def make_workspace(self, images):
"""Make a workspace """
module = S.StackImages()
pipeline = cpp.Pipeline()
object_set = cpo.ObjectSet()
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(
pipeline,
module,
image_set,
object_set,
cpmeas.Measurements(),
image_set_list,
)
# setup the input images
names = [INPUT_IMAGE_BASENAME + str(i) for i, img in enumerate(images)]
for img, nam in zip(images, names):
image_set.add(nam, cpi.Image(img))
# setup the input images settings
module.stack_image_name.value = OUTPUT_IMAGE_NAME
nimgs = len(images)
while len(module.stack_channels) < nimgs:
module.add_stack_channel_cb()
for sc, imname in zip(module.stack_channels, names):
sc.image_name.value = imname
return workspace, module
def test_02_00_no_measurements(self):
m = cpmeas.Measurements()
m.add_measurement(OBJECT_NAME, M_FEATURES[0], np.array([], float))
rules = R.Rules()
rules.rules += [
R.Rules.Rule(
OBJECT_NAME, M_FEATURES[0], ">", 0, np.array([[1.0, -1.0], [-1.0, 1.0]])
)
]
score = rules.score(m)
self.assertEqual(score.shape[0], 0)
self.assertEqual(score.shape[1], 2)
def save_pipeline(self, workspace, outf=None):
"""Save the pipeline in Batch_data.mat
Save the pickled image_set_list state in a setting and put this
module in batch mode.
if outf is not None, it is used as a file object destination.
"""
if outf is None:
if self.wants_default_output_directory.value:
path = cpprefs.get_default_output_directory()
else:
path = cpprefs.get_absolute_path(
self.custom_output_directory.value)
h5_path = os.path.join(path, F_BATCH_DATA_H5)
else:
h5_path = outf
image_set_list = workspace.image_set_list
pipeline = workspace.pipeline
m = cpmeas.Measurements(copy=workspace.measurements, filename=h5_path)
try:
assert isinstance(pipeline, cpp.Pipeline)
assert isinstance(m, cpmeas.Measurements)
orig_pipeline = pipeline
pipeline = pipeline.copy()
# this use of workspace.frame is okay, since we're called from
# prepare_run which happens in the main wx thread.
target_workspace = cpw.Workspace(pipeline, None, None, None, m,
image_set_list, workspace.frame)
pipeline.prepare_to_create_batch(target_workspace, self.alter_path)
bizarro_self = pipeline.module(self.module_num)
bizarro_self.revision.value = int(
re.sub(r"\.|rc\d{1}", "", cellprofiler.__version__))
if self.wants_default_output_directory:
bizarro_self.custom_output_directory.value = self.alter_path(
cpprefs.get_default_output_directory())
bizarro_self.default_image_directory.value = self.alter_path(
cpprefs.get_default_image_directory())
bizarro_self.batch_mode.value = True
pipeline.write_pipeline_measurement(m)
orig_pipeline.write_pipeline_measurement(m, user_pipeline=True)
#
# Write the path mappings to the batch measurements
#
m.write_path_mappings([(mapping.local_directory.value,
mapping.remote_directory.value)
for mapping in self.mappings])
return h5_path
finally:
m.close()
def make_workspace(image, mask):
"""Make a workspace for testing FilterByObjectMeasurement"""
module = S.SmoothMultichannel()
pipeline = cpp.Pipeline()
object_set = cpo.ObjectSet()
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
image_set.add(INPUT_IMAGE_NAME, cpi.Image(image, mask, scale=1))
module.image_name.value = INPUT_IMAGE_NAME
module.filtered_image_name.value = OUTPUT_IMAGE_NAME
return workspace, module
def test_02_01_score_one_positive(self):
m = cpmeas.Measurements()
m.add_measurement(OBJECT_NAME, M_FEATURES[0], np.array([1.5], float))
rules = R.Rules()
rules.rules += [
R.Rules.Rule(
OBJECT_NAME, M_FEATURES[0], ">", 0, np.array([[1.0, -0.5], [-2.0, 0.6]])
)
]
score = rules.score(m)
self.assertEqual(score.shape[0], 1)
self.assertEqual(score.shape[1], 2)
self.assertAlmostEqual(score[0, 0], 1.0)
self.assertAlmostEqual(score[0, 1], -0.5)
def test_03_01_score_two_rules(self):
m = cpmeas.Measurements()
m.add_measurement(OBJECT_NAME, M_FEATURES[0], np.array([1.5], float))
m.add_measurement(OBJECT_NAME, M_FEATURES[1], np.array([-1.5], float))
rules = R.Rules()
rules.rules += [
R.Rules.Rule(
OBJECT_NAME, M_FEATURES[0], ">", 0, np.array([[1.0, -0.5], [-2.0, 0.6]])
),
R.Rules.Rule(
OBJECT_NAME, M_FEATURES[1], ">", 0, np.array([[1.5, -0.7], [-2.3, 0.9]])
),
]
score = rules.score(m)
self.assertEqual(score.shape[0], 1)
self.assertEqual(score.shape[1], 2)
self.assertAlmostEqual(score[0, 0], 1.0 - 2.3)
self.assertAlmostEqual(score[0, 1], -0.5 + 0.9)
def test_02_03_score_one_nan(self):
m = cpmeas.Measurements()
m.add_measurement(OBJECT_NAME, M_FEATURES[0], np.array([np.NaN], float))
rules = R.Rules()
rules.rules += [
R.Rules.Rule(
OBJECT_NAME,
M_FEATURES[0],
">",
2.0,
np.array([[1.0, -0.5], [-2.0, 0.6]]),
)
]
score = rules.score(m)
self.assertEqual(score.shape[0], 1)
self.assertEqual(score.shape[1], 2)
self.assertTrue(score[0, 0], -2)
self.assertTrue(score[0, 1], 0.6)
def make_workspace(image, outlier_percentile):
"""Make a workspace """
module = C.ClipRange()
pipeline = cpp.Pipeline()
object_set = cpo.ObjectSet()
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
# setup the input images
image_set.add(INPUT_IMAGE_NAME, cpi.Image(image))
# setup the input images settings
module.x_name.value = INPUT_IMAGE_NAME
module.y_name.value = OUTPUT_IMAGE_NAME
module.outlier_percentile.value = outlier_percentile
return workspace, module
def test_03_02_score_two_objects(self):
m = cpmeas.Measurements()
m.add_measurement(OBJECT_NAME, M_FEATURES[0], np.array([1.5, 2.5], float))
rules = R.Rules()
rules.rules += [
R.Rules.Rule(
OBJECT_NAME,
M_FEATURES[0],
"<",
2.0,
np.array([[1.0, -0.5], [-2.0, 0.6]]),
)
]
score = rules.score(m)
self.assertEqual(score.shape[0], 2)
self.assertEqual(score.shape[1], 2)
self.assertAlmostEqual(score[0, 0], 1.0)
self.assertAlmostEqual(score[0, 1], -0.5)
self.assertAlmostEqual(score[1, 0], -2.0)
self.assertAlmostEqual(score[1, 1], 0.6)
def merge_files(destination, sources, force_headless=False):
is_headless = force_headless or get_headless()
if not is_headless:
import wx
if len(sources) == 0:
return
if not is_headless:
progress = wx.ProgressDialog(
"Writing " + destination,
"Loading " + sources[0],
maximum=len(sources) * 4 + 1,
style=wx.PD_CAN_ABORT
| wx.PD_APP_MODAL
| wx.PD_ELAPSED_TIME
| wx.PD_REMAINING_TIME,
)
count = 0
try:
pipeline = cpp.Pipeline()
has_error = [False]
def callback(caller, event):
if isinstance(event, cpp.event.LoadException):
has_error = True
wx.MessageBox(
message="Could not load %s: %s" %
(sources[0], event.error),
caption="Failed to load %s" % sources[0],
)
has_error[0] = True
pipeline.add_listener(callback)
pipeline.load(sources[0])
if has_error[0]:
return
if destination.lower().endswith(".h5"):
mdest = cpmeas.Measurements(filename=destination,
multithread=False)
h5_dest = True
else:
mdest = cpmeas.Measurements(multithread=False)
h5_dest = False
for source in sources:
if not is_headless:
count += 1
keep_going, skip = progress.Update(count,
"Loading " + source)
if not keep_going:
return
if h5py.is_hdf5(source):
msource = cpmeas.Measurements(filename=source,
mode="r",
multithread=False)
else:
msource = cpmeas.load_measurements(source)
dest_image_numbers = mdest.get_image_numbers()
source_image_numbers = msource.get_image_numbers()
if len(dest_image_numbers) == 0 or len(
source_image_numbers) == 0:
offset_source_image_numbers = source_image_numbers
else:
offset_source_image_numbers = (
np.max(dest_image_numbers) -
np.min(source_image_numbers) + source_image_numbers +
1)
for object_name in msource.get_object_names():
if object_name in mdest.get_object_names():
destfeatures = mdest.get_feature_names(object_name)
else:
destfeatures = []
for feature in msource.get_feature_names(object_name):
if object_name == cpmeas.EXPERIMENT:
if not mdest.has_feature(object_name, feature):
src_value = msource.get_experiment_measurement(
feature)
mdest.add_experiment_measurement(
feature, src_value)
continue
src_values = msource.get_measurement(
object_name,
feature,
image_set_number=source_image_numbers)
mdest[object_name, feature,
offset_source_image_numbers] = src_values
destset = set(destfeatures)
if not is_headless:
keep_going, skip = progress.Update(count + 1,
"Saving to " + destination)
if not keep_going:
return
if not h5_dest:
pipeline.save_measurements(destination, mdest)
finally:
if not is_headless:
progress.Destroy()