def __init__(self, index, operation):
self.__index = index
self.__operation = operation
self.__operand_choice = cps.Choice(
self.operand_choice_text(),
MC_ALL,
doc="""Indicate whether the operand is an image or object measurement.""",
)
self.__operand_objects = cps.ObjectNameSubscriber(
self.operand_objects_text(),
"None",
doc="""Choose the objects you want to measure for this operation.""",
)
self.__operand_measurement = cps.Measurement(
self.operand_measurement_text(),
self.object_fn,
doc="""\
Enter the category that was used to create the measurement. You
will be prompted to add additional information depending on
the type of measurement that is requested.""",
)
self.__multiplicand = cps.Float(
"Multiply the above operand by",
1,
doc="""Enter the number by which you would like to multiply the above operand.""",
)
self.__exponent = cps.Float(
"Raise the power of above operand by",
1,
doc="""Enter the power by which you would like to raise the above operand.""",
)
def add_channel(self, can_remove=True):
"""Add another channel to the channels list"""
group = cps.SettingsGroup()
group.can_remove = can_remove
group.append(
"channel_choice",
cps.Integer(
text="Channel number",
value=len(self.channels) + 1,
minval=1,
doc="""\
*(Used only when splitting images)*
This setting chooses a channel to be processed. For example, *1*
is the first
channel in a .TIF or the red channel in a traditional image file.
*2* and *3* are the second and third channels of a TIF or
the green and blue channels in other formats. *4* is the
transparency channel for image formats that support transparency and is
channel # 4 for a .TIF file. **ColorToGray** will fail to process an
image if you select a channel that is not supported by that image, for
example, “5” for a three-channel .PNG file.""",
),
)
group.append(
"contribution",
cps.Float(
"Relative weight of the channel",
1,
0,
doc="""\
*(Used only when combining channels)*
Relative weights: If all relative weights are equal, all three colors
contribute equally in the final image. To weight colors relative to each
other, increase or decrease the relative weights.""",
),
)
group.append(
"image_name",
cps.ImageNameProvider(
"Image name",
value="Channel%d" % (len(self.channels) + 1),
doc="""\
*(Used only when splitting images)*
Select the name of the output grayscale image.""",
),
)
if group.can_remove:
group.append(
"remover",
cps.RemoveSettingButton("", "Remove this channel",
self.channels, group),
)
self.channels.append(group)
def add_stack_channel_cb(self, can_remove=True):
group = cps.SettingsGroup()
default_color = DEFAULT_COLORS[len(self.stack_channels) % len(DEFAULT_COLORS)]
group.append(
"image_name",
cps.ImageNameSubscriber(
"Image name",
"None",
doc="""\
*(Used only if "%(SCHEME_STACK)s" or "%(SCHEME_COMPOSITE)s" is chosen)*
Select the input image to add to the stacked image.
"""
% globals(),
),
)
group.append(
"color",
cps.Color(
"Color",
default_color,
doc="""\
*(Used only if "%(SCHEME_COMPOSITE)s" is chosen)*
The color to be assigned to the above image.
"""
% globals(),
),
)
group.append(
"weight",
cps.Float(
"Weight",
1.0,
minval=0.5 / 255,
doc="""\
*(Used only if "%(SCHEME_COMPOSITE)s" is chosen)*
The weighting of the above image relative to the others. The image’s
pixel values are multiplied by this weight before assigning the color.
"""
% globals(),
),
)
if can_remove:
group.append(
"remover",
cps.RemoveSettingButton(
"", "Remove this image", self.stack_channels, group
),
)
self.stack_channels.append(group)
def add_single_measurement(self, can_delete=True):
"""Add a single measurement to the group of single measurements
can_delete - True to include a "remove" button, False if you're not
allowed to remove it.
"""
group = cps.SettingsGroup()
if can_delete:
group.append("divider", cps.Divider(line=True))
group.append(
"object_name",
cps.ObjectNameSubscriber(
"Select the object to be classified",
"None",
doc="""\
The name of the objects to be classified. You can choose from objects
created by any previous module. See **IdentifyPrimaryObjects**,
**IdentifySecondaryObjects**, **IdentifyTertiaryObjects**, or **Watershed**
""",
),
)
def object_fn():
return group.object_name.value
group.append(
"measurement",
cps.Measurement(
"Select the measurement to classify by",
object_fn,
doc="""\
*(Used only if using a single measurement)*
Select a measurement made by a previous module. The objects will be
classified according to their values for this measurement.
""",
),
)
group.append(
"bin_choice",
cps.Choice(
"Select bin spacing",
[BC_EVEN, BC_CUSTOM],
doc="""\
*(Used only if using a single measurement)*
Select how you want to define the spacing of the bins. You have the
following options:
- *%(BC_EVEN)s:* Choose this if you want to specify bins of equal
size, bounded by upper and lower limits. If you want two bins, choose
this option and then provide a single threshold when asked.
- *%(BC_CUSTOM)s:* Choose this option to create the indicated number
of bins at evenly spaced intervals between the low and high
threshold. You also have the option to create bins for objects that
fall below or above the low and high threshold.
""" % globals(),
),
)
group.append(
"bin_count",
cps.Integer(
"Number of bins",
3,
minval=1,
doc="""\
*(Used only if using a single measurement)*
This is the number of bins that will be created between
the low and high threshold""",
),
)
group.append(
"low_threshold",
cps.Float(
"Lower threshold",
0,
doc="""\
*(Used only if using a single measurement and "%(BC_EVEN)s" selected)*
This is the threshold that separates the lowest bin from the others. The
lower threshold, upper threshold, and number of bins define the
thresholds of bins between the lowest and highest.
""" % globals(),
),
)
group.append(
"wants_low_bin",
cps.Binary(
"Use a bin for objects below the threshold?",
False,
doc="""\
*(Used only if using a single measurement)*
Select "*Yes*" if you want to create a bin for objects whose values
fall below the low threshold. Select "*No*" if you do not want a bin
for these objects.
""" % globals(),
),
)
def min_upper_threshold():
return group.low_threshold.value + np.finfo(float).eps
group.append(
"high_threshold",
cps.Float(
"Upper threshold",
1,
minval=cps.NumberConnector(min_upper_threshold),
doc="""\
*(Used only if using a single measurement and "%(BC_EVEN)s" selected)*
This is the threshold that separates the last bin from the others. Note
that if you would like two bins, you should select "*%(BC_CUSTOM)s*".
""" % globals(),
),
)
group.append(
"wants_high_bin",
cps.Binary(
"Use a bin for objects above the threshold?",
False,
doc="""\
*(Used only if using a single measurement)*
Select "*Yes*" if you want to create a bin for objects whose values
are above the high threshold.
Select "*No*" if you do not want a bin for these objects.
""" % globals(),
),
)
group.append(
"custom_thresholds",
cps.Text(
"Enter the custom thresholds separating the values between bins",
"0,1",
doc="""\
*(Used only if using a single measurement and "%(BC_CUSTOM)s" selected)*
This setting establishes the threshold values for the bins. You should
enter one threshold between each bin, separating thresholds with commas
(for example, *0.3, 1.5, 2.1* for four bins). The module will create one
more bin than there are thresholds.
""" % globals(),
),
)
group.append(
"wants_custom_names",
cps.Binary(
"Give each bin a name?",
False,
doc="""\
*(Used only if using a single measurement)*
Select "*Yes*" to assign custom names to bins you have specified.
Select "*No*" for the module to automatically assign names based on
the measurements and the bin number.
""" % globals(),
),
)
group.append(
"bin_names",
cps.Text(
"Enter the bin names separated by commas",
"None",
doc="""\
*(Used only if "Give each bin a name?" is checked)*
Enter names for each of the bins, separated by commas.
An example including three bins might be *First,Second,Third*.""",
),
)
group.append(
"wants_images",
cps.Binary(
"Retain an image of the classified objects?",
False,
doc="""\
Select "*Yes*" to keep an image of the objects which is color-coded
according to their classification, for use later in the pipeline (for
example, to be saved by a **SaveImages** module).
""" % globals(),
),
)
group.append(
"image_name",
cps.ImageNameProvider(
"Name the output image",
"ClassifiedNuclei",
doc=
"""Enter the name to be given to the classified object image.""",
),
)
group.can_delete = can_delete
def number_of_bins():
"""Return the # of bins in this classification"""
if group.bin_choice == BC_EVEN:
value = group.bin_count.value
else:
value = len(group.custom_thresholds.value.split(",")) - 1
if group.wants_low_bin:
value += 1
if group.wants_high_bin:
value += 1
return value
group.number_of_bins = number_of_bins
def measurement_name():
"""Get the measurement name to use inside the bin name
Account for conflicts with previous measurements
"""
measurement_name = group.measurement.value
other_same = 0
for other in self.single_measurements:
if id(other) == id(group):
break
if other.measurement.value == measurement_name:
other_same += 1
if other_same > 0:
measurement_name += str(other_same)
return measurement_name
def bin_feature_names():
"""Return the feature names for each bin"""
if group.wants_custom_names:
return [
name.strip() for name in group.bin_names.value.split(",")
]
return [
"_".join((measurement_name(), "Bin_%d" % (i + 1)))
for i in range(number_of_bins())
]
group.bin_feature_names = bin_feature_names
def validate_group():
bin_name_count = len(bin_feature_names())
bin_count = number_of_bins()
if bin_count < 1:
bad_setting = (group.bin_count if group.bin_choice == BC_EVEN
else group.custom_thresholds)
raise cps.ValidationError(
"You must have at least one bin in order to take measurements. "
"Either add more bins or ask for bins for objects above or below threshold",
bad_setting,
)
if bin_name_count != number_of_bins():
raise cps.ValidationError(
"The number of bin names (%d) does not match the number of bins (%d)."
% (bin_name_count, bin_count),
group.bin_names,
)
for bin_feature_name in bin_feature_names():
cps.AlphanumericText.validate_alphanumeric_text(
bin_feature_name, group.bin_names, True)
if group.bin_choice == BC_CUSTOM:
try:
[
float(x.strip())
for x in group.custom_thresholds.value.split(",")
]
except ValueError:
raise cps.ValidationError(
"Custom thresholds must be a comma-separated list "
'of numbers (example: "1.0, 2.3, 4.5")',
group.custom_thresholds,
)
group.validate_group = validate_group
if can_delete:
group.remove_settings_button = cps.RemoveSettingButton(
"", "Remove this classification", self.single_measurements,
group)
self.single_measurements.append(group)
def create_settings(self):
self.objects_name = cps.ObjectNameSubscriber(
"Select the input objects",
"None",
doc="""\
Select the objects you would like to split or merge (that is,
whose object numbers you want to reassign). You can
use any objects that were created in previous modules, such as
**IdentifyPrimaryObjects** or **IdentifySecondaryObjects**.""",
)
self.output_objects_name = cps.ObjectNameProvider(
"Name the new objects",
"RelabeledNuclei",
doc="""\
Enter a name for the objects that have been split or merged (that is,
whose numbers have been reassigned).
You can use this name in subsequent modules that take objects as inputs.""",
)
self.relabel_option = cps.Choice(
"Operation",
[OPTION_MERGE, OPTION_SPLIT],
doc="""\
You can choose one of the following options:
- *%(OPTION_MERGE)s:* Assign adjacent or nearby objects the same label
based on certain criteria. It can be useful, for example, to merge
together touching objects that were incorrectly split into two pieces
by an **Identify** module.
- *%(OPTION_SPLIT)s:* Assign a unique number to separate objects that
currently share the same label. This can occur if you applied certain
operations in the **Morph** module to objects.""" % globals(),
)
self.merge_option = cps.Choice(
"Merging method",
[UNIFY_DISTANCE, UNIFY_PARENT],
doc="""\
*(Used only with the "%(OPTION_MERGE)s" option)*
You can merge objects in one of two ways:
- *%(UNIFY_DISTANCE)s:* All objects within a certain pixel radius from
each other will be merged.
- *%(UNIFY_PARENT)s:* All objects which share the same parent
relationship to another object will be merged. This is not to be
confused with using the **RelateObjects** module, in which the
related objects remain as individual objects. See **RelateObjects**
for more details.""" % globals(),
)
self.merging_method = cps.Choice(
"Output object type",
[UM_DISCONNECTED, UM_CONVEX_HULL],
doc="""\
*(Used only with the "%(UNIFY_PARENT)s" merging method)*
**SplitOrMergeObjects** can either merge the child objects and keep them
disconnected or it can find the smallest convex polygon (the convex
hull) that encloses all of a parent’s child objects. The convex hull
will be truncated to include only those pixels in the parent - in that
case it may not truly be convex. Choose *%(UM_DISCONNECTED)s* to leave
the children as disconnected pieces. Choose *%(UM_CONVEX_HULL)s* to
create an output object that is the convex hull around them all.""" %
globals(),
)
self.parent_object = cps.Choice(
"Select the parent object",
["None"],
choices_fn=self.get_parent_choices,
doc="""\
Select the parent object that will be used to merge the child objects.
Please note the following:
- You must have established a parent-child relationship between the
objects using a prior **RelateObjects** module.
- Primary objects and their associated secondary objects are already in
a one-to-one parent-child relationship, so it makes no sense to merge
them here.""",
)
self.distance_threshold = cps.Integer(
"Maximum distance within which to merge objects",
0,
minval=0,
doc="""\
*(Used only with the "%(OPTION_MERGE)s" option and the "%(UNIFY_DISTANCE)s"
method)*
Objects that are less than or equal to the distance you enter here, in
pixels, will be merged. If you choose zero (the default), only objects
that are touching will be merged. Note that *%(OPTION_MERGE)s* will
not actually connect or bridge the two objects by adding any new pixels;
it simply assigns the same object number to the portions of the object.
The new, merged object may therefore consist of two or more unconnected
components. If you want to add pixels around objects, see
**ExpandOrShrink** or **Morph**.""" % globals(),
)
self.wants_image = cps.Binary(
"Merge using a grayscale image?",
False,
doc="""\
*(Used only with the "%(OPTION_MERGE)s" option)*
Select *Yes* to use the objects’ intensity features to determine
whether two objects should be merged. If you choose to use a grayscale
image, *%(OPTION_MERGE)s* will merge two objects only if they are
within the distance you have specified *and* certain criteria about the
objects within the grayscale image are met.""" % globals(),
)
self.image_name = cps.ImageNameSubscriber(
"Select the grayscale image to guide merging",
"None",
doc="""\
*(Used only if a grayscale image is to be used as a guide for
merging)*
Select the name of an image loaded or created by a previous module.""",
)
self.minimum_intensity_fraction = cps.Float(
"Minimum intensity fraction",
0.9,
minval=0,
maxval=1,
doc="""\
*(Used only if a grayscale image is to be used as a guide for
merging)*
Select the minimum acceptable intensity fraction. This will be used as
described for the method you choose in the next setting.""",
)
self.where_algorithm = cps.Choice(
"Method to find object intensity",
[CA_CLOSEST_POINT, CA_CENTROIDS],
doc="""\
*(Used only if a grayscale image is to be used as a guide for
merging)*
You can use one of two methods to determine whether two objects should
merged, assuming they meet the distance criteria (as specified
above):
- *%(CA_CENTROIDS)s:* When the module considers merging two objects,
this method identifies the centroid of each object, records the
intensity value of the dimmer of the two centroids, multiplies this
value by the *minimum intensity fraction* to generate a threshold,
and draws a line between the centroids. The method will merge the two
objects only if the intensity of every point along the line is above
the threshold. For instance, if the intensity of one centroid is 0.75
and the other is 0.50 and the *minimum intensity fraction* has been
chosen to be 0.9, all points along the line would need to have an
intensity of min(0.75, 0.50) \* 0.9 = 0.50 \* 0.9 = 0.45.
This method works well for round cells whose maximum intensity is in
the center of the cell: a single cell that was incorrectly segmented
into two objects will typically not have a dim line between the
centroids of the two halves and will be correctly merged.
- *%(CA_CLOSEST_POINT)s:* This method is useful for unifying
irregularly shaped cells that are connected. It starts by assigning
background pixels in the vicinity of the objects to the nearest
object. Objects are then merged if each object has background pixels
that are:
- Within a distance threshold from each object;
- Above the minimum intensity fraction of the nearest object pixel;
- Adjacent to background pixels assigned to a neighboring object.
An example of a feature that satisfies the above constraints is a
line of pixels that connects two neighboring objects and is roughly
the same intensity as the boundary pixels of both (such as an axon
connecting two neurons' soma).""" % globals(),
)
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",
"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 create_settings(self):
self.image_name = cps.ImageNameSubscriber(
"Select the input image",
"None",
doc="""Select the image whose edges you want to enhance.""",
)
self.output_image_name = cps.ImageNameProvider(
"Name the output image",
"EdgedImage",
doc="""Enter a name for the resulting image with edges enhanced.""",
)
self.method = cps.Choice(
"Select an edge-finding method",
[M_SOBEL, M_PREWITT, M_ROBERTS, M_LOG, M_CANNY, M_KIRSCH],
doc="""\
There are several methods that can be used to enhance edges. Often, it
is best to test them against each other empirically:
- *%(M_SOBEL)s:* Finds edges using the %(M_SOBEL)s approximation to
the derivative. The %(M_SOBEL)s method derives a horizontal and
vertical gradient measure and returns the square-root of the sum of
the two squared signals.
- *%(M_PREWITT)s:* Finds edges using the %(M_PREWITT)s approximation
to the derivative. It returns edges at those points where the
gradient of the image is maximum.
- *%(M_ROBERTS)s:* Finds edges using the Roberts approximation to the
derivative. The %(M_ROBERTS)s method looks for gradients in the
diagonal and anti-diagonal directions and returns the square-root of
the sum of the two squared signals. This method is fast, but it
creates diagonal artifacts that may need to be removed by smoothing.
- *%(M_LOG)s:* Applies a Laplacian of Gaussian filter to the image and
finds zero crossings.
- *%(M_CANNY)s:* Finds edges by looking for local maxima of the
gradient of the image. The gradient is calculated using the
derivative of a Gaussian filter. The method uses two thresholds to
detect strong and weak edges, and includes the weak edges in the
output only if they are connected to strong edges. This method is
therefore less likely than the others to be fooled by noise, and more
likely to detect true weak edges.
- *%(M_KIRSCH)s:* Finds edges by calculating the gradient among the 8
compass points (North, North-east, etc.) and selecting the maximum as
the pixel’s value.
""" % globals(),
)
self.wants_automatic_threshold = cps.Binary(
"Automatically calculate the threshold?",
True,
doc="""\
*(Used only with the "%(M_CANNY)s" option and automatic thresholding)*
Select *Yes* to automatically calculate the threshold using a
three-category Otsu algorithm performed on the Sobel transform of the
image.
Select *No* to manually enter the threshold value.
""" % globals(),
)
self.manual_threshold = cps.Float(
"Absolute threshold",
0.2,
0,
1,
doc="""\
*(Used only with the "%(M_CANNY)s" option and manual thresholding)*
The upper cutoff for Canny edges. All Sobel-transformed pixels with this
value or higher will be marked as an edge. You can enter a threshold
between 0 and 1.
""" % globals(),
)
self.threshold_adjustment_factor = cps.Float(
"Threshold adjustment factor",
1,
doc="""\
*(Used only with the "%(M_CANNY)s" option and automatic thresholding)*
This threshold adjustment factor is a multiplier that is applied to both
the lower and upper Canny thresholds if they are calculated
automatically. An adjustment factor of 1 indicates no adjustment. The
adjustment factor has no effect on any threshold entered manually.
""" % globals(),
)
self.direction = cps.Choice(
"Select edge direction to enhance",
[E_ALL, E_HORIZONTAL, E_VERTICAL],
doc="""\
*(Used only with "%(M_PREWITT)s" and "%(M_SOBEL)s" methods)*
Select the direction of the edges you aim to identify in the image
(predominantly horizontal, predominantly vertical, or both).
""" % globals(),
)
self.wants_automatic_sigma = cps.Binary(
"Calculate Gaussian's sigma automatically?",
True,
doc="""\
Select *Yes* to automatically calculate the Gaussian's sigma.
Select *No* to manually enter the value.
""" % globals(),
)
self.sigma = cps.Float("Gaussian's sigma value",
10,
doc="""Set a value for Gaussian's sigma.""")
self.wants_automatic_low_threshold = cps.Binary(
"Calculate value for low threshold automatically?",
True,
doc="""\
*(Used only with the "%(M_CANNY)s" option and automatic thresholding)*
Select *Yes* to automatically calculate the low / soft threshold
cutoff for the %(M_CANNY)s method.
Select *No* to manually enter the low threshold value.
""" % globals(),
)
self.low_threshold = cps.Float(
"Low threshold value",
0.1,
0,
1,
doc="""\
*(Used only with the "%(M_CANNY)s" option and manual thresholding)*
Enter the soft threshold cutoff for the %(M_CANNY)s method. The
%(M_CANNY)s method will mark all %(M_SOBEL)s-transformed pixels with
values below this threshold as not being edges.
""" % globals(),
)
def create_settings(self):
# XXX needs to use cps.SettingsGroup
class Operand(object):
"""Represents the collection of settings needed by each operand"""
def __init__(self, index, operation):
self.__index = index
self.__operation = operation
self.__operand_choice = cps.Choice(
self.operand_choice_text(),
MC_ALL,
doc="""Indicate whether the operand is an image or object measurement.""",
)
self.__operand_objects = cps.ObjectNameSubscriber(
self.operand_objects_text(),
"None",
doc="""Choose the objects you want to measure for this operation.""",
)
self.__operand_measurement = cps.Measurement(
self.operand_measurement_text(),
self.object_fn,
doc="""\
Enter the category that was used to create the measurement. You
will be prompted to add additional information depending on
the type of measurement that is requested.""",
)
self.__multiplicand = cps.Float(
"Multiply the above operand by",
1,
doc="""Enter the number by which you would like to multiply the above operand.""",
)
self.__exponent = cps.Float(
"Raise the power of above operand by",
1,
doc="""Enter the power by which you would like to raise the above operand.""",
)
@property
def operand_choice(self):
"""Either MC_IMAGE for image measurements or MC_OBJECT for object"""
return self.__operand_choice
@property
def operand_objects(self):
"""Get measurements from these objects"""
return self.__operand_objects
@property
def operand_measurement(self):
"""The measurement providing the value of the operand"""
return self.__operand_measurement
@property
def multiplicand(self):
"""Premultiply the measurement by this value"""
return self.__multiplicand
@property
def exponent(self):
"""Raise the measurement to this power"""
return self.__exponent
@property
def object(self):
"""The name of the object for measurement or "Image\""""
if self.operand_choice == MC_IMAGE:
return cpmeas.IMAGE
else:
return self.operand_objects.value
def object_fn(self):
if self.__operand_choice == MC_IMAGE:
return cpmeas.IMAGE
elif self.__operand_choice == MC_OBJECT:
return self.__operand_objects.value
else:
raise NotImplementedError(
"Measurement type %s is not supported"
% self.__operand_choice.value
)
def operand_name(self):
"""A fancy name based on what operation is being performed"""
if self.__index == 0:
return (
"first operand"
if self.__operation in (O_ADD, O_MULTIPLY)
else "minuend"
if self.__operation == O_SUBTRACT
else "numerator"
)
elif self.__index == 1:
return (
"second operand"
if self.__operation in (O_ADD, O_MULTIPLY)
else "subtrahend"
if self.__operation == O_SUBTRACT
else "denominator"
)
def operand_choice_text(self):
return self.operand_text("Select the %s measurement type")
def operand_objects_text(self):
return self.operand_text("Select the %s objects")
def operand_text(self, format):
return format % self.operand_name()
def operand_measurement_text(self):
return self.operand_text("Select the %s measurement")
def settings(self):
"""The operand settings to be saved in the output file"""
return [
self.operand_choice,
self.operand_objects,
self.operand_measurement,
self.multiplicand,
self.exponent,
]
def visible_settings(self):
"""The operand settings to be displayed"""
self.operand_choice.text = self.operand_choice_text()
self.operand_objects.text = self.operand_objects_text()
self.operand_measurement.text = self.operand_measurement_text()
result = [self.operand_choice]
result += (
[self.operand_objects] if self.operand_choice == MC_OBJECT else []
)
result += [self.operand_measurement, self.multiplicand, self.exponent]
return result
self.output_feature_name = cps.AlphanumericText(
"Name the output measurement",
"Measurement",
doc="""Enter a name for the measurement calculated by this module.""",
)
self.operation = cps.Choice(
"Operation",
O_ALL,
doc="""\
Choose the arithmetic operation you would like to perform. *None* is
useful if you simply want to select some of the later options in the
module, such as multiplying or exponentiating your image by a constant.
""",
)
self.operands = (Operand(0, self.operation), Operand(1, self.operation))
self.spacer_1 = cps.Divider(line=True)
self.spacer_2 = cps.Divider(line=True)
self.spacer_3 = cps.Divider(line=True)
self.wants_log = cps.Binary(
"Take log10 of result?",
False,
doc="""Select *Yes* if you want the log (base 10) of the result."""
% globals(),
)
self.final_multiplicand = cps.Float(
"Multiply the result by",
1,
doc="""\
*(Used only for operations other than "None")*
Enter the number by which you would like to multiply the result.
""",
)
self.final_exponent = cps.Float(
"Raise the power of result by",
1,
doc="""\
*(Used only for operations other than "None")*
Enter the power by which you would like to raise the result.
""",
)
self.final_addend = cps.Float(
"Add to the result",
0,
doc="""Enter the number you would like to add to the result.""",
)
self.constrain_lower_bound = cps.Binary(
"Constrain the result to a lower bound?",
False,
doc="""Select *Yes* if you want the result to be constrained to a lower bound."""
% globals(),
)
self.lower_bound = cps.Float(
"Enter the lower bound",
0,
doc="""Enter the lower bound of the result here.""",
)
self.constrain_upper_bound = cps.Binary(
"Constrain the result to an upper bound?",
False,
doc="""Select *Yes* if you want the result to be constrained to an upper bound."""
% globals(),
)
self.upper_bound = cps.Float(
"Enter the upper bound",
1,
doc="""Enter the upper bound of the result here.""",
)
self.rounding = cps.Choice(
"How should the output value be rounded?",
ROUNDING,
doc="""\
Choose how the values should be rounded- not at all, to a specified number of decimal places,
to the next lowest integer ("floor rounding"), or to the next highest integer ("ceiling rounding").
Note that for rounding to an arbitrary number of decimal places, Python uses "round to even" rounding,
such that ties round to the nearest even number. Thus, 1.5 and 2.5 both round to to 2 at 0 decimal
places, 2.45 rounds to 2.4, 2.451 rounds to 2.5, and 2.55 rounds to 2.6 at 1 decimal place. See the
numpy documentation for more information.
""",
)
self.rounding_digit = cps.Integer(
"Enter how many decimal places the value should be rounded to",
0,
doc="""\
Enter how many decimal places the value should be rounded to. 0 will round to an integer (e.g. 1, 2), 1 to
one decimal place (e.g. 0.1, 0.2), -1 to one value before the decimal place (e.g. 10, 20), etc.
""",
)
def add_image(self, can_remove=True):
group = GranularitySettingsGroup()
group.can_remove = can_remove
if can_remove:
group.append("divider", cps.Divider(line=True))
group.append(
"image_name",
cps.ImageNameSubscriber(
"Select an image to measure",
"None",
doc=
"Select the grayscale images whose granularity you want to measure.",
),
)
group.append(
"subsample_size",
cps.Float(
"Subsampling factor for granularity measurements",
0.25,
minval=np.finfo(float).eps,
maxval=1,
doc="""\
If the textures of interest are larger than a few pixels, we recommend
you subsample the image with a factor <1 to speed up the processing.
Down sampling the image will let you detect larger structures with a
smaller sized structure element. A factor >1 might increase the accuracy
but also require more processing time. Images are typically of higher
resolution than is required for granularity measurements, so the default
value is 0.25. For low-resolution images, increase the subsampling
fraction; for high-resolution images, decrease the subsampling fraction.
Subsampling by 1/4 reduces computation time by (1/4) :sup:`3` because the
size of the image is (1/4) :sup:`2` of original and the range of granular
spectrum can be 1/4 of original. Moreover, the results are sometimes
actually a little better with subsampling, which is probably because
with subsampling the individual granular spectrum components can be used
as features, whereas without subsampling a feature should be a sum of
several adjacent granular spectrum components. The recommendation on the
numerical value cannot be determined in advance; an analysis as in this
reference may be required before running the whole set. See this `pdf`_,
slides 27-31, 49-50.
.. _pdf: http://www.ravkin.net/presentations/Statistical%20properties%20of%20algorithms%20for%20analysis%20of%20cell%20images.pdf""",
),
)
group.append(
"image_sample_size",
cps.Float(
"Subsampling factor for background reduction",
0.25,
minval=np.finfo(float).eps,
maxval=1,
doc="""\
It is important to remove low frequency image background variations as
they will affect the final granularity measurement. Any method can be
used as a pre-processing step prior to this module; we have chosen to
simply subtract a highly open image. To do it quickly, we subsample the
image first. The subsampling factor for background reduction is usually
[0.125 – 0.25]. This is highly empirical, but a small factor should be
used if the structures of interest are large. The significance of
background removal in the context of granulometry is that image volume
at certain granular size is normalized by total image volume, which
depends on how the background was removed.""",
),
)
group.append(
"element_size",
cps.Integer(
"Radius of structuring element",
10,
minval=1,
doc="""\
This radius should correspond to the radius of the textures of interest
*after* subsampling; i.e., if textures in the original image scale have
a radius of 40 pixels, and a subsampling factor of 0.25 is used, the
structuring element size should be 10 or slightly smaller, and the range
of the spectrum defined below will cover more sizes.""",
),
)
group.append(
"granular_spectrum_length",
cps.Integer(
"Range of the granular spectrum",
16,
minval=1,
doc="""\
You may need a trial run to see which granular
spectrum range yields informative measurements. Start by using a wide spectrum and
narrow it down to the informative range to save time.""",
),
)
group.append(
"add_objects_button",
cps.DoSomething(
"",
"Add another object",
group.add_objects,
doc="""\
Press this button to add granularity measurements for objects, such as
those identified by a prior **IdentifyPrimaryObjects** module.
**MeasureGranularity** will measure the image’s granularity within each
object at the requested scales.""",
),
)
group.objects = []
group.object_count = cps.HiddenCount(group.objects, "Object count")
if can_remove:
group.append(
"remover",
cps.RemoveSettingButton("", "Remove this image", self.images,
group),
)
self.images.append(group)
return group
def create_settings(self):
"""Create the settings for the module
Create the settings for the module during initialization.
"""
self.image_name = cps.ImageNameSubscriber(
"Select the input image",
"None",
doc="""\
The name of a binary image from a previous module. **IdentifyDeadWorms**
will use this image to establish the foreground and background for the
fitting operation. You can use **ApplyThreshold** to threshold a
grayscale image and create the binary mask. You can also use a module
such as **IdentifyPrimaryObjects** to label each worm and then use
**ConvertObjectsToImage** to make the result a mask.
""",
)
self.object_name = cps.ObjectNameProvider(
"Name the dead worm objects to be identified",
"DeadWorms",
doc="""\
This is the name for the dead worm objects. You can refer
to this name in subsequent modules such as
**IdentifySecondaryObjects**""",
)
self.worm_width = cps.Integer(
"Worm width",
10,
minval=1,
doc="""\
This is the width (the short axis), measured in pixels,
of the diamond used as a template when
matching against the worm. It should be less than the width
of a worm.""",
)
self.worm_length = cps.Integer(
"Worm length",
100,
minval=1,
doc="""\
This is the length (the long axis), measured in pixels,
of the diamond used as a template when matching against the
worm. It should be less than the length of a worm""",
)
self.angle_count = cps.Integer(
"Number of angles",
32,
minval=1,
doc="""\
This is the number of different angles at which the template will be
tried. For instance, if there are 12 angles, the template will be
rotated by 0°, 15°, 30°, 45° … 165°. The shape is bilaterally symmetric;
that is, you will get the same shape after rotating it by 180°.
""",
)
self.wants_automatic_distance = cps.Binary(
"Automatically calculate distance parameters?",
True,
doc="""\
This setting determines whether or not **IdentifyDeadWorms**
automatically calculates the parameters used to determine whether two
found-worm centers belong to the same worm.
Select "*Yes*" to have **IdentifyDeadWorms** automatically calculate
the distance from the worm length and width. Select "*No*" to set the
distances manually.
""" % globals(),
)
self.space_distance = cps.Float(
"Spatial distance",
5,
minval=1,
doc="""\
*(Used only if not automatically calculating distance parameters)*
Enter the distance for calculating the worm centers, in units of pixels.
The worm centers must be at least many pixels apart for the centers to
be considered two separate worms.
""",
)
self.angular_distance = cps.Float(
"Angular distance",
30,
minval=1,
doc="""\
*(Used only if automatically calculating distance parameters)*
**IdentifyDeadWorms** calculates the worm centers at different angles.
Two worm centers are considered to represent different worms if their
angular distance is larger than this number. The number is measured in
degrees.
""",
)
def create_settings(self):
self.image_name = cps.ImageNameSubscriber(
"Select the input image",
"None",
doc=
"""Select the multichannel image you want to convert to grayscale.""",
)
self.combine_or_split = cps.Choice(
"Conversion method",
[COMBINE, SPLIT],
doc="""\
How do you want to convert the color image?
- *%(SPLIT)s:* Splits the channels of a color
image (e.g., red, green, blue) into separate grayscale images.
- *%(COMBINE)s:* Converts a color image to a grayscale image by
combining channels together (e.g., red, green, blue).""" % globals(),
)
self.rgb_or_channels = cps.Choice(
"Image type",
[CH_RGB, CH_HSV, CH_CHANNELS],
doc="""\
This setting provides three options to choose from:
- *%(CH_RGB)s:* The RGB (red, green, blue) color space is the typical
model in which color images are stored. Choosing this option will
split the image into red, green, and blue component images.
- *%(CH_HSV)s:* The HSV (hue, saturation, value) color space is based
on color characteristics such as tint, shade, and tone.
Choosing this option will split the image into the hue,
saturation, and value component images.
- *%(CH_CHANNELS)s:* Many images contain color channels other than RGB
or HSV. For instance, GIF and PNG formats can have an alpha
channel that encodes transparency. TIF formats can have an arbitrary
number of channels which represent pixel measurements made by
different detectors, filters or lighting conditions. This setting
allows you to handle a more complex model for images that
have more than three channels.""" % globals(),
)
# The following settings are used for the combine option
self.grayscale_name = cps.ImageNameProvider(
"Name the output image",
"OrigGray",
doc="""\
*(Used only when combining channels)*
Enter a name for the resulting grayscale image.""",
)
self.red_contribution = cps.Float(
"Relative weight of the red channel",
1,
0,
doc="""\
*(Used only when combining channels)*
Relative weights: If all relative weights are equal, all three colors
contribute equally in the final image. To weight colors relative to each
other, increase or decrease the relative weights.""",
)
self.green_contribution = cps.Float(
"Relative weight of the green channel",
1,
0,
doc="""\
*(Used only when combining channels)*
Relative weights: If all relative weights are equal, all three colors
contribute equally in the final image. To weight colors relative to each
other, increase or decrease the relative weights.""",
)
self.blue_contribution = cps.Float(
"Relative weight of the blue channel",
1,
0,
doc="""\
*(Used only when combining channels)*
Relative weights: If all relative weights are equal, all three colors
contribute equally in the final image. To weight colors relative to each
other, increase or decrease the relative weights.""",
)
# The following settings are used for the split RGB option
self.use_red = cps.Binary(
"Convert red to gray?",
True,
doc="""\
*(Used only when splitting RGB images)*
Select *"Yes"* to extract the red channel to grayscale. Otherwise, the
red channel will be ignored.
""" % globals(),
)
self.red_name = cps.ImageNameProvider(
"Name the output image",
"OrigRed",
doc="""\
*(Used only when splitting RGB images)*
Enter a name for the resulting grayscale image coming from the red channel.""",
)
self.use_green = cps.Binary(
"Convert green to gray?",
True,
doc="""\
*(Used only when splitting RGB images)*
Select *"Yes"* to extract the green channel to grayscale. Otherwise, the
green channel will be ignored.
""" % globals(),
)
self.green_name = cps.ImageNameProvider(
"Name the output image",
"OrigGreen",
doc="""\
*(Used only when splitting RGB images)*
Enter a name for the resulting grayscale image coming from the green channel.""",
)
self.use_blue = cps.Binary(
"Convert blue to gray?",
True,
doc="""\
*(Used only when splitting RGB images)*
Select *"Yes"* to extract the blue channel to grayscale. Otherwise, the
blue channel will be ignored.
""" % globals(),
)
self.blue_name = cps.ImageNameProvider(
"Name the output image",
"OrigBlue",
doc="""\
*(Used only when splitting RGB images)*
Enter a name for the resulting grayscale image coming from the blue channel.""",
)
# The following settings are used for the split HSV option
self.use_hue = cps.Binary(
"Convert hue to gray?",
True,
doc="""\
*(Used only when splitting HSV images)*
Select *"Yes"* to extract the hue to grayscale. Otherwise, the hue
will be ignored.
""" % globals(),
)
self.hue_name = cps.ImageNameProvider(
"Name the output image",
"OrigHue",
doc="""\
*(Used only when splitting HSV images)*
Enter a name for the resulting grayscale image coming from the hue.""",
)
self.use_saturation = cps.Binary(
"Convert saturation to gray?",
True,
doc="""\
*(Used only when splitting HSV images)*
Select *"Yes"* to extract the saturation to grayscale. Otherwise, the
saturation will be ignored.
""" % globals(),
)
self.saturation_name = cps.ImageNameProvider(
"Name the output image",
"OrigSaturation",
doc="""\
*(Used only when splitting HSV images)*
Enter a name for the resulting grayscale image coming from the saturation.""",
)
self.use_value = cps.Binary(
"Convert value to gray?",
True,
doc="""\
*(Used only when splitting HSV images)*
Select *"Yes"* to extract the value to grayscale. Otherwise, the
value will be ignored.
""" % globals(),
)
self.value_name = cps.ImageNameProvider(
"Name the output image",
"OrigValue",
doc="""\
*(Used only when splitting HSV images)*
Enter a name for the resulting grayscale image coming from the value.""",
)
# The alternative model:
self.channels = []
self.add_channel(False)
self.channel_button = cps.DoSomething("", "Add another channel",
self.add_channel)
self.channel_count = cps.HiddenCount(self.channels, "Channel count")
def create_settings(self):
self.image_name = cps.ImageNameSubscriber(
"Select the input image",
"None",
doc="Choose the image you want to flip or rotate.",
)
self.output_name = cps.ImageNameProvider(
"Name the output image",
"FlippedOrigBlue",
doc="Provide a name for the transformed image.",
)
self.flip_choice = cps.Choice(
"Select method to flip image",
FLIP_ALL,
doc="""\
Select how the image is to be flipped.""",
)
self.rotate_choice = cps.Choice(
"Select method to rotate image",
ROTATE_ALL,
doc="""\
- *%(ROTATE_NONE)s:* Leave the image unrotated. This should be used if
you want to flip the image only.
- *%(ROTATE_ANGLE)s:* Provide the numerical angle by which the image
should be rotated.
- *%(ROTATE_COORDINATES)s:* Provide the X,Y pixel locations of two
points in the image that should be aligned horizontally or
vertically.
- *%(ROTATE_MOUSE)s:* CellProfiler will pause so you can select the
rotation interactively. When prompted during the analysis run, grab
the image by clicking the left mouse button, rotate the image by
dragging with the mouse, then release the mouse button. Press the
*Done* button on the image after rotating the image appropriately.
""" % globals(),
)
self.wants_crop = cps.Binary(
"Crop away the rotated edges?",
True,
doc="""\
*(Used only when rotating images)*
When an image is rotated, there will be black space at the
corners/edges; select *Yes* to crop away the incomplete rows and
columns of the image, or select *No* to leave it as-is.
This cropping will produce an image that is not exactly the same size as
the original, which may affect downstream modules.
""" % globals(),
)
self.how_often = cps.Choice(
"Calculate rotation",
IO_ALL,
doc="""\
*(Used only when using “%(ROTATE_MOUSE)s” to rotate images)*
Select the cycle(s) at which the calculation is requested and
calculated.
- *%(IO_INDIVIDUALLY)s:* Determine the amount of rotation for each image individually, e.g., for each cycle.
- *%(IO_ONCE)s:* Define the rotation only once (on the first image), then apply it to all images.
""" % globals(),
)
self.first_pixel = cps.Coordinates(
"Enter coordinates of the top or left pixel",
(0, 0),
doc="""\
*(Used only when using {ROTATE_COORDINATES} to rotate images)*
After rotation, if the specified points are aligned horizontally, this point on the image will be positioned to the
left of the other point. If the specified points are aligned vertically, this point of the image will be positioned
above the other point.
""".format(**{"ROTATE_COORDINATES": ROTATE_COORDINATES}),
)
self.second_pixel = cps.Coordinates(
"Enter the coordinates of the bottom or right pixel",
(0, 100),
doc="""\
*(Used only when using {ROTATE_COORDINATES} to rotate images)*
After rotation, if the specified points are aligned horizontally, this point on the image will be positioned to the
right of the other point. If the specified points are aligned vertically, this point of the image will be positioned
below the other point.
""".format(**{"ROTATE_COORDINATES": ROTATE_COORDINATES}),
)
self.horiz_or_vert = cps.Choice(
"Select how the specified points should be aligned",
C_ALL,
doc="""\
*(Used only when using “%(ROTATE_COORDINATES)s” to rotate images)*
Specify whether you would like the coordinate points that you entered to
be horizontally or vertically aligned after the rotation is complete.""" %
globals(),
)
self.angle = cps.Float(
"Enter angle of rotation",
0,
doc="""\
*(Used only when using “%(ROTATE_ANGLE)s” to rotate images)*
Enter the angle you would like to rotate the image. This setting is in
degrees, with positive angles corresponding to counterclockwise and
negative as clockwise.""" % globals(),
)
def add_image(self, can_remove=True):
group = cps.SettingsGroup()
group.can_remove = can_remove
if can_remove:
group.append("divider", cps.Divider())
idx = len(self.outputs)
default_name = STAINS_BY_POPULARITY[idx % len(STAINS_BY_POPULARITY)]
default_name = default_name.replace(" ", "")
group.append(
"image_name",
cps.ImageNameProvider(
"Name the output image",
default_name,
doc="""\
Use this setting to name one of the images produced by the
module for a particular stain. The image can be used in
subsequent modules in the pipeline.
""",
),
)
choices = list(sorted(STAIN_DICTIONARY.keys())) + [CHOICE_CUSTOM]
group.append(
"stain_choice",
cps.Choice(
"Stain",
choices=choices,
doc="""\
Use this setting to choose the absorbance values for a particular stain.
The stains are:
|Unmix_image0|
(Information taken from `here`_,
`here <http://en.wikipedia.org/wiki/Staining>`__, and
`here <http://stainsfile.info>`__.)
You can choose *{CHOICE_CUSTOM}* and enter your custom values for the
absorbance (or use the estimator to determine values from single-stain
images).
.. _here: http://en.wikipedia.org/wiki/Histology#Staining
.. |Unmix_image0| image:: {UNMIX_COLOR_CHART}
""".format(
**{
"UNMIX_COLOR_CHART":
cellprofiler.gui.help.content.image_resource(
"UnmixColors.png"),
"CHOICE_CUSTOM":
CHOICE_CUSTOM,
}),
),
)
group.append(
"red_absorbance",
cps.Float(
"Red absorbance",
0.5,
0,
1,
doc="""\
*(Used only if "%(CHOICE_CUSTOM)s" is selected for the stain)*
The red absorbance setting estimates the dye’s absorbance of light in
the red channel.You should enter a value between 0 and 1 where 0 is no
absorbance and 1 is complete absorbance. You can use the estimator to
calculate this value automatically.
""" % globals(),
),
)
group.append(
"green_absorbance",
cps.Float(
"Green absorbance",
0.5,
0,
1,
doc="""\
*(Used only if "%(CHOICE_CUSTOM)s" is selected for the stain)*
The green absorbance setting estimates the dye’s absorbance of light in
the green channel. You should enter a value between 0 and 1 where 0 is
no absorbance and 1 is complete absorbance. You can use the estimator to
calculate this value automatically.
""" % globals(),
),
)
group.append(
"blue_absorbance",
cps.Float(
"Blue absorbance",
0.5,
0,
1,
doc="""\
*(Used only if "%(CHOICE_CUSTOM)s" is selected for the stain)*
The blue absorbance setting estimates the dye’s absorbance of light in
the blue channel. You should enter a value between 0 and 1 where 0 is no
absorbance and 1 is complete absorbance. You can use the estimator to
calculate this value automatically.
""" % globals(),
),
)
def on_estimate():
result = self.estimate_absorbance()
if result is not None:
(
group.red_absorbance.value,
group.green_absorbance.value,
group.blue_absorbance.value,
) = result
group.append(
"estimator_button",
cps.DoSomething(
"Estimate absorbance from image",
"Estimate",
on_estimate,
doc="""\
Press this button to load an image of a sample stained only with the dye
of interest. **UnmixColors** will estimate appropriate red, green and
blue absorbance values from the image.
""",
),
)
if can_remove:
group.append(
"remover",
cps.RemoveSettingButton("", "Remove this image", self.outputs,
group),
)
self.outputs.append(group)
def create_settings(self):
"""Create the settings for the module
Create the settings for the module during initialization.
"""
self.contrast_choice = cps.Choice(
"Make each classification decision on how many measurements?",
[BY_SINGLE_MEASUREMENT, BY_TWO_MEASUREMENTS],
doc="""\
This setting controls how many measurements are used to make a
classifications decision for each object:
- *%(BY_SINGLE_MEASUREMENT)s:* Classifies each object based on a
single measurement.
- *%(BY_TWO_MEASUREMENTS)s:* Classifies each object based on a pair
of measurements taken together (that is, an object must meet two
criteria to belong to a class).
""" % globals(),
)
############### Single measurement settings ##################
#
# A list holding groupings for each of the single measurements
# to be done
#
self.single_measurements = []
#
# A count of # of measurements
#
self.single_measurement_count = cps.HiddenCount(
self.single_measurements)
#
# Add one single measurement to start off
#
self.add_single_measurement(False)
#
# A button to press to get another measurement
#
self.add_measurement_button = cps.DoSomething(
"", "Add another classification", self.add_single_measurement)
#
############### Two-measurement settings #####################
#
# The object for the contrasting method
#
self.object_name = cps.ObjectNameSubscriber(
"Select the object name",
"None",
doc="""\
Choose the object that you want to measure from the list. This should be
an object created by a previous module such as
**IdentifyPrimaryObjects**, **IdentifySecondaryObjects**, **IdentifyTertiaryObjects**, or **Watershed**
""",
)
#
# The two measurements for the contrasting method
#
def object_fn():
return self.object_name.value
self.first_measurement = cps.Measurement(
"Select the first measurement",
object_fn,
doc="""\
*(Used only if using a pair of measurements)*
Choose a measurement made on the above object. This is the first of two
measurements that will be contrasted together. The measurement should be
one made on the object in a prior module.
""",
)
self.first_threshold_method = cps.Choice(
"Method to select the cutoff",
[TM_MEAN, TM_MEDIAN, TM_CUSTOM],
doc="""\
*(Used only if using a pair of measurements)*
Objects are classified as being above or below a cutoff value for a
measurement. You can set this cutoff threshold in one of three ways:
- *%(TM_MEAN)s*: At the mean of the measurement’s value for all
objects in the image cycle.
- *%(TM_MEDIAN)s*: At the median of the measurement’s value for all
objects in the image set.
- *%(TM_CUSTOM)s*: You specify a custom threshold value.
""" % globals(),
)
self.first_threshold = cps.Float(
"Enter the cutoff value",
0.5,
doc="""\
*(Used only if using a pair of measurements)*
This is the cutoff value separating objects in the two classes.""",
)
self.second_measurement = cps.Measurement(
"Select the second measurement",
object_fn,
doc="""\
*(Used only if using a pair of measurements)*
Select a measurement made on the above object. This is
the second of two measurements that will be contrasted together.
The measurement should be one made on the object in a prior
module.""",
)
self.second_threshold_method = cps.Choice(
"Method to select the cutoff",
[TM_MEAN, TM_MEDIAN, TM_CUSTOM],
doc="""\
*(Used only if using a pair of measurements)*
Objects are classified as being above or below a cutoff value for a
measurement. You can set this cutoff threshold in one of three ways:
- *%(TM_MEAN)s:* At the mean of the measurement’s value for all
objects in the image cycle.
- *%(TM_MEDIAN)s:* At the median of the measurement’s value for all
objects in the image set.
- *%(TM_CUSTOM)s:* You specify a custom threshold value.
""" % globals(),
)
self.second_threshold = cps.Float(
"Enter the cutoff value",
0.5,
doc="""\
*(Used only if using a pair of measurements)*
This is the cutoff value separating objects in the two classes.""",
)
self.wants_custom_names = cps.Binary(
"Use custom names for the bins?",
False,
doc="""\
*(Used only if using a pair of measurements)*
Select "*Yes*" if you want to specify the names of each bin
measurement.
Select "*No*" to create names based on the measurements. For instance,
for “Intensity_MeanIntensity_Green” and
“Intensity_TotalIntensity_Blue”, the module generates measurements
such as
“Classify_Intensity_MeanIntensity_Green_High_Intensity_TotalIntensity_Low”.
""" % globals(),
)
self.low_low_custom_name = cps.AlphanumericText(
"Enter the low-low bin name",
"low_low",
doc="""\
*(Used only if using a pair of measurements)*
Name of the measurement for objects that fall below the threshold for
both measurements.
""",
)
self.low_high_custom_name = cps.AlphanumericText(
"Enter the low-high bin name",
"low_high",
doc="""\
*(Used only if using a pair of measurements)*
Name of the measurement for objects whose
first measurement is below threshold and whose second measurement
is above threshold.
""",
)
self.high_low_custom_name = cps.AlphanumericText(
"Enter the high-low bin name",
"high_low",
doc="""\
*(Used only if using a pair of measurements)*
Name of the measurement for objects whose
first measurement is above threshold and whose second measurement
is below threshold.""",
)
self.high_high_custom_name = cps.AlphanumericText(
"Enter the high-high bin name",
"high_high",
doc="""\
*(Used only if using a pair of measurements)*
Name of the measurement for objects that
are above the threshold for both measurements.""",
)
self.wants_image = cps.Binary(
"Retain an image of the classified objects?",
False,
doc="""\
Select "*Yes*" to retain the image of the objects color-coded
according to their classification, for use later in the pipeline (for
example, to be saved by a **SaveImages** module).
""" % globals(),
)
self.image_name = cps.ImageNameProvider(
"Enter the image name",
"None",
doc="""\
*(Used only if the classified object image is to be retained for later use in the pipeline)*
Enter the name to be given to the classified object image.""",
)
def create_settings(self):
"""Create the settings that control this module"""
self.object_name = cps.ObjectNameSubscriber(
"Select objects to be masked",
"None",
doc="""\
Select the objects that will be masked (that is, excluded in whole or in
part based on the other settings in the module). You can choose from any
objects created by a previous object processing module, such as
**IdentifyPrimaryObjects**, **IdentifySecondaryObjects** or
**IdentifyTertiaryObjects**.
""",
)
self.remaining_objects = cps.ObjectNameProvider(
"Name the masked objects",
"MaskedNuclei",
doc="""\
Enter a name for the objects that remain after
the masking operation. You can refer to the masked objects in
subsequent modules by this name.
""",
)
self.mask_choice = cps.Choice(
"Mask using a region defined by other objects or by binary image?",
[MC_OBJECTS, MC_IMAGE],
doc="""\
You can mask your objects by defining a region using objects you
previously identified in your pipeline (*%(MC_OBJECTS)s*) or by
defining a region based on the white regions in a binary image
previously loaded or created in your pipeline (*%(MC_IMAGE)s*).
""" % globals(),
)
self.masking_objects = cps.ObjectNameSubscriber(
"Select the masking object",
"None",
doc="""\
*(Used only if mask is to be made from objects)*
Select the objects that will be used to define the masking region. You
can choose from any objects created by a previous object processing
module, such as **IdentifyPrimaryObjects**,
**IdentifySecondaryObjects**, or **IdentifyTertiaryObjects**.
""",
)
self.masking_image = cps.ImageNameSubscriber(
"Select the masking image",
"None",
doc="""\
*(Used only if mask is to be made from an image)*
Select an image that was either loaded or created by a previous module.
The image should be a binary image where the white portion of the image
is the region(s) you will use for masking. Binary images can be loaded
from disk using the **NamesAndTypes** module by selecting “Binary mask”
for the image type. You can also create a binary image from a grayscale
image using **ApplyThreshold**.
""",
)
self.wants_inverted_mask = cps.Binary(
"Invert the mask?",
False,
doc="""\
This option reverses the foreground/background relationship of the mask.
- Select "*No*" for the mask to be composed of the foreground (white
portion) of the masking image or the area within the masking objects.
- Select "*Yes*" for the mask to instead be composed of the
*background* (black portions) of the masking image or the area
*outside* the masking objects.
""" % globals(),
)
self.overlap_choice = cps.Choice(
"Handling of objects that are partially masked",
[P_MASK, P_KEEP, P_REMOVE, P_REMOVE_PERCENTAGE],
doc="""\
An object might partially overlap the mask region, with pixels both
inside and outside the region. **MaskObjects** can handle this in one
of three ways:
- *%(P_MASK)s:* Choosing this option will reduce the size of partially
overlapping objects. The part of the object that overlaps the masking
region will be retained. The part of the object that is outside of the
masking region will be removed.
- *%(P_KEEP)s:* If you choose this option, **MaskObjects** will keep
the whole object if any part of it overlaps the masking region.
- *%(P_REMOVE)s:* Objects that are partially outside of the masking
region will be completely removed if you choose this option.
- *%(P_REMOVE_PERCENTAGE)s:* Determine whether to remove or keep an
object depending on how much of the object overlaps the masking
region. **MaskObjects** will keep an object if at least a certain
fraction (which you enter below) of the object falls within the
masking region. **MaskObjects** completely removes the object if too
little of it overlaps the masking region.""" % globals(),
)
self.overlap_fraction = cps.Float(
"Fraction of object that must overlap",
0.5,
minval=0,
maxval=1,
doc="""\
*(Used only if removing based on overlap)*
Specify the minimum fraction of an object that must overlap the masking
region for that object to be retained. For instance, if the fraction is
0.75, then 3/4 of an object must be within the masking region for that
object to be retained.
""",
)
self.retain_or_renumber = cps.Choice(
"Numbering of resulting objects",
[R_RENUMBER, R_RETAIN],
doc="""\
Choose how to number the objects that remain after masking, which
controls how remaining objects are associated with their predecessors:
- *%(R_RENUMBER)s:* The objects that remain will be renumbered using
consecutive numbers. This is a good choice if you do not plan to use
measurements from the original objects; your object measurements for
the masked objects will not have gaps (where removed objects are
missing).
- *%(R_RETAIN)s:* The original labels for the objects will be
retained. This allows any measurements you make from the masked
objects to be directly aligned with measurements you might have made
of the original, unmasked objects (or objects directly associated
with them).
""" % globals(),
)
def create_settings(self):
self.scheme_choice = cps.Choice(
"Select a color scheme",
[SCHEME_RGB, SCHEME_CMYK, SCHEME_STACK, SCHEME_COMPOSITE],
doc="""\
This module can use one of two color schemes to combine images:
- *%(SCHEME_RGB)s*: Each input image determines the intensity of one
of the color channels: red, green, and blue.
- *%(SCHEME_CMYK)s*: Three of the input images are combined to
determine the colors (cyan, magenta, and yellow) and a fourth is used
only for brightness. The cyan image adds equally to the green and
blue intensities. The magenta image adds equally to the red and blue
intensities. The yellow image adds equally to the red and green
intensities.
- *%(SCHEME_STACK)s*: The channels are stacked in the order listed,
from top to bottom. An arbitrary number of channels is allowed.
For example, you could create a 5-channel image by providing
5 grayscale images. The first grayscale image you provide will fill
the first channel, the second grayscale image you provide will fill
the second channel, and so on.
- *%(SCHEME_COMPOSITE)s*: A color is assigned to each grayscale image.
Each grayscale image is converted to color by multiplying the
intensity by the color and the resulting color images are added
together. An arbitrary number of channels can be composited into a
single color image.
""" % globals(),
)
# # # # # # # # # # # # # # # #
#
# RGB settings
#
# # # # # # # # # # # # # # # #
self.red_image_name = cps.ImageNameSubscriber(
"Select the image to be colored red",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Select the input image to be displayed in red.
""" % globals(),
)
self.green_image_name = cps.ImageNameSubscriber(
"Select the image to be colored green",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Select the input image to be displayed in green.
""" % globals(),
)
self.blue_image_name = cps.ImageNameSubscriber(
"Select the image to be colored blue",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Select the input image to be displayed in blue.
""" % globals(),
)
self.rgb_image_name = cps.ImageNameProvider(
"Name the output image",
"ColorImage",
doc="""Enter a name for the resulting image.""",
)
self.red_adjustment_factor = cps.Float(
"Relative weight for the red image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Enter the relative weight for the red image. If all relative weights are
equal, all three colors contribute equally in the final image. To weight
colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
self.green_adjustment_factor = cps.Float(
"Relative weight for the green image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Enter the relative weight for the green image. If all relative weights
are equal, all three colors contribute equally in the final image. To
weight colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
self.blue_adjustment_factor = cps.Float(
"Relative weight for the blue image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_RGB)s" is selected as the color scheme)*
Enter the relative weight for the blue image. If all relative weights
are equal, all three colors contribute equally in the final image. To
weight colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
# # # # # # # # # # # # # #
#
# CYMK settings
#
# # # # # # # # # # # # # #
self.cyan_image_name = cps.ImageNameSubscriber(
"Select the image to be colored cyan",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Select the input image to be displayed in cyan.
""" % globals(),
)
self.magenta_image_name = cps.ImageNameSubscriber(
"Select the image to be colored magenta",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Select the input image to be displayed in magenta.
""" % globals(),
)
self.yellow_image_name = cps.ImageNameSubscriber(
"Select the image to be colored yellow",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Select the input image to be displayed in yellow.
""" % globals(),
)
self.gray_image_name = cps.ImageNameSubscriber(
"Select the image that determines brightness",
can_be_blank=True,
blank_text=LEAVE_THIS_BLACK,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Select the input image that will determine each pixel's brightness.
""" % globals(),
)
self.cyan_adjustment_factor = cps.Float(
"Relative weight for the cyan image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Enter the relative weight for the cyan image. If all relative weights
are equal, all colors contribute equally in the final image. To weight
colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
self.magenta_adjustment_factor = cps.Float(
"Relative weight for the magenta image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Enter the relative weight for the magenta image. If all relative weights
are equal, all colors contribute equally in the final image. To weight
colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
self.yellow_adjustment_factor = cps.Float(
"Relative weight for the yellow image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Enter the relative weight for the yellow image. If all relative weights
are equal, all colors contribute equally in the final image. To weight
colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
self.gray_adjustment_factor = cps.Float(
"Relative weight for the brightness image",
value=1,
minval=0,
doc="""\
*(Used only if "%(SCHEME_CMYK)s" is selected as the color scheme)*
Enter the relative weight for the brightness image. If all relative
weights are equal, all colors contribute equally in the final image. To
weight colors relative to each other, increase or decrease the relative
weights.
""" % globals(),
)
# # # # # # # # # # # # # #
#
# Stack settings
#
# # # # # # # # # # # # # #
self.stack_channels = []
self.stack_channel_count = cps.HiddenCount(self.stack_channels)
self.add_stack_channel_cb(can_remove=False)
self.add_stack_channel = cps.DoSomething(
"Add another channel",
"Add another channel",
self.add_stack_channel_cb,
doc="""\
Press this button to add another image to the stack.
""",
)
def create_settings(self):
self.image_name = cps.ImageNameSubscriber(
"Select the input image",
"None",
doc="Select the images to be made into a projection.",
)
self.projection_type = cps.Choice(
"Type of projection",
P_ALL,
doc="""\
The final projection image can be created by the following methods:
- *%(P_AVERAGE)s:* Use the average pixel intensity at each pixel
position.
- *%(P_MAXIMUM)s:* Use the maximum pixel value at each pixel position.
- *%(P_MINIMUM)s:* Use the minimum pixel value at each pixel position.
- *%(P_SUM)s:* Add the pixel values at each pixel position.
- *%(P_VARIANCE)s:* Compute the variance at each pixel position.
The variance method is described in Selinummi et al (2009). The
method is designed to operate on a Z-stack of brightfield images
taken at different focus planes. Background pixels will have
relatively uniform illumination whereas cytoplasm pixels will have
higher variance across the Z-stack.
- *%(P_POWER)s:* Compute the power at a given frequency at each pixel
position.
The power method is experimental. The method computes the power at a
given frequency through the Z-stack. It might be used with a phase
contrast image where the signal at a given pixel will vary
sinusoidally with depth. The frequency is measured in Z-stack steps
and pixels that vary with the given frequency will have a higher
score than other pixels with similar variance, but different
frequencies.
- *%(P_BRIGHTFIELD)s:* Perform the brightfield projection at each
pixel position.
Artifacts such as dust appear as black spots that are most strongly
resolved at their focal plane with gradually increasing signals
below. The brightfield method scores these as zero since the dark
appears in the early Z-stacks. These pixels have a high score for the
variance method but have a reduced score when using the brightfield
method.
- *%(P_MASK)s:* Compute a binary image of the pixels that are masked
in any of the input images.
The mask method operates on any masks that might have been applied to
the images in a group. The output is a binary image where the “1”
pixels are those that are not masked in all of the images and the “0”
pixels are those that are masked in one or more of the images.
You can use the output of the mask method to mask or crop all of the
images in a group similarly. Use the mask method to combine all of
the masks in a group, save the image and then use **Crop**,
**MaskImage** or **MaskObjects** in another pipeline to mask all
images or objects in the group similarly.
References
^^^^^^^^^^
- Selinummi J, Ruusuvuori P, Podolsky I, Ozinsky A, Gold E, et al.
(2009) “Bright field microscopy as an alternative to whole cell
fluorescence in automated analysis of macrophage images”, *PLoS ONE*
4(10): e7497 `(link)`_.
.. _(link): https://doi.org/10.1371/journal.pone.0007497
""" % globals(),
)
self.projection_image_name = cps.ImageNameProvider(
"Name the output image",
"ProjectionBlue",
doc="Enter the name for the projected image.",
provided_attributes={
"aggregate_image": True,
"available_on_last": True,
},
)
self.frequency = cps.Float(
"Frequency",
6.0,
minval=1.0,
doc="""\
*(Used only if "%(P_POWER)s" is selected as the projection method)*
This setting controls the frequency at which the power is measured. A
frequency of 2 will respond most strongly to pixels that alternate
between dark and light in successive z-stack slices. A frequency of N
will respond most strongly to pixels whose brightness cycles every N
slices.""" % globals(),
)
