def test_01_03_load_v02():
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20130522170932
ModuleCount:1
HasImagePlaneDetails:False
SmoothMultichannel:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Select the input image:InputImage
Name the output image:OutputImage
Select smoothing method:Median Filter
Calculate artifact diameter automatically?:Yes
Typical artifact diameter, in pixels:19.0
Edge intensity difference:0.2
Clip intensity at 0 and 1:No
"""
pipeline = cpp.Pipeline()
cpmodules.fill_modules()
cpmodules.add_module_for_tst(S.SmoothMultichannel)
pipeline.load(io.StringIO(data))
assert len(pipeline.modules()) == 1
smooth = pipeline.modules()[0]
assert isinstance(smooth, S.SmoothMultichannel)
assert smooth.image_name == "InputImage"
assert smooth.filtered_image_name == "OutputImage"
assert smooth.wants_automatic_object_size
assert smooth.object_size == 19
assert smooth.smoothing_method == S.MEDIAN_FILTER
assert not smooth.clip
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_01_06_run_pipeline(self):
import cellprofiler_core.pipeline as cpp
import cellprofiler_core.module as cpm
def callback(caller, event):
self.assertFalse(
isinstance(event,
(cpp.event.LoadException, cpp.event.RunException)))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
fly_pipe = get_test_resources_directory("../ExampleFlyURL.cppipe")
pipeline.load(fly_pipe)
while True:
removed_something = False
for module in reversed(pipeline.modules()):
self.assertTrue(isinstance(module, cpm.Module))
if module.module_name in ("SaveImages", "CalculateStatistics",
"ExportToSpreadsheet",
"ExportToDatabase"):
pipeline.remove_module(module.module_num)
removed_something = True
break
if not removed_something:
break
for module in pipeline.modules():
module.show_window = False
m = pipeline.run(image_set_end=1)
def test_01_05_load_pipeline(self):
import cellprofiler_core.pipeline as cpp
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.event.LoadException))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
fly_pipe = get_test_resources_directory("../ExampleFlyURL.cppipe")
pipeline.load(fly_pipe)
def load_pipeline(pipeline_path):
pipeline = cpp.Pipeline()
pipeline.load(pipeline_path)
# Remove first 4 modules: Images, Metadata, NamesAndTypes, Groups...
# (replaced by InjectImage module below)
for i in range(4):
print('Remove module: ', pipeline.modules()[0].module_name)
pipeline.remove_module(1)
print('Pipeline modules:')
for module in pipeline.modules():
print(module.module_num, module.module_name)
return pipeline
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_01_05_load_pipeline(self):
import cellprofiler_core.pipeline as cpp
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.event.LoadException))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
try:
fd = urlopen(self.fly_url)
except IOError as e:
def bad_url(e=e):
raise e
unittest.expectedFailure(bad_url)()
pipeline.load(fd)
fd.close()
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 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()
def do_job(self, job):
"""Handle a work request to its completion
job - request.Work
"""
import cellprofiler_core.pipeline as cpp
job_measurements = []
try:
send_dictionary = job.wants_dictionary
logging.info("Starting job")
# Fetch the pipeline and preferences for this analysis if we don't have it
current_pipeline, current_preferences = self.pipelines_and_preferences.get(
self.current_analysis_id, (None, None)
)
if not current_pipeline:
logging.debug("Fetching pipeline and preferences")
rep = self.send(PipelinePreferences(self.current_analysis_id))
logging.debug("Received pipeline and preferences response")
preferences_dict = rep.preferences
# update preferences to match remote values
set_preferences_from_dict(preferences_dict)
logging.debug("Loading pipeline")
current_pipeline = cpp.Pipeline()
pipeline_chunks = rep.pipeline_blob.tolist()
pipeline_io = io.StringIO("".join(pipeline_chunks))
current_pipeline.loadtxt(pipeline_io, raise_on_error=True)
logging.debug("Pipeline loaded")
current_pipeline.add_listener(self.pipeline_listener.handle_event)
current_preferences = rep.preferences
self.pipelines_and_preferences[self.current_analysis_id] = (
current_pipeline,
current_preferences,
)
else:
# update preferences to match remote values
set_preferences_from_dict(current_preferences)
# Reset the listener's state
self.pipeline_listener.reset()
logging.debug("Getting initial measurements")
# Fetch the path to the intial measurements if needed.
current_measurements = self.initial_measurements.get(
self.current_analysis_id
)
if current_measurements is None:
logging.debug("Sending initial measurements request")
rep = self.send(InitialMeasurements(self.current_analysis_id))
logging.debug("Got initial measurements")
current_measurements = self.initial_measurements[
self.current_analysis_id
] = load_measurements_from_buffer(rep.buf)
else:
logging.debug("Has initial measurements")
# Make a copy of the measurements for writing during this job
current_measurements = Measurements(copy=current_measurements)
all_measurements.add(current_measurements)
job_measurements.append(current_measurements)
successful_image_set_numbers = []
image_set_numbers = job.image_set_numbers
worker_runs_post_group = job.worker_runs_post_group
logging.info("Doing job: " + ",".join(map(str, image_set_numbers)))
self.pipeline_listener.image_set_number = image_set_numbers[0]
if not worker_runs_post_group:
# Get the shared state from the first imageset in this run.
shared_dicts = self.send(
SharedDictionary(self.current_analysis_id)
).dictionaries
assert len(shared_dicts) == len(current_pipeline.modules())
for module, new_dict in zip(current_pipeline.modules(), shared_dicts):
module.set_dictionary_for_worker(new_dict)
# Run prepare group if this is the first image in the group. We do
# this here (even if there's no grouping in the pipeline) to ensure
# that any changes to the modules' shared state dictionaries get
# propagated correctly.
should_process = True
if current_measurements["Image", "Group_Index", image_set_numbers[0]] == 1:
workspace = Workspace(
current_pipeline, None, None, None, current_measurements, None, None
)
if not current_pipeline.prepare_group(
workspace,
current_measurements.get_grouping_keys(),
image_set_numbers,
):
# exception handled elsewhere, possibly cancelling this run.
should_process = False
del workspace
# process the images
if should_process:
abort = False
for image_set_number in image_set_numbers:
try:
self.pipeline_listener.image_set_number = image_set_number
last_workspace = current_pipeline.run_image_set(
current_measurements,
image_set_number,
self.interaction_handler,
self.display_handler,
self.cancel_handler,
)
if self.pipeline_listener.should_abort:
abort = True
break
elif self.pipeline_listener.should_skip:
# Report skipped image sets as successful so that
# analysis can complete.
# Report their measurements because some modules
# may have provided measurements before skipping.
pass
successful_image_set_numbers.append(image_set_number)
# Send an indication that the image set finished successfully.
if send_dictionary:
# The jobserver would like a copy of our modules'
# run_state dictionaries.
dicts = [
m.get_dictionary_for_worker()
for m in current_pipeline.modules()
]
req = ImageSetSuccessWithDictionary(
self.current_analysis_id,
image_set_number=image_set_number,
shared_dicts=dicts,
)
else:
req = ImageSetSuccess(
self.current_analysis_id,
image_set_number=image_set_number,
)
rep = self.send(req)
except CancelledException:
logging.info("Aborting job after cancellation")
abort = True
except Exception as e:
try:
logging.error("Error in pipeline", exc_info=True)
if (
self.handle_exception(image_set_number=image_set_number)
== ED_STOP
):
abort = True
break
except:
logging.error(
"Error in handling of pipeline exception", exc_info=True
)
# this is bad. We can't handle nested exceptions
# remotely so we just fail on this run.
abort = True
if abort:
current_measurements.close()
job_measurements.remove(current_measurements)
return
if worker_runs_post_group:
last_workspace.interaction_handler = self.interaction_handler
last_workspace.cancel_handler = self.cancel_handler
last_workspace.post_group_display_handler = (
self.post_group_display_handler
)
# There might be an exception in this call, but it will be
# handled elsewhere, and there's nothing we can do for it
# here.
current_pipeline.post_group(
last_workspace, current_measurements.get_grouping_keys()
)
del last_workspace
# send measurements back to server
req = MeasurementsReport(
self.current_analysis_id,
buf=current_measurements.file_contents(),
image_set_numbers=image_set_numbers,
)
rep = self.send(req)
except CancelledException:
# Main thread received shutdown signal
raise
except Exception:
logging.error("Error in worker", exc_info=True)
if self.handle_exception() == ED_STOP:
raise CancelledException("Cancelling after user-requested stop")
finally:
# Clean up any measurements owned by us
for m in job_measurements:
m.close()