENTÉS - COMMENT FAIRE ?
figure = cellprofiler.gui.figure.Figure(frm)
labeled_image = workspace.display_data.labeled_image
objects = object_set.get_objects(
"nuclei"
) #SET D'OBJETS CONTIENT JUSTE LE NOM "NUCLEI" MAIS PAS D'IMAGE, COMMENT OBTENIR L'IMAGE QUI VA AVEC L'OBJET ?
from matplotlib.figure import Figure
#x.display(workspace,figure)
figure.set_subplots((1, 1))
print("[EXTRACTING DATA]")
## ATTENTION : MODIFICATION DU CODE FIGURE.PY QUI RESORT L'IMAGE AVEC LES NOYAUX COLORÉS :
stuff = figure.subplot_imshow_labels(0,
0,
labeled_image,
title=None,
use_imshow=False)
print("[DEBUG] FLAG 2")
data_primary = stuff[1] # = image avec noyaux colorés
## test - save data to txt files
#np.save("data_primary.npy", data_primary)