def create_settings(self):
"""Create the settings during initialization
"""
self.directory = cps.DirectoryPath(
"Input image file location",
support_urls=True,
doc="""\
Choose the folder containing the image(s) to be loaded. Generally, it is
best to store the image you want to load in either the Default Input or
Output Folder, so that the correct image is loaded into the pipeline and
typos are avoided.
{IO_FOLDER_CHOICE_HELP_TEXT}
{IO_WITH_METADATA_HELP_TEXT}
""".format(
**{
"IO_FOLDER_CHOICE_HELP_TEXT": IO_FOLDER_CHOICE_HELP_TEXT,
"IO_WITH_METADATA_HELP_TEXT": IO_WITH_METADATA_HELP_TEXT
}))
self.file_settings = []
self.add_file(can_remove=False)
self.add_button = cps.DoSomething("", "Add another image",
self.add_file)
def test_04_05_alter_absolute_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.ABSOLUTE_FOLDER_NAME
s.custom_path = os.path.join(self.root_directory, "..", "1", "2")
s.alter_for_create_batch_files(TestDirectoryPath.fn_alter_path)
self.assertEqual(s.get_absolute_path(),
os.path.join(self.root_directory, "2altered"))
def test_02_04_get_output_subfolder_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.DEFAULT_OUTPUT_SUBFOLDER_NAME
s.custom_path = "0"
self.assertEqual(
s.get_absolute_path(),
os.path.join(cpprefs.get_default_output_directory(), "0"))
def add_dose_value(self, can_remove=True):
'''Add a dose value measurement to the list
can_delete - set this to False to keep from showing the "remove"
button for images that must be present.'''
group = cps.SettingsGroup()
group.append("measurement",
cps.Measurement("Select the image measurement describing the treatment dose",
lambda: cpmeas.IMAGE,
doc="""\
The V and Z’ factors, metrics of assay quality, and the EC50,
indicating dose-response, are calculated by this module based on each
image being specified as a particular treatment dose. Choose a
measurement that gives the dose of some treatment for each of your
images. See the help for the previous setting for details."""))
group.append("log_transform", cps.Binary(
"Log-transform the dose values?", False, doc='''\
Select *%(YES)s* if you have dose-response data and you want to
log-transform the dose values before fitting a sigmoid curve.
Select *%(NO)s* if your data values indicate only positive vs. negative
controls.
''' % globals()))
group.append('wants_save_figure', cps.Binary(
'''Create dose-response plots?''', False, doc='''Select *%(YES)s* if you want to create and save dose-response plots.
You will be asked for information on how to save the plots.''' % globals()))
group.append('figure_name', cps.Text(
"Figure prefix", "", doc='''\
*(Used only when creating dose-response plots)*
CellProfiler will create a file name by appending the measurement name
to the prefix you enter here. For instance, if you specify a prefix
of “Dose\_”, when saving a file related to objects you have chosen (for
example, *Cells*) and a particular measurement (for example, *AreaShape_Area*),
CellProfiler will save the figure as *Dose_Cells_AreaShape_Area.m*.
Leave this setting blank if you do not want a prefix.
'''
))
group.append('pathname', cps.DirectoryPath(
"Output file location",
dir_choices=[
cps.DEFAULT_OUTPUT_FOLDER_NAME, cps.DEFAULT_INPUT_FOLDER_NAME,
cps.ABSOLUTE_FOLDER_NAME, cps.DEFAULT_OUTPUT_SUBFOLDER_NAME,
cps.DEFAULT_INPUT_SUBFOLDER_NAME], doc="""\
*(Used only when creating dose-response plots)*
This setting lets you choose the folder for the output files. %(IO_FOLDER_CHOICE_HELP_TEXT)s
%(IO_WITH_METADATA_HELP_TEXT)s
""" % globals()))
group.append("divider", cps.Divider())
group.append("remover", cps.RemoveSettingButton("", "Remove this dose measurement",
self.dose_values,
group))
self.dose_values.append(group)
def test_01_02_split_and_join(self):
gibberish = "aqwura[oijs|fd"
for dir_choice in cps.DirectoryPath.DIR_ALL + [cps.NO_FOLDER_NAME]:
s = cps.DirectoryPath("whatever")
value = s.join_parts(dir_choice, gibberish)
out_dir_choice = s.dir_choice
custom_path = s.custom_path
self.assertEqual(dir_choice, out_dir_choice)
self.assertEqual(custom_path, gibberish)
def test_01_03_is_custom_choice(self):
for dir_choice, expected in ((cps.DEFAULT_INPUT_FOLDER_NAME, False),
(cps.DEFAULT_INPUT_SUBFOLDER_NAME, True),
(cps.DEFAULT_OUTPUT_FOLDER_NAME, False),
(cps.DEFAULT_OUTPUT_SUBFOLDER_NAME,
True), (cps.ABSOLUTE_FOLDER_NAME, True),
(cps.URL_FOLDER_NAME, True)):
s = cps.DirectoryPath("whatever")
s.dir_choice = dir_choice
self.assertEqual(s.is_custom_choice, expected)
def test_04_04_alter_output_subfolder_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.DEFAULT_OUTPUT_SUBFOLDER_NAME
s.custom_path = "0"
def fn_alter_path(path, **kwargs):
self.assertEqual(path, "0")
return "5"
s.alter_for_create_batch_files(fn_alter_path)
self.assertEqual(
s.get_absolute_path(),
os.path.join(cpprefs.get_default_output_directory(), "5"))
def create_settings(self):
"""Create the settings during initialization
"""
self.directory = cps.DirectoryPath(
"Input image file location", support_urls=True, doc='''
Select the folder containing the image(s) to be loaded. Generally,
it is best to store the image you want to load in either the Default Input or
Output Folder, so that the correct image is loaded into the pipeline
and typos are avoided. %(IO_FOLDER_CHOICE_HELP_TEXT)s
<p>%(IO_WITH_METADATA_HELP_TEXT)s %(USING_METADATA_TAGS_REF)s For instance,
if you have a "Plate" metadata tag, and your single files are
organized in subfolders named with the "Plate" tag, you can select one of the
subfolder options and then specify a subfolder name of "\g<Plate>"
to get the files from the subfolder associated with that image's plate. The module will
substitute the metadata values for the current image set for any metadata tags in the
folder name. %(USING_METADATA_HELP_REF)s.</p>''' % globals())
self.file_settings = []
self.add_file(can_remove=False)
self.add_button = cps.DoSomething("", "Add another image", self.add_file)
def test_03_07_metadata(self):
m = cpmeas.Measurements()
m.add_image_measurement("Metadata_Path", "2")
s = cps.DirectoryPath("whatever", allow_metadata=True)
for dir_choice, expected in (
(cps.DEFAULT_INPUT_SUBFOLDER_NAME,
os.path.join(cpprefs.get_default_image_directory(), "0", "2")),
(cps.DEFAULT_OUTPUT_SUBFOLDER_NAME,
os.path.join(cpprefs.get_default_output_directory(), "0",
"2")), (cps.ABSOLUTE_FOLDER_NAME,
os.path.join(self.root_directory, "2")),
(cps.URL_FOLDER_NAME, "http://www.cellprofiler.org/2")):
s.dir_choice = dir_choice
if dir_choice in (cps.DEFAULT_INPUT_SUBFOLDER_NAME,
cps.DEFAULT_OUTPUT_SUBFOLDER_NAME):
s.custom_path = "0" + os.path.sep.replace('\\',
'\\\\') + "\\g<Path>"
elif dir_choice == cps.ABSOLUTE_FOLDER_NAME:
s.custom_path = self.root_directory + os.path.sep.replace(
'\\', '\\\\') + "\\g<Path>"
else:
s.custom_path = "http://www.cellprofiler.org/\\g<Path>"
self.assertEqual(s.get_absolute_path(m), expected)
def test_02_06_get_url(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.URL_FOLDER_NAME
s.custom_path = "http://www.cellprofiler.org"
self.assertEqual(s.get_absolute_path(), "http://www.cellprofiler.org")
def test_02_05_get_absolute_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.ABSOLUTE_FOLDER_NAME
s.custom_path = os.path.join(self.root_directory, "..", "1", "2")
self.assertEqual(s.get_absolute_path(),
os.path.join(self.root_directory, "2"))
def create_settings(self):
'''Create the settings for the ExportToCellH5 module'''
self.directory = cps.DirectoryPath("Output file location",
doc="""
This setting lets you choose the folder for the output files.
%(IO_FOLDER_CHOICE_HELP_TEXT)s
""" % globals())
def get_directory_fn():
'''Get the directory for the CellH5 file'''
return self.directory.get_absolute_path()
def set_directory_fn(path):
dir_choice, custom_path = self.directory.get_parts_from_path(path)
self.directory.join_parts(dir_choice, custom_path)
self.file_name = cps.FilenameText("Output file name",
"DefaultOut.ch5",
get_directory_fn=get_directory_fn,
set_directory_fn=set_directory_fn,
metadata=True,
browse_msg="Choose CellH5 file",
mode=cps.FilenameText.MODE_APPEND,
exts=[("CellH5 file (*.cellh5)",
"*.ch5"),
("HDF5 file (*.h5)", "*.h5"),
("All files (*.*", "*.*")],
doc="""
This setting lets you name your CellH5 file. If you choose an
existing file, CellProfiler will add new data to the file
or overwrite existing locations.
<p>%(IO_WITH_METADATA_HELP_TEXT)s %(USING_METADATA_TAGS_REF)s.
For instance, if you have a metadata tag named
"Plate", you can create a per-plate folder by selecting one the subfolder options
and then specifying the subfolder name as "\g<Plate>". The module will
substitute the metadata values for the current image set for any metadata tags in the
folder name.%(USING_METADATA_HELP_REF)s.</p>
""" % globals())
self.overwrite_ok = cps.Binary(
"Overwrite existing data without warning?",
False,
doc="""
Select <i>%(YES)s</i> to automatically overwrite any existing data
for a site. Select <i>%(NO)s</i> to be prompted first.
If you are running the pipeline on a computing cluster,
select <i>%(YES)s</i> unless you want execution to stop because you
will not be prompted to intervene. Also note that two instances
of CellProfiler cannot write to the same file at the same time,
so you must ensure that separate names are used on a cluster.
""" % globals())
self.repack = cps.Binary("Repack after analysis",
True,
doc="""
This setting determines whether CellProfiler in multiprocessing mode
repacks the data at the end of analysis. If you select <i>%(YES)s</i>,
CellProfiler will combine all of the satellite files into a single
file upon completion. This option requires some extra temporary disk
space and takes some time at the end of analysis, but results in
a single file which may occupy less disk space. If you select
<i>%(NO)s</i>, CellProfiler will create a master file using the
name that you give and this file will have links to individual
data files that contain the actual data. Using the data generated by
this option requires that you keep the master file and the linked
files together when copying them to a new folder.
""" % globals())
self.plate_metadata = cps.Choice("Plate metadata", [],
value="Plate",
choices_fn=self.get_metadata_choices,
doc="""
This is the metadata tag that identifies the plate name of
the images for the current cycle. Choose <i>None</i> if
your assay does not have metadata for plate name. If your
assay is slide-based, you can use a metadata item that identifies
the slide as the choice for this setting and set the well
and site metadata items to <i>None</i>.""")
self.well_metadata = cps.Choice(
"Well metadata", [],
value="Well",
choices_fn=self.get_metadata_choices,
doc="""This is the metadata tag that identifies the well name
for the images in the current cycle. Choose <i>None</i> if
your assay does not have metadata for the well.""")
self.site_metadata = cps.Choice(
"Site metadata", [],
value="Site",
choices_fn=self.get_metadata_choices,
doc="""This is the metadata tag that identifies the site name
for the images in the current cycle. Choose <i>None</i> if
your assay doesn't divide wells up into sites or if this
tag is not required for other reasons.""")
self.divider = cps.Divider()
self.wants_to_choose_measurements = cps.Binary("Choose measurements?",
False,
doc="""
This setting lets you choose between exporting all measurements or
just the ones that you choose. Select <i>%(YES)s</i> to pick the
measurements to be exported. Select <i>%(NO)s</i> to automatically
export all measurements available at this stage of the pipeline.
""" % globals())
self.measurements = cps.MeasurementMultiChoice(
"Measurements to export",
doc="""
<i>(Used only if choosing measurements.)</i>
<br>
This setting lets you choose individual measurements to be exported.
Check the measurements you want to export.
""")
self.objects_to_export = []
self.add_objects_button = cps.DoSomething("Add objects to export",
"Add objects",
self.add_objects)
self.images_to_export = []
self.add_image_button = cps.DoSomething("Add an image to export",
"Add image", self.add_image)
self.objects_count = cps.HiddenCount(self.objects_to_export)
self.images_count = cps.HiddenCount(self.images_to_export)
def test_02_02_get_default_output_folder_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.DEFAULT_OUTPUT_FOLDER_NAME
self.assertEqual(s.get_absolute_path(),
cpprefs.get_default_output_directory())
def create_settings(self):
self.input_type = cps.Choice("Select the type of input", [IF_IMAGE],
IF_IMAGE)
self.image_name = cps.ImageNameSubscriber("Select the image to save",
cps.NONE,
doc="""
<i>(Used only if "%(IF_IMAGE)s", "%(IF_MASK)s" or "%(IF_CROPPING)s" are selected to save)</i><br>
Select the image you want to save.""" % globals())
self.input_object_name = cps.ObjectNameSubscriber(
"Select the objects to save", cps.NONE)
self.objects_name = cps.ObjectNameSubscriber(
"Select the objects to crop and save",
cps.NONE,
doc="""
Select the objects that you want to save.""" % globals())
self.file_name_method = cps.Choice(
"Select method for constructing file names", [
FN_FROM_IMAGE,
],
doc="""
Several choices are available for constructing the image file name:
<ul>
<li><i>%(FN_FROM_IMAGE)s:</i> The filename will be constructed based
on the original filename of an input image specified in <b>NamesAndTypes</b>.
You will have the opportunity to prefix or append
additional text.
<p>If you have metadata associated with your images, you can append an text
to the image filename using a metadata tag. This is especially useful if you
want your output given a unique label according to the metadata corresponding
to an image group. The name of the metadata to substitute can be provided for
each image for each cycle using the <b>Metadata</b> module.
%(USING_METADATA_TAGS_REF)s%(USING_METADATA_HELP_REF)s.</p></li>
</ul>""" % globals())
self.file_image_name = cps.FileImageNameSubscriber(
"Select image name for file prefix",
cps.NONE,
doc="""
<i>(Used only when "%(FN_FROM_IMAGE)s" is selected for contructing the filename)</i><br>
Select an image loaded using <b>NamesAndTypes</b>. The original filename will be
used as the prefix for the output filename.""" % globals())
self.wants_file_name_suffix = cps.Binary(
"Append a suffix to the image file name?",
False,
doc="""
Select <i>%(YES)s</i> to add a suffix to the image's file name.
Select <i>%(NO)s</i> to use the image name as-is.""" % globals())
self.file_name_suffix = cps.Text("Text to append to the image name",
"",
metadata=True,
doc="""
<i>(Used only when constructing the filename from the image filename)</i><br>
Enter the text that should be appended to the filename specified above."""
)
self.file_format = cps.Choice("Saved file format", [FF_TIFF],
value=FF_TIFF,
doc="""
<i>(Used only when saving non-movie files)</i><br>
Select the image or movie format to save the image(s). Most common
image formats are available; MAT-files are readable by MATLAB.""")
self.pathname = SaveImagesDirectoryPath("Output file location",
self.file_image_name,
doc="""
<i>(Used only when saving non-movie files)</i><br>
This setting lets you choose the folder for the output
files. %(IO_FOLDER_CHOICE_HELP_TEXT)s
<p>An additional option is the following:
<ul>
<li><i>Same folder as image</i>: Place the output file in the same folder
that the source image is located.</li>
</ul></p>
<p>%(IO_WITH_METADATA_HELP_TEXT)s %(USING_METADATA_TAGS_REF)s.
For instance, if you have a metadata tag named
"Plate", you can create a per-plate folder by selecting one the subfolder options
and then specifying the subfolder name as "\g<Plate>". The module will
substitute the metadata values for the current image set for any metadata tags in the
folder name.%(USING_METADATA_HELP_REF)s.</p>
<p>If the subfolder does not exist when the pipeline is run, CellProfiler will
create it.</p>
<p>If you are creating nested subfolders using the sub-folder options, you can
specify the additional folders separated with slashes. For example, "Outlines/Plate1" will create
a "Plate1" folder in the "Outlines" folder, which in turn is under the Default
Input/Output Folder. The use of a forward slash ("/") as a folder separator will
avoid ambiguity between the various operating systems.</p>""" %
globals())
self.bit_depth = cps.Choice(
"Image bit depth", [BIT_DEPTH_8, BIT_DEPTH_16, BIT_DEPTH_FLOAT],
doc="""
<i>(Used only when saving files in a non-MAT format)</i><br>
Select the bit-depth at which you want to save the images.
<i>%(BIT_DEPTH_FLOAT)s</i> saves the image as floating-point decimals
with 32-bit precision in its raw form, typically scaled between
0 and 1.
<b>%(BIT_DEPTH_16)s and %(BIT_DEPTH_FLOAT)s images are supported only
for TIF formats. Currently, saving images in 12-bit is not supported.</b>"""
% globals())
self.object_extension = cps.Integer("Object extension",
value=1,
doc="""
How many pixels should the bounding box of the objects be extended
before cropping""")
self.overwrite = cps.Binary(
"Overwrite existing files without warning?",
False,
doc="""
Select <i>%(YES)s</i> to automatically overwrite a file if it already exists.
Select <i>%(NO)s</i> to be prompted for confirmation first.
<p>If you are running the pipeline on a computing cluster,
select <i>%(YES)s</i> since you will not be able to intervene and answer the confirmation prompt.</p>"""
% globals())
self.when_to_save = cps.Choice("When to save", [WS_FIRST_CYCLE],
doc="""<a name='when_to_save'>
<i>(Used only when saving non-movie files)</i><br>
Specify at what point during pipeline execution to save file(s). </a>
<ul>
<li><i>%(WS_EVERY_CYCLE)s:</i> Useful for when the image of interest is created every cycle and is
not dependent on results from a prior cycle.</li>
</ul> """ % globals())
self.update_file_names = cps.Binary(
"Record the file and path information to the saved image?",
False,
doc="""
Select <i>%(YES)s</i> to store filename and pathname data for each of the new files created
via this module as a per-image measurement.
<p>Instances in which this information may be useful include:
<ul>
<li>Exporting measurements to a database, allowing
access to the saved image. If you are using the machine-learning tools or image
viewer in CellProfiler Analyst, for example, you will want to enable this setting if you want
the saved images to be displayed along with the original images.</li>
<li>Allowing downstream modules (e.g., <b>CreateWebPage</b>) to access
the newly saved files.</li>
</ul></p>""" % globals())
self.create_subdirectories = cps.Binary(
"Create subfolders in the output folder?",
False,
doc="""
Select <i>%(YES)s</i> to create subfolders to match the input image folder structure."""
% globals())
self.root_dir = cps.DirectoryPath("Base image folder",
doc="""
<i>Used only if creating subfolders in the output folder</i>
In subfolder mode, <b>SaveImages</b> determines the folder for
an image file by examining the path of the matching input file.
The path that SaveImages uses is relative to the image folder
chosen using this setting. As an example, input images might be stored
in a folder structure of "images%(sep)s<i>experiment-name</i>%(sep)s
<i>date</i>%(sep)s<i>plate-name</i>". If the image folder is
"images", <b>SaveImages</b> will store images in the subfolder,
"<i>experiment-name</i>%(sep)s<i>date</i>%(sep)s<i>plate-name</i>".
If the image folder is "images%(sep)s<i>experiment-name</i>",
<b>SaveImages</b> will store images in the subfolder,
<i>date</i>%(sep)s<i>plate-name</i>".
""" % dict(sep=os.path.sep))
def test_04_07_no_folder(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.NO_FOLDER_NAME
s.custom_path = "gibberish"
self.assertEqual(s.get_absolute_path(), '')
def test_04_02_alter_output_folder_path(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.DEFAULT_OUTPUT_FOLDER_NAME
s.alter_for_create_batch_files(TestDirectoryPath.fn_alter_path)
self.assertEqual(s.get_absolute_path(),
cpprefs.get_default_output_directory())
def add_dose_value(self, can_remove=True):
'''Add a dose value measurement to the list
can_delete - set this to False to keep from showing the "remove"
button for images that must be present.'''
group = cps.SettingsGroup()
group.append("measurement",
cps.Measurement("Select the image measurement describing the treatment dose",
lambda: cpmeas.IMAGE,
doc="""
The V and Z' factor, a measure of assay quality, and the EC50, indicating
dose/response, are calculated by this module based on each image being
specified as a particular treatment dose. Choose a measurement that gives
the dose of some treatment for each of your images. <p>
The typical way to provide this information in the pipeline is to create
a comma-delimited text file (CSV) outside of CellProfiler and then load that file into the pipeline
using <b>Metadata</b> or the <b>LoadData</b>. In that case, choose the
measurement that matches the column header of the measurement
in the CSV input file. See <b>LoadData</b> help for an example text file.
"""))
group.append("log_transform", cps.Binary(
"Log-transform the dose values?", False, doc='''
Select <i>%(YES)s</i> if you have dose-response data and you want to log-transform
the dose values before fitting a sigmoid curve.
<p>Select <i>%(NO)s</i> if your data values indicate only positive vs. negative
controls.</p>''' % globals()))
group.append('wants_save_figure', cps.Binary(
'''Create dose/response plots?''', False, doc='''<a name='wants_save_figure'></a>
Select <i>%(YES)s</i> if you want to create and save
dose response plots. You will be asked for information on how to save the plots.''' % globals()))
group.append('figure_name', cps.Text(
"Figure prefix", "", doc='''
<i>(Used only when creating dose/response plots)</i><br>
CellProfiler will create a file name by appending the measurement name
to the prefix you enter here. For instance, if you have objects
named, "Cells", the "AreaShape_Area measurement", and a prefix of "Dose_",
CellProfiler will save the figure as <i>Dose_Cells_AreaShape_Area.m</i>.
Leave this setting blank if you do not want a prefix.'''
))
group.append('pathname', cps.DirectoryPath(
"Output file location",
dir_choices=[
cps.DEFAULT_OUTPUT_FOLDER_NAME, cps.DEFAULT_INPUT_FOLDER_NAME,
cps.ABSOLUTE_FOLDER_NAME, cps.DEFAULT_OUTPUT_SUBFOLDER_NAME,
cps.DEFAULT_INPUT_SUBFOLDER_NAME], doc="""
<i>(Used only when creating dose/response plots)</i><br>
This setting lets you choose the folder for the output
files. %(IO_FOLDER_CHOICE_HELP_TEXT)s
<p>%(IO_WITH_METADATA_HELP_TEXT)s %(USING_METADATA_TAGS_REF)s
For instance, if you have a metadata tag named
"Plate", you can create a per-plate folder by selecting one of the subfolder options
and then specifying the subfolder name as "\g<Plate>". The module will
substitute the metadata values for the current image set for any metadata tags in the
folder name. %(USING_METADATA_HELP_REF)s.</p>""" % globals()))
group.append("divider", cps.Divider())
group.append("remover", cps.RemoveSettingButton("", "Remove this dose measurement",
self.dose_values,
group))
self.dose_values.append(group)
def add_measurement(self, flag_settings, can_delete=True):
measurement_settings = flag_settings.measurement_settings
group = cps.SettingsGroup()
group.append("divider1", cps.Divider(line=False))
group.append(
"source_choice",
cps.Choice(
"Flag is based on",
S_ALL,
doc="""\
- *%(S_IMAGE)s:* A per-image measurement, such as intensity or
granularity.
- *%(S_AVERAGE_OBJECT)s:* The average of all object measurements in
the image.
- *%(S_ALL_OBJECTS)s:* All the object measurements in an image,
without averaging. In other words, if *any* of the objects meet the
criteria, the image will be flagged.
- *%(S_RULES)s:* Use a text file of rules produced by CellProfiler
Analyst. With this option, you will have to ensure that this pipeline
produces every measurement in the rules file upstream of this module.
- *%(S_CLASSIFIER)s:* Use a classifier built by CellProfiler Analyst.
""" % globals(),
),
)
group.append(
"object_name",
cps.ObjectNameSubscriber(
"Select the object to be used for flagging",
cps.NONE,
doc="""\
*(Used only when flag is based on an object measurement)*
Select the objects whose measurements you want to use for flagging.
""",
),
)
def object_fn():
if group.source_choice == S_IMAGE:
return cpmeas.IMAGE
return group.object_name.value
group.append(
"rules_directory",
cps.DirectoryPath(
"Rules file location",
doc="""\
*(Used only when flagging using "%(S_RULES)s")*
Select the location of the rules file that will be used for flagging images.
%(IO_FOLDER_CHOICE_HELP_TEXT)s
""" % globals(),
),
)
def get_directory_fn():
"""Get the directory for the rules file name"""
return group.rules_directory.get_absolute_path()
def set_directory_fn(path):
dir_choice, custom_path = group.rules_directory.get_parts_from_path(
path)
group.rules_directory.join_parts(dir_choice, custom_path)
group.append(
"rules_file_name",
cps.FilenameText(
"Rules file name",
"rules.txt",
get_directory_fn=get_directory_fn,
set_directory_fn=set_directory_fn,
doc="""\
*(Used only when flagging using "%(S_RULES)s")*
The name of the rules file, most commonly from CellProfiler Analyst's
Classifier. This file should be a plain text file
containing the complete set of rules.
Each line of this file should be a rule naming a measurement to be made
on an image, for instance:
IF (Image_ImageQuality_PowerLogLogSlope_DNA < -2.5, [0.79, -0.79], [-0.94, 0.94])
The above rule will score +0.79 for the positive category and -0.94
for the negative category for images whose power log slope is less
than -2.5 pixels and will score the opposite for images whose slope is
larger. The filter adds positive and negative and flags the images
whose positive score is higher than the negative score.
""" % globals(),
),
)
def get_rules_class_choices(group=group):
"""Get the available choices from the rules file"""
try:
if group.source_choice == S_CLASSIFIER:
return self.get_bin_labels(group)
elif group.source_choice == S_RULES:
rules = self.get_rules(group)
nclasses = len(rules.rules[0].weights[0])
return [str(i) for i in range(1, nclasses + 1)]
else:
return ["None"]
rules = self.get_rules(group)
nclasses = len(rules.rules[0].weights[0])
return [str(i) for i in range(1, nclasses + 1)]
except:
return [str(i) for i in range(1, 3)]
group.append(
"rules_class",
cps.MultiChoice(
"Class number",
choices=["1", "2"],
doc="""\
*(Used only when flagging using "%(S_RULES)s")*
Select which classes to flag when filtering. The CellProfiler Analyst
Classifier user interface lists the names of the classes in order. By
default, these are the positive (class 1) and negative (class 2)
classes. **FlagImage** uses the first class from CellProfiler Analyst
if you choose “1”, etc.
Please note the following:
- The flag is set if the image falls into the selected class.
- You can make multiple class selections. If you do so, the module will
set the flag if the image falls into any of the selected classes.
""" % globals(),
),
)
group.rules_class.get_choices = get_rules_class_choices
group.append(
"measurement",
cps.Measurement(
"Which measurement?",
object_fn,
doc="""Choose the measurement to be used as criteria.""",
),
)
group.append(
"wants_minimum",
cps.Binary(
"Flag images based on low values?",
True,
doc="""\
Select *Yes* to flag images with measurements below the specified
cutoff. If the measurement evaluates to Not-A-Number (NaN), then the
image is not flagged.
""" % globals(),
),
)
group.append(
"minimum_value",
cps.Float("Minimum value",
0,
doc="""Set a value as a lower limit."""),
)
group.append(
"wants_maximum",
cps.Binary(
"Flag images based on high values?",
True,
doc="""\
Select *Yes* to flag images with measurements above the specified
cutoff. If the measurement evaluates to Not-A-Number (NaN), then the
image is not flagged.
""" % globals(),
),
)
group.append(
"maximum_value",
cps.Float("Maximum value",
1,
doc="""Set a value as an upper limit."""),
)
if can_delete:
group.append(
"remover",
cps.RemoveSettingButton("", "Remove this measurement",
measurement_settings, group),
)
group.append("divider2", cps.Divider(line=True))
measurement_settings.append(group)
def test_01_05_get_parts_from_output_folder_path(self):
s = cps.DirectoryPath("whatever")
dir_choice, custom_path = s.get_parts_from_path(
cpprefs.get_default_output_directory())
self.assertEqual(dir_choice, cps.DEFAULT_OUTPUT_FOLDER_NAME)
def test_01_07_get_parts_from_output_subfolder_path(self):
s = cps.DirectoryPath("whatever")
dir_choice, custom_path = s.get_parts_from_path(
os.path.join(cpprefs.get_default_output_directory(), "2"))
self.assertEqual(dir_choice, cps.DEFAULT_OUTPUT_SUBFOLDER_NAME)
self.assertEqual(custom_path, "2")
def test_04_06_alter_url(self):
s = cps.DirectoryPath("whatever")
s.dir_choice = cps.URL_FOLDER_NAME
s.custom_path = "http://www.cellprofiler.org"
s.alter_for_create_batch_files(TestDirectoryPath.fn_alter_path)
self.assertEqual(s.get_absolute_path(), "http://www.cellprofiler.org")
def test_01_08_get_parts_from_abspath(self):
s = cps.DirectoryPath("whatever")
dir_choice, custom_path = s.get_parts_from_path(self.root_directory)
self.assertEqual(dir_choice, cps.ABSOLUTE_FOLDER_NAME)
self.assertEqual(custom_path, self.root_directory)
def create_settings(self):
'''Create the UI settings for the module'''
self.seed_objects_name = cps.ObjectNameSubscriber(
"Select the seed objects",
cps.NONE,
doc="""\
Select the previously identified objects that you want to use as the
seeds for measuring branches and distances. Branches and trunks are assigned
per seed object. Seed objects are typically not single points/pixels but
instead are usually objects of varying sizes.""")
self.image_name = cps.ImageNameSubscriber(
"Select the skeletonized image",
cps.NONE,
doc="""\
Select the skeletonized image of the dendrites and/or axons as produced
by the **Morph** module’s *Skel* operation.""")
self.wants_branchpoint_image = cps.Binary(
"Retain the branchpoint image?",
False,
doc="""\
Select "*%(YES)s*" if you want to save the color image of branchpoints and
trunks. This is the image that is displayed in the output window for
this module.""" % globals())
self.branchpoint_image_name = cps.ImageNameProvider(
"Name the branchpoint image",
"BranchpointImage",
doc="""\
*(Used only if a branchpoint image is to be retained)*
Enter a name for the branchpoint image here. You can then use this image
in a later module, such as **SaveImages**.""")
self.wants_to_fill_holes = cps.Binary("Fill small holes?",
True,
doc="""\
The algorithm reskeletonizes the image and this can leave artifacts
caused by small holes in the image prior to skeletonizing. These holes
result in false trunks and branchpoints. Select "*%(YES)s*" to fill in
these small holes prior to skeletonizing.""" % globals())
self.maximum_hole_size = cps.Integer("Maximum hole size",
10,
minval=1,
doc="""\
*(Used only when filling small holes)*
This is the area of the largest hole to fill, measured in pixels. The
algorithm will fill in any hole whose area is this size or smaller.""")
self.wants_objskeleton_graph = cps.Binary(
"Export the skeleton graph relationships?",
False,
doc="""\
Select "*%(YES)s*" to produce an edge file and a vertex file that gives the
relationships between vertices (trunks, branchpoints and endpoints).""" %
globals())
self.intensity_image_name = cps.ImageNameSubscriber("Intensity image",
cps.NONE,
doc="""\
Select the image to be used to calculate
the total intensity along the edges between the vertices (trunks, branchpoints, and endpoints)."""
)
self.directory = cps.DirectoryPath(
"File output directory",
doc=
'Select the directory you want to save the graph relationships to.',
dir_choices=[
cps.DEFAULT_OUTPUT_FOLDER_NAME, cps.DEFAULT_INPUT_FOLDER_NAME,
cps.ABSOLUTE_FOLDER_NAME, cps.DEFAULT_OUTPUT_SUBFOLDER_NAME,
cps.DEFAULT_INPUT_SUBFOLDER_NAME
])
self.directory.dir_choice = cps.DEFAULT_OUTPUT_FOLDER_NAME
self.vertex_file_name = cps.Text("Vertex file name",
"vertices.csv",
doc="""\
*(Used only when exporting graph relationships)*
Enter the name of the file that will hold the edge information. You can
use metadata tags in the file name.
Each line of the file is a row of comma-separated values. The first
row is the header; this names the file’s columns. Each subsequent row
represents a vertex in the skeleton graph: either a trunk, a
branchpoint or an endpoint. The file has the following columns:
- *image\_number:* The image number of the associated image.
- *vertex\_number:* The number of the vertex within the image.
- *i:* The I coordinate of the vertex.
- *j:* The J coordinate of the vertex.
- *label:* The label of the seed object associated with the vertex.
- *kind:* The vertex type, with the following choices:
- **T:** Trunk
- **B:** Branchpoint
- **E:** Endpoint
""")
self.edge_file_name = cps.Text("Edge file name",
"edges.csv",
doc="""\
*(Used only when exporting graph relationships)*
Enter the name of the file that will hold the edge information. You can
use metadata tags in the file name. Each line of the file is a row of
comma-separated values. The first row is the header; this names the
file’s columns. Each subsequent row represents an edge or connection
between two vertices (including between a vertex and itself for certain
loops). Note that vertices include trunks, branchpoints, and endpoints.
The file has the following columns:
- *image\_number:* The image number of the associated image.
- *v1:* The zero-based index into the vertex table of the first vertex
in the edge.
- *v2:* The zero-based index into the vertex table of the second vertex
in the edge.
- *length:* The number of pixels in the path connecting the two
vertices, including both vertex pixels.
- *total\_intensity:* The sum of the intensities of the pixels in the
edge, including both vertex pixel intensities.
""")
def create_settings(self):
self.mode = cps.Choice("Classify or train?",
[MODE_CLASSIFY, MODE_TRAIN])
self.advanced_or_automatic = cps.Choice(
"Configuration mode", [AA_AUTOMATIC, AA_ADVANCED],
doc="""Do you want to automatically choose the training parameters
or use the defaults?""")
self.radius = cps.Integer("Radius", DEFAULT_RADIUS, 1)
self.n_features = cps.Integer(
"Number of features",
DEFAULT_N_FEATURES,
1,
doc="""The classifier runs a feature reduction set. This creates
<i>Eigentextures</i> which are representative texture patches
found throughout the image. The module scores each patch around
a pixel according to how much it has each of these textures and
those scores are fed into the final classifier. Raise the number of
features if some of the textures or edges of your classes are
misclassified. Lower the number of features to improve processing
time or to reduce overfitting if you have a smaller amount of
ground truth.
""")
self.n_estimators = cps.Integer(
"Number of estimators",
DEFAULT_N_ESTIMATORS,
1,
doc="""The classifier uses a voting scheme where it trains this
many estimators. It purposefully does a bad job training and makes
up for this deficit by having many poor classification judges.
This protects against overfitting by not relying on having a single
classifier that is very good at classifying the ground truth, but
mistakenly uses irrelevant information to do so. Raise the number
of estimators if the classifier is making obvious mistakes with
unwarranted certainty. Lower the number of estimators to improve
processing speed.""")
self.min_samples_per_leaf = cps.Integer(
"Minimum samples per leaf",
DEFAULT_MIN_SAMPLES_PER_LEAF,
1,
doc="""This setting determines the minimum number of ground truth
pixels that the classifier will use to split a decision tree.
There must be at least this number of example pixels in each branch
for the classifier to have confidence that the split is real and
not just an artifact of an irrelevant measurement.
Lower this setting if the classifier does a good job on most of the
pixels but does not draw sharp distinctions between one class and
another at the border between the classes (e.g. at the edges of
cells). Raise this setting if the classifier misclassifies pixels
that are clearly not the right class - this is overtraining.
""")
self.path = cps.DirectoryPath("Classifier folder")
def get_directory_fn():
'''Get the directory for the file name'''
return self.path.get_absolute_path()
def set_directory_fn(path):
dir_choice, custom_path = self.path.get_parts_from_path(path)
self.path.join_parts(dir_choice, custom_path)
self.filename = cps.FilenameText(
"Classifier file",
"Classifier.cpclassifier",
get_directory_fn=get_directory_fn,
set_directory_fn=set_directory_fn,
exts=[("Pixel classifier (*.cpclassifier)", "*.cpclassifier"),
("All files (*.*)", "*.*")])
self.gt_source = cps.Choice("Ground truth source",
[SRC_OBJECTS, SRC_ILASTIK],
doc="""
The ground truth data can either be taken from objects or can be
the exported TIF "labels" output of Ilastik.
""")
self.labels_image = cps.ImageNameSubscriber("Ilastik labels image",
"labels.tif",
doc="""
<i>Used only if the ground truth source is "Ilastik"</i>
<br>
This image should be the exported labels image from Ilastik.
""")
self.wants_background_class = cps.Binary(
"Do you want a background class?", True)
self.background_class_name = cps.Text("Background class name",
"Background")
self.object_classes = []
self.object_class_count = cps.HiddenCount(self.object_classes,
"Object class count")
self.add_objects(False)
self.add_objects_button = cps.DoSomething("Add another class", "Add",
self.add_objects)
self.label_classes = []
self.label_class_count = cps.HiddenCount(self.label_classes,
"Label class count")
self.add_labels(False)
self.add_labels_button = cps.DoSomething("Add another class", "Add",
self.add_labels)
self.images = []
self.image_count = cps.HiddenCount(self.images, "Image count")
self.add_image(False)
self.add_image_button = cps.DoSomething("Add another image", "Add",
self.add_image)
self.outputs = []
self.output_count = cps.HiddenCount(self.outputs, "Output count")
self.add_output(False)
self.add_output_button = cps.DoSomething("Add another output", "Add",
self.add_output)
