def run(self, workspace):
statistics = []
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
image_set = workspace.image_set
for file_setting in self.file_settings:
wants_images = self.file_wants_images(file_setting)
image_name = file_setting.image_name.value if wants_images else \
file_setting.objects_name.value
m_path, m_file, m_md5_digest, m_scaling, m_height, m_width = [
"_".join((c, image_name))
for c in (C_PATH_NAME if wants_images else C_OBJECTS_PATH_NAME,
C_FILE_NAME if wants_images else C_OBJECTS_FILE_NAME,
C_MD5_DIGEST, C_SCALING, C_HEIGHT, C_WIDTH)
]
pathname = m.get_current_image_measurement(m_path)
filename = m.get_current_image_measurement(m_file)
rescale = (wants_images and file_setting.rescale.value)
provider = LoadImagesImageProvider(image_name, pathname, filename,
rescale)
image = provider.provide_image(image_set)
pixel_data = image.pixel_data
digest = hashlib.md5()
if wants_images:
digest.update(np.ascontiguousarray(pixel_data).data)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
digest.hexdigest())
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
image_set.providers.append(provider)
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
#
# Add outlines if appropriate
#
if file_setting.wants_outlines:
outlines = cellprofiler.cpmath.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_setting.outlines_name.value,
outline_image)
statistics += [(image_name, filename)]
workspace.display_data.col_labels = ("Image name", "File")
workspace.display_data.statistics = statistics
def run(self, workspace):
statistics = []
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
image_set = workspace.image_set
for file_setting in self.file_settings:
wants_images = self.file_wants_images(file_setting)
image_name = file_setting.image_name.value if wants_images else \
file_setting.objects_name.value
m_path, m_file, m_md5_digest, m_scaling, m_height, m_width = [
"_".join((c, image_name)) for c in (
C_PATH_NAME if wants_images else C_OBJECTS_PATH_NAME,
C_FILE_NAME if wants_images else C_OBJECTS_FILE_NAME,
C_MD5_DIGEST, C_SCALING, C_HEIGHT, C_WIDTH)]
pathname = m.get_current_image_measurement(m_path)
filename = m.get_current_image_measurement(m_file)
rescale = (wants_images and file_setting.rescale.value)
provider = LoadImagesImageProvider(
image_name, pathname, filename, rescale)
image = provider.provide_image(image_set)
pixel_data = image.pixel_data
digest = hashlib.md5()
if wants_images:
digest.update(np.ascontiguousarray(pixel_data).data)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
digest.hexdigest())
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
image_set.providers.append(provider)
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
#
# Add outlines if appropriate
#
if file_setting.wants_outlines:
outlines = cellprofiler.cpmath.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_setting.outlines_name.value,
outline_image)
statistics += [(image_name, filename)]
workspace.display_data.col_labels = ("Image name","File")
workspace.display_data.statistics = statistics
def run(self, workspace):
dict = self.get_file_names(workspace)
root = self.get_base_directory(workspace)
statistics = [("Image name", "File")]
m = workspace.measurements
for image_name in dict.keys():
file_settings = self.get_file_settings(image_name)
rescale = (file_settings.image_objects_choice == IO_IMAGES
and file_settings.rescale)
provider = LoadImagesImageProvider(image_name, root,
dict[image_name], rescale)
image = provider.provide_image(workspace.image_set)
pixel_data = image.pixel_data
if file_settings.image_objects_choice == IO_IMAGES:
workspace.image_set.providers.append(provider)
#
# Add measurements
#
path_name_category = C_PATH_NAME
file_name_category = C_FILE_NAME
digest = hashlib.md5()
digest.update(np.ascontiguousarray(pixel_data).data)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
digest.hexdigest())
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
#
# Add measurements
#
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
path_name_category = C_OBJECTS_PATH_NAME
file_name_category = C_OBJECTS_FILE_NAME
#
# Add outlines if appropriate
#
if file_settings.wants_outlines:
outlines = cellprofiler.cpmath.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_settings.outlines_name.value,
outline_image)
m.add_image_measurement(file_name_category + '_' + image_name,
dict[image_name])
m.add_image_measurement(path_name_category + '_' + image_name,
root)
statistics += [(image_name, dict[image_name])]
if workspace.frame:
title = "Load single image: image cycle # %d" % (
workspace.measurements.image_set_number + 1)
figure = workspace.create_or_find_figure(
title="LoadSingleImage, image cycle #%d" %
(workspace.measurements.image_set_number),
subplots=(1, 1))
figure.subplot_table(0, 0, statistics)
def run(self, workspace):
statistics = []
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
#
# Hack: if LoadSingleImage is first, no paths are populated
#
if self.file_wants_images(self.file_settings[0]):
m_path = "_".join((C_PATH_NAME, self.file_settings[0].image_name.value))
else:
m_path = "_".join((C_OBJECTS_PATH_NAME, self.file_settings[0].objects_name.value))
if m.get_current_image_measurement(m_path) is None:
self.prepare_run(workspace)
image_set = workspace.image_set
for file_setting in self.file_settings:
wants_images = self.file_wants_images(file_setting)
image_name = file_setting.image_name.value if wants_images else file_setting.objects_name.value
m_path, m_file, m_md5_digest, m_scaling, m_height, m_width = [
"_".join((c, image_name))
for c in (
C_PATH_NAME if wants_images else C_OBJECTS_PATH_NAME,
C_FILE_NAME if wants_images else C_OBJECTS_FILE_NAME,
C_MD5_DIGEST,
C_SCALING,
C_HEIGHT,
C_WIDTH,
)
]
pathname = m.get_current_image_measurement(m_path)
filename = m.get_current_image_measurement(m_file)
rescale = wants_images and file_setting.rescale.value
provider = LoadImagesImageProvider(image_name, pathname, filename, rescale)
image = provider.provide_image(image_set)
pixel_data = image.pixel_data
if wants_images:
md5 = provider.get_md5_hash(m)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)), md5)
m.add_image_measurement("_".join((C_SCALING, image_name)), image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)), int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)), int(pixel_data.shape[1]))
image_set.providers.append(provider)
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
#
# Add outlines if appropriate
#
if file_setting.wants_outlines:
outlines = centrosome.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_setting.outlines_name.value, outline_image)
statistics += [(image_name, filename)]
workspace.display_data.col_labels = ("Image name", "File")
workspace.display_data.statistics = statistics
def run(self, workspace):
dict = self.get_file_names(workspace)
root = self.get_base_directory(workspace)
statistics = [("Image name","File")]
m = workspace.measurements
for image_name in dict.keys():
file_settings = self.get_file_settings(image_name)
rescale = (file_settings.image_objects_choice == IO_IMAGES and
file_settings.rescale)
provider = LoadImagesImageProvider(
image_name, root, dict[image_name], rescale)
image = provider.provide_image(workspace.image_set)
pixel_data = image.pixel_data
if file_settings.image_objects_choice == IO_IMAGES:
workspace.image_set.providers.append(provider)
#
# Add measurements
#
path_name_category = C_PATH_NAME
file_name_category = C_FILE_NAME
digest = hashlib.md5()
digest.update(np.ascontiguousarray(pixel_data).data)
m.add_image_measurement("_".join((C_MD5_DIGEST, image_name)),
digest.hexdigest())
m.add_image_measurement("_".join((C_SCALING, image_name)),
image.scale)
m.add_image_measurement("_".join((C_HEIGHT, image_name)),
int(pixel_data.shape[0]))
m.add_image_measurement("_".join((C_WIDTH, image_name)),
int(pixel_data.shape[1]))
else:
#
# Turn image into objects
#
labels = convert_image_to_objects(pixel_data)
objects = cpo.Objects()
objects.segmented = labels
object_set = workspace.object_set
assert isinstance(object_set, cpo.ObjectSet)
object_set.add_objects(objects, image_name)
#
# Add measurements
#
add_object_count_measurements(m, image_name, objects.count)
add_object_location_measurements(m, image_name, labels)
path_name_category = C_OBJECTS_PATH_NAME
file_name_category = C_OBJECTS_FILE_NAME
#
# Add outlines if appropriate
#
if file_settings.wants_outlines:
outlines = cellprofiler.cpmath.outline.outline(labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(file_settings.outlines_name.value,
outline_image)
m.add_image_measurement(file_name_category + '_' + image_name,
dict[image_name])
m.add_image_measurement(path_name_category + '_' + image_name, root)
statistics += [(image_name, dict[image_name])]
if workspace.frame:
title = "Load single image: image cycle # %d"%(workspace.measurements.image_set_number+1)
figure = workspace.create_or_find_figure(title="LoadSingleImage, image cycle #%d"%(
workspace.measurements.image_set_number),
subplots=(1,1))
figure.subplot_table(0,0, statistics)
