add FilterObjects_StringMatch (#254)

Uses FilterObjects as template for filtering objects that match a given string.

active_plugins/filterobjects_stringmatch.py

@@ -0,0 +1,179 @@
+from cellprofiler_core.module.image_segmentation import ObjectProcessing
+from cellprofiler_core.setting import (
+    Divider,
+)
+from cellprofiler_core.setting.text import Alphanumeric
+
+__doc__ = ""
+
+import logging
+import os
+
+import numpy
+import scipy
+import scipy.ndimage
+import scipy.sparse
+
+import cellprofiler_core.object
+from cellprofiler.utilities.rules import Rules
+
+LOGGER = logging.getLogger(__name__)
+
+
+class FilterObjects_StringMatch(ObjectProcessing):
+    module_name = "FilterObjects_StringMatch"
+
+    variable_revision_number = 1
+
+    def __init__(self):
+        self.rules = Rules()
+
+        super(FilterObjects_StringMatch, self).__init__()
+
+    def create_settings(self):
+        super(FilterObjects_StringMatch, self).create_settings()
+
+        self.x_name.text = """Select the objects to filter"""
+
+        self.x_name.doc = ""
+
+        self.y_name.text = """Name the output objects"""
+
+        self.y_name.doc = "Enter a name for the collection of objects that are retained after applying the filter(s)."
+
+        self.spacer_1 = Divider(line=False)
+
+        self.filter_out = Alphanumeric(
+            "What string to filter out",
+            "AAAA",
+            doc="""Enter a name for the measurement calculated by this module.""",
+        )
+
+        self.rules.create_settings()
+
+    def settings(self):
+        settings = super(FilterObjects_StringMatch, self).settings()
+        settings += [self.filter_out]
+        return settings
+
+    def visible_settings(self):
+        visible_settings = super(FilterObjects_StringMatch, self).visible_settings()
+        visible_settings += [
+                self.filter_out
+            ]
+        return visible_settings              
+
+    def run(self, workspace):
+        """Filter objects for this image set, display results"""
+        src_objects = workspace.get_objects(self.x_name.value)
+
+        indexes = self.keep_by_string(workspace, src_objects)
+
+        #
+        # Create an array that maps label indexes to their new values
+        # All labels to be deleted have a value in this array of zero
+        #
+        new_object_count = len(indexes)
+        max_label = numpy.max(src_objects.segmented)
+        label_indexes = numpy.zeros((max_label + 1,), int)
+        label_indexes[indexes] = numpy.arange(1, new_object_count + 1)
+        #
+        # Loop over both the primary and additional objects
+        #
+        object_list = [(self.x_name.value, self.y_name.value)]
+        m = workspace.measurements
+        first_set = True
+        for src_name, target_name in object_list:
+            src_objects = workspace.get_objects(src_name)
+            target_labels = src_objects.segmented.copy()
+            #
+            # Reindex the labels of the old source image
+            #
+            target_labels[target_labels > max_label] = 0
+            target_labels = label_indexes[target_labels]
+            #
+            # Make a new set of objects - retain the old set's unedited
+            # segmentation for the new and generally try to copy stuff
+            # from the old to the new.
+            #
+            target_objects = cellprofiler_core.object.Objects()
+            target_objects.segmented = target_labels
+            target_objects.unedited_segmented = src_objects.unedited_segmented
+            #
+            # Remove the filtered objects from the small_removed_segmented
+            # if present. "small_removed_segmented" should really be
+            # "filtered_removed_segmented".
+            #
+            small_removed = src_objects.small_removed_segmented.copy()
+            small_removed[(target_labels == 0) & (src_objects.segmented != 0)] = 0
+            target_objects.small_removed_segmented = small_removed
+            if src_objects.has_parent_image:
+                target_objects.parent_image = src_objects.parent_image
+            workspace.object_set.add_objects(target_objects, target_name)
+
+            self.add_measurements(workspace, src_name, target_name)
+            if self.show_window and first_set:
+                workspace.display_data.src_objects_segmented = src_objects.segmented
+                workspace.display_data.target_objects_segmented = target_objects.segmented
+                workspace.display_data.dimensions = src_objects.dimensions
+                first_set = False
+
+    def display(self, workspace, figure):
+        """Display what was filtered"""
+        src_name = self.x_name.value
+        src_objects_segmented = workspace.display_data.src_objects_segmented
+        target_objects_segmented = workspace.display_data.target_objects_segmented
+        dimensions = workspace.display_data.dimensions
+
+        target_name = self.y_name.value
+
+        figure.set_subplots((2, 2), dimensions=dimensions)
+
+        figure.subplot_imshow_labels(
+            0, 0, src_objects_segmented, title="Original: %s" % src_name
+        )
+
+        figure.subplot_imshow_labels(
+            1,
+            0,
+            target_objects_segmented,
+            title="Filtered: %s" % target_name,
+            sharexy=figure.subplot(0, 0),
+        )
+
+        pre = numpy.max(src_objects_segmented)
+        post = numpy.max(target_objects_segmented)
+
+        statistics = [[pre], [post], [pre - post]]
+
+        figure.subplot_table(
+            0,
+            1,
+            statistics,
+            row_labels=(
+                "Number of objects pre-filtering",
+                "Number of objects post-filtering",
+                "Number of objects removed",
+            ),
+        )
+
+    def keep_by_string(self, workspace, src_objects):
+        """
+        workspace - workspace passed into Run
+        src_objects - the Objects instance to be filtered
+        """
+        src_name = self.x_name.value
+        m = workspace.measurements
+        values = m.get_current_measurement(src_name, "Barcode_BarcodeCalled")
+        # Is this structure still necessary or is it an artifact?
+        # Could be just values == self.filter_out.value
+        # Make an array of True
+        hits = numpy.ones(len(values), bool)
+        # Fill with False for those where we want to filter out
+        hits[values == self.filter_out.value] = False
+        # Get object numbers for things that are True
+        indexes = numpy.argwhere(hits)[:, 0]
+        # Objects are 1 counted, Python is 0 counted
+        indexes = indexes + 1
+
+        return indexes