#!/usr/bin/env python # -*- coding: utf-8 -*- # # # Copyright (c) 2020 University of Dundee. # # 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. # # Version: 1.0 # import numpy import os import omero.clients from omero.gateway import BlitzGateway from getpass import getpass from collections import OrderedDict from ilastik import app from ilastik.applets.dataSelection.opDataSelection import PreloadedArrayDatasetInfo # noqa # Connect to the server def connect(hostname, username, password): conn = BlitzGateway(username, password, host=hostname, secure=True) conn.connect() return conn # Load-images def load_images(conn, dataset_id): return conn.getObjects('Image', opts={'dataset': dataset_id}) # Create-dataset def create_dataset(conn, dataset_id): dataset = omero.model.DatasetI() v = "ilastik_probabilities_from_dataset_%s" % dataset_id dataset.setName(omero.rtypes.rstring(v)) v = "ilatisk results probabilities from Dataset:%s" % dataset_id dataset.setDescription(omero.rtypes.rstring(v)) return conn.getUpdateService().saveAndReturnObject(dataset) # Load-data def load_numpy_array(image): pixels = image.getPrimaryPixels() size_z = image.getSizeZ() size_c = image.getSizeC() size_t = image.getSizeT() size_y = image.getSizeY() size_x = image.getSizeX() z, t, c = 0, 0, 0 # first plane of the image zct_list = [] for t in range(size_t): for z in range(size_z): # get the Z-stack for c in range(size_c): # all channels zct_list.append((z, c, t)) values = [] # Load all the planes as YX numpy array planes = pixels.getPlanes(zct_list) j = 0 k = 0 tmp_c = [] tmp_z = [] s = "z:%s t:%s c:%s y:%s x:%s" % (size_z, size_t, size_c, size_y, size_x) print(s) # axis tzyxc print("Downloading image %s" % image.getName()) for i, p in enumerate(planes): if k < size_z: if j < size_c: tmp_c.append(p) j = j + 1 if j == size_c: # use dstack to have c at the end tmp_z.append(numpy.dstack(tmp_c)) tmp_c = [] j = 0 k = k + 1 if k == size_z: # done with the stack values.append(numpy.stack(tmp_z)) tmp_z = [] k = 0 return numpy.stack(values) # Analyze-data def analyze(conn, images, model, new_dataset): # Prepare ilastik os.environ["LAZYFLOW_THREADS"] = "2" os.environ["LAZYFLOW_TOTAL_RAM_MB"] = "2000" args = app.parse_args([]) args.headless = True args.project = model shell = app.main(args) for image in images: input_data = load_numpy_array(image) # run ilastik headless print('running ilastik using %s and %s' % (model, image.getName())) data = [ {"Raw Data": PreloadedArrayDatasetInfo(preloaded_array=input_data, axistags=vigra.defaultAxistags("tzyxc"))}] # noqa predictions = shell.workflow.batchProcessingApplet.run_export(data, export_to_array=True) # noqa for d in predictions: save_results(conn, image, d, new_dataset) # Save-results def save_results(conn, image, data, dataset): filename, file_extension = os.path.splitext(image.getName()) # Save the probabilities file as an image print("Saving Probabilities as an Image in OMERO") name = filename + "_Probabilities" desc = "ilastik probabilities from Image:%s" % image.getId() # Re-organise array from tzyxc to zctyx order expected by OMERO data = data.swapaxes(0, 1).swapaxes(3, 4).swapaxes(2, 3).swapaxes(1, 2) def plane_gen(): """ Set up a generator of 2D numpy arrays. The createImage method below expects planes in the order specified here (for z.. for c.. for t..) """ size_z = data.shape[0]-1 for z in range(data.shape[0]): # all Z sections data.shape[0] print('z: %s/%s' % (z, size_z)) for c in range(data.shape[1]): # all channels for t in range(data.shape[2]): # all time-points yield data[z][c][t] conn.createImageFromNumpySeq(plane_gen(), name, data.shape[0], data.shape[1], data.shape[2], description=desc, dataset=dataset) # Disconnect def disconnect(conn): conn.close() # main def main(): try: # Collect user credentials host = input("Host [wss://workshop.openmicroscopy.org/omero-ws]: ") or 'wss://workshop.openmicroscopy.org/omero-ws' # noqa username = input("Username [trainer-1]: ") or 'trainer-1' password = getpass("Password: ") dataset_id = input("Dataset ID [6210]: ") or '6210' # Connect to the server conn = connect(host, username, password) conn.c.enableKeepAlive(60) # path to the ilastik project ilastik_project = "../notebooks/pipelines/pixel-class-133.ilp" # Load the images in the dataset images = load_images(conn, dataset_id) new_dataset = create_dataset(conn, dataset_id) analyze(conn, images, ilastik_project, new_dataset) finally: disconnect(conn) print("done") if __name__ == "__main__": main()