MATLAB and Python

MATLAB functionality can be mixed into Python scripts using the MATLAB Engine API for Python.

Installing MATLAB Engine API

To install the MATLAB Engine API for Python follow the installation guide. You only need to run python install, most likely as an administrator. It is possible to install the engine into a virtual environment.

Example MATLAB scripts

Below are some sample scripts showing MATLAB being launched from OMERO.scripts. MATLAB functions can also call the OMERO Java language bindings interface to access the server from the MATLAB functions.

Calling a simple MATLAB function

import omero.scripts as scripts

import matlab.engine

client = scripts.client('',
This script checks if the specified number is a prime number.
        "x", optional=False, grouping="1",
        description="Number to check."))
    # process the list of args above.
    params = {}
    for key in client.getInputKeys():
        if client.getInput(key):
            params[key] = client.getInput(key, unwrap=True)
    x = params.get("x")
    # start the MATLAB engine
    eng = matlab.engine.start_matlab("-nodisplay")
    tf = eng.isprime(x)

Using the OMERO interface inside MATLAB

This example shows the MATLAB script being called, passed to the client object and accessing the same client instance as the script.

You will need to have the OMERO.matlab toolbox installed on the server:

  • download the toolbox from the OMERO Downloads page

  • unzip

  • enter the full path to the toolbox in the OMERO script below.

Create a frap.m, copy the MATLAB function below. Save the file to the server. Enter the full path to the directory containing the scipt in the example OMERO script below.

Note that this script expects to run on timelapse images with at least one Ellipse not linked to a T-index.

import omero

import omero.scripts as scripts
from omero.rtypes import rlong
from omero.gateway import BlitzGateway
from omero.rtypes import robject, rstring

import matlab.engine

dataTypes = [rstring('Dataset')]
client = scripts.client('',
This script does simple FRAP analysis using Ellipse ROIs previously
saved on images. If matplotlib is installed, data is plotted and new
OMERO images are created from the plots.
Call the matlab frap code.
        "Data_Type", optional=False, grouping="1",
        description="Choose source of images",
        values=dataTypes, default="Dataset"),

        "ID", optional=False, grouping="2",
        description="Dataset ID."))
    # process the list of args above.
    params = {}
    for key in client.getInputKeys():
        if client.getInput(key):
            params[key] = client.getInput(key, unwrap=True)
    dataset_id = params.get("ID")
    # wrap client to use the Blitz Gateway
    conn = BlitzGateway(client_obj=client)
    # start the MATLAB engine
    eng = matlab.engine.start_matlab("-nodisplay")
    # Add the OMERO.matlab toolbox the MATLABPATH
    # Add the frap function to the MATLABPATH.
    # For convenience this could
    # be placed in the OMERO.matlab toolbox folder
    eng.frap(conn.getEventContext().sessionUuid, dataset_id, nargout=0)
    client.setOutput("Message", rstring("frap script completed"))


The MATLAB frap function

function T = frap(sessionId, datasetId)

p = inputParser;
p.addRequired('sessionId',@(x) isscalar(x));
p.addRequired('datasetId',@(x) isscalar(x));

client = loadOmero();
% Join an OMERO session
session = client.joinSession(sessionId);
% Initiliaze the service used to load the Regions of Interest (ROI)
service = session.getRoiService();

% Retrieve the Dataset with the Images
dataset = getDatasets(session, datasetId, true);
images = toMatlabList(dataset.linkedImageList);

% Iterate through the images

for i = 1 : numel(images)
    image = images(i);
    imageId = image.getId().getValue();
    pixels = image.getPrimaryPixels();
    sizeT = pixels.getSizeT().getValue(); % The number of timepoints

    % Load the ROIs linked to the Image. Only keep the Ellipses
    roiResult = service.findByImage(imageId, []);
    rois = roiResult.rois;
    if rois.size == 0
    toAnalyse = java.util.ArrayList;
    for thisROI  = 1:rois.size
        roi = rois.get(thisROI-1);
        for ns = 1:roi.sizeOfShapes
            shape = roi.getShape(ns-1);
            if (isa(shape, 'omero.model.Ellipse'))

    % We analyse the first z and the first channel
    keys = strings(1, sizeT);
    values = strings(1, sizeT);
    means = zeros(1, sizeT);
    for t = 0:sizeT-1
        % OMERO index starts at 0
        stats = service.getShapeStatsRestricted(toAnalyse, 0, t, [0]);
        calculated = stats(1,1);
        mean = calculated.mean(1,1);
        index = t+1;
        keys(1, index) = num2str(t);
        values(1, index) = num2str(mean);
        means(1, index) = mean;
    % create a map annotation and link it to the Image
    mapAnnotation = writeMapAnnotation(session, cellstr(keys), cellstr(values), 'namespace', 'demo.simple_frap_data');
    linkAnnotation(session, mapAnnotation, 'image', imageId);

    % Create a CSV
    headers = 'Image_name,ImageID,Timepoint,Mean';
    tmpName = [tempname,'.csv'];
    [filepath,imageName,ext] = fileparts(tmpName);
    f = fullfile(filepath, 'results_frap.csv');
    fileID = fopen(f,'w');
    for j = 1 : numel(keys)
        row = strcat(char(imageName), ',', num2str(imageId), ',', keys(1, j), ',', values(1, j));
    % Create a file annotation
    fileAnnotation = writeFileAnnotation(session, f, 'mimetype', 'text/csv', 'namespace', 'training.demo');
    linkAnnotation(session, fileAnnotation, 'image', imageId);

    % Plot the result
    time = 1:sizeT;
    fig = plot(means);
    xlabel('Timepoint'), ylabel('Values');
    % Save the plot as png
    name = strcat(char(image.getName().getValue()),'_FRAP_plot.png');
    % Upload the Image as an attachment
    fileAnnotation = writeFileAnnotation(session, name);
    linkAnnotation(session, fileAnnotation, 'image', imageId);
    % Delete the local file