def test_01_01_load_matlab(self):
data = ('eJzzdQzxcXRSMNUzUPB1DNFNy8xJ1VEIyEksScsvyrVSCHAO9/TTUXAuSk0s'
'SU1RyM+zUgjJKFXwKs1RMDQDIitDCytjMwUjAwNLBZIBA6OnLz8DA0MWEwND'
'xZy3EXfzLhuIlG1aIr0i5HaehJD4zidt9RN25EQ4dh+5lTVbTbHwsVleumbn'
'4iMro+Z1HH9QWMHzZtKWzqkm9ryKHDGRFwvPfa+1O26/8TYzQ48804XE88ph'
'qR9Wr1ioe1Rr+3rBpTdm/tYKOBB549/cq9EvEi02XtDdwvSk2bfzFm+6W17Y'
'u2dXVQ2ZuXkXypanpUrcEH7yc3EkC098/0ehyOs/tJlOCu2MXcj6zbDQqeaE'
'/ra8Ek7Rkuo1u/i3VU5osdFYM+/YrZ318jl/p07ZI3v8T//hquZpX162q84J'
'OttWUnW8Mu7JBxmVCr4DCSuazR5NYfFjZK7NebN4+vT71U51O2v3LTqydUF+'
'+9vST1t070fuKLe0CO2YfvvXFN3XXYV1Mr1cL8tYYvOkzzFVbpm1Lm9Wawk/'
'a2zefOHqJtkrL/5MELIRqrgZNGfG8++/Qhf/rdmaXrO/+t910eevbb/+stzx'
'22nOHTmBs4oh3H8+ON9K2ZP/b2PFfbM14SV2na9/G1tzix48yTmHpy3Ffm8d'
'37p1H3O894hb7hBWjMtLPn6EO4ftwqUNvM9nB2dP/FPKape32bjJbvGJ3S1f'
'1mn4+z94uO2gtXDyqsnLX+1w+dhxftPrF5Fs/hdrls4VfyYX8zM4Y6HIvk1/'
'z86KD/356tzyG/bPLe+c/O57t/Z91/nHRo8nbKtfI37xjnLj7bh5276t1py3'
'pfrzNPfkaKWi/8d2nOqrv7Z53R/Rq7/X/0//fdNsHrdm9f5pDp/qF6x6/Ui4'
'cZmg9YeQv+/WWS467Odd+ej+ZT8/u9S0uWW77+56J+r6JSRy2tHqUjk/ztv7'
'beq2/Cv8wx+Qd8/m5kP5qdt+/WfJeirzEgCAxWF+')
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
#
# There are 3 ImageMath modules:
# 1)
# image_name[0] = DNA, factor[0] = 1, operation = invert, truncation
# output_image_name = DNAAfterMath
# 2)
# image_name[0] = DNA, factor[0] = 1
# image_name[1] = Cytoplasm, factor[1] = 2
# operation = add, exponent = 4, multiply = 5, no truncation
# output_image_name = ImageAfterMath
# 3) DNA, Cytoplasm, Actin
self.assertEqual(len(pipeline.modules()), 4)
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, I.ImageMath))
self.assertEqual(len(module.images), 2)
self.assertEqual(module.images[0].image_name.value, 'DNA')
self.assertEqual(module.output_image_name.value, 'DNAAfterMath')
self.assertEqual(module.operation.value, I.O_INVERT)
self.assertTrue(module.truncate_low.value)
self.assertTrue(module.truncate_high.value)
module = pipeline.modules()[2]
self.assertTrue(isinstance(module, I.ImageMath))
self.assertEqual(len(module.images), 2)
self.assertEqual(module.images[0].image_name.value, 'DNA')
self.assertEqual(module.images[0].factor.value, 1)
self.assertEqual(module.images[1].image_name.value, 'Cytoplasm')
self.assertEqual(module.images[1].factor.value, 2)
self.assertEqual(module.output_image_name.value, 'ImageAfterMath')
self.assertEqual(module.operation.value, I.O_ADD)
self.assertEqual(module.exponent.value, 4)
self.assertEqual(module.after_factor.value, 5)
self.assertFalse(module.truncate_low.value)
self.assertFalse(module.truncate_high.value)
module = pipeline.modules()[3]
self.assertTrue(isinstance(module, I.ImageMath))
self.assertEqual(len(module.images), 3)
self.assertEqual(module.images[0].image_name.value, 'DNA')
self.assertEqual(module.images[1].image_name.value, 'Cytoplasm')
self.assertEqual(module.images[2].image_name.value, 'Actin')
self.assertEqual(module.output_image_name.value, 'ImageAfterMath')
self.assertEqual(module.operation.value, I.O_ADD)
def do_job(self, job):
'''Handle a work request to its completion
job - WorkRequest
'''
import cellprofiler.pipeline as cpp
job_measurements = []
try:
send_dictionary = job.wants_dictionary
logger.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:
logger.debug("Fetching pipeline and preferences")
rep = self.send(PipelinePreferencesRequest(
self.current_analysis_id))
logger.debug("Received pipeline and preferences response")
preferences_dict = rep.preferences
# update preferences to match remote values
cpprefs.set_preferences_from_dict(preferences_dict)
logger.debug("Loading pipeline")
pipeline_blob = rep.pipeline_blob.tostring()
current_pipeline = cpp.Pipeline()
current_pipeline.loadtxt(StringIO.StringIO(pipeline_blob),
raise_on_error=True)
logger.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
cpprefs.set_preferences_from_dict(current_preferences)
# Reset the listener's state
self.pipeline_listener.reset()
logger.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:
logger.debug("Sending initial measurements request")
rep = self.send(InitialMeasurementsRequest(
self.current_analysis_id))
logger.debug("Got initial measurements")
current_measurements = \
self.initial_measurements[self.current_analysis_id] = \
cpmeas.load_measurements_from_buffer(rep.buf)
else:
logger.debug("Has initial measurements")
# Make a copy of the measurements for writing during this job
current_measurements = cpmeas.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
logger.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(
SharedDictionaryRequest(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[cpmeas.IMAGE,
cpmeas.GROUP_INDEX,
image_set_numbers[0]] == 1:
workspace = cpw.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:
gc.collect()
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 cpp.CancelledException:
logging.info("Aborting job after cancellation")
abort = True
except Exception:
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 cpp.CancelledException:
# Main thread received shutdown signal
raise
except Exception:
logging.error("Error in worker", exc_info=True)
if self.handle_exception() == ED_STOP:
raise cpp.CancelledException("Cancelling after user-requested stop")
finally:
# Clean up any measurements owned by us
for m in job_measurements:
m.close()
def test_01_03_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10016
Morph:[module_num:1|svn_version:\'9935\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input image:InputImage
Name the output image:MorphImage
Select the operation to perform:bothat
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:branchpoints
Repeat operation:Forever
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:bridge
Repeat operation:Custom
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:clean
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:close
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:convex hull
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:diag
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:dilate
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:distance
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:endpoints
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:erode
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:fill
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:fill small holes
Repeat operation:Once
Maximum hole area:2
Scale\x3A:3
Select the operation to perform:hbreak
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:invert
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:majority
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:open
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:remove
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:shrink
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:skel
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:skelpe
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:spur
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:thicken
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:thin
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:tophat
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:vbreak
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
ops = [
morph.F_BOTHAT, morph.F_BRANCHPOINTS, morph.F_BRIDGE,
morph.F_CLEAN, morph.F_CLOSE, morph.F_CONVEX_HULL, morph.F_DIAG,
morph.F_DILATE, morph.F_DISTANCE, morph.F_ENDPOINTS, morph.F_ERODE,
morph.F_FILL, morph.F_FILL_SMALL, morph.F_HBREAK, morph.F_INVERT,
morph.F_MAJORITY, morph.F_OPEN, morph.F_REMOVE, morph.F_SHRINK,
morph.F_SKEL, morph.F_SKELPE, morph.F_SPUR, morph.F_THICKEN,
morph.F_THIN, morph.F_TOPHAT, morph.F_VBREAK
]
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, morph.Morph))
self.assertEqual(module.image_name, "InputImage")
self.assertEqual(module.output_image_name, "MorphImage")
self.assertEqual(len(module.functions), len(ops))
for op, function in zip(ops, module.functions):
self.assertEqual(function.function, op)
if op == morph.F_BRANCHPOINTS:
self.assertEqual(function.repeats_choice, morph.R_FOREVER)
elif op == morph.F_BRIDGE:
self.assertEqual(function.repeats_choice, morph.R_CUSTOM)
else:
self.assertEqual(function.repeats_choice, morph.R_ONCE)
self.assertEqual(function.custom_repeats, 2)
self.assertEqual(function.scale, 3)
fn0 = module.functions[0]
self.assertEqual(fn0.structuring_element, morph.SE_DISK)
self.assertEqual(fn0.x_offset, 1)
self.assertEqual(fn0.y_offset, 1)
self.assertEqual(fn0.angle, 0)
self.assertEqual(fn0.width, 3)
self.assertEqual(fn0.height, 3)
strel = np.array(fn0.strel.get_matrix())
self.assertEqual(strel.shape[0], 3)
self.assertEqual(strel.shape[1], 3)
self.assertTrue(np.all(strel))
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20120112154631
ModuleCount:1
HasImagePlaneDetails:False
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)]
Extract metadata?:Yes
Extraction method count:2
Extraction method:Manual
Source:From file name
Regular expression:^Channel(?P<ChannelNumber>\x5B12\x5D)-(?P<Index>\x5B0-9\x5D+)-(?P<WellRow>\x5BA-H\x5D)-(?P<WellColumn>\x5B0-9\x5D{2}).tif$
Regular expression:(?P<Date>\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$
Filter images:All images
:or (file does contain "Channel2")
Metadata file location\x3A:
Match file and image metadata:\x5B\x5D
Case insensitive matching:No
Extraction method:Import metadata
Source:From folder name
Regular expression:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
Regular expression:Example(?P<Project>\x5B^\\\\\\\\\x5D+)Images
Filter images:Images selected using a filter
:or (file does contain "")
Metadata file location\x3A:/imaging/analysis/metadata.csv
Match file and image metadata:\x5B{\'Image Metadata\'\x3A u\'ChannelNumber\', \'CSV Metadata\'\x3A u\'Wavelength\'}\x5D
Case insensitive matching:Yes
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, M.Metadata))
self.assertTrue(module.wants_metadata)
self.assertEqual(len(module.extraction_methods), 2)
em0, em1 = module.extraction_methods
self.assertEqual(em0.extraction_method, M.X_MANUAL_EXTRACTION)
self.assertEqual(em0.source, M.XM_FILE_NAME)
self.assertEqual(
em0.file_regexp.value,
r"^Channel(?P<ChannelNumber>[12])-(?P<Index>[0-9]+)-(?P<WellRow>[A-H])-(?P<WellColumn>[0-9]{2}).tif$"
)
self.assertEqual(em0.folder_regexp.value,
r"(?P<Date>[0-9]{4}_[0-9]{2}_[0-9]{2})$")
self.assertEqual(em0.filter_choice, M.F_ALL_IMAGES)
self.assertEqual(em0.filter, 'or (file does contain "Channel2")')
self.assertFalse(em0.wants_case_insensitive)
self.assertEqual(em1.extraction_method, M.X_IMPORTED_EXTRACTION)
self.assertEqual(em1.source, M.XM_FOLDER_NAME)
self.assertEqual(em1.filter_choice, M.F_FILTERED_IMAGES)
self.assertEqual(em1.csv_location, "/imaging/analysis/metadata.csv")
self.assertEqual(
em1.csv_joiner.value,
"[{'Image Metadata': u'ChannelNumber', 'CSV Metadata': u'Wavelength'}]"
)
self.assertTrue(em1.wants_case_insensitive)
def test_01_03_load_v1(self):
data = ('eJztXNtu2zYYphwna1pgSLeLFRiG6SoYCkdQTluWmymHZjEQJ8EcdO1NM0Wi'
'bQ4yaUhUEu9J9lh7lD7CRFu2ZEaObFmWLJdCFJs0v/8jP/48/JKg2tHNxdGx'
'vK+ocu3oZquBLChfWzptELt9KGNakU9sqFNoygQfyjWC5VNoyOovsnpwuPvz'
'obov76jqryDZIVVrX3sf/34CYM37fOGdJf+nVT8thU6WrkNKEW46q6AM3vj5'
'n73zvW4j/c6C73XLhU5AMciv4ga56XaGP9WI6VrwUm+HC3vHpdu+g7Zz1RgA'
'/Z+v0SO06ugfyDVhUOwPeI8cRLCP9+3zuUNeQjneeos8nNledTj7xzo1WnXq'
'9cBofk+3SqCbxOnGPjdD+az8OQjKlyN0fh0qv+GnETbRPTJd3ZJRW28Oa83s'
'HcTYe8HZY+krGzWPvS5KAx/Xnm85PEvXPefxXLwNdce1YRti6gzbo8XY2+Ds'
'sfPmgYwYS2wHPtKtd4+6QeU26/JJ9Fnj7LD0pWtYEE3WnpccnqVPiYwJlV3H'
'9zdmR42xI43YkcBOQtx2QtwumMwfV7n2svS2WtlTJ8TH6T0rPq7d5RF8GVwS'
'DLPs55UROyvgozcb8Lg1Djc4Brh1EPB9iOELz18bfrqKKcQOot1b9q1ps6Up'
'yBvMDrPpGYcrjeBKHm4yvklw0+gXN/99w+nH0u/uIba6stPRDW9Nv0PYyc/e'
'd5w9lj6FDd21qFxli418imxoUGJ3c/HPaeeh/YS43YQ4BUw2z7/idGbpK+q4'
'8u8WudOtyPamqRPfL6qyk6i9g3l6XuNzkvVovroczHXdTKrLk3pWtrPVJYIv'
'TVxSXfj1Q1XU7Sx1iZrfslh3tBi+9RG+fnq4Ro9p7zT81zH8P3L8LH2GbIdW'
'6tAg2KzctJBtVs6Ia9NW5Qw1aGum/UKSOOakpWMMrZ1F0DNrv5s17puWd1ud'
'r79HxXMnlu44qIGg2d/hp2lnVn9LY18zjX5Fjauy3m8mmdeC2KemP0YEQkmv'
'+1wQQ6eI4NsT6Bm1bz+E6jkvPSfBpRkfRV0fOnEdStpbJmwgHA5okvphDZpI'
'x8Xyw3nFL0/3u/u5r1vz3c+rqY2/j2PqsUx6bft6pTnOsujfea2LUeu4slNR'
'9ivKQR7jTYvhy/s6w6Jcl1s0vqRxy1dgtD9Z2iIPt965VPNQmnEK06eFmq0s'
'6+sN64WIb5LoxbRiDpX1vMTu2xmsDTDY3+UV38TVPyr+7+nG/oXqMa99ctT+'
'5E/ocbPHAu7ZDXBswBTqkef9mjT9Lep+2xmxYdMmLjaz16kGdZxH/LXo8/ys'
'82ae95WyHldZ67II/TDJOCpK+5Z9HC2iLmnu6xcZl2T/c0EevL9itXNe+kTF'
'Wefe9uoixThrkXFJ9PG0YRIVqp1ZXidi4pynGIcWBSfm5QCnged1mTSuT8r/'
'39sAJ3E49rkZys86Hus9zMwCsk76doriH+cx7Y2K+8nd39CgvQbLCJuwk2P9'
'i4LTwPM6R12fCum8sHaKor/ACZzACZzACZzACZzAPY/TQrhJ48YgDuqHBUWu'
'd1HbvyzxSVF0KwpOA1+WPwucwAmcwC0aTgvhvoTrywIncMuI00I4sZ8aj9PA'
'8zqJeEDg5oHTgBifAidwAidwAidwAidwAidwy4LTQrg89vetUoCTOJzkf5dC'
'5f+Kqe9brr4sbUDL6tiEvY/ZVtq9lwY7ikV0s/8WXuXC+1oNvZCX8XRieDSO'
'RxvHg0yIKWp0O7bH5lLS1ikylKqfe+3lHg1yGe9jDO8xx3s8jtd/kW7/GgAa'
'vMxGqfWzr3rZ3Iuvev0Rw7/H8e+N4zf6Dz93+xVwFP9h6G6f2cmLL/z8/3oE'
'X9jfSn769Q8rm9+D58cZAKP+Hfj959+S8pbLJUkCT8fpqxg80/EleHowO2+k'
'6cbbT2B8+UGbl6l8kn6S2AFm1zfgKw/rNuBZhvL/A/z19oA=')
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()),5)
module = pipeline.modules()[-2]
self.assertTrue(isinstance(module, C.ClassifyObjects))
self.assertEqual(module.contrast_choice.value, C.BY_SINGLE_MEASUREMENT)
self.assertEqual(len(module.single_measurements), 2)
group = module.single_measurements[0]
self.assertEqual(group.object_name, "Nuclei")
self.assertEqual(group.measurement.value, "Intensity_IntegratedIntensity_OrigBlue")
self.assertEqual(group.bin_choice, C.BC_EVEN)
self.assertEqual(group.bin_count, 3)
self.assertAlmostEqual(group.low_threshold.value, 0.2)
self.assertTrue(group.wants_low_bin)
self.assertAlmostEqual(group.high_threshold.value, 0.8)
self.assertTrue(group.wants_high_bin)
self.assertTrue(group.wants_custom_names)
for name, expected in zip(group.bin_names.value.split(','),
('First','Second','Third','Fourth','Fifth')):
self.assertEqual(name, expected)
self.assertTrue(group.wants_images)
self.assertEqual(group.image_name, "ClassifiedNuclei")
group = module.single_measurements[1]
self.assertEqual(group.object_name, "Nuclei")
self.assertEqual(group.measurement.value, "Intensity_MaxIntensity_OrigBlue")
self.assertEqual(group.bin_choice, C.BC_CUSTOM)
self.assertEqual(group.custom_thresholds, ".2,.5,.8")
self.assertFalse(group.wants_custom_names)
self.assertFalse(group.wants_images)
module = pipeline.modules()[-1]
self.assertTrue(isinstance(module, C.ClassifyObjects))
self.assertEqual(module.contrast_choice, C.BY_TWO_MEASUREMENTS)
self.assertEqual(module.object_name, "Nuclei")
self.assertEqual(module.first_measurement, "Location_Center_X")
self.assertEqual(module.first_threshold_method, C.TM_MEDIAN)
self.assertEqual(module.second_measurement, "Location_Center_Y")
self.assertEqual(module.second_threshold_method, C.TM_MEDIAN)
def test_04_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:9207
MeasureImageQuality:[module_num:1|svn_version:\'9143\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Select an image to measure:Alpha
Check for blur?:Yes
Window size for blur measurements:25
Check for saturation?:Yes
Calculate threshold?:Yes
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.2
Calculate quartiles and sum of radial power spectrum?:Yes
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select an image to measure:Beta
Check for blur?:No
Window size for blur measurements:15
Check for saturation?:No
Calculate threshold?:No
Select a thresholding method:MoG Global
Typical fraction of the image covered by objects:0.3
Calculate quartiles and sum of radial power spectrum?:No
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Entropy
Assign pixels in the middle intensity class to the foreground or the background?:Background
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, miq.MeasureImageQuality))
self.assertEqual(len(module.image_groups),2)
group = module.image_groups[0]
thr = group.threshold_groups[0]
self.assertEqual(group.image_names, "Alpha")
self.assertTrue(group.check_blur)
self.assertEqual(group.scale_groups[0].scale, 25)
self.assertTrue(group.check_saturation)
self.assertTrue(group.calculate_threshold)
self.assertEqual(thr.threshold_method, miq.cpthresh.TM_OTSU)
self.assertAlmostEqual(thr.object_fraction.value, 0.2)
self.assertEqual(thr.two_class_otsu, miq.O_THREE_CLASS)
self.assertEqual(thr.use_weighted_variance, miq.O_WEIGHTED_VARIANCE)
self.assertEqual(thr.assign_middle_to_foreground, miq.O_FOREGROUND)
group = module.image_groups[1]
thr = group.threshold_groups[0]
self.assertEqual(group.image_names, "Beta")
self.assertFalse(group.check_blur)
self.assertEqual(group.scale_groups[0].scale, 15)
self.assertFalse(group.check_saturation)
self.assertFalse(group.calculate_threshold)
self.assertEqual(thr.threshold_method, miq.cpthresh.TM_MOG)
self.assertAlmostEqual(thr.object_fraction.value, 0.3)
self.assertEqual(thr.two_class_otsu, miq.O_TWO_CLASS)
self.assertEqual(thr.use_weighted_variance, miq.O_ENTROPY)
self.assertEqual(thr.assign_middle_to_foreground, miq.O_BACKGROUND)
def test_01_05_load_v3_batch_data(self):
'''Test loading a version 3 pipeline with batch data in it'''
data = ('eJztnX9vG0d6xynHzsVO2kuCAtf+EYAHFHDSRvLO71m3yEm2klio5egi937g'
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'Onsn5dYsyi7icO1pvbRdLl2lzrT6yUBHxnSKd8e9/nDY228NW29ag2Lt62rF'
'y97maMVs22tmvb1yXxwWb1rDvUPzwmDtabXiiVnxjVlh9Z55evcn6Pm8cWe0'
'/Iv379377LNPPrl378Of3f/o5z//+P54+8e4p+G1c7/hc9D//WrePO6urKzc'
'vXvnTvlnxfzzwXvjPH7ntffhlPZMnf++MfuPaf+/3jsfp33eiL+/MVqX3p/e'
'n95//vf/P0+1wjU=')
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
module = pipeline.modules()[-1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
batch_data = zlib.decompress(module.batch_state)
image_set_list = cpi.ImageSetList()
image_set_list.load_state(batch_data)
self.assertEqual(image_set_list.count(), 96)
self.assertEqual(image_set_list.legacy_fields['PathnamerawDNA'],
'\\\\iodine\\imaging_analysis\\People\\Lee\\ExampleImages\\ExampleSBSImages')
def test_03_09_flag_image_abort(self):
#
# Regression test of issue #1210
# Make a pipeline that aborts during FlagImage
#
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20140918122611
GitHash:ded6939
ModuleCount:6
HasImagePlaneDetails:False
Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
:
Filter images?:Images only
Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.")
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Extract metadata?:No
Metadata data type:Text
Metadata types:{}
Extraction method count:1
Metadata extraction method:Extract from file/folder names
Metadata source:File name
Regular expression:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
Regular expression:(?P<Date>\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$
Extract metadata from:All images
Select the filtering criteria:and (file does contain "")
Metadata file location:
Match file and image metadata:\x5B\x5D
Use case insensitive matching?:No
NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:5|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Assign a name to:All images
Select the image type:Grayscale image
Name to assign these images:DNA
Match metadata:\x5B\x5D
Image set matching method:Order
Set intensity range from:Image metadata
Assignments count:1
Single images count:0
Select the rule criteria:and (file does contain "")
Name to assign these images:DNA
Name to assign these objects:Cell
Select the image type:Grayscale image
Set intensity range from:Image metadata
Retain outlines of loaded objects?:No
Name the outline image:LoadedOutlines
Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Do you want to group your images?:No
grouping metadata count:1
Metadata category:None
FlagImage:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:1
Hidden:1
Name the flag\'s category:Metadata
Name the flag:QCFlag
Flag if any, or all, measurement(s) fails to meet the criteria?:Flag if any fail
Skip image set if flagged?:Yes
Flag is based on:Whole-image measurement
Select the object to be used for flagging:None
Which measurement?:Height_DNA
Flag images based on low values?:No
Minimum value:0.0
Flag images based on high values?:Yes
Maximum value:100.0
Rules file location:Elsewhere...\x7C
Rules file name:rules.txt
Class number:
MeasureImageIntensity:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the image to measure:DNA
Measure the intensity only from areas enclosed by objects?:No
Select the input objects:None
"""
self.awthread = self.AWThread(self.announce_addr)
self.awthread.start()
self.set_work_socket()
self.awthread.ex(self.awthread.aw.do_job,
cpanalysis.WorkReply(
image_set_numbers = [1],
worker_runs_post_group = False,
wants_dictionary = True))
#
# The worker should ask for the pipeline and preferences next.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cpanalysis.PipelinePreferencesRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
input_dir = os.path.join(example_images_directory(), "ExampleSBSImages")
cpprefs.set_default_image_directory(input_dir)
preferences = {cpprefs.DEFAULT_IMAGE_DIRECTORY:
cpprefs.config_read(cpprefs.DEFAULT_IMAGE_DIRECTORY) }
rep = cpanalysis.Reply(
pipeline_blob = np.array(data),
preferences = preferences)
req.reply(rep)
#
# The worker asks for the initial measurements.
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cpanalysis.InitialMeasurementsRequest)
self.assertEqual(req.analysis_id, self.analysis_id)
m = get_measurements_for_good_pipeline()
pipeline = cpp.Pipeline()
pipeline.loadtxt(StringIO(data))
pipeline.write_pipeline_measurement(m)
try:
req.reply(cpanalysis.Reply(buf = m.file_contents()))
finally:
m.close()
#
# Next, the worker asks for the shared dictionary
#
req = self.awthread.recv(self.work_socket)
self.assertIsInstance(req, cpanalysis.SharedDictionaryRequest)
shared_dictionaries = [{ ("foo%d" % i):"bar%d" % i} for i in range(1,7)]
rep = cpanalysis.SharedDictionaryReply(
dictionaries = shared_dictionaries)
req.reply(rep)
#
# MeasureImageIntensity follows FlagImage and it is poised to ask
# for a display. So if we get that, we know the module has been run
# and we fail the test.
#
req = self.awthread.recv(self.work_socket)
self.assertFalse(isinstance(req, cpanalysis.DisplayRequest))
self.assertFalse(isinstance(req, cpanalysis.ExceptionReport))
def test_01_03_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10252
MeasureGranularity:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Image count:2
Object count:2
Select an image to measure:DNA
Subsampling factor for granularity measurements:0.25
Subsampling factor for background reduction:0.25
Radius of structuring element:10
Range of the granular spectrum:16
Object name:Nuclei
Object name:Cells
Object count:3
Select an image to measure:Actin
Subsampling factor for granularity measurements:0.33
Subsampling factor for background reduction:0.5
Radius of structuring element:12
Range of the granular spectrum:20
Object name:Nuclei
Object name:Cells
Object name:Cytoplasm
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, M.MeasureGranularity))
self.assertEqual(len(module.images), 2)
for image_setting, image_name, subsample_size, bsize, elsize, glen, objs in (
(module.images[0], "DNA", 0.25, 0.25, 10, 16, ("Nuclei", "Cells")),
(
module.images[1],
"Actin",
0.33,
0.50,
12,
20,
("Nuclei", "Cells", "Cytoplasm"),
),
):
# self.assertTrue(isinstance(image_setting, M.MeasureGranularity))
self.assertEqual(image_setting.image_name, image_name)
self.assertAlmostEqual(image_setting.subsample_size.value,
subsample_size)
self.assertAlmostEqual(image_setting.image_sample_size.value,
bsize)
self.assertEqual(image_setting.element_size.value, elsize)
self.assertEqual(image_setting.granular_spectrum_length.value,
glen)
self.assertEqual(len(image_setting.objects), len(objs))
self.assertEqual(image_setting.object_count.value, len(objs))
self.assertTrue(
all([
ob.objects_name.value in objs
for ob in image_setting.objects
]))
def test_01_03_load_v3(self):
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10865
MeasureTexture:[module_num:1|svn_version:\'1\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:Yes
Number of angles to compute for Gabor:6
MeasureTexture:[module_num:2|svn_version:\'1\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:No
Number of angles to compute for Gabor:6
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 2)
for i, wants_gabor in enumerate((True, False)):
module = pipeline.modules()[i]
self.assertTrue(isinstance(module, M.MeasureTexture))
self.assertEqual(len(module.image_groups), 2)
self.assertEqual(module.image_groups[0].image_name, "rawDNA")
self.assertEqual(module.image_groups[1].image_name, "rawGFP")
self.assertEqual(len(module.object_groups), 2)
self.assertEqual(module.object_groups[0].object_name, "Cells")
self.assertEqual(module.object_groups[1].object_name, "Nuclei")
self.assertEqual(len(module.scale_groups), 2)
self.assertEqual(module.scale_groups[0].scale, 3)
angles = module.scale_groups[0].angles.get_selections()
self.assertEqual(len(angles), 2)
self.assertTrue(M.H_HORIZONTAL in angles)
self.assertTrue(M.H_VERTICAL in angles)
angles = module.scale_groups[1].angles.get_selections()
self.assertEqual(len(angles), 2)
self.assertTrue(M.H_DIAGONAL in angles)
self.assertTrue(M.H_ANTIDIAGONAL in angles)
self.assertEqual(module.scale_groups[1].scale, 5)
self.assertEqual(module.wants_gabor, wants_gabor)
self.assertEqual(module.gabor_angles, 6)
self.assertEqual(module.images_or_objects, M.IO_BOTH)
def test_01_04_load_v4(self):
data = """CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20141017202435
GitHash:b261e94
ModuleCount:3
HasImagePlaneDetails:False
MeasureTexture:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:Yes
Number of angles to compute for Gabor:6
Measure images or objects?:Images
MeasureTexture:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:No
Number of angles to compute for Gabor:6
Measure images or objects?:Objects
MeasureTexture:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Hidden:2
Hidden:2
Hidden:2
Select an image to measure:rawDNA
Select an image to measure:rawGFP
Select objects to measure:Cells
Select objects to measure:Nuclei
Texture scale to measure:3
Angles to measure:Horizontal,Vertical
Texture scale to measure:5
Angles to measure:Diagonal,Anti-diagonal
Measure Gabor features?:No
Number of angles to compute for Gabor:6
Measure images or objects?:Both
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 3)
for i, (wants_gabor, io_choice) in enumerate(
((True, M.IO_IMAGES),
(False, M.IO_OBJECTS),
(False, M.IO_BOTH))):
module = pipeline.modules()[i]
self.assertTrue(isinstance(module, M.MeasureTexture))
self.assertEqual(len(module.image_groups), 2)
self.assertEqual(module.image_groups[0].image_name, "rawDNA")
self.assertEqual(module.image_groups[1].image_name, "rawGFP")
self.assertEqual(len(module.object_groups), 2)
self.assertEqual(module.object_groups[0].object_name, "Cells")
self.assertEqual(module.object_groups[1].object_name, "Nuclei")
self.assertEqual(len(module.scale_groups), 2)
self.assertEqual(module.scale_groups[0].scale, 3)
angles = module.scale_groups[0].angles.get_selections()
self.assertEqual(len(angles), 2)
self.assertTrue(M.H_HORIZONTAL in angles)
self.assertTrue(M.H_VERTICAL in angles)
angles = module.scale_groups[1].angles.get_selections()
self.assertEqual(len(angles), 2)
self.assertTrue(M.H_DIAGONAL in angles)
self.assertTrue(M.H_ANTIDIAGONAL in angles)
self.assertEqual(module.scale_groups[1].scale, 5)
self.assertEqual(module.wants_gabor, wants_gabor)
self.assertEqual(module.gabor_angles, 6)
def test_01_10_img_787(self):
data = ('eJyVk/s7E4oDxkeJU5xJhJPLcr91rEMuyxR6ODqZ+yS5Lmm5bC5z35Rrclml'
'EqYdB5PIDuUyxhzRYpg7oWJhmNvcRmM7Pef7F3zfH97f3ud5n8/zvg5Wbtes'
'rEEXfgWDHKzczt1GhgQagJxC/KNuoyJCISAnGw97mAHIJiLQPyrwFggVBgG5'
'YQJBDv4RILA5CHwech4MAZuAfgOfB4P+bwFE7B2AAABgWRwAiCXMeXo5ToFl'
'o0vAE2K13N+44Hnxd6WjxiaryGxMNfDdA1AZtLm7pB69+jH8eotvI7Ge3mW8'
'1HU8QqLEvvF3OMaFuL6XyA7Q/mQlcbh09p2iICwNIeVQk/N7Ta+U5hRI0x+b'
'dMR9kLMoWEffDLBC4zs0SSJ7d+K7eFMrD32zW5d8K6aldKI20LiIWEyyPzD1'
'a+tDuVIa86PEKwecyA0qC8lG2yR2eJapznJiKqIpIytrOmeIty3cjFV80hGo'
'tbZCoWLfgsep2KniItqZnBMK3fHBqcpIqdqoY8fZpSLszPTSuRoQFnXP9Q3C'
'a/hr7rbjUUBLxiVDYyVNYsq63/GwSx09efnVOGypiDxwO7gHL+rlTNDA5Yrx'
'tpuSTGeoQ+inSnRssxZXyLogGTsqRtWDHLuaNXDRaLF18AC+iZyI+RwGar08'
'hl005TSm+YibtPfezxSSBPUls3qfdnUe63xtQ9ygk360gM04gZyFpovrwd/5'
'MZtb8CdV3dTAjS23YY+IoiE/M4/FAr6YPKuNf8UZqx3sfetTUiiQEHw8bYSW'
'KSxL3Os596Fcbg/8jZGaJ0s3yWtPVKPrQGGTOxbbSrdYkVirJOXaIwQ9IG/g'
'L2/NRmuV1AsaK34P7OBAHWHBJL4sHtVGxt4pmzNapWjrq2rx7bkHycWcthmR'
'yKDdtzMaO28ydjHDkGhjrOaUy+VYyfpfns/BjYhNX0xV+iZbbnKqQsy0LPVO'
'mMmNGFSqkqgTP6BYmDO/hf+iMnZ6e3zAWd/L8lCCGOfRjYIKwpUDa4PFobod'
'FQxjwaXh7WeJvRT0T++5CSlqLHgUlZGex9uvDREEruLxz/KbfTLIJFpGThQH'
'V/PGq1s5LHNKVq1alXzxBKyIMpfeJEKKIowJqd/+C26RCXYqVblrk/isL/nP'
'R01ZB/jV2/Eqry6cQcYxitb/+uZmQqijJelChG93WzrX5YeA1eryCU+m2x/u'
'kem0NO2v5QEtjBph4BZxWykqK9I6DjisC9QiuK/+sDIe2k70b2i7z1HXq7SF'
'rTXDH/cQCleufwpjgWWllJsN7/ItZVTvDZVldaobWYeyxWypfYjj8GW9Tp0/'
'ygWhl8qj25Ovcye4opVXNDgRzv1cyvj4H9PRp5mWYXZN9Wh+7bs5SqdP4ikz'
'+b3GjKl/Nlv1blalBsutM2YmBIm8hvYdsaY8mbBZoJO2rae24z+spGcCJBUq'
'svZRl+d4QEP/rKaQciJolPaA/Wj2rCCFjQl/Wxhzmo1SiGnsNH+y1yqDYyqq'
'QVuxIrCGtmvckRzEqwel3KzlzXuDDonMnQTvryHTeluIp+1oSZILpF/Bgpvx'
'Kq9fusfHX8lFeWTM23BNjl0Zoqxi9D1znfKSHQNtbCR5hVusL8bWxf2569me'
'0LchkyQ5ZC99f8lV9PKBwrzOYya5Mg42Lv/s4Ml3vXfjHw3JMDY5gh/UkorK'
'O78b+gHbvwj1xRdlF9gKcPOKhWKKhOp5/GTzFifanFZYmKSSm+NlgvsCseJi'
'RXBFfCnsGD9/1NvdLokl2anba2nQSY5oU9/Zvthz3155ZPK6/NLbPu6j+VnP'
'UaoT9Y6wOWD7pOoH172fK+KkMTMGElbfVbEkVmO5axnKFXMX4kuKjYXHFI/1'
'5cEI7nzyaniVl10Dy7jr/dsGTnJXy/OtwKtAZuEQ8mFGUCiR8Xo7zX7DOVEX'
'uRjAZAg3fvVzxSOLX5u5KzdvucIdfdsRHIHLU1UviAN3o7rn8MM9Qm579O68'
'qy2QaxdnW5OhDXqYzZn1fNy/btGGoDao+6IEQ3wcnEkaM/IRWkw9xYw99fn8'
'fWyV3tDGJOrjemz2yYTNmOI16jlL0fdH57cWNo3dx3OD6Oot+NJ8U3A3/IrD'
'nH+o3Lr8/wb4wtkxBSxNZxyL7LkZlaVl+WJvsRR3JL9rUSbHljdTVFmtRyot'
'0+/eXS1PNx+7PHlWFqyODBNfGRd644cCRACT/kYd0dKe0ocLfcUdwG0r+xki'
'5cvR/c8b06ETNwaFvYsCM7bTDJpIxZ7N6Lo8uBOW3Ppkwx0j8IB92WcWaWcN'
'n47dhZDrlD43u3GsRjyMfT98PtWUEDwd1fr6oHnM0YdQF+v+hl59OIo5wIS7'
'ERHe8cvwpB/gPGDOVGqoz/cGwl10w1a3cE62S5+xIJeRXZqg2oIkBHIYdwPj'
'69byo+arQrDNW82VffowxSCPGXSSqGJ2mmt3+MfXLTpeUZYN4SGhxdUrfBSw'
'evwkz779b06XfiXkMF+OSalfjul25Oe+3DnE2M3c6iTOFkC0kVgzjRcD5ykl'
'6JJe+0LHlwO99xVyLNKpBYzTevuXZMymVR0e0ad5XeqYipR7j3Vv1bgOWkCv'
'mlWIsrczbJBNIFV/pWvehpK5sWSbB8UAm3RZirYoTyPt4gaWCqhqMtS4GZkT'
'nHxWs2B8ELJsFYPFiKnw1k8SxGRzBglptg7jte3mPuEquIZgmicj0/RPCcsk'
'a9XKLorhRpPQq5T3CKu3oJ8ZCeX9FIiIgMZIwxTocFFtS18TNVrQ1OHLVhRm'
'BUdrHbaczqmpGqGIsJol0fviys/9ow0QFciCYljpYMiFAEjdxtDP/QnIsF0f'
'c/ElniF+fh/131K+Ifz8BgABJqAFQIDtMQm/tE1/gJbla5zbIdoFCLCjWXv/'
'Cz7DUEw=')
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(
StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
module = pipeline.modules()[2]
self.assertTrue(isinstance(module, R.RescaleIntensity))
self.assertEqual(module.image_name, "OrigGreen")
self.assertEqual(module.rescaled_image_name, "Rescaledgreen")
self.assertEqual(module.rescale_method, R.M_MANUAL_INPUT_RANGE)
self.assertEqual(module.wants_automatic_low, R.LOW_EACH_IMAGE)
self.assertEqual(module.wants_automatic_high, R.HIGH_EACH_IMAGE)
def test_01_09_load_v1(self):
data = ('eJztWl9P2zAQd2lhMKSNaRKbtBc/7AE2GqXlj6CaoB3dtG6UVYA2IcSYaV3w'
'5MRV4rB2ExIfa498pH2ExW3SpKYlbSm0aIlkNXfx737ns++SOM1n9rYyb+Gy'
'osJ8Zi9eJhTDAkW8zAwtBXW+ADcNjDguQaan4HuDwI8WhQkVJpKpxZWUugyT'
'qroG+jsiufwj8fsSgAn7Z9JuY86lcUeO+JqQdzHnRD8xx0EMPHf0l3b7ggyC'
'jin+gqiFTY/C1ef0MturVZqX8qxkUbyNNH9n+9i2tGNsmJ/LLtC5XCBVTHfJ'
'LywNwe22g8+ISZju4B37srbJy7jEK+JwOePFISLFIWq3WZ9e9P8AvP6xNnF7'
'4us/48hEL5EzUrIQhURDJ00vhD01wF60xV4UZLczN8KlA3Azkv+i7eEqj7+r'
'oiKHGuLF0274x1rsjIFtBrrin5b4hbyDzSKiuCTG0G3cIi12ImCxS1ysBRez'
'/daxwJ0G4JYkv4W8ecqYiaFZwUVSJkV4Vs8TyBk8xtDAJubiHMGiZXKmQQPp'
'J9gdX1CcHkp8Qs4yqDMOLdNJGHm8E5Id93DtTIH2uF7mdxC4u/AzKI+fgdb4'
'CjmLy8iiHOZEEsMsMXCRM6N2q/7LeayoiYHxDdJPOW9UZW2tn/Ht29XxJn6u'
'BvA9AK3zKmRVSSzY/vY1H8PxN2n7uzqUfAvKm6eSv0LebRQ6p8zVq2DT3uiv'
'Y3V5UPfpUYlzN/fLRBvcLdeLruLc6f7cr5/pAL4p0BpnIW/WOKtQZGo+OxcB'
'dj5JdoT8bW6j8EY8+ON15fX8kZC+Ykp32M/1g0y8cDjvajYZtTR9/UCNrx3+'
'Tiwkzxudd4mNrCvn28Z9kPMlr4+kgwt6LlqVxi1k4fs+RoYzoKXz+bhQ5ZnO'
'Tx1d0tFlUc3T3GR8hQA/X0h+Cjmnc6ybhNeO8hjpntTpOfSu69cw1vmo1etO'
'63LU/Bx03eoXdxHg532qU73g7kud6hc3+bi3fYzbqiPt3kvrmx4nBrMqg7cz'
'anW03f6J5zckeglXbpO/3f4JO/5hv6l6gRt2fQxxIS7Ehbj/EZf24YZx/whx'
'dzufo/ocE+JGA5cG16+fsB6EuBDXO64a6fw+LGT/fr3o/x1cn4evQGseCrmI'
'Ka0YTPx/w1C0+p8MTIUyVGp85Ve27NOc74O/4KkE8KQlnnQnHg0j0zJwnYq4'
'W5NKvqGtszY3LLvZ/1iReFc68RqNj/EepfN1vsl2dd6m2vD54z9mS7PRmWvn'
'W55nb/7/bvTDF41Ernx/mw7AxXw+iUPg/4De1tncNf3dMd5V/38aZ6rY')
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(
StringIO.StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()), 3)
module = pipeline.modules()[2]
self.assertTrue(isinstance(module, R.RescaleIntensity))
#
# image_name = DNA
# rescaled_image_name = RescaledDNA
# rescaling_method = M_MANUAL_IO_RANGE
# manual intensities
# source_low = .01
# source_high = .99
# src_scale = .1 to .9
# dest_scale = .2 to .8
# low_truncation_choice = R_SET_TO_CUSTOM
# custom_low_truncation = .05
# high_truncation_choice = R_SET_TO_CUSTOM
# custom_high_truncation = .95
# matching_image_name = Cytoplasm
# divisor_value = 2
# divisor_measurement = Intensity_MeanIntensity_DNA
self.assertEqual(module.image_name.value, "DNA")
self.assertEqual(module.rescaled_image_name.value, "RescaledDNA")
self.assertEqual(module.rescale_method.value, R.M_MANUAL_IO_RANGE)
self.assertEqual(module.wants_automatic_high.value, R.CUSTOM_VALUE)
self.assertEqual(module.wants_automatic_low.value, R.CUSTOM_VALUE)
self.assertAlmostEqual(module.source_low.value, .01)
self.assertAlmostEqual(module.source_high.value, .99)
self.assertAlmostEqual(module.source_scale.min, .1)
self.assertAlmostEqual(module.source_scale.max, .9)
self.assertEqual(module.low_truncation_choice.value, R.R_SET_TO_CUSTOM)
self.assertEqual(module.high_truncation_choice.value,
R.R_SET_TO_CUSTOM)
self.assertAlmostEqual(module.custom_low_truncation.value, .05)
self.assertAlmostEqual(module.custom_high_truncation.value, .95)
self.assertEqual(module.matching_image_name.value, "Cytoplasm")
self.assertAlmostEqual(module.divisor_value.value, 2)
self.assertEqual(module.divisor_measurement.value,
"Intensity_MeanIntensity_DNA")
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20130220214757
ModuleCount:5
HasImagePlaneDetails:False
SendEmail:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Hidden:2
Hidden:6
Sender address:[email protected]
Subject line:Hello, world
Server name:smtp.cellprofiler.org
Port:587
Select connection security:STARTTLS
Username and password required to login?:Yes
Username:[email protected]
Password:rumplestiltskin
Recipient address:[email protected]
Recipient address:[email protected]
When should the email be sent?:After first cycle
Image cycle number:1
Image cycle count:1
Message text:First cycle
When should the email be sent?:After last cycle
Image cycle number:1
Image cycle count:1
Message text:Last cycle
When should the email be sent?:After group start
Image cycle number:1
Image cycle count:1
Message text:Group start
When should the email be sent?:After group end
Image cycle number:1
Image cycle count:1
Message text:Group end
When should the email be sent?:Every # of cycles
Image cycle number:1
Image cycle count:5
Message text:Every fifth cycle
When should the email be sent?:After cycle #
Image cycle number:17
Image cycle count:1
Message text:Cycle 17
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
assert isinstance(module, SE.SendEmail)
self.assertEqual(module.from_address, "*****@*****.**")
self.assertEqual(module.subject, "Hello, world")
self.assertEqual(module.smtp_server, "smtp.cellprofiler.org")
self.assertEqual(module.port, 587)
self.assertEqual(module.connection_security, SE.C_STARTTLS)
self.assertTrue(module.use_authentication)
self.assertEqual(module.username, "*****@*****.**")
self.assertEqual(module.password, "rumplestiltskin")
self.assertEqual(len(module.recipients), 2)
self.assertEqual(module.recipients[0].recipient,
"*****@*****.**")
self.assertEqual(module.recipients[1].recipient,
"*****@*****.**")
self.assertEqual(len(module.when), 6)
self.assertEqual(module.when[0].choice, SE.S_FIRST)
self.assertEqual(module.when[0].message, "First cycle")
self.assertEqual(module.when[1].choice, SE.S_LAST)
self.assertEqual(module.when[2].choice, SE.S_GROUP_START)
self.assertEqual(module.when[3].choice, SE.S_GROUP_END)
self.assertEqual(module.when[4].choice, SE.S_EVERY_N)
self.assertEqual(module.when[4].image_set_count, 5)
self.assertEqual(module.when[5].choice, SE.S_CYCLE_N)
self.assertEqual(module.when[5].image_set_number, 17)
def make_workspace(
self, control_points, lengths, radii, image, mask=None, auximage=None
):
"""Create a workspace containing the control point measurements
control_points - an n x 2 x m array where n is the # of control points,
and m is the number of objects.
lengths - the length of each object
radii - the radii_from_training defining the radius at each control pt
image - the image to be straightened
mask - the mask associated with the image (default = no mask)
auximage - a second image to be straightnened (default = no second image)
"""
module = S.StraightenWorms()
module.objects_name.value = OBJECTS_NAME
module.straightened_objects_name.value = STRAIGHTENED_OBJECTS_NAME
module.images[0].image_name.value = IMAGE_NAME
module.images[0].straightened_image_name.value = STRAIGHTENED_IMAGE_NAME
module.flip_image.value = IMAGE_NAME
module.module_num = 1
# Trick the module into thinking it's read the data file
class P:
def __init__(self):
self.radii_from_training = radii
module.training_set_directory.dir_choice = cps.URL_FOLDER_NAME
module.training_set_directory.custom_path = "http://www.cellprofiler.org"
module.training_set_file_name.value = "TrainingSet.xml"
module.training_params = {"TrainingSet.xml": (P(), "URL")}
pipeline = cpp.Pipeline()
pipeline.add_module(module)
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
m = cpmeas.Measurements()
for i, (y, x) in enumerate(control_points):
for v, f in ((x, S.F_CONTROL_POINT_X), (y, S.F_CONTROL_POINT_Y)):
feature = "_".join((S.C_WORM, f, str(i + 1)))
m.add_measurement(OBJECTS_NAME, feature, v)
feature = "_".join((S.C_WORM, S.F_LENGTH))
m.add_measurement(OBJECTS_NAME, feature, lengths)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(IMAGE_NAME, cpi.Image(image, mask))
if auximage is not None:
image_set.add(AUX_IMAGE_NAME, cpi.Image(auximage))
module.add_image()
module.images[1].image_name.value = AUX_IMAGE_NAME
module.images[1].straightened_image_name.value = AUX_STRAIGHTENED_IMAGE_NAME
object_set = cpo.ObjectSet()
objects = cpo.Objects()
labels = np.zeros(image.shape, int)
for i in range(control_points.shape[2]):
if lengths[i] == 0:
continue
self.rebuild_worm_from_control_points_approx(
control_points[:, :, i], radii, labels, i + 1
)
objects.segmented = labels
object_set.add_objects(objects, OBJECTS_NAME)
workspace = cpw.Workspace(
pipeline, module, image_set, object_set, m, image_set_list
)
return workspace, module
def run_group_request(self, session_id, message_type, message):
'''Handle a run-group request message'''
pipeline = cpp.Pipeline()
m = cpmeas.Measurements()
image_group = m.hdf5_dict.hdf5_file.create_group("ImageData")
if len(message) < 2:
self.raise_cellprofiler_exception(session_id,
"Missing run request sections")
return
pipeline_txt = message.pop(0).bytes
image_metadata = message.pop(0).bytes
n_image_sets = None
try:
image_metadata = json.loads(image_metadata)
channel_names = []
for channel_name, channel_metadata in image_metadata:
channel_names.append(channel_name)
if len(message) < 1:
self.raise_cellprofiler_exception(
session_id,
"Missing binary data for channel %s" % channel_name)
return None, None, None
pixel_data = self.decode_image(channel_metadata,
message.pop(0).bytes,
grouping_allowed=True)
if pixel_data.ndim < 3:
self.raise_cellprofiler_exception(
session_id,
"The image for channel %s does not have a Z or T dimension"
)
return
if n_image_sets is None:
n_image_sets = pixel_data.shape[0]
elif n_image_sets != pixel_data.shape[0]:
self.raise_cellprofiler_exception(
session_id,
"The images passed have different numbers of Z or T planes"
)
return
image_group.create_dataset(channel_name, data=pixel_data)
except Exception as e:
self.raise_cellprofiler_exception(session_id, e.message)
return None, None, None
try:
pipeline.loadtxt(StringIO(pipeline_txt))
except Exception as e:
logger.warning(
"Failed to load pipeline: sending pipeline exception",
exc_info=1)
self.raise_pipeline_exception(session_id, str(e))
return
image_numbers = np.arange(1, n_image_sets + 1)
for image_number in image_numbers:
m[cpmeas.IMAGE, cpmeas.GROUP_NUMBER, image_number] = 1
m[cpmeas.IMAGE, cpmeas.GROUP_INDEX, image_number] = image_number
input_modules, other_modules = self.split_pipeline(pipeline)
workspace = cpw.Workspace(pipeline, None, m, None, m, None)
logger.info("Preparing group")
for module in other_modules:
module.prepare_group(
workspace, dict([("image_number", i) for i in image_numbers]),
image_numbers)
for image_index in range(n_image_sets):
object_set = cpo.ObjectSet()
m.next_image_set(image_index + 1)
for channel_name in channel_names:
dataset = image_group[channel_name]
pixel_data = dataset[image_index]
m.add(channel_name, cpi.Image(pixel_data))
for module in other_modules:
workspace = cpw.Workspace(pipeline, module, m, object_set, m,
None)
try:
logger.info("Running module # %d: %s" %
(module.module_num, module.module_name))
pipeline.run_module(module, workspace)
if workspace.disposition in \
(cpw.DISPOSITION_SKIP, cpw.DISPOSITION_CANCEL):
break
except Exception as e:
msg = "Encountered error while running module, \"%s\": %s" % (
module.module_name, e.message)
logger.warning(msg, exc_info=1)
self.raise_cellprofiler_exception(session_id, msg)
return
else:
continue
if workspace.disposition == cpw.DISPOSITION_CANCEL:
break
for module in other_modules:
module.post_group(
workspace, dict([("image_number", i) for i in image_numbers]))
logger.info("Finished group")
type_names, feature_dict = self.find_measurements(
other_modules, pipeline)
double_features = []
double_data = []
float_features = []
float_data = []
int_features = []
int_data = []
string_features = []
string_data = []
metadata = [
double_features, float_features, int_features, string_features
]
for object_name, features in feature_dict.items():
df = []
double_features.append((object_name, df))
ff = []
float_features.append((object_name, ff))
intf = []
int_features.append((object_name, intf))
sf = []
string_features.append((object_name, sf))
if object_name == cpmeas.IMAGE:
object_counts = [] * n_image_sets
else:
object_numbers = m[object_name, cpmeas.OBJECT_NUMBER,
image_numbers]
object_counts = [len(x) for x in object_numbers]
for feature, data_type in features:
if data_type == 'java.lang.String':
continue
if not m.has_feature(object_name, feature):
data = np.zeros(np.sum(object_counts))
else:
data = m[object_name, feature, image_numbers]
temp = []
for i, (di, count) in enumerate(zip(data, object_counts)):
if count == 0:
continue
di = np.atleast_1d(di)
if len(di) > count:
di = di[:count]
elif len(di) == count:
temp.append(di)
else:
temp += [di + np.zeros(len(di) - count)]
if len(temp) > 0:
data = np.hstack(temp)
if type_names[data_type] == 'java.lang.Double':
df.append((feature, len(data)))
if len(data) > 0:
double_data.append(data.astype("<f8"))
elif type_names[data_type] == 'java.lang.Float':
ff.append((feature, len(data)))
if len(data) > 0:
float_data.append(data.astype('<f4'))
elif type_names[data_type] == 'java.lang.Integer':
intf.append((feature, len(data)))
if len(data) > 0:
int_data.append(data.astype('<i4'))
data = np.hstack([
np.frombuffer(
np.ascontiguousarray(np.hstack(ditem)).data, np.uint8)
for ditem in (double_data, float_data, int_data) if len(ditem) > 0
])
data = np.ascontiguousarray(data)
self.socket.send_multipart([
zmq.Frame(session_id),
zmq.Frame(),
zmq.Frame(RUN_REPLY_1),
zmq.Frame(json.dumps(metadata)),
zmq.Frame(data)
])
def test_01_02_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10891
StraightenWorms:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input untangled worm objects:OverlappingWorms
Name the output straightened worm objects:StraightenedWorms
Worm width:20
Training set file location:Default Input Folder\x7CNone
Training set file name:TrainingSet.mat
Image count:1
Measure intensity distribution?:Yes
Number of segments:4
Align worms?:Top brightest
Alignment image:Brightfield
Select an input image to straighten:Brightfield
Name the output straightened image:StraightenedImage
StraightenWorms:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input untangled worm objects:OverlappingWorms
Name the output straightened worm objects:StraightenedWorms
Worm width:20
Training set file location:Default Input Folder\x7CNone
Training set file name:TrainingSet.mat
Image count:1
Measure intensity distribution?:Yes
Number of segments:4
Align worms?:Bottom brightest
Alignment image:Brightfield
Select an input image to straighten:Brightfield
Name the output straightened image:StraightenedImage
StraightenWorms:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input untangled worm objects:OverlappingWorms
Name the output straightened worm objects:StraightenedWorms
Worm width:20
Training set file location:Default Input Folder\x7CNone
Training set file name:TrainingSet.mat
Image count:1
Measure intensity distribution?:Yes
Number of segments:4
Align worms?:Do not align
Alignment image:Brightfield
Select an input image to straighten:Brightfield
Name the output straightened image:StraightenedImage
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 3)
for alignment, module in zip(
(S.FLIP_TOP, S.FLIP_BOTTOM, S.FLIP_NONE), pipeline.modules()
):
self.assertTrue(isinstance(module, S.StraightenWorms))
self.assertEqual(module.objects_name, "OverlappingWorms")
self.assertEqual(module.straightened_objects_name, "StraightenedWorms")
self.assertEqual(module.width, 20)
self.assertEqual(
module.training_set_directory.dir_choice, cps.DEFAULT_INPUT_FOLDER_NAME
)
self.assertEqual(module.training_set_file_name, "TrainingSet.mat")
self.assertEqual(len(module.images), 1)
self.assertTrue(module.wants_measurements)
self.assertEqual(module.number_of_segments, 4)
self.assertEqual(module.number_of_stripes, 1)
self.assertEqual(module.flip_worms, alignment)
self.assertEqual(module.flip_image, "Brightfield")
self.assertEqual(module.images[0].image_name, "Brightfield")
self.assertEqual(
module.images[0].straightened_image_name, "StraightenedImage"
)
def test_01_02_load_v1(self):
data = ('eJztnN1u2zYUgCnnB806tO52sWG90cUGNFtiyGmyNsGQ2km2xVudGk3Qoii6'
'jLHpmIMkGhKVxhsK7HKPtUfYY+xyjzBRliyJUSJZlpXIoQDBOjQ/Hp6jQ1LU'
'D5v1o+f1HXmjosjN+tFqF6tIbqmQdomhbck6XZF3DQQp6shE35KbRJd/slR5'
'TZGVza319a2qYh8rmyDdJjWa9+yfv1oALNq/d+y95P614MpSYGfyIaIU66fm'
'ApgHn7vpf9v7K2hgeKKiV1C1kOmr8NIbepccDfqjv5qkY6noAGrBzPZ2YGkn'
'yDBfdD3Q/buFz5F6iH9HnAletpfoDJuY6C7vls+njvQSyullfvj3K98PEucH'
'lv4wkM7y7wM//3yE3x4E8pddGesdfIY7FlRlrMHTUS1YeUpMeXOh8ubA3kF9'
'Iu5pDLfI1X/ROT9tFeFhfWsxfJnj2d5gRjcRNC0DaUinaf344uQ31KaBggJ+'
'TFOvI3ROV78/h20qa5C2e0n8WgqVUwIHBGTi13HP5xs7ipLovQPCepnctFSK'
'++ogCz6u3lKIl8BjkMze+RA3b/tZR0nqu8DVl8lVZWVdidC7yPHe5vFL7m9a'
'vU7cJ+Sj4mMY78ni+yOOZ/IekXVCZctEvh1p7U8bn9PSlzY+Jm2Hl7X/admZ'
'tb5WjL4vOP8wuaFTpJuYDo6b8NwX2JAyKrcWU+49rlwm1+2rnMMe7KNjdpRs'
'fP2MK4fJe6gL7S5JdtqbvIcNu9UQYzBT8c7rW6soqbhqBJe2nuNwtZh63gXh'
'88rkF9S05B9VcgLVifWP66fHGfr3JsbPpHFw3eNh3v5Nc72qVBRnW6m6B4Hy'
'xLiYrZ22r6vTHBeD88CyKwfGRQT1iwNjkrj5lCuXyf642EIG1hBFRqRfbkK/'
'vcTVfynolwz0j9vO13Put6Pm47Ps340p+5efL1anbB/fb1WV67+eTzuejTMe'
'TMs+/vw9ieCmGZ/fThiff8ZwP4Nwe2TyL4+etb5jN3DRduWb5WMmvUaq+pK8'
'335bX229W/ZSdolqafr2W2V1890f1ZW1D8PMh9gmncTlSLtnad7yZMLz04vh'
'noLw+WEy8/EbBA3X8esflldZUpPotOemrblpe3Dgp0yzX8/zfs2sc3nH+axz'
'wp/ZcsKf2XJJ/Klc032uInJ5XzfOOpf3/ZFZ50T/mS2XrP/cuPZ63mSuBq72'
'Z9TzjKP3RG6r0DTdN0CKZG/e3D642r9R99tfI3zaY69NnbEXhPQ2KqDdNy2O'
'o+apPxADnRrE0jvFs7f3pc9JHMfS+fe98vSr83IYc2w//3Ki+iviPG/zCyrS'
'ec6z3wn4ScZ6B/ULaHee4+BtjKtZ52ogmzjIqpyi+E1wghOc4AQnxqsi+E1w'
't5OrgavjvAzCcc52fx46nBYVyV7BCU5wgpsVbj/ARfXfn4Bw/81kYlEV6+jC'
'ja0i2S04wQlOcIIrBlcLcJM87yqKvYITnOAEJzjBCW72uH9KPidxnOSWJQXy'
'/xrQE3X983Ugf9mV20hV+wZh64gZFc1Z7MqsqAR2hqtNVZ7bh43AwlNMTz9G'
'T43TU7tMjzZcjslRhb0PUyvuIk2O1tHnqpnqxR2kU9wd9A1btUWJBiluVxpu'
'astOrXupTO95jN4dTu9OjL3DZzjQsCX2pbZn8HCJgtEH3MnPq8LpVy7T34Zq'
'22LfGtqm9Sq7ntS0pTz1BNcTWIrQE4zrkiuX5x6W7t+/uj0BEG5Hfvv671la'
'vXPSnCRJF+cXd2N45r+PwcXNuZ8mjdeuH4HL83s239b8/wMM1XyW')
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
#
# 2 CalculateMath modules: 4 & 5
# 4: ImageMeasurement, multiply
# Image - Intensity_MaxIntensity_DNA, multiply by 2, raise to 3
# Image - Intensity_MeanIntensity_DNA, multiply by 4, raise to 5
# multiply by 6 raise to 7
# 5: ObjectMeasurement, multiply
# Nuclei - AreaShape_Area
# Nuclei - AreaShape_Perimeter
#
self.assertEqual(len(pipeline.modules()), 6)
module = pipeline.modules()[4]
module.output_feature_name.value = "ImageMeasurement"
self.assertEqual(module.operands[0].operand_choice, C.MC_IMAGE)
self.assertEqual(module.operands[1].operand_choice, C.MC_IMAGE)
self.assertEqual(module.operands[0].operand_measurement,
"Intensity_MaxIntensity_DNA")
self.assertEqual(module.operands[1].operand_measurement,
"Intensity_MeanIntensity_DNA")
self.assertEqual(module.operands[0].multiplicand, 2)
self.assertEqual(module.operands[0].exponent, 3)
self.assertEqual(module.operands[1].multiplicand, 4)
self.assertEqual(module.operands[1].exponent, 5)
self.assertEqual(module.final_multiplicand, 6)
self.assertEqual(module.final_exponent, 7)
module = pipeline.modules()[5]
self.assertEqual(module.operands[0].operand_choice, C.MC_OBJECT)
self.assertEqual(module.operands[1].operand_choice, C.MC_OBJECT)
self.assertEqual(module.operands[0].object, "Nuclei")
self.assertEqual(module.operands[1].object, "Nuclei")
self.assertEqual(module.operands[0].operand_measurement,
"AreaShape_Area")
self.assertEqual(module.operands[1].operand_measurement,
"AreaShape_Perimeter")
def test_04_04_load_v4(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10908
MeasureImageQuality:[module_num:1|svn_version:\'10368\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Calculate metrics for which images?:All loaded images
Image count:1
Scale count:1
Threshold count:0
Select the images to measure:Alpha
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:Yes
MeasureImageQuality:[module_num:2|svn_version:\'10368\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Calculate metrics for which images?:Select...
Image count:1
Scale count:1
Threshold count:1
Select the images to measure:Alpha
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:Yes
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
MeasureImageQuality:[module_num:3|svn_version:\'10368\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Calculate metrics for which images?:Select...
Image count:1
Scale count:1
Threshold count:1
Select the images to measure:Delta,Beta
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:Yes
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
MeasureImageQuality:[module_num:4|svn_version:\'10368\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Calculate metrics for which images?:Select...
Image count:2
Scale count:1
Scale count:1
Threshold count:1
Threshold count:1
Select the images to measure:Delta
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:No
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select the images to measure:Epsilon
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:Yes
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
MeasureImageQuality:[module_num:5|svn_version:\'10368\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Calculate metrics for which images?:Select...
Image count:1
Scale count:2
Threshold count:2
Select the images to measure:Zeta
Include the image rescaling value?:Yes
Calculate blur metrics?:Yes
Window size for blur measurements:20
Window size for blur measurements:30
Calculate saturation metrics?:Yes
Calculate intensity metrics?:Yes
Calculate thresholds?:Yes
Use all thresholding methods?:No
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Two classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
Select a thresholding method:Otsu Global
Typical fraction of the image covered by objects:0.1
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Foreground
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
self.assertEqual(len(pipeline.modules()), 5)
for module in pipeline.modules():
self.assertTrue(isinstance(module, miq.MeasureImageQuality))
module = pipeline.modules()[0]
self.assertEqual(len(module.image_groups),1)
group = module.image_groups[0]
self.assertEqual(group.threshold_groups, [])
self.assertEqual(module.images_choice, miq.O_ALL_LOADED)
self.assertTrue(group.check_blur)
self.assertEqual(group.scale_groups[0].scale, 20)
self.assertTrue(group.check_saturation)
self.assertTrue(group.check_intensity)
self.assertTrue(group.calculate_threshold)
self.assertTrue(group.use_all_threshold_methods)
module = pipeline.modules()[1]
self.assertEqual(len(module.image_groups),1)
group = module.image_groups[0]
self.assertEqual(module.images_choice, miq.O_SELECT)
self.assertEqual(group.image_names, "Alpha")
module = pipeline.modules()[2]
self.assertEqual(len(module.image_groups),1)
group = module.image_groups[0]
self.assertEqual(module.images_choice, miq.O_SELECT)
self.assertEqual(group.image_names, "Delta,Beta")
module = pipeline.modules()[3]
self.assertEqual(len(module.image_groups),2)
group = module.image_groups[0]
self.assertEqual(module.images_choice, miq.O_SELECT)
self.assertEqual(group.image_names, "Delta")
self.assertTrue(group.check_intensity)
self.assertFalse(group.use_all_threshold_methods)
thr = group.threshold_groups[0]
self.assertEqual(thr.threshold_method, miq.cpthresh.TM_OTSU)
self.assertEqual(thr.use_weighted_variance, miq.O_WEIGHTED_VARIANCE)
self.assertEqual(thr.two_class_otsu, miq.O_TWO_CLASS)
group = module.image_groups[1]
self.assertEqual(group.image_names, "Epsilon")
module = pipeline.modules()[4]
self.assertEqual(len(module.image_groups),1)
group = module.image_groups[0]
self.assertEqual(module.images_choice, miq.O_SELECT)
self.assertEqual(group.image_names, "Zeta")
self.assertFalse(group.use_all_threshold_methods)
thr = group.threshold_groups[0]
self.assertEqual(thr.threshold_method, miq.cpthresh.TM_OTSU)
self.assertEqual(thr.use_weighted_variance, miq.O_WEIGHTED_VARIANCE)
self.assertEqual(thr.two_class_otsu, miq.O_TWO_CLASS)
thr = group.threshold_groups[1]
self.assertEqual(thr.threshold_method, miq.cpthresh.TM_OTSU)
self.assertEqual(thr.use_weighted_variance, miq.O_WEIGHTED_VARIANCE)
self.assertEqual(thr.two_class_otsu, miq.O_THREE_CLASS)
self.assertEqual(thr.assign_middle_to_foreground, miq.O_FOREGROUND)
def test_01_01_load_matlab(self):
data = ('eJwBTgSx+01BVExBQiA1LjAgTUFULWZpbGUsIFBsYXRmb3JtOiBQQ1dJTiwg'
'Q3JlYXRlZCBvbjogRnJpIEp1bCAxNyAxNjoxNzoxNyAyMDA5ICAgICAgICAg'
'ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAAAUlN'
'DwAAAMYDAAB4nOxYT2/TMBR3t3ZsIFWDCYljDhw4bCUZTNqRwSbooS1i1SSO'
'buplRm5cJc7U8sk48pH4CMRZ0iYmm/PHaVo0S9bTc/x+fv/sZ6cNADh9DcCO'
'T3f9vgXuWivkG7HO+UvEGLYttwWa4FU4/tvvV9DBcETQFSQecsGiReNd+5oO'
'59PFpx4dewT14SQ+2W99bzJCjju4jgTDz1/xDJFL/BOBZIumfUO32MXUDuVD'
'fHF0sS5lwrptv/96ufRDQ/ADHz+IjfP5H8ByfjPFb/ux+fthH6IZO7qYQZNp'
'E8jMG45zKsHZFXA4P3Cw9dF39SbItwR5zncn0EKhH2XyO4I85/ueSRDOFodn'
'gjznP91A20bk+Eg3gnGOo0twGgmcRjBfhf5V278nyHO+azNku5jNw3GVOGXz'
'KW8cjIzrpuWhoR++j+Ios/+pIM/5c6rZlGmei7L7sS3gcH5IGSQxZ/KxLxKc'
'AwGH8xzCciBD42RkVNpXdJ/I5LYTctvgu39Gq9S77nNSlR0RlflzK4G3Bfq0'
'3jwwSsqt2n9152PddTOvP2V4aXV4wFxP+0zoCJLMOKrO4VXXRVV2RbToPipj'
'V1CnzhwEc+Ck3YN7cLZw0cXYquV8FPe3fmgk5OuuF3nj+y6n/c2EfBPoHd1Q'
'kV/rcr7L8LLu26z+uK/eFpWT3f+eC/pzHttjfIvHHiQa5qUieuXW6RfVeVLU'
'n2XsP/MY9R/s2NwA+0W5Dvg/7D8uob//Jpq7JiTR/yMV98Cq7RfzuHOiRl+Z'
'//Kes0Xuoef8lEJq5Ks+D1T744mAx/nuxOohWPx9MRj9QCYLIBZ65M0T+y1U'
'ui9ktGzcRPpm7+H/t/F6WTb+QXG1HOpNNxcn7T1GgzxaAqnEEWmR+00MV/Pv'
'Omgaw6tKTxlu2vtmGY87NYvgFtW3qB2q8mHd8vwxPuuNsynxuY9WZV9W/FXv'
't0daLZXVxRcgGSfOU48RbKN/CuND6/zxQf4CAAD//2NjYGDgAGJGBghgReKz'
'IYnzAbEDVAwkz4JFPReSegEo3yc/McUzNzE9tRhhjgcBc0TRzAHxfVMTi0uL'
'UsFGeeaVpOYVZ5ZUUmCeZ0pqXklmWmVAUWauY2lJfm5iSWYykeaJoZknhnCf'
'f1JWanIJkgOJCTdeNPNAfOfEnOTSnMSSVN/Ekgwiw59YcyyQzGHDYg5yvDNB'
'+UIswuyCghD9BgTcwciA7A5GBkMK7GVl4mFiY8P0PyH9IHfxAHGMzCYZfxlt'
'mbfSb6UZoO7wYESYQ0y612DArR4GRqp6ALPX4U6p2wdz')
pipeline = cpp.Pipeline()
# def callback(caller,event):
# self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
# pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
#
# 6 modules: last two are CalculateMath
#
# 1) Image: Intensity_TotalIntensity_OrigBlue
# Image: Intensity_TotalArea_OrigBlue
# Divide
# ImgMeas
# 2) Nuclei: Intensity_IntegratedIntensity_OrigBlue
# Nuclei: Intensity_MaxIntensity_OrigBlue
# Exponent = 2, multiplier = .5
# Divide
# ObjectMeas
#
self.assertEqual(len(pipeline.modules()), 6)
module = pipeline.modules()[4]
self.assertTrue(isinstance(module, C.CalculateMath))
self.assertEqual(module.output_feature_name.value, "ImgMeas")
self.assertEqual(module.operation.value, C.O_DIVIDE)
self.assertEqual(module.operands[0].operand_choice, C.MC_IMAGE)
self.assertEqual(module.operands[1].operand_choice, C.MC_IMAGE)
self.assertEqual(module.operands[0].operand_measurement.value,
"Intensity_TotalIntensity_OrigBlue")
self.assertEqual(module.operands[1].operand_measurement.value,
"Intensity_TotalArea_OrigBlue")
module = pipeline.modules()[5]
self.assertTrue(isinstance(module, C.CalculateMath))
self.assertEqual(module.output_feature_name.value, "ObjectMeas")
self.assertEqual(module.operation.value, C.O_DIVIDE)
self.assertEqual(module.operands[0].operand_choice, C.MC_OBJECT)
self.assertEqual(module.operands[1].operand_choice, C.MC_OBJECT)
self.assertEqual(module.operands[0].operand_objects, "Nuclei")
self.assertEqual(module.operands[1].operand_objects, "Nuclei")
self.assertEqual(module.operands[0].operand_measurement.value,
"Intensity_IntegratedIntensity_OrigBlue")
self.assertEqual(module.operands[1].operand_measurement.value,
"Intensity_MaxIntensityEdge_OrigBlue")
self.assertEqual(module.final_exponent.value, 2)
self.assertEqual(module.final_multiplicand.value, .5)
def test_03_01_save_and_load(self):
'''Save a pipeline to batch data, open it to check and load it'''
data = ('eJztWW1PGkEQXhC1WtPYTzb9tB+llROoGiWNgi9NSYUSIbZGbbvCApvu7ZJ7'
'UWlj0o/9Wf1J/QndxTs4tsoBRS3JHbkcMzfPPDOzs8uxl8uU9jPbcFWLw1ym'
'FKsSimGBIqvKDT0FmbUEdwyMLFyBnKVgycYwY9dgIgET8dTqRmolCZPx+AYY'
'7ghlc0/EJf4cgClxfSTOsHNr0pFDnlPKRWxZhNXMSRABzxz9L3EeIoOgM4oP'
'EbWx2aFw9VlW5aVmo30rxys2xXmke43Fkbf1M2yY76su0LldIJeYFsk3rKTg'
'mh3gc2ISzhy841/Vtnm5pfDKOhTmOnUIKXWQdVnw6KX9W9Cxj9xQt6ce+3lH'
'JqxCzknFRhQSHdXaUbTGwcffRJe/CXAk0BKX9sHNK3HIs4QvrdjeJSpbUEdW'
'uS79rPv4mVb8SLmcOrGw3ugr/lAXPgRe9Zl3uAsXBnkO+sp7VolXyrscMm5B'
'28T91/02/lHgphSce7i4GdCJ0y/fOSVfKe9RE1/UsYE1TXP91H38rCh+pCzG'
'uYwpbRhcLlHGiWXY7OuJaCC9ISZ3q5NdqbhdzLZb+yH4hpmXy3I2ioVtGTFE'
'myYZxbwcdpzvu68eku8+cGmf/GZAdz9IeaeOGMM0ERtx3P30z24+c6d86jqc'
'uOP8Il18EdE/DP8L3w8fvnegezyl/Glxq/BaPljhTe1l9LOUPoj15YBfbB5n'
'YoXTqKvZ4dTW2eZxPLZx+j2xlLy6Ni4SgWwpozfmPUj8fuvhuhK/lGUMRxgZ'
'TmArV9GYVOU4s+qOLunodlGzo3mgeZMcxbwZir9p8QZFpj4C/kHnUfKO+YJ5'
'NJ7z6OPsYP8rxuV3NcAFuAAX4P43XNqD63c/pLUZUzO43YCEVXBjnPINcOON'
'S4OgXwPc8DipvO35Ut2/kfZfQO9+ewG6+03K3s04TW9topsa5ahyvYut7Yuv'
'Wc+Gdj/P52sKz9ptPOXWK5AzuU8tb5ja9TuRbal4Q1rvCNT6zdzA561DWHwW'
'pnvXXa13Zxx+bw3DFwn9zffYBxdxKidxP8Fg47zYw97NbVj7P/nFW+E=')
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
for windows_mode in ((False, True) if sys.platform.startswith("win")
else (False,)):
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
ipath = os.path.join(T.example_images_directory(),'ExampleSBSImages')
bpath = tempfile.mkdtemp()
bfile = os.path.join(bpath,C.F_BATCH_DATA)
hfile = os.path.join(bpath, C.F_BATCH_DATA_H5)
try:
li = pipeline.modules()[0]
self.assertTrue(isinstance(li, LI.LoadImages))
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
li.location.dir_choice = LI.ABSOLUTE_FOLDER_NAME
li.location.custom_path = ipath
module.wants_default_output_directory.value = False
module.custom_output_directory.value = bpath
module.remote_host_is_windows.value = windows_mode
self.assertEqual(len(module.mappings), 1)
mapping = module.mappings[0]
mapping.local_directory.value = ipath
self.assertFalse(pipeline.in_batch_mode())
measurements = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
result = pipeline.prepare_run(
cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list))
self.assertFalse(pipeline.in_batch_mode())
self.assertFalse(result)
self.assertFalse(module.batch_mode.value)
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
fd = open(hfile,'rb')
try:
pipeline.load(fd)
fd.seek(0)
finally:
fd.close()
measurements = cpmeas.load_measurements(hfile)
image_set_list = cpi.ImageSetList()
self.assertTrue(pipeline.in_batch_mode())
module = pipeline.modules()[1]
self.assertTrue(isinstance(module, C.CreateBatchFiles))
self.assertTrue(module.batch_mode.value)
image_numbers = measurements.get_image_numbers()
self.assertTrue([x == i+1 for i, x in enumerate(image_numbers)])
workspace = cpw.Workspace(pipeline, None, None, None,
measurements, image_set_list)
pipeline.prepare_run(workspace)
pipeline.prepare_group(workspace, {}, range(1,97))
for i in range(96):
image_set = image_set_list.get_image_set(i)
for image_name in ('DNA', 'Cytoplasm'):
pathname = measurements.get_measurement(
cpmeas.IMAGE, "PathName_"+image_name, i+1)
self.assertEqual(pathname,
'\\imaging\\analysis' if windows_mode
else '/imaging/analysis')
del measurements
finally:
if os.path.exists(bfile):
os.unlink(bfile)
if os.path.exists(hfile):
os.unlink(hfile)
os.rmdir(bpath)
def test_load_with_extracted_operations(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20160418141927
GitHash:9969f42
ModuleCount:5
HasImagePlaneDetails:False
Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
:
Filter images?:Images only
Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.")
Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Extract metadata?:No
Metadata data type:Text
Metadata types:{}
Extraction method count:1
Metadata extraction method:Extract from file/folder names
Metadata source:File name
Regular expression:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)_w(?P<ChannelNumber>\x5B0-9\x5D)
Regular expression:(?P<Date>\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$
Extract metadata from:All images
Select the filtering criteria:and (file does contain "")
Metadata file location:
Match file and image metadata:\x5B\x5D
Use case insensitive matching?:No
NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Assign a name to:All images
Select the image type:Grayscale image
Name to assign these images:DNA
Match metadata:\x5B\x5D
Image set matching method:Order
Set intensity range from:Image metadata
Assignments count:1
Single images count:0
Maximum intensity:255.0
Select the rule criteria:and (file does contain "")
Name to assign these images:DNA
Name to assign these objects:Cell
Select the image type:Grayscale image
Set intensity range from:Image metadata
Retain outlines of loaded objects?:No
Name the outline image:LoadedOutlines
Maximum intensity:255.0
Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Do you want to group your images?:No
grouping metadata count:1
Metadata category:None
Morph:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
Select the input image:DNA
Name the output image:MorphBlue
Select the operation to perform:bothat
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:close
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:dilate
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:erode
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:fill small holes
Number of times to repeat operation:Once
Maximum hole area:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:invert
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:open
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:skel
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
Select the operation to perform:tophat
Number of times to repeat operation:Once
Repetition number:2
Diameter:3.0
Structuring element:Disk
X offset:1.0
Y offset:1.0
Angle:0.0
Width:3.0
Height:3.0
Custom:5,5,1111111111111111111111111
Rescale values from 0 to 1?:Yes
"""
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
module = pipeline.modules()[-1]
with pytest.raises(cps.ValidationError):
module.test_valid(pipeline)
def test_01_01_load_v1(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10268
UnmixColors:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Stain count:13
Color image\x3A:Color
Image name\x3A:Hematoxylin
Stain:Hematoxylin
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:Eosin
Stain:Eosin
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:DAB
Stain:DAB
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:FastRed
Stain:Fast red
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:FastBlue
Stain:Fast blue
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:MethylGreen
Stain:Methyl green
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:AEC
Stain:AEC
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name:AnilineBlue
Stain:Aniline blue
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:Azocarmine
Stain:Azocarmine
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:AlicanBlue
Stain:Alican blue
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:PAS
Stain:PAS
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:HematoxylinAndPAS
Stain:Hematoxylin and PAS
Red absorbance\x3A:0.5
Green absorbance\x3A:0.5
Blue absorbance\x3A:0.5
Image name\x3A:RedWine
Stain:Custom
Red absorbance\x3A:0.1
Green absorbance\x3A:0.2
Blue absorbance\x3A:0.3
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, U.UnmixColors))
self.assertEqual(module.input_image_name, "Color")
self.assertEqual(module.stain_count.value, 13)
self.assertEqual(module.outputs[0].image_name, "Hematoxylin")
self.assertEqual(module.outputs[-1].image_name, "RedWine")
for i, stain in enumerate((
U.CHOICE_HEMATOXYLIN, U.CHOICE_EOSIN, U.CHOICE_DAB,
U.CHOICE_FAST_RED, U.CHOICE_FAST_BLUE, U.CHOICE_METHYL_GREEN,
U.CHOICE_AEC, U.CHOICE_ANILINE_BLUE, U.CHOICE_AZOCARMINE,
U.CHOICE_ALICAN_BLUE, U.CHOICE_PAS)):
self.assertEqual(module.outputs[i].stain_choice, stain)
self.assertAlmostEqual(module.outputs[-1].red_absorbance.value, .1)
self.assertAlmostEqual(module.outputs[-1].green_absorbance.value, .2)
self.assertAlmostEqual(module.outputs[-1].blue_absorbance.value, .3)
def test_01_03_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10016
Morph:[module_num:1|svn_version:\'9935\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input image:InputImage
Name the output image:MorphImage
Select the operation to perform:branchpoints
Repeat operation:Forever
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:bridge
Repeat operation:Custom
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:clean
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:convex hull
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:diag
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:distance
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:endpoints
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:fill
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:hbreak
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:majority
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:remove
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:shrink
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:skelpe
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:spur
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:thicken
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:thin
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
Select the operation to perform:vbreak
Repeat operation:Once
Custom # of repeats:2
Scale\x3A:3
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(io.StringIO(data))
ops = [
morph.F_BRANCHPOINTS,
morph.F_BRIDGE,
morph.F_CLEAN,
morph.F_CONVEX_HULL,
morph.F_DIAG,
morph.F_DISTANCE,
morph.F_ENDPOINTS,
morph.F_FILL,
morph.F_HBREAK,
morph.F_MAJORITY,
morph.F_REMOVE,
morph.F_SHRINK,
morph.F_SKELPE,
morph.F_SPUR,
morph.F_THICKEN,
morph.F_THIN,
morph.F_VBREAK,
]
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, morph.Morph))
self.assertEqual(module.image_name, "InputImage")
self.assertEqual(module.output_image_name, "MorphImage")
self.assertEqual(len(module.functions), len(ops))
def test_01_04_load_v2(self):
data = ('eJztne9z2jYYxwUhXdPe7dJtL/qmN7/a7XrEA5bs0rwZ+VEW7kLCDa5rX6WO'
'LUA7I3G2nIT91fkTZoEdG8XExtgGU3EhIOHP80hfP5L1iBBax92L4xPpQK5I'
'rePuXg/pUGrrCu0RY3gkYVqWTg2oUKhJBB9JLYKlM6hKtapUPTiq/XG0/0Gq'
'VSofQLxbodn63n5o9wF4YT++tO9F56Vtp1zw3Vm5AylFuG9ugxJ469Q/2PdP'
'ioGUGx1+UnQLmp4Lt76Je6Q7Hj2+1CKapcNLZeg/2L5dWsMbaJhXPRd0Xm6j'
'e6h30H+Q64J72N/wFpmIYId37PO1j34J5fx2BuSuYdjN4eyfKFQddKh9Bmbr'
'mW4PFU+3Aqcbe3zvq2fHnwPv+FKAzm98x+86ZYQ1dIs0S9ElNFT6j61m9g5D'
'7L3k7LHylYH6J/YpYnwlhC/M8AVQjdiPHzi/rNyxg8YO7SFUTMuAQ4hpdF1+'
'4uyxcltBhkR6foOma68eYm+Xs8fuXXhP9z7eKyqVhuyUR9H3BWeHlS8tVYdo'
'Neen5vQ/7nldlPsdROvnNtdPVq5WyvuViPyyOofxYf0uzfAlcEkwjBJnrzi/'
'rHxGJEyoZJnOfBLF/9aMnS3wxZ4FeO4Fx7k3l9sBnr/PIf5+4drNyk1MITYR'
'HV+zZ32DXZK8Ojdql9MzjCvOcEWbi+YvCreIfnHmv4+3EOtjyRwpqn0tv0HY'
'XJ29oPn0DPYUS6dSk11kpAbRNWi49rKOz7jzUFbzngyiXWdeczqz8hU1Lekv'
'ndwoemB/k9SJPy8VuRarv+48ndb4jHJe0tQlKF6T5OLq8uT8lasZx8thquMq'
'ri789aMiV6p5jZdF2lkP8bcz429afrxGz+lvqvETEK+LcHHWz6cDBWOo1/Ko'
'17LzSdr5Bu+3WllOn3aIv5+59rJyAxkmLXegSrBW7g6QoZUbxDLooNxAPTpY'
'av25bLwtOk8egHzFV1rr+qznyaD8/1RXTBP1ENSmmWGSdtLSLQq3iG55zcOz'
'jq8485aXK7eU+4DEOe7+4AVRFYoIvj6FtlHj+rOvnaucV5LMp3/k+s3Kp5ZJ'
'yXBPgz2E/Qlw3DhsQQ0pOF+6pbUuf5oHHGScd1RW4i+J8fdlTjuSvH6t276C'
'G8dxxt10HKfb3qT3/xbVpxoQz9Hicn+j5qM016Npxos9/S3lL05eIdfK8kFZ'
'PozCf8fxrKyTu2v7nvk81L0jksrWwNC7Hmc9ruPozfQaoP4gzXVM0PXjH2g7'
'ZW/v37I3srEK5/Q/Sd2SzH+ZZizQ0oyzoPfRGsSAfYNYWEtfr3lxFtbuoP2l'
'iV7s1xz/6zy/h/U3qXw46/k+6X2ZdciD4+qTNbfp/UtTlxZU8KrbuQ66JL1e'
'yyMnxlEwJ8ZRMCfGUTCX9Xpknbk465gLcmf/5KufaekTlJef20v/iyXz8rxw'
'cfSxtWES5aqfWe4LMXHOl9ynyCMn5mWPq4PndYmThy/U7t88rsBx7JH/3EOW'
'+0KTD0mwjaHR8v7zwp2D53UK2nckN/9ClU6EkhDW4Mhnrx5iT+gef3/cp/va'
'2smL/oITnOAEJzjBCU5wgnueq/u4qHmjt66fpgl56q/gvk2uDpKJ86TsrLof'
'm5Lf5iX+BCc4wX07XN3Hif1SwQlOcIITnOAEt075XF64Onhep6TyTqG34AQn'
'OMEJTnCCE5zgBCc4wW0mV/dxq8gHB0WPK3BcwXle8B3/NaS9/r+z33XKKtT1'
'kUHY90gY8nDyZQemrBNFm357gHxhP236vkiA+RmF+Klzfurz/CANYop645Fh'
'e7MoGSoUqXLTqW3btcduLfN7H+L3hPN7Ms+v8yUA0z0A5P5zNbk1rb6aVHP/'
'2HNyPkL873P+9+f5V6cfrhhPG2DKzoctxlPP5qr8+T9ftBPgzx9vRaf85t3W'
'+3fg+XEGwGx8e3H/8Gdcv6XSVrEIno7T1yE80/EVeHpjdt4WFhtvv4L5x7t9'
'3qTj45ynAruB5fX1/JUe2+b62YTj/wfn4gog')
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(zlib.decompress(base64.b64decode(data))))
self.assertEqual(len(pipeline.modules()),5)
module = pipeline.modules()[-2]
self.assertTrue(isinstance(module, C.ClassifyObjects))
self.assertEqual(module.contrast_choice.value, C.BY_SINGLE_MEASUREMENT)
self.assertEqual(len(module.single_measurements), 2)
group = module.single_measurements[0]
self.assertEqual(group.object_name, "Nuclei")
self.assertEqual(group.measurement.value, "Intensity_IntegratedIntensity_OrigBlue")
self.assertEqual(group.bin_choice, C.BC_EVEN)
self.assertEqual(group.bin_count, 3)
self.assertAlmostEqual(group.low_threshold.value, 0.2)
self.assertTrue(group.wants_low_bin)
self.assertAlmostEqual(group.high_threshold.value, 0.8)
self.assertTrue(group.wants_high_bin)
self.assertTrue(group.wants_custom_names)
for name, expected in zip(group.bin_names.value.split(','),
('First','Second','Third','Fourth','Fifth')):
self.assertEqual(name, expected)
self.assertTrue(group.wants_images)
self.assertEqual(group.image_name, "ClassifiedNuclei")
group = module.single_measurements[1]
self.assertEqual(group.object_name, "Nuclei")
self.assertEqual(group.measurement.value, "Intensity_MaxIntensity_OrigBlue")
self.assertEqual(group.bin_choice, C.BC_CUSTOM)
self.assertEqual(group.custom_thresholds, ".2,.5,.8")
self.assertFalse(group.wants_custom_names)
self.assertFalse(group.wants_images)
module = pipeline.modules()[-1]
self.assertTrue(isinstance(module, C.ClassifyObjects))
self.assertEqual(module.contrast_choice, C.BY_TWO_MEASUREMENTS)
self.assertEqual(module.object_name, "Nuclei")
self.assertEqual(module.first_measurement, "Location_Center_X")
self.assertEqual(module.first_threshold_method, C.TM_MEDIAN)
self.assertEqual(module.second_measurement, "Location_Center_Y")
self.assertEqual(module.second_threshold_method, C.TM_CUSTOM)
self.assertAlmostEqual(module.second_threshold.value, .4)
def test_02_01_compare_to_matlab(self):
expected = {
'EC50_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
3.982812,
'EC50_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
4.139827,
'EC50_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
4.178600,
'EC50_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
4.059770,
'EC50_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
4.066357,
'EC50_DistCytoplasm_Math_Ratio1':
4.491367,
'EC50_DistCytoplasm_Math_Ratio2':
3.848722,
'EC50_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
4.948056,
'EC50_DistCytoplasm_Texture_Entropy_CorrGreen_1':
4.687104,
'EC50_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
5.0285,
'EC50_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
4.319017,
'EC50_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
4.548876,
'EC50_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
4.779139,
'EC50_DistCytoplasm_Texture_Variance_CorrGreen_1':
4.218379,
'EC50_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
3.708711,
'EC50_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
4.135146,
'EC50_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
4.5372,
'EC50_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
4.1371,
'EC50_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
4.033999,
'EC50_DistanceCells_Intensity_MinIntensity_CorrGreen':
4.079470,
'EC50_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
5.118689,
'EC50_DistanceCells_Texture_Correlation_CorrGreen_1':
4.002074,
'EC50_DistanceCells_Texture_Entropy_CorrGreen_1':
5.008000,
'EC50_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
3.883586,
'EC50_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
3.977216,
'EC50_DistanceCells_Texture_SumAverage_CorrGreen_1':
4.9741,
'EC50_DistanceCells_Texture_SumEntropy_CorrGreen_1':
5.1455,
'EC50_DistanceCells_Texture_SumVariance_CorrGreen_1':
4.593041,
'EC50_DistanceCells_Texture_Variance_CorrGreen_1':
4.619517,
'EC50_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
3.751133,
'EC50_Nuclei_Math_Ratio1':
4.491367,
'EC50_Nuclei_Math_Ratio2':
3.848722,
'EC50_Nuclei_Texture_SumAverage_CorrGreen_1':
3.765297,
'EC50_PropCells_AreaShape_Area':
4.740853,
'EC50_PropCells_AreaShape_MajorAxisLength':
5.064460,
'EC50_PropCells_AreaShape_MinorAxisLength':
4.751471,
'EC50_PropCells_AreaShape_Perimeter':
4.949292,
'EC50_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
3.772565,
'EC50_PropCells_Texture_GaborX_CorrGreen_1':
5.007167,
'EC50_PropCells_Texture_InfoMeas2_CorrBlue_1':
4.341353,
'EC50_PropCells_Texture_SumVariance_CorrBlue_1':
4.298359,
'EC50_PropCells_Texture_SumVariance_CorrGreen_1':
4.610826,
'EC50_PropCells_Texture_Variance_CorrBlue_1':
4.396352,
'EC50_PropCells_Texture_Variance_CorrGreen_1':
4.632468,
'EC50_PropCytoplasm_AreaShape_Area':
4.669679,
'EC50_PropCytoplasm_AreaShape_MinorAxisLength':
4.754476,
'EC50_PropCytoplasm_AreaShape_Perimeter':
4.949292,
'EC50_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
4.072830,
'EC50_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
4.0934,
'EC50_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
3.925800,
'EC50_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
3.9252,
'EC50_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
4.777481,
'EC50_PropCytoplasm_Texture_Entropy_CorrGreen_1':
4.4432,
'EC50_PropCytoplasm_Texture_GaborX_CorrGreen_1':
5.163371,
'EC50_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
4.701046,
'EC50_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
4.510543,
'EC50_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
4.560315,
'EC50_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
4.966674,
'EC50_ThresholdedCells_Texture_Entropy_CorrBlue_1':
4.457866,
'EC50_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
4.624049,
'EC50_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
4.686706,
'EC50_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
4.537378,
'EC50_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
4.322820,
'EC50_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
4.742158,
'EC50_ThresholdedCells_Texture_Variance_CorrBlue_1':
4.265549,
'EC50_ThresholdedCells_Texture_Variance_CorrGreen_1':
4.860020,
'OneTailedZfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Math_Ratio1':
0.622059,
'OneTailedZfactor_DistCytoplasm_Math_Ratio2':
-4.508284,
'OneTailedZfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
-4.645887,
'OneTailedZfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
-4.279118,
'OneTailedZfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
-4.765570,
'OneTailedZfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
-4.682335,
'OneTailedZfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
-4.415607,
'OneTailedZfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
-4.200105,
'OneTailedZfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
-4.316452,
'OneTailedZfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
-4.316452,
'OneTailedZfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.202500,
'OneTailedZfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
-4.404815,
'OneTailedZfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
-4.508513,
'OneTailedZfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
-4.225356,
'OneTailedZfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
-4.382768,
'OneTailedZfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.492125,
'OneTailedZfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.477360,
'OneTailedZfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.563780,
'OneTailedZfactor_Nuclei_Math_Ratio1':
0.622059,
'OneTailedZfactor_Nuclei_Math_Ratio2':
-4.508284,
'OneTailedZfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.426178,
'OneTailedZfactor_PropCells_AreaShape_Area':
-4.216674,
'OneTailedZfactor_PropCells_AreaShape_MajorAxisLength':
-4.119131,
'OneTailedZfactor_PropCells_AreaShape_MinorAxisLength':
-4.109793,
'OneTailedZfactor_PropCells_AreaShape_Perimeter':
-4.068050,
'OneTailedZfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.765440,
'OneTailedZfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.114982,
'OneTailedZfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.108409,
'OneTailedZfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.191251,
'OneTailedZfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.559865,
'OneTailedZfactor_PropCells_Texture_Variance_CorrBlue_1':
0.254078,
'OneTailedZfactor_PropCells_Texture_Variance_CorrGreen_1':
0.556108,
'OneTailedZfactor_PropCytoplasm_AreaShape_Area':
-4.223021,
'OneTailedZfactor_PropCytoplasm_AreaShape_MinorAxisLength':
-4.095632,
'OneTailedZfactor_PropCytoplasm_AreaShape_Perimeter':
-4.068050,
'OneTailedZfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
-4.194663,
'OneTailedZfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
-4.443338,
'OneTailedZfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.207265,
'OneTailedZfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
-4.297250,
'OneTailedZfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
-4.525324,
'OneTailedZfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.167795,
'OneTailedZfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.067560,
'OneTailedZfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.478527,
'OneTailedZfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.155119,
'OneTailedZfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.535907,
'OneTailedZfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.572801,
'OneTailedZfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.423454,
'OneTailedZfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.440500,
'Vfactor_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.500429,
'Vfactor_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.325675,
'Vfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.323524,
'Vfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
0.138487,
'Vfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
0.128157,
'Vfactor_DistCytoplasm_Math_Ratio1':
0.503610,
'Vfactor_DistCytoplasm_Math_Ratio2':
0.319610,
'Vfactor_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.522880,
'Vfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
0.504303,
'Vfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.289432,
'Vfactor_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
0.234123,
'Vfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
0.591687,
'Vfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.520356,
'Vfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
-0.007649,
'Vfactor_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
0.761198,
'Vfactor_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
0.234655,
'Vfactor_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
0.252240,
'Vfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
0.195125,
'Vfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
0.138299,
'Vfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
0.126784,
'Vfactor_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
0.342691,
'Vfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.314396,
'Vfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
0.311771,
'Vfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
0.410631,
'Vfactor_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
0.170576,
'Vfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
0.223147,
'Vfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
0.269519,
'Vfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.571528,
'Vfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.566272,
'Vfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.705051,
'Vfactor_Nuclei_Math_Ratio1':
0.503610,
'Vfactor_Nuclei_Math_Ratio2':
0.319610,
'Vfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.553708,
'Vfactor_PropCells_AreaShape_Area':
0.340093,
'Vfactor_PropCells_AreaShape_MajorAxisLength':
0.243838,
'Vfactor_PropCells_AreaShape_MinorAxisLength':
0.320691,
'Vfactor_PropCells_AreaShape_Perimeter':
0.238915,
'Vfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.723520,
'Vfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.213161,
'Vfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.199791,
'Vfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.078959,
'Vfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.642844,
'Vfactor_PropCells_Texture_Variance_CorrBlue_1':
0.199105,
'Vfactor_PropCells_Texture_Variance_CorrGreen_1':
0.640818,
'Vfactor_PropCytoplasm_AreaShape_Area':
0.325845,
'Vfactor_PropCytoplasm_AreaShape_MinorAxisLength':
0.312258,
'Vfactor_PropCytoplasm_AreaShape_Perimeter':
0.238915,
'Vfactor_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.337565,
'Vfactor_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
0.292900,
'Vfactor_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.175528,
'Vfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.193308,
'Vfactor_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.276152,
'Vfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
0.239567,
'Vfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.332380,
'Vfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.379141,
'Vfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.337740,
'Vfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
0.334520,
'Vfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
0.192882,
'Vfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.276245,
'Vfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.139166,
'Vfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.465237,
'Vfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.355399,
'Vfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.453937,
'Vfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.564371,
'Vfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.360566,
'Vfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.548770,
'Zfactor_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.531914,
'Zfactor_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.265558,
'Zfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.178586,
'Zfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
0.084566,
'Zfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
0.086476,
'Zfactor_DistCytoplasm_Math_Ratio1':
0.623284,
'Zfactor_DistCytoplasm_Math_Ratio2':
0.358916,
'Zfactor_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.429510,
'Zfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
0.508275,
'Zfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.068695,
'Zfactor_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
0.347949,
'Zfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
0.646576,
'Zfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.494276,
'Zfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
0.179011,
'Zfactor_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
0.824686,
'Zfactor_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
0.027644,
'Zfactor_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
0.088491,
'Zfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
0.065056,
'Zfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
0.089658,
'Zfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
0.078017,
'Zfactor_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
0.238131,
'Zfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.301107,
'Zfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
0.251143,
'Zfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
0.564957,
'Zfactor_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
0.302767,
'Zfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
0.036459,
'Zfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
0.159798,
'Zfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.516938,
'Zfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.501186,
'Zfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.691408,
'Zfactor_Nuclei_Math_Ratio1':
0.623284,
'Zfactor_Nuclei_Math_Ratio2':
0.358916,
'Zfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.587347,
'Zfactor_PropCells_AreaShape_Area':
0.132425,
'Zfactor_PropCells_AreaShape_MajorAxisLength':
0.034809,
'Zfactor_PropCells_AreaShape_MinorAxisLength':
0.113864,
'Zfactor_PropCells_AreaShape_Perimeter':
0.005984,
'Zfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.717632,
'Zfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.251023,
'Zfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.149719,
'Zfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.102050,
'Zfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.611960,
'Zfactor_PropCells_Texture_Variance_CorrBlue_1':
0.197090,
'Zfactor_PropCells_Texture_Variance_CorrGreen_1':
0.614879,
'Zfactor_PropCytoplasm_AreaShape_Area':
0.205042,
'Zfactor_PropCytoplasm_AreaShape_MinorAxisLength':
0.072682,
'Zfactor_PropCytoplasm_AreaShape_Perimeter':
0.005984,
'Zfactor_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.272017,
'Zfactor_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
0.115327,
'Zfactor_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.141850,
'Zfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.105803,
'Zfactor_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.107640,
'Zfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
0.067896,
'Zfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.136688,
'Zfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.334749,
'Zfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.208829,
'Zfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
0.263467,
'Zfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
0.124355,
'Zfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.236433,
'Zfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.125845,
'Zfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.449333,
'Zfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.323243,
'Zfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.507477,
'Zfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.599000,
'Zfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.361424,
'Zfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.481393
}
temp_dir = tempfile.mkdtemp()
try:
cpprefs.set_headless()
cpprefs.set_default_output_directory(temp_dir)
print "Writing output to %s" % temp_dir
path = os.path.split(__file__)[0]
measurements = loadmat(os.path.join(path,
'calculatestatistics.mat'),
struct_as_record=True)
measurements = measurements['m']
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
m = cpmeas.Measurements()
doses = [
0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 10,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 0, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0
]
for i, dose in enumerate(doses):
m.add_image_measurement("Dose", dose)
for object_name in measurements.dtype.fields:
omeasurements = measurements[object_name][0, 0]
for feature_name in omeasurements.dtype.fields:
data = omeasurements[feature_name][0, 0][0, i]
m.add_measurement(object_name, feature_name, data)
if i < len(doses) - 1:
m.next_image_set()
pipeline = cpp.Pipeline()
module = C.CalculateStatistics()
module.grouping_values.value = "Dose"
module.dose_values[0].log_transform.value = False
module.dose_values[0].measurement.value = "Dose"
module.dose_values[0].wants_save_figure.value = True
module.dose_values[0].figure_name.value = "EC49_"
module.module_num = 1
pipeline.add_module(module)
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
workspace = cpw.Workspace(pipeline, module, image_set,
cpo.ObjectSet(), m, image_set_list)
module.post_run(workspace)
for feature_name in m.get_feature_names(cpmeas.EXPERIMENT):
if not expected.has_key(feature_name):
print "Missing measurement: %s" % feature_name
continue
value = m.get_experiment_measurement(feature_name)
e_value = expected[feature_name]
diff = abs(value - e_value) * 2 / abs(value + e_value)
self.assertTrue(
diff < .05, "%s: Matlab: %f, Python: %f diff: %f" %
(feature_name, e_value, value, diff))
if diff > .01:
print(
"Warning: > 1%% difference for %s: Matlab: %f, Python: %f diff: %f"
% (feature_name, e_value, value, diff))
if feature_name.startswith("EC50"):
filename = "EC49_" + feature_name[5:] + ".pdf"
self.assertTrue(
os.path.isfile(os.path.join(temp_dir, filename)))
finally:
try:
workspace.measurements.hdf5_dict.hdf5_file.close()
except:
pass
for filename in os.listdir(temp_dir):
path = os.path.join(temp_dir, filename)
os.remove(path)
os.rmdir(temp_dir)
def test_2_5_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:10308
ColorToGray:[module_num:1|svn_version:\'10300\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D]
Select the input image:DNA
Conversion method:Combine
Image type\x3A:Channels
Name the output image:OrigGrayw
Relative weight of the red channel:1
Relative weight of the green channel:3
Relative weight of the blue channel:5
Convert red to gray?:Yes
Name the output image:OrigRedx
Convert green to gray?:Yes
Name the output image:OrigGreeny
Convert blue to gray?:Yes
Name the output image:OrigBluez
Channel count:3
Channel number\x3A:Red\x3A 1
Relative weight of the channel:1
Image name\x3A:RedChannel1
Channel number\x3A:Blue\x3A 3
Relative weight of the channel:2
Image name\x3A:GreenChannel2
Channel number\x3A:Green\x3A 2
Relative weight of the channel:3
Image name\x3A:BlueChannel3
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 1)
module = pipeline.modules()[0]
self.assertTrue(isinstance(module, cpm_ctg.ColorToGray))
self.assertEqual(module.image_name, "DNA")
self.assertEqual(module.combine_or_split, cpm_ctg.COMBINE)
self.assertEqual(module.rgb_or_channels, cpm_ctg.CH_CHANNELS)
self.assertEqual(module.grayscale_name, "OrigGrayw")
self.assertEqual(module.red_contribution, 1)
self.assertEqual(module.green_contribution, 3)
self.assertEqual(module.blue_contribution, 5)
self.assertTrue(module.use_red)
self.assertTrue(module.use_green)
self.assertTrue(module.use_blue)
self.assertEqual(module.red_name, "OrigRedx")
self.assertEqual(module.green_name, "OrigGreeny")
self.assertEqual(module.blue_name, "OrigBluez")
self.assertEqual(module.channel_count.value, 3)
self.assertEqual(module.channels[0].channel_choice,
module.channel_names[0])
self.assertEqual(module.channels[1].channel_choice,
module.channel_names[2])
self.assertEqual(module.channels[2].channel_choice,
module.channel_names[1])
self.assertEqual(module.channels[0].contribution, 1)
self.assertEqual(module.channels[1].contribution, 2)
self.assertEqual(module.channels[2].contribution, 3)
self.assertEqual(module.channels[0].image_name, "RedChannel1")
self.assertEqual(module.channels[1].image_name, "GreenChannel2")
self.assertEqual(module.channels[2].image_name, "BlueChannel3")
def test_01_03_load_v2(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:1
SVNRevision:8925
LoadImages:[module_num:1|svn_version:\'8913\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D]
What type of files are you loading?:individual images
How do you want to load these files?:Text-Exact match
How many images are there in each group?:3
Type the text that the excluded images have in common:Do not use
Analyze all subfolders within the selected folder?:No
Image location:Default Image Folder
Enter the full path to the images:
Do you want to check image sets for missing or duplicate files?:Yes
Do you want to group image sets by metadata?:No
Do you want to exclude certain files?:No
What metadata fields do you want to group by?:
Type the text that these images have in common (case-sensitive):hoe
What do you want to call this image in CellProfiler?:Cytoplasm
What is the position of this image in each group?:1
Do you want to extract metadata from the file name, the subfolder path or both?:None
Type the regular expression that finds metadata in the file name\x3A:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
Type the regular expression that finds metadata in the subfolder path\x3A:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
Type the text that these images have in common (case-sensitive):ax2
What do you want to call this image in CellProfiler?:Speckles
What is the position of this image in each group?:2
Do you want to extract metadata from the file name, the subfolder path or both?:None
Type the regular expression that finds metadata in the file name\x3A:^(?P<Plate>.*)_(?P<Well>\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P<Site>\x5B0-9\x5D)
Type the regular expression that finds metadata in the subfolder path\x3A:.*\x5B\\\\/\x5D(?P<Date>.*)\x5B\\\\/\x5D(?P<Run>.*)$
Smooth:[module_num:2|svn_version:\'8664\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select the input image:Cytoplasm
Name the output image:SmoothedCytoplasm
Select smoothing method\x3A:Smooth Keeping Edges
Calculate object size automatically?:No
Size of objects\x3A:100.0
Edge intensity difference\x3A:0.1
Morph:[module_num:3|svn_version:\'8780\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select input image\x3A:SmoothedCytoplasm
Name the output image\x3A:DNA
Select operation to perform\x3A:erode
Repeat operation\x3A:Custom
Custom # of repeats:20
IdentifyPrimAutomatic:[module_num:4|svn_version:\'8826\'|variable_revision_number:4|show_window:False|notes:\x5B\x5D]
Select the input image:DNA
Name the identified primary objects:Nuclei
Typical diameter of objects, in pixel units (Min,Max)\x3A:50,400
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:Yes
Discard objects touching the border of the image?:Yes
Select the thresholding method:Otsu Global
Threshold correction factor:1.0
Lower and upper bounds on threshold\x3A:0.000000,1.000000
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter\x3A:10
Suppress local maxima within this distance\x3A:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:PrimaryOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum size of local maxima?:Yes
Enter manual threshold\x3A:0.0
Select binary image\x3A:None
Save outlines of the identified objects?:No
Calculate the Laplacian of Gaussian threshold automatically?:Yes
Enter Laplacian of Gaussian threshold\x3A:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter\x3A :5
IdentifySecondary:[module_num:5|svn_version:\'8826\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D]
Select the input objects:Nuclei
Name the identified objects:Cells
Select the method to identify the secondary objects:Propagation
Select the input image:Cytoplasm
Select the thresholding method:Otsu Global
Threshold correction factor:1.0
Lower and upper bounds on threshold\x3A:0.000000,1.000000
Approximate fraction of image covered by objects?:0.01
Number of pixels by which to expand the primary objects\x3A:10
Regularization factor\x3A:0.05
Name the outline image:SecondaryOutlines
Enter manual threshold\x3A:0.0
Select binary image\x3A:None
Save outlines of the identified objects?:No
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Do you want to discard objects that touch the edge of the image?:No
Do you want to discard associated primary objects?:No
New primary objects name\x3A:FilteredNuclei
IdentifyTertiarySubregion:[module_num:6|svn_version:\'8886\'|variable_revision_number:1|show_window:False|notes:\x5B\x5D]
Select the larger identified objects:Cells
Select the smaller identified objects:Nuclei
Name the identified subregion objects:Cytoplasm
Name the outline image:CytoplasmOutlines
Retain the outlines for use later in the pipeline (for example, in SaveImages)?:No
IdentifyPrimAutomatic:[module_num:7|svn_version:\'8826\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D]
Select the input image:Speckles
Name the identified primary objects:Speckles
Typical diameter of objects, in pixel units (Min,Max)\x3A:4,30
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:No
Discard objects touching the border of the image?:Yes
Select the thresholding method:Otsu Global
Threshold correction factor:1.0
Lower and upper bounds on threshold\x3A:0.000000,1.000000
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter\x3A:10
Suppress local maxima within this distance\x3A:7
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:PrimaryOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum size of local maxima?:Yes
Enter manual threshold\x3A:0.0
Select binary image\x3A:None
Save outlines of the identified objects?:No
Calculate the Laplacian of Gaussian threshold automatically?:Yes
Enter Laplacian of Gaussian threshold\x3A:0.5
Two-class or three-class thresholding?:Three classes
Minimize the weighted variance or the entropy?:Weighted variance
Assign pixels in the middle intensity class to the foreground or the background?:Background
Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes
Enter LoG filter diameter\x3A :5
Relate:[module_num:8|svn_version:\'8866\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D]
Select the input child objects:Speckles
Select the input parent objects:Cells
Find distances?:Both
Calculate per-parent means for all child measurements?:No
Find distances to other parents?:Yes
Parent name\x3A:Cytoplasm
Parent name\x3A:Nuclei
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO(data))
self.assertEqual(len(pipeline.modules()), 8)
module = pipeline.modules()[-1]
self.assertTrue(isinstance(module, R.Relate))
self.assertEqual(module.sub_object_name, "Speckles")
self.assertEqual(module.parent_name, "Cells")
self.assertEqual(module.find_parent_child_distances, R.D_BOTH)
self.assertFalse(module.wants_per_parent_means)
self.assertTrue(module.wants_step_parent_distances)
self.assertEqual(len(module.step_parent_names), 2)
for group, expected in zip(module.step_parent_names,
("Cytoplasm", "Nuclei")):
self.assertEqual(group.step_parent_name, expected)
def test_01_04_load_v3(self):
data = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20130402163959
ModuleCount:1
HasImagePlaneDetails:False
Morph:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Select the input image:Thresh
Name the output image:MorphedThresh
Select the operation to perform:dilate
Number of times to repeat operation:Once
Repetition number:2
Scale:7.0
Structuring element:Custom
X offset:1
Y offset:1
Angle:45
Width:3
Height:3
Custom:5,7,11111110000000011111000000001111111
Select the operation to perform:close
Number of times to repeat operation:Once
Repetition number:2
Scale:3
Structuring element:Diamond
X offset:1
Y offset:1
Angle:0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
Select the operation to perform:open
Number of times to repeat operation:Once
Repetition number:2
Scale:4.0
Structuring element:Line
X offset:1
Y offset:1
Angle:60.0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
Select the operation to perform:erode
Number of times to repeat operation:Once
Repetition number:2
Scale:3
Structuring element:Octagon
X offset:1
Y offset:1
Angle:0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
Select the operation to perform:close
Number of times to repeat operation:Once
Repetition number:2
Scale:3
Structuring element:Pair
X offset:4.0
Y offset:2.0
Angle:0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
Select the operation to perform:dilate
Number of times to repeat operation:Once
Repetition number:2
Scale:15.0
Structuring element:Periodic line
X offset:-1.0
Y offset:-3.0
Angle:0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
Select the operation to perform:erode
Number of times to repeat operation:Once
Repetition number:2
Scale:3
Structuring element:Rectangle
X offset:1
Y offset:1
Angle:0
Width:5.0
Height:8.0
Custom:5,5,1111111111111111111111111
Select the operation to perform:open
Number of times to repeat operation:Once
Repetition number:2
Scale:9.0
Structuring element:Square
X offset:1
Y offset:1
Angle:0
Width:3
Height:3
Custom:5,5,1111111111111111111111111
"""
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(StringIO.StringIO(data))
module = pipeline.modules()[0]
assert isinstance(module, morph.Morph)
self.assertEqual(module.image_name, "Thresh")
self.assertEqual(module.output_image_name, "MorphedThresh")
self.assertEqual(len(module.functions), 8)
f0 = module.functions[0]
matrix = f0.strel.get_matrix()
self.assertEqual(len(matrix), 5)
self.assertEqual(len(matrix[0]), 7)
expected = [[1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1]]
np.testing.assert_array_equal(matrix, np.array(expected, bool))
for f, se in zip(
module.functions,
(morph.SE_ARBITRARY, morph.SE_DIAMOND, morph.SE_LINE,
morph.SE_OCTAGON, morph.SE_PAIR, morph.SE_PERIODIC_LINE,
morph.SE_RECTANGLE, morph.SE_SQUARE)):
self.assertEqual(f.structuring_element, se)
if se == morph.SE_LINE:
self.assertEqual(f.angle, 60)
self.assertEqual(f.scale, 4.0)
elif se == morph.SE_PERIODIC_LINE:
self.assertEqual(f.x_offset, -1)
self.assertEqual(f.y_offset, -3)
elif se == morph.SE_RECTANGLE:
self.assertEqual(f.width, 5)
self.assertEqual(f.height, 8)
def test_03_03_run_missing_measurement(self):
# Regression test of knime-bridge issue #6
#
# Missing measurement causes exception
#
pipeline = cpp.Pipeline()
load_images = LoadImages()
load_images.module_num = 1
load_images.images[0].channels[0].image_name.value = "Foo"
pipeline.add_module(load_images)
identify = IdentifyPrimaryObjects()
identify.module_num = 2
identify.use_advanced.value = True
identify.image_name.value = "Foo"
identify.object_name.value = "dizzy"
identify.threshold_scope.value = TS_GLOBAL
identify.global_operation.value = TM_MANUAL
identify.manual_threshold.value = .5
identify.exclude_size.value = False
pipeline.add_module(identify)
flag_module = FlagImage()
flag_module.module_num = 3
flag = flag_module.flags[0]
flag.wants_skip.value = True
criterion = flag.measurement_settings[0]
criterion.source_choice.value = S_IMAGE
criterion.measurement.value = "Count_dizzy"
criterion.wants_minimum.value = True
criterion.minimum_value.value = 1000
pipeline.add_module(flag_module)
measureobjectsizeshape = MeasureObjectSizeShape()
measureobjectsizeshape.module_num = 4
measureobjectsizeshape.object_groups[0].name.value = "dizzy"
pipeline.add_module(measureobjectsizeshape)
pipeline_txt = StringIO()
pipeline.savetxt(pipeline_txt)
image = np.zeros((11, 17))
image[2:-2, 2:-2] = 1
image_metadata = [[
"Foo",
[["Y", image.shape[0], image.strides[0] / 8],
["X", image.shape[1], image.strides[1] / 8]]
]]
message = [
zmq.Frame(self.session_id),
zmq.Frame(),
zmq.Frame(RUN_REQ_1),
zmq.Frame(pipeline_txt.getvalue()),
zmq.Frame(json.dumps(image_metadata)),
zmq.Frame(image)
]
self.socket.send_multipart(message)
response = self.socket.recv_multipart()
self.assertEqual(response.pop(0), self.session_id)
self.assertEqual(response.pop(0), "")
self.assertEqual(response.pop(0), RUN_REPLY_1)
metadata = json.loads(response.pop(0))
data = response.pop(0)
measurements = self.decode_measurements(metadata, data)
self.assertEqual(measurements[cpmeas.IMAGE]["Count_dizzy"][0], 1)
self.assertEqual(measurements["dizzy"]["Location_Center_Y"][0], 5)
self.assertEqual(len(measurements["dizzy"]["AreaShape_Area"]), 0)
