57a14faee7b0198e056e474c04ea346d148a2835,cellprofiler/modules/identifyprimaryobjects.py,IdentifyPrimaryObjects,display,#IdentifyPrimaryObjects#Any#Any#,1194

Before Change

            orig_axes     = figure.subplot(0,0)
            label_axes    = figure.subplot(1,0, sharexy = orig_axes)
            outlined_axes = figure.subplot(0,1, sharexy = orig_axes)
            table_axes    = figure.subplot(1,1, sharexy = orig_axes)
    
            title = "Input image, cycle //%d"%(workspace.measurements.image_number,)
            figure.subplot_imshow_grayscale(0, 0,
                                              workspace.display_data.image,
                                              title)
            figure.subplot_imshow_labels(1, 0, workspace.display_data.labeled_image, 
                                           self.object_name.value)
    
            if workspace.display_data.image.ndim == 2:
                // Outline the size-excluded pixels in red
                outline_img = np.ndarray(shape=(workspace.display_data.image.shape[0],
                                                   workspace.display_data.image.shape[1],3))
                outline_img[:,:,0] = workspace.display_data.image 
                outline_img[:,:,1] = workspace.display_data.image
                outline_img[:,:,2] = workspace.display_data.image
            else:
                outline_img = workspace.display_data.image.copy()
            
            // Outline the accepted objects pixels
            draw_outline(outline_img, workspace.display_data.outline_image,
                         cpp.get_primary_outline_color())
            
            // Outline the size-excluded pixels
            draw_outline(outline_img,
                         workspace.display_data.outline_size_excluded_image,
                         cpp.get_secondary_outline_color())
            
            // Outline the border-excluded pixels in yellow
            draw_outline(outline_img,
                         workspace.display_data.outline_border_excluded_image,
                         cpp.get_tertiary_outline_color())
            
            title = "%s outlines"%(self.object_name.value) 
            figure.subplot_imshow(0, 1, outline_img, title, normalize=False)
            
            table_axes.clear()
            table = table_axes.table(cellText=workspace.display_data.statistics,
                                     colWidths=[.7,.3],
                                     loc="center",
                                     cellLoc="left")
            table_axes.set_frame_on(False)
            table_axes.set_axis_off()
            table.auto_set_font_size(False)
            table.set_fontsize(cpp.get_table_font_size())
    
    def calc_smoothing_filter_size(self):

After Change

            figure.subplot_table(
                1, 1, 
                [[x[1]] for x in workspace.display_data.statistics],
                row_labels = [x[0] for x in workspace.display_data.statistics])
    
    def calc_smoothing_filter_size(self):
        Return the size of the smoothing filter, calculating it if in automatic mode

Instances