#!/usr/bin/env python # -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Copyright (C) 2020 University of Dundee. All rights reserved. # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, # OR PROFITS; OR BUSINESS INTERRUPTION) # HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ------------------------------------------------------------------------------ """ Simple FRAP plot from Ellipse ROI on time-lapse image. This an OMERO script that runs server-side. """ from omero.gateway import BlitzGateway, MapAnnotationWrapper from omero.model import EllipseI from PIL import Image import numpy as np import matplotlib from matplotlib import pyplot as plt # OMERO.script specific imports import omero.scripts as scripts from omero.rtypes import rlong, rstring, robject def get_channel_index(image, label): """ Get the channel index of a specific channel :param image: The image :param label: The channel name :return: The channel index (None if not found) """ labels = image.getChannelLabels() if label in labels: idx = labels.index(label) return idx return None def get_ellipse_roi(conn, image): """ Get the first ellipse ROI found in the image :param conn: The BlitzGateway :param image: The Image :return: The shape ID of the first ellipse ROI found """ roi_service = conn.getRoiService() result = roi_service.findByImage(image.getId(), None) shape_id = None for roi in result.rois: for s in roi.copyShapes(): if type(s) == EllipseI: shape_id = s.id.val return shape_id def get_mean_intensities(conn, image, the_c, shape_id): """ Get the mean pixel intensities of an roi in a time series image :param conn: The BlitzGateway :param image: The image :param the_c: The channel index :param shape_id: The ROI shape id :return: List of mean intensity values (one for each timepoint) """ roi_service = conn.getRoiService() the_z = 0 size_t = image.getSizeT() meanvalues = [] for t in range(size_t): stats = roi_service.getShapeStatsRestricted([shape_id], the_z, t, [the_c]) meanvalues.append(stats[0].mean[0]) return meanvalues def plot(values, plot_filename): """ Create a simple plot of the given values and saves it. :param values: The values :param plot_filename: The file name :return: Nothing """ matplotlib.use('Agg') fig = plt.figure() plt.subplot(111) plt.plot(values) fig.canvas.draw() fig.savefig(plot_filename) def save_values(conn, image, values): """ Attach the values as map annotation to the image :param conn: The BlitzGateway :param image: The image :param values: The values :return: Nothing """ namespace = "demo.simple_frap_data" key_value_data = [[str(t), str(value)] for t, value in enumerate(values)] map_ann = MapAnnotationWrapper(conn) map_ann.setNs(namespace) map_ann.setValue(key_value_data) map_ann.save() image.linkAnnotation(map_ann) def save_plot(conn, image, plot_filename): """ Save the plot to OMERO :param conn: The BlitzGateway :param image: The image :param plot_filename: The path to the plot image :return: The plot image """ pil_img = Image.open(plot_filename) np_array = np.asarray(pil_img) red = np_array[::, ::, 0] green = np_array[::, ::, 1] blue = np_array[::, ::, 2] plane_gen = iter([red, green, blue]) plot_image = conn.createImageFromNumpySeq(plane_gen, plot_filename, sizeC=3, dataset=image.getParent()) return plot_image def delete_old_values(conn, image): """ Delete previously attached Map annotations :param conn: The BlitzGateway :param image: The image """ namespace = "demo.simple_frap_data" to_delete = [] for ann in image.listAnnotations(ns=namespace): to_delete.append(ann.id) if len(to_delete) > 0: conn.deleteObjects('Annotation', to_delete, wait=True) def analyse(conn, image_id, channel_name): # Step 2 - Load iamge img = conn.getObject("Image", image_id) # - ci = get_channel_index(img, channel_name) shape_id = get_ellipse_roi(conn, img) values = get_mean_intensities(conn, img, ci, shape_id) delete_old_values(conn, img) save_values(conn, img, values) plot_name = "{}_plot.png".format(img.getName()) plot(values, plot_name) return save_plot(conn, img, plot_name) if __name__ == "__main__": client = scripts.client( 'simple_frap_server.py', """ This script does simple FRAP analysis using an Ellipse ROI previously saved on a time-lapse image. Data is plotted and a new OMERO images is created from the plot. """, scripts.String( "Data_Type", optional=False, grouping="1", description="Data type", values=[rstring('Image')], default="Image"), scripts.List( "IDs", optional=False, grouping="2", description="Image IDs.").ofType(rlong(0)), scripts.String( "Channel_Name", optional=False, grouping="3", description="Channel name:"), authors=["OME Team"], institutions=["University of Dundee"], contact="training-support@openmicroscopy.org", ) try: # Wrap the script parameters into a dictionary scriptParams = {} for key in client.getInputKeys(): if client.getInput(key): scriptParams[key] = client.getInput(key, unwrap=True) # Use existing session via client object conn = BlitzGateway(client_obj=client) plots = [] for image_id in scriptParams["IDs"]: plots.append(analyse(conn, image_id, scriptParams["Channel_Name"])) client.setOutput("Image", robject(plots[0]._obj)) client.setOutput("Message", rstring("Created {} plot(s)." .format(len(plots)))) finally: client.closeSession()