def setUpClass(cls):
from cellprofiler.modules.tests \
import example_images_directory, maybe_download_sbs
maybe_download_sbs()
cls.root_dir = example_images_directory()
J.attach()
def test_03_03_measurement_columns(self):
'''Check that results of get_measurement_columns match the actual column names output'''
data = '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'
# alternate data: 1 image, 1 object, tex scale=3, gabor angles=6
# data = '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'
maybe_download_sbs()
cpprefs.set_default_image_directory(
os.path.join(example_images_directory(), "ExampleSBSImages"))
fd = StringIO(zlib.decompress(base64.b64decode(data)))
pipeline = cpp.Pipeline()
pipeline.load(fd)
module = pipeline.modules()[3]
measurements = pipeline.run(image_set_end=1)
for x in module.get_measurement_columns(pipeline):
assert x[1] in measurements.get_feature_names(
x[0]
), '%s does not match any measurement output by pipeline' % (
str(x))
for obname in measurements.get_object_names():
for m in measurements.get_feature_names(obname):
if m.startswith(M.TEXTURE):
assert (
obname, m, 'float'
) in module.get_measurement_columns(
pipeline
), 'no entry matching %s in get_measurement_columns.' % (
(obname, m, 'float'))
def setUpClass(cls):
from cellprofiler.modules.tests \
import example_images_directory, maybe_download_sbs
maybe_download_sbs()
cls.root_dir = example_images_directory()
J.attach()
def setUpClass(cls):
maybe_download_sbs()
cls.test_filename = "1-162hrh2ax2.tif"
cls.test_folder = "loadsingleimage"
cls.test_shape = (27, 18)
path = make_12_bit_image(cls.test_folder, cls.test_filename, (27, 18))
cls.test_path = os.path.dirname(path)
with open(path, "rb") as fd:
cls.test_md5 = hashlib.md5(fd.read()).hexdigest()
def setUpClass(cls):
maybe_download_sbs()
cls.test_filename = "1-162hrh2ax2.tif"
cls.test_folder = "loadsingleimage"
cls.test_shape = (27, 18)
path = make_12_bit_image(cls.test_folder, cls.test_filename, (27, 18))
cls.test_path = os.path.dirname(path)
with open(path, "rb") as fd:
cls.test_md5 = hashlib.md5(fd.read()).hexdigest()
def setUpClass(cls):
maybe_download_sbs()
cls.test_folder = "loaddata"
cls.test_path = os.path.join(example_images_directory(),
cls.test_folder)
cls.test_filename = "image.tif"
cls.test_shape = (13, 15)
path = maybe_download_example_image([cls.test_folder],
cls.test_filename,
shape=cls.test_shape)
with open(path, "rb") as fd:
cls.test_md5 = hashlib.md5(fd.read()).hexdigest()
def test_01_05_load_pipeline(self):
import cellprofiler.pipeline as cpp
import os
maybe_download_sbs()
pipeline_file = os.path.join(self.example_dir(),
"ExampleSBSImages", "ExampleSBS.cppipe")
self.assertTrue(os.path.isfile(pipeline_file), "No ExampleSBS.cppipe file")
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(pipeline_file)
def setUpClass(cls):
cls.zmq_context = cpaw.the_zmq_context
cls.notify_pub_socket = cpaw.get_the_notify_pub_socket()
#
# Install a bogus display_post_group method in FlipAndRotate
# to elicit a post-group interaction request
#
from cellprofiler.modules.flipandrotate import FlipAndRotate
def bogus_display_post_group(self, workspace, figure):
pass
FlipAndRotate.display_post_group = bogus_display_post_group
maybe_download_sbs()
def setUpClass(cls):
maybe_download_sbs()
cls.test_folder = "loaddata"
cls.test_path = os.path.join(
example_images_directory(), cls.test_folder)
cls.test_filename = "image.tif"
cls.test_shape = (13, 15)
path = maybe_download_example_image([cls.test_folder],
cls.test_filename,
shape=cls.test_shape)
with open(path, "rb") as fd:
cls.test_md5 = hashlib.md5(fd.read()).hexdigest()
def test_01_05_load_pipeline(self):
import cellprofiler.pipeline as cpp
import os
maybe_download_sbs()
pipeline_file = os.path.join(self.example_dir(), "ExampleSBSImages",
"ExampleSBS.cppipe")
self.assertTrue(os.path.isfile(pipeline_file),
"No ExampleSBS.cppipe file")
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
pipeline.add_listener(callback)
pipeline.load(pipeline_file)
def test_13_06_load_default_input_folder(self):
# Regression test of issue #1365 - load a file from the default
# input folder and check that PathName_xxx is absolute
path = maybe_download_sbs()
root_dir, path_name = os.path.split(path)
file_name = 'Channel2-01-A-01.tif'
csv_text = '''"Image_FileName_DNA","Image_PathName_DNA"\n"%s","%s"''' \
% (file_name, path_name)
pipeline, module, filename = self.make_pipeline(csv_text)
try:
assert isinstance(module, L.LoadData)
module.image_directory.dir_choice = cps.ABSOLUTE_FOLDER_NAME
module.image_directory.custom_path = root_dir
m = cpmeas.Measurements(mode="memory")
workspace = cpw.Workspace(pipeline, module, m, cpo.ObjectSet(),
m, cpi.ImageSetList())
self.assertTrue(module.prepare_run(workspace))
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "FileName_DNA", 1),
file_name)
path_out = m.get_measurement(cpmeas.IMAGE, "PathName_DNA", 1)
self.assertEqual(path, path_out)
self.assertEqual(m.get_measurement(cpmeas.IMAGE, "URL_DNA", 1),
L.pathname2url(os.path.join(path, file_name)))
module.prepare_group(workspace, {}, [1])
module.run(workspace)
img = workspace.image_set.get_image("DNA", must_be_grayscale=True)
self.assertEqual(tuple(img.pixel_data.shape), (640, 640))
finally:
os.remove(filename)
def test_02_03_prepare_run(self):
# regression test for issue #673 and #1161
#
# If LoadSingleImage appears first, pathname data does not show
# up in the measurements.
#
maybe_download_sbs()
folder = "ExampleSBSImages"
path = os.path.join(example_images_directory(), folder)
filename = "Channel1-01-A-01.tif"
pipeline = cpp.Pipeline()
lsi = L.LoadSingleImage()
lsi.module_num = 1
lsi.directory.dir_choice = cps.ABSOLUTE_FOLDER_NAME
lsi.directory.custom_path = path
lsi.file_settings[0].image_name.value = self.get_image_name(0)
lsi.file_settings[0].file_name.value = filename
pipeline.add_module(lsi)
li = LI.LoadImages()
li.module_num = 2
pipeline.add_module(li)
li.match_method.value = LI.MS_EXACT_MATCH
li.location.dir_choice = cps.ABSOLUTE_FOLDER_NAME
li.location.custom_path = path
li.images[0].common_text.value = "Channel2-"
m = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, lsi, m, cpo.ObjectSet(), m,
cpi.ImageSetList())
self.assertTrue(pipeline.prepare_run(workspace))
self.assertGreater(m.image_set_count, 1)
pipeline.prepare_group(workspace, {}, m.get_image_numbers())
#
# Skip to the second image set
#
m.next_image_set(2)
lsi.run(workspace)
#
# Are the measurements populated?
#
m_file = "_".join((cpmeas.C_FILE_NAME, self.get_image_name(0)))
self.assertEqual(m[cpmeas.IMAGE, m_file, 2], filename)
#
# Can we retrieve the image?
#
pixel_data = m.get_image(self.get_image_name(0)).pixel_data
self.assertFalse(np.isscalar(pixel_data))
def test_02_03_prepare_run(self):
# regression test for issue #673 and #1161
#
# If LoadSingleImage appears first, pathname data does not show
# up in the measurements.
#
maybe_download_sbs()
folder = "ExampleSBSImages"
path = os.path.join(example_images_directory(), folder)
filename = "Channel1-01-A-01.tif"
pipeline = cpp.Pipeline()
lsi = L.LoadSingleImage()
lsi.module_num = 1
lsi.directory.dir_choice = cps.ABSOLUTE_FOLDER_NAME
lsi.directory.custom_path = path
lsi.file_settings[0].image_name.value = self.get_image_name(0)
lsi.file_settings[0].file_name.value = filename
pipeline.add_module(lsi)
li = LI.LoadImages()
li.module_num = 2
pipeline.add_module(li)
li.match_method.value = LI.MS_EXACT_MATCH
li.location.dir_choice = cps.ABSOLUTE_FOLDER_NAME
li.location.custom_path = path
li.images[0].common_text.value = "Channel2-"
m = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, lsi, m, cpo.ObjectSet(), m,
cpi.ImageSetList())
self.assertTrue(pipeline.prepare_run(workspace))
self.assertGreater(m.image_set_count, 1)
pipeline.prepare_group(workspace, {}, m.get_image_numbers())
#
# Skip to the second image set
#
m.next_image_set(2)
lsi.run(workspace)
#
# Are the measurements populated?
#
m_file = "_".join((cpmeas.C_FILE_NAME, self.get_image_name(0)))
self.assertEqual(m[cpmeas.IMAGE, m_file,2], filename)
#
# Can we retrieve the image?
#
pixel_data = m.get_image(self.get_image_name(0)).pixel_data
self.assertEqual(tuple(pixel_data.shape), (640, 640))
def test_03_03_measurement_columns(self):
'''Check that results of get_measurement_columns match the actual column names output'''
data = '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'
# alternate data: 1 image, 1 object, tex scale=3, gabor angles=6
# data = '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'
maybe_download_sbs()
cpprefs.set_default_image_directory(
os.path.join(example_images_directory(),"ExampleSBSImages"))
fd = StringIO(zlib.decompress(base64.b64decode(data)))
pipeline = cpp.Pipeline()
pipeline.load(fd)
module = pipeline.modules()[3]
measurements = pipeline.run(image_set_end=1)
for x in module.get_measurement_columns(pipeline):
assert x[1] in measurements.get_feature_names(x[0]), '%s does not match any measurement output by pipeline'%(str(x))
for obname in measurements.get_object_names():
for m in measurements.get_feature_names(obname):
if m.startswith(M.TEXTURE):
assert (obname, m, 'float') in module.get_measurement_columns(pipeline), 'no entry matching %s in get_measurement_columns.'%((obname, m, 'float'))
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))
T.maybe_download_sbs()
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(mode="memory")
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)
self.assertTrue(
measurements.has_feature(cpmeas.EXPERIMENT,
cpp.M_PIPELINE))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
image_set_list = cpi.ImageSetList()
measurements = cpmeas.Measurements(mode="memory")
workspace = cpw.Workspace(pipeline, None, None, None,
cpmeas.Measurements(),
image_set_list)
workspace.load(hfile, True)
measurements = workspace.measurements
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)])
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')
measurements.close()
finally:
if os.path.exists(bfile):
os.unlink(bfile)
if os.path.exists(hfile):
os.unlink(hfile)
os.rmdir(bpath)
def test_05_04_convert_objects(self):
pipeline_text = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20120831182904
ModuleCount:2
HasImagePlaneDetails:False
LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B"Load the images by matching files in the folder against the unique text pattern for each stain\x3A \'Channel1-\' for nuclei, \'Channel2-\' for the GFP image. The two images together comprise an image set."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
File type to be loaded:individual images
File selection method:Text-Exact match
Number of images in each group?:3
Type the text that the excluded images have in common:Do not use
Analyze all subfolders within the selected folder?:None
Input image file location:Default Input Folder\x7C
Check image sets for unmatched or duplicate files?:Yes
Group images by metadata?:No
Exclude certain files?:No
Specify metadata fields to group by:
Select subfolders to analyze:
Image count:2
Text that these images have in common (case-sensitive):Channel1-
Position of this image in each group:1
Extract metadata from where?:File name
Regular expression that finds metadata in the file name:.*-(?P<ImageNumber>\\\\d*)-(?P<Row>.*)-(?P<Column>\\\\d*)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P<Date>.*)\x5B\\\\\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:2
Load the input as images or objects?:Images
Name this loaded image:rawGFP
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:NucleiOutlines
Channel number:1
Rescale intensities?:Yes
Text that these images have in common (case-sensitive):Channel2-
Position of this image in each group:2
Extract metadata from where?:File name
Regular expression that finds metadata in the file name:.*-(?P<ImageNumber>\\\\d*)-(?P<Row>.*)-(?P<Column>\\\\d*)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P<Date>.*)\x5B\\\\\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:3
Load the input as images or objects?:Images
Name this loaded image:DNA
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:LoadedImageOutlines
Channel number:1
Rescale intensities?:Yes
LoadSingleImage:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:5|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Input image file location:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):Channel1ILLUM.mat
Load as images or objects?:Objects
Name the image that will be loaded:IllumGFP
Name this loaded object:MyObjects
Retain outlines of loaded objects?:No
Name the outlines:NucleiOutlines
Rescale intensities?:No
"""
maybe_download_sbs()
directory = os.path.join(example_images_directory(),
"ExampleSBSImages")
self.convtester(pipeline_text, directory)
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))
T.maybe_download_sbs()
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(mode="memory")
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)
self.assertTrue(measurements.has_feature(
cpmeas.EXPERIMENT, cpp.M_PIPELINE))
pipeline = cpp.Pipeline()
pipeline.add_listener(callback)
image_set_list = cpi.ImageSetList()
measurements = cpmeas.Measurements(mode="memory")
workspace = cpw.Workspace(pipeline, None, None, None,
cpmeas.Measurements(),
image_set_list)
workspace.load(hfile, True)
measurements = workspace.measurements
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)])
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')
measurements.close()
finally:
if os.path.exists(bfile):
os.unlink(bfile)
if os.path.exists(hfile):
os.unlink(hfile)
os.rmdir(bpath)
def setUpClass(cls):
maybe_download_sbs()
def test_05_04_convert_objects(self):
pipeline_text = r"""CellProfiler Pipeline: http://www.cellprofiler.org
Version:3
DateRevision:20120831182904
ModuleCount:2
HasImagePlaneDetails:False
LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B"Load the images by matching files in the folder against the unique text pattern for each stain\x3A \'Channel1-\' for nuclei, \'Channel2-\' for the GFP image. The two images together comprise an image set."\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
File type to be loaded:individual images
File selection method:Text-Exact match
Number of images in each group?:3
Type the text that the excluded images have in common:Do not use
Analyze all subfolders within the selected folder?:None
Input image file location:Default Input Folder\x7C
Check image sets for unmatched or duplicate files?:Yes
Group images by metadata?:No
Exclude certain files?:No
Specify metadata fields to group by:
Select subfolders to analyze:
Image count:2
Text that these images have in common (case-sensitive):Channel1-
Position of this image in each group:1
Extract metadata from where?:File name
Regular expression that finds metadata in the file name:.*-(?P<ImageNumber>\\\\d*)-(?P<Row>.*)-(?P<Column>\\\\d*)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P<Date>.*)\x5B\\\\\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:2
Load the input as images or objects?:Images
Name this loaded image:rawGFP
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:NucleiOutlines
Channel number:1
Rescale intensities?:Yes
Text that these images have in common (case-sensitive):Channel2-
Position of this image in each group:2
Extract metadata from where?:File name
Regular expression that finds metadata in the file name:.*-(?P<ImageNumber>\\\\d*)-(?P<Row>.*)-(?P<Column>\\\\d*)
Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\\\\\/\x5D(?P<Date>.*)\x5B\\\\\\\\/\x5D(?P<Run>.*)$
Channel count:1
Group the movie frames?:No
Grouping method:Interleaved
Number of channels per group:3
Load the input as images or objects?:Images
Name this loaded image:DNA
Name this loaded object:Nuclei
Retain outlines of loaded objects?:No
Name the outline image:LoadedImageOutlines
Channel number:1
Rescale intensities?:Yes
LoadSingleImage:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:5|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True]
Input image file location:Default Input Folder\x7CNone
Filename of the image to load (Include the extension, e.g., .tif):Channel1ILLUM.mat
Load as images or objects?:Objects
Name the image that will be loaded:IllumGFP
Name this loaded object:MyObjects
Retain outlines of loaded objects?:No
Name the outlines:NucleiOutlines
Rescale intensities?:No
"""
maybe_download_sbs()
directory = os.path.join(example_images_directory(),
"ExampleSBSImages")
self.convtester(pipeline_text, directory)
def setUpClass(cls):
maybe_download_sbs()
