示例#1
0
    def run(self, workspace):
        objects_name = self.objects_name.value
        objects = workspace.object_set.get_objects(objects_name)
        assert isinstance(objects, cpo.Objects)
        labels = objects.segmented
        if self.relabel_option == OPTION_SPLIT:
            output_labels, count = scind.label(labels > 0, np.ones((3, 3),
                                                                   bool))
        else:
            if self.merge_option == UNIFY_DISTANCE:
                mask = labels > 0
                if self.distance_threshold.value > 0:
                    #
                    # Take the distance transform of the reverse of the mask
                    # and figure out what points are less than 1/2 of the
                    # distance from an object.
                    #
                    d = scind.distance_transform_edt(~mask)
                    mask = d < self.distance_threshold.value / 2 + 1
                output_labels, count = scind.label(mask, np.ones((3, 3), bool))
                output_labels[labels == 0] = 0
                if self.wants_image:
                    output_labels = self.filter_using_image(workspace, mask)
            elif self.merge_option == UNIFY_PARENT:
                parents_name = self.parent_object.value
                parents_of = workspace.measurements[objects_name, "_".join(
                    (C_PARENT, parents_name))]
                output_labels = labels.copy().astype(np.uint32)
                output_labels[labels > 0] = parents_of[labels[labels > 0] - 1]
                if self.merging_method == UM_CONVEX_HULL:
                    ch_pts, n_pts = morph.convex_hull(output_labels)
                    ijv = morph.fill_convex_hulls(ch_pts, n_pts)
                    output_labels[ijv[:, 0], ijv[:, 1]] = ijv[:, 2]

        output_objects = cpo.Objects()
        output_objects.segmented = output_labels
        if objects.has_small_removed_segmented:
            output_objects.small_removed_segmented = \
                copy_labels(objects.small_removed_segmented, output_labels)
        if objects.has_unedited_segmented:
            output_objects.unedited_segmented = \
                copy_labels(objects.unedited_segmented, output_labels)
        output_objects.parent_image = objects.parent_image
        workspace.object_set.add_objects(output_objects,
                                         self.output_objects_name.value)

        measurements = workspace.measurements
        add_object_count_measurements(measurements,
                                      self.output_objects_name.value,
                                      np.max(output_objects.segmented))
        add_object_location_measurements(measurements,
                                         self.output_objects_name.value,
                                         output_objects.segmented)

        #
        # Relate the output objects to the input ones and record
        # the relationship.
        #
        children_per_parent, parents_of_children = \
            objects.relate_children(output_objects)
        measurements.add_measurement(
            self.objects_name.value,
            FF_CHILDREN_COUNT % self.output_objects_name.value,
            children_per_parent)
        measurements.add_measurement(self.output_objects_name.value,
                                     FF_PARENT % self.objects_name.value,
                                     parents_of_children)

        if self.show_window:
            workspace.display_data.orig_labels = objects.segmented
            workspace.display_data.output_labels = output_objects.segmented
            if self.merge_option == UNIFY_PARENT:
                workspace.display_data.parent_labels = \
                    workspace.object_set.get_objects(self.parent_object.value).segmented
    def run(self, workspace):
        objects_name = self.objects_name.value
        objects = workspace.object_set.get_objects(objects_name)
        assert isinstance(objects, cpo.Objects)
        labels = objects.segmented
        if self.relabel_option == OPTION_SPLIT:
            output_labels, count = scind.label(labels > 0, np.ones((3,3),bool))
        else:
            if self.unify_option == UNIFY_DISTANCE:
                mask = labels > 0
                if self.distance_threshold.value > 0:
                    #
                    # Take the distance transform of the reverse of the mask
                    # and figure out what points are less than 1/2 of the
                    # distance from an object.
                    #
                    d = scind.distance_transform_edt(~mask)
                    mask = d < self.distance_threshold.value/2+1
                output_labels, count = scind.label(mask, np.ones((3,3), bool))
                output_labels[labels == 0] = 0
                if self.wants_image:
                    output_labels = self.filter_using_image(workspace, mask)
            elif self.unify_option == UNIFY_PARENT:
                parents_name = self.parent_object.value
                parents_of = workspace.measurements[
                    objects_name, "_".join((C_PARENT, parents_name))]
                output_labels = labels.copy().astype(np.uint32)
                output_labels[labels > 0] = parents_of[labels[labels > 0]-1]
                if self.unification_method == UM_CONVEX_HULL:
                    ch_pts, n_pts = morph.convex_hull(output_labels)
                    ijv = morph.fill_convex_hulls(ch_pts, n_pts)
                    output_labels[ijv[:, 0], ijv[:, 1]] = ijv[:, 2]

        output_objects = cpo.Objects()
        output_objects.segmented = output_labels
        if objects.has_small_removed_segmented:
            output_objects.small_removed_segmented = \
                copy_labels(objects.small_removed_segmented, output_labels)
        if objects.has_unedited_segmented:
            output_objects.unedited_segmented = \
                copy_labels(objects.unedited_segmented, output_labels)
        output_objects.parent_image = objects.parent_image
        workspace.object_set.add_objects(output_objects, self.output_objects_name.value)

        measurements = workspace.measurements
        add_object_count_measurements(measurements,
                                      self.output_objects_name.value,
                                      np.max(output_objects.segmented))
        add_object_location_measurements(measurements,
                                         self.output_objects_name.value,
                                         output_objects.segmented)

        #
        # Relate the output objects to the input ones and record
        # the relationship.
        #
        children_per_parent, parents_of_children = \
            objects.relate_children(output_objects)
        measurements.add_measurement(self.objects_name.value,
                                     FF_CHILDREN_COUNT %
                                     self.output_objects_name.value,
                                     children_per_parent)
        measurements.add_measurement(self.output_objects_name.value,
                                     FF_PARENT%self.objects_name.value,
                                     parents_of_children)
        if self.wants_outlines:
            outlines = centrosome.outline.outline(output_labels)
            outline_image = cpi.Image(outlines.astype(bool))
            workspace.image_set.add(self.outlines_name.value,
                                    outline_image)

        if self.show_window:
            workspace.display_data.orig_labels = objects.segmented
            workspace.display_data.output_labels = output_objects.segmented
            if self.unify_option == UNIFY_PARENT:
                workspace.display_data.parent_labels = \
                    workspace.object_set.get_objects(self.parent_object.value).segmented