How to use the identify.add_object_location_measurements function in identify

child_count, parents = orig_objects.relate_children(filtered_objects)
        m.add_measurement(filtered_objects_name,
                          I.FF_PARENT%(orig_objects_name),
                          parents)
        m.add_measurement(orig_objects_name,
                          I.FF_CHILDREN_COUNT%(filtered_objects_name),
                          child_count)
        #
        # The object count
        #
        I.add_object_count_measurements(m, filtered_objects_name,
                                        object_count)
        #
        # The object locations
        #
        I.add_object_location_measurements(m, filtered_objects_name,
                                           filtered_labels)
        #
        # Outlines if we want them
        #
        if self.wants_outlines:
            outlines_name = self.outlines_name.value
            outlines = outline(filtered_labels)
            outlines_image = cpi.Image(outlines.astype(bool))
            workspace.image_set.add(outlines_name, outlines_image)
        #
        # Do the drawing here
        #
        if workspace.frame is not None:
            figure = workspace.create_or_find_figure(subplots=(2,1))
            figure.subplot_imshow_labels(0, 0, orig_labels, orig_objects_name)
            figure.subplot_imshow_labels(1, 0, filtered_labels,

workspace.object_set.add_objects(objects, objects_name)

        ##################
        #
        # Add measurements
        #
        m = workspace.measurements
        #
        # The object count
        #
        object_count = np.max(labels)
        I.add_object_count_measurements(m, objects_name, object_count)
        #
        # The object locations
        #
        I.add_object_location_measurements(m, objects_name, labels)
        #
        # Outlines if we want them
        #
        if self.wants_outlines:
            outlines_name = self.outlines_name.value
            outlines = outline(labels)
            outlines_image = cpi.Image(outlines.astype(bool))
            workspace.image_set.add(outlines_name, outlines_image)
        #
        # Do the drawing here
        #
        if workspace.frame is not None:
            figure = workspace.create_or_find_figure(subplots=(2,1))
            figure.subplot_imshow_labels(0, 0, labels, objects_name)
            figure.subplot_imshow(1, 0, self.draw_outlines(pixel_data, labels))

np.array([ave_threshold],
                                                     dtype=float))
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_ORIG_THRESHOLD%(objname),
                                         np.array([global_threshold],
                                                      dtype=float))
            wv = cpthresh.weighted_variance(img, mask, local_threshold)
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_WEIGHTED_VARIANCE%(objname),
                                         np.array([wv],dtype=float))
            entropies = cpthresh.sum_of_entropies(img, mask, local_threshold)
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_SUM_OF_ENTROPIES%(objname),
                                         np.array([entropies],dtype=float))
        cpmi.add_object_count_measurements(measurements, objname, object_count)
        cpmi.add_object_location_measurements(measurements, objname,
                                              segmented_out)
        #
        # Relate the secondary objects to the primary ones and record
        # the relationship.
        #
        children_per_parent, parents_of_children = \
            objects.relate_children(objects_out)
        measurements.add_measurement(self.primary_objects.value,
                                     cpmi.FF_CHILDREN_COUNT%objname,
                                     children_per_parent)
        measurements.add_measurement(objname,
                                     cpmi.FF_PARENT%self.primary_objects.value,
                                     parents_of_children)
        #
        # If primary objects were created, add them
        #

objname = self.object_name.value
        measurements = workspace.measurements
        cpmi.add_object_count_measurements(measurements,
                                           objname, object_count)
        self.add_threshold_measurements(measurements, img, mask,
                                        local_threshold, global_threshold,
                                        self.object_name.value)
        # Add label matrices to the object set
        objects = cellprofiler.objects.Objects()
        objects.segmented = labeled_image
        objects.unedited_segmented = unedited_labels
        objects.small_removed_segmented = small_removed_labels
        objects.parent_image = image
        
        workspace.object_set.add_objects(objects,self.object_name.value)
        cpmi.add_object_location_measurements(workspace.measurements, 
                                              self.object_name.value,
                                              labeled_image)
        if self.should_save_outlines.value:
            out_img = cpi.Image(outline_image.astype(bool),
                                parent_image = image)
            workspace.image_set.add(self.save_outlines.value, out_img)

measurements.add_measurement(self.primary_objects.value,
                                     cpmi.FF_CHILDREN_COUNT%objname,
                                     children_per_parent)
        measurements.add_measurement(objname,
                                     cpmi.FF_PARENT%self.primary_objects.value,
                                     parents_of_children)
        #
        # If primary objects were created, add them
        #
        if self.wants_discard_edge and self.wants_discard_primary:
            workspace.object_set.add_objects(new_objects,
                                             self.new_primary_objects_name.value)
            cpmi.add_object_count_measurements(measurements,
                                               self.new_primary_objects_name.value,
                                               np.max(new_objects.segmented))
            cpmi.add_object_location_measurements(measurements,
                                                  self.new_primary_objects_name.value,
                                                  new_objects.segmented)
            for parent_objects, parent_name, child_objects, child_name in (
                (objects, self.primary_objects.value,
                 new_objects, self.new_primary_objects_name.value),
                (new_objects, self.new_primary_objects_name.value,
                 objects_out, objname)):
                children_per_parent, parents_of_children = \
                    parent_objects.relate_children(child_objects)
                measurements.add_measurement(parent_name,
                                             cpmi.FF_CHILDREN_COUNT%child_name,
                                             children_per_parent)
                measurements.add_measurement(child_name,
                                             cpmi.FF_PARENT%parent_name,
                                             parents_of_children)

cpmi.FF_PARENT%(parent_objects_name.value),
                              parents_of)
            m.add_measurement(parent_objects_name.value,
                              cpmi.FF_CHILDREN_COUNT%(self.subregion_objects_name.value),
                              child_count)
        object_count = np.max(tertiary_labels)
        #
        # The object count
        #
        cpmi.add_object_count_measurements(workspace.measurements,
                                           self.subregion_objects_name.value,
                                           object_count)
        #
        # The object locations
        #
        cpmi.add_object_location_measurements(workspace.measurements,
                                              self.subregion_objects_name.value,
                                              tertiary_labels)
        #
        # The outlines
        #
        if self.use_outlines.value:
            out_img = cpi.Image(tertiary_outlines.astype(bool),
                                parent_image = tertiary_image)
            workspace.image_set.add(self.outlines_name.value, out_img)

if np.max(original_objects.segmented) == 0:
            child_count = np.array([],int)
        else:
            child_count = fix(scind.sum(labels, original_objects.segmented,
                                        np.arange(1, nobjects+1)))
            child_count = (child_count > 0).astype(int)
        m.add_measurement(object_name,
                          I.FF_CHILDREN_COUNT % remaining_object_name,
                          child_count)
        if self.retain_or_renumber == R_RETAIN:
            remaining_object_count = nobjects
        else:
            remaining_object_count = len(unique_labels)
        I.add_object_count_measurements(m, remaining_object_name,
                                        remaining_object_count)
        I.add_object_location_measurements(m, remaining_object_name, labels)
        #
        # Add an outline if asked to do so
        #
        if self.wants_outlines.value:
            outline_image = cpi.Image(outline(labels) > 0,
                                      parent_image = original_objects.parent_image)
            workspace.image_set.add(self.outlines_name.value, outline_image)
        #
        # Save the input, mask and output images for display
        #
        if workspace.frame is not None:
            workspace.display_data.original_labels = original_objects.segmented
            workspace.display_data.final_labels = labels
            workspace.display_data.mask = mask