OMERO MATLAB language bindings¶

See Developing OMERO clients and OME-Remote Objects, for an introduction to Object.

Installing the OMERO.matlab toolbox¶

Download the latest released version from the Downloads page.
Unzip the directory anywhere on your system.
In MATLAB, move to the newly unzipped directory and run loadOmero;.
The MATLAB files are now on your path, and the necessary jars are on your Java classpath. You can change directories and still have access to OMERO.

Once OMERO.matlab is installed, the typical workflow is:

Creating a connection
Keeping your session alive
Creating an unencrypted session (optional)
Do some work (load objects, work with them, upload to the server, etc.)
Closing your connection
Unloading OMERO (optional)

As a quickstart example, the following lines create a secure connection to a server, read a series of images and close the connection.

client = loadOmero(servername);
session = client.createSession(user, password);
client.enableKeepAlive(60);
images = getImages(session, ids);
client.closeSession();

Examples of usage of the OMERO.matlab toolbox are provided in the training examples directory.

Configuring the OMERO.matlab connection¶

Creating a connection¶

As described under Working with OMERO, there are several ways to configure your connection to an OMERO server. OMERO.matlab comes with a few conveniences for making this work.

If you run client = loadOmero(); (i.e. loadOmero without an input argument), then OMERO.matlab will try to configure the omero.client object for you. First, it checks the ICE_CONFIG environment variable. If set, it will let the omero.client constructor initialize itself. Otherwise, it looks for the file ice.config in the current directory. The OMERO.matlab toolbox comes with a default ice.config file pointing at localhost. To use this configuration file, you should replace localhost by your server address.

Alternatively, you can pass the server address to loadOmero; to create a client:

>> client = loadOmero(servername);

Or, if you want a session created directly using the configuration ice.config file:

>> [client, session] = loadOmero('ice.config');

This is equivalent to:

>> client = loadOmero(servername, port);
>> session = client.createSession(username, password)

where the variables servername, port, username and password are the values set in ice.config for the previous example. The default port will be used if not specified.

Keeping your session alive¶

For executing any long running task, you will need a background thread which keeps your session alive. If you are familiar with MATLAB Timers you can use omeroKeepAlive.m directly or modify it to your liking. By default the function creates a default 60-second timer.

>> [client, session] = loadOmero('ice.config');
>> timer = omeroKeepAlive(client); % Create timer and starts it.
>> …
>> delete(timer);             % Disable the keep-alive

Alternatively, you can use the Java-based enableKeepAlive method, but it is not configurable from within MATLAB. In that case, you will need to specify the time interval:

client.enableKeepAlive(60); % Call session.keepAlive() every 60 seconds
client.closeSession();      % Close session to end the keep-alive

Working in a different group¶

Each session is created within a given context, defining not only the session user but also the session group. The session context can be retrieved using the administration service:

eventContext = session.getAdminService().getEventContext();
groupId = eventContext.groupId;

Most read and write operations described below are performed in the context of the session group when using the default parameters. Since OMERO 5.1.4, it is possible to specify a different context than the session group for reading and writing data using the group parameter/key value in the OMERO.matlab functions. Retrieving objects by identifiers is also done across all groups by default.

See also

OMERO permissions querying, usage and history: Developer documentation about the OMERO permissions system

Creating an unencrypted session¶

Once a session has been created, if you want to speed up the data transfer, you can create and use an unencrypted session as:

unsecureClient = client.createClient(false);
sessionUnencrypted = unsecureClient.getSession();

Closing your connection¶

When you are done with OMERO, it is critical that you close your connection to save resources:

client.closeSession();
clear client;
clear session;

If you created an unencrypted session, you will need to close the unsecure session as well:

client.closeSession();
unsecureClient.closeSession();
clear client;
clear unsecureClient;
clear session;
clear sessionUnencrypted;

Unloading OMERO¶

Then if you would like, you can unload OMERO as well:

unloadOmero();

You may see the following warning when unloading OMERO:

>> unloadOmero()
Warning: Objects of omero/client class exist - not clearing java
> In javaclasspath>doclear at 377
  In javaclasspath>local_javapath at 194
  In javaclasspath at 105
  In javarmpath at 48
  In unloadOmero at 75

===============================================================
While unloading OMERO, found java objects left in workspace.
Please remove with 'clear <name>' and then run 'unloadOmero'
again.  Printing all objects...
===============================================================

  Name      Size            Bytes  Class           Attributes

  c         1x1                    omero.client

Closing session(s) for 1 found client(s): c

This means that there is still an OMERO.matlab object in your workspace. If not listed, use whos to find such objects, and clear to remove them. After that, run unloadOmero() again:

>> clear c
>> unloadOmero()

Warning

You should also unload OMERO before installing a new version of OMERO.matlab or calling loadOmero again.

If you need to create another session without unloading/loading OMERO again, use the omero.client object directly:

>> client = loadOmero(servername,port);
>> client = omero.client(username_1, password_1);
>> session = c.createSession();

Reading data¶

The IContainer service provides methods to load the data management hierarchy in OMERO – projects, datasets, etc.. A list of examples follows indicating how to load projects, datasets, screens.

Projects

The projects owned by the session user in the context of the session group can be retrieved using the getProjects function:

projects = getProjects(session)

If the project identifiers are known, they can be retrieved independently of their owner or group using:

projects = getProjects(session, ids)

If the projects contain datasets, the datasets will automatically be loaded:

for j = 1 : numel(projects) % Matlab list, index starts at 1
    % Get all the datasets in the Project
    datasetsList = projects(j).linkedDatasetList; % Java List
    % convert it to a Matlab list for convenience
    datasets = toMatlabList(datasetsList);
    % Iterate through datasets
    for i = 1 : numel(datasets)
        d = datasets(i);
    end
end

If the datasets contain images, the images are not automatically loaded. To load the whole graph (projects, datasets, images), pass true as an optional argument:

% Load the specified Projects and the whole graph
loadedProjects = getProjects(session, ids, true)
% Get the first project
project_1 = loadedProjects(1) % Matlab array, index starts at 1
% Get all the datasets in the Project
datasets = project_1.linkedDatasetList;
% Get the first dataset in the Java list, index starts at 0
dataset_1 = datasets.get(0);
dataset_name = dataset_1.getName().getValue(); % dataset's name
dataset_id = dataset_1.getId().getValue(); % dataset's id
% Retrieve all the images in the datasets as a Java List (index will start at 0)
imageList = dataset_1.linkedImageList;
% convert it to a Matlab list for convenience
images = toMatlabList(imageList);
% Iterate through the images
for i = 1 : numel(images)
    image = images(i);
    image_name = image.getName().getValue(); % image's name
    image_id = image.getId().getValue(); % image's id
end

Warning

Loading the entire projects/datasets/images graph can be time-consuming and memory-consuming depending on the amount of data.

To return the orphaned datasets i.e. datasets not in a project, as well as the projects, you can query the second output argument of getProjects:

[projects, orphanedDatasets] = getProjects(session)

To filter projects by owner, use the owner parameter/key value. A value of -1 means projects are retrieved independently of their owner:

% Returns all projects owned by the specified user in the context of the
% session group
projects = getProjects(session, 'owner', ownerId);
% Returns all projects with the input identifiers owned by the specified
% user
projects = getProjects(session, ids, 'owner', ownerId);
% Returns all projects owned by any user in the context of the session
% group
projects = getProjects(session, 'owner', -1);

To filter projects by group, use the group parameter/key value. A value of -1 means projects are retrieved independently of their group:

% Returns all projects owned by the session user in the specified group
projects = getProjects(session, 'group', groupId);
% Returns all projects with the input identifiers in the specified group
projects = getProjects(session, ids, 'group', groupId);
% Returns all projects owned by the session user across groups
projects = getProjects(session, 'group', -1);

Datasets

The datasets owned by the session user in the context of the session group can be retrieved using the getDatasets function:

datasets = getDatasets(session)

If the dataset identifiers are known, they can be retrieved independently of their owner or group using:

datasets = getDatasets(session, ids)

If the datasets contain images, the images are not automatically loaded. To load the whole graph (datasets, images), pass true as an optional argument:

loadedDatasets = getDatasets(session, ids, true);
% Get the first dataset
dataset_1 = loadedDatasets(1); % Matlab array, index starts at 1
% Get the all the images in the dataset as the Java list, index starts at 0
imageList = dataset_1.linkedImageList;

To filter datasets by owner, use the owner parameter/key value. A value of -1 means datasets are retrieved independently of their owner:

% Returns all datasets owned by the specified user in the context of the
% session group
datasets = getDatasets(session, 'owner', ownerId);
% Returns all datasets with the input identifiers owned by the specified
% user
datasets = getDatasets(session, ids, 'owner', ownerId);
% Returns all datasets owned by any user in the context of the session
% group
datasets = getDatasets(session, 'owner', -1);

To filter datasets by group, use the group parameter/key value. A value of -1 means datasets are retrieved independently of their group:

% Returns all datasets owned by the session user in the specified group
datasets = getDatasets(session, 'group', groupId);
% Returns all datasets with the input identifiers in the specified group
datasets = getDatasets(session, ids, 'group', groupId);
% Returns all datasets owned by the session user across groups
datasets = getDatasets(session, 'group', -1);

Images

The images owned by the session user in the context of the session group can be retrieved using the getImages function:

images = getImages(session)

If the image identifiers are known, they can be retrieved independently of their owner or group using:

images = getImages(session, ids)

All the images contained in a subset of datasets of known identifiers datasetsIds can be returned independently of their owner or group using:

datasetImages = getImages(session, 'dataset', datasetsIds)

All the images contained in all the datasets under a subset of projects of known identifiers projectIds can be returned independently of their owner or group using:

projectImages = getImages(session, 'project', projectIds)

To filter images by owner, use the owner parameter/key value. A value of -1 means images are retrieved independently of their owner:

% Returns all images owned by the specified user in the context of the
% session group
images = getImages(session, 'owner', ownerId);
% Returns all images with the input identifiers owned by the specified user
images = getImages(session, ids, 'owner', ownerId);
% Returns all images owned by any user in the context of the session
% group
images = getImages(session, 'owner', -1);

To filter images by group, use the group parameter/key value. A value of -1 means images are retrieved independently of their group:

% Returns all images owned by the session user in the specified group
images = getImages(session, 'group', groupId);
% Returns all images with the input identifiers in the specified group
images = getImages(session, ids, 'group', groupId);
% Returns all images owned by the session user across groups
images = getImages(session, 'group', -1);

The Image-Pixels model (see OME-Remote Objects) implies you need to use the Pixels objects to access valuable data about the Image:

pixels = image.getPrimaryPixels();
sizeZ = pixels.getSizeZ().getValue(); % The number of z-sections.
sizeT = pixels.getSizeT().getValue(); % The number of timepoints.
sizeC = pixels.getSizeC().getValue(); % The number of channels.
sizeX = pixels.getSizeX().getValue(); % The number of pixels along the X-axis.
sizeY = pixels.getSizeY().getValue(); % The number of pixels along the Y-axis.

Screens

The screens owned by the session user in the context of the session group can be retrieved using the getScreens function:

screens = getScreens(session)

If the screen identifiers are known, they can be retrieved independently of their owner or group using:

screens = getScreens(session, ids)

Note that the wells are not loaded. The plate objects can be accessed using:

for j = 1 : numel(screens), % Matlab array, index start at 1
    platesList = screens(j).linkedPlateList; % Java List, index start at 0
    for i = 0 : platesList.size()-1,
        plate = platesList.get(i);
        plateAcquisitionList = plate.copyPlateAcquisitions(); % Java List
        for k = 0 : plateAcquisitionList.size()-1,
            pa = plateAcquisitionList.get(i);
    end
end

To return the orphaned plates as well as the screens, you can query the second output argument of getScreens:

[screens, orphanedPlates] = getScreens(session)

To filter screens by owner, use the owner parameter/key value. A value of -1 means screens are retrieved independently of their owner:

% Returns all screens owned by the specified user in the context of the
% session group
screens = getScreens(session, 'owner', ownerId);
% Returns all screens with the input identifiers owned by the specified
% user
screens = getScreens(session, ids, 'owner', ownerId);
% Returns all screens owned by any user in the context of the session
% group
screens = getScreens(session, 'owner', -1);

To filter screens by group, use the group parameter/key value. A value of -1 means screens are retrieved independently of their group:

% Returns all screens owned by the session user in the specified group
screens = getScreens(session, 'group', groupId);
% Returns all screens with the input identifiers in the specified group
screens = getScreens(session, ids, 'group', groupId);
% Returns all screens owned by the session user across groups
screens = getScreens(session, 'group', -1);

Plates

The screens owned by the session user in the context of the session group can be retrieved using the getPlates function:

plates = getPlates(session)

If the plate identifiers are known, they can be retrieved independently of their owner or group using:

plates = getPlates(session, ids)

To filter plates by owner, use the owner parameter/key value. A value of -1 means plates are retrieved independently of their owner:

% Returns all plates owned by the specified user in the context of the
% session group
plates = getPlates(session, 'owner', ownerId);
% Returns all plates with the input identifiers owned by the specified user
plates = getPlates(session, ids, 'owner', ownerId);
% Returns all plates owned by any user in the context of the session
% group
plates = getPlates(session, 'owner', -1);

To filter plates by group, use the group parameter/key value. A value of -1 means plates are retrieved independently of their group:

% Returns all plates owned by the session user in the specified group
plates = getPlates(session, 'group', groupId);
% Returns all plates with the input identifiers in the specified group
plates = getPlates(session, ids, 'group', groupId);
% Returns all plates owned by the session user across groups
plates = getPlates(session, 'group', -1);

Wells

Given a plate identifier, the wells can be loaded using the findAllByQuery method:

wellList = session.getQueryService().findAllByQuery(
['select well from Well as well '...
'left outer join fetch well.plate as pt '...
'left outer join fetch well.wellSamples as ws '...
'left outer join fetch ws.plateAcquisition as pa '...
'left outer join fetch ws.image as img '...
'left outer join fetch img.pixels as pix '...
'left outer join fetch pix.pixelsType as pt '...
'where well.plate.id = ', num2str(plateId)], []);
% wellList is a Java List, index starts at 0
for j = 0 : wellList.size()-1,
    well = wellList.get(j);
    wellsSampleList = well.copyWellSamples();
    well.getId().getValue()
    % The wellList returned from the server is not sorted by wellIds,
    % please extract the wellRow and wellColumn for every well,
    % to populate your results appropriately
    wellRow = well.getRow().getValue();
    wellColumn = well.getColumn().getValue();
    for i = 0 : wellsSampleList.size()-1,
        ws = wellsSampleList.get(i);
        ws.getId().getValue()
        pa = ws.getPlateAcquisition();
    end
end

Channel

A channel associated to an image has an object called a logicalChannel associated to it. That entity contains valuable information e.g. emission wavelength, name, etc. Given an Image, retrieve channels associated to an image on the OMERO server and the name of the channel:

channels = loadChannels(session, image);
for j = 1 : numel(channels) % Matlab array
    channel = channels(j);
    channelId = channel.getId().getValue();
    lc = channel.getLogicalChannel();
    channelName = lc.getName().getValue();
end

Raw data access¶

You can retrieve data, plane by plane or retrieve a stack. The values are z in [0, sizeZ - 1], c in [0, sizeC - 1] and t in [0, sizeT - 1].

Plane

The plane of an input image at coordinates (z, c, t) can be retrieved using the getPlane function:

plane = getPlane(session, image, z, c, t);

Alternatively, the image identifier can be passed to the function:

plane = getPlane(session, imageId, z, c, t);

Tile

The tile of an input image at coordinates (z, c, t) originated at (x, y) (where x in [0, sizeX - 1], y in [0, sizeY - 1]) and of dimensions (w, h) can be retrieved using the getTile function:

tile = getTile(session, image, z, c, t, x, y, w, h);

Alternatively, the image identifier can be passed to the function:

tile = getTile(session, imageId, z, c, t, x, y, w, h);

Stack

The stack of an input image at coordinates (c, t) can be retrieved using the getStack function:

stack = getStack(session, image, c, t);

Alternatively, the image identifier can be passed to the function:

stack = getStack(session, imageId, c, t);

All the methods described above will internally initialize a raw pixels store to retrieve the pixels data and close this store at the end of the call. This is inefficient when multiple planes/tiles/stacks need to be retrieved. For each function, it is possible to initialize a pixels store and pass this store directly to the pixel retrieval function, e.g.:

[store, pixels] = getRawPixelsStore(session, image);
for z = 0 : sizeZ - 1
  for c = 0 : sizeC - 1
    for t = 0 : sizeT - 1
      plane = getPlane(pixels, store, z, c, t);
    end
  end
end
store.close();

Hypercube

This is useful when you need the Pixels intensity.

% Create the store to load the stack. No access via the gateway
store = session.createRawPixelsStore();
% Indicate the pixels set you are working on
store.setPixelsId(pixelsId, false);

% Offset values in each dimension XYZCT
offset = java.util.ArrayList;
offset.add(java.lang.Integer(0));
offset.add(java.lang.Integer(0));
offset.add(java.lang.Integer(0));
offset.add(java.lang.Integer(0));
offset.add(java.lang.Integer(0));

size = java.util.ArrayList;
size.add(java.lang.Integer(sizeX));
size.add(java.lang.Integer(sizeY));
size.add(java.lang.Integer(sizeZ));
size.add(java.lang.Integer(sizeC));
size.add(java.lang.Integer(sizeT));

% Indicate the step in each direction,
% step = 1, will return values at index 0, 1, 2.
% step = 2, values at index 0, 2, 4, etc.
step = java.util.ArrayList;
step.add(java.lang.Integer(1));
step.add(java.lang.Integer(1));
step.add(java.lang.Integer(1));
step.add(java.lang.Integer(1));
step.add(java.lang.Integer(1));
% Retrieve the data
store.getHypercube(offset, size, step);
% Close the store
store.close();

See also

RawDataAccess.m: Example script showing methods to retrieve the pixel data from an image

Annotations¶

Reading annotations by ID

If the identifier of the annotation of a given type is known, the annotation can be retrieved from the server using the generic getAnnotations function:

tagAnnotations = getAnnotations(session, 'tag', tagIds);

Shortcut functions are available for the main object and annotation types, e.g. to retrieve tag annotations:

tagAnnotations = getTagAnnotations(session, tagIds);

Reading annotations linked to an object

The annotations of a given type linked to a given object can be retrieved using the generic getObjectAnnotations function:

tagAnnotations = getObjectAnnotations(session, 'tag', 'image', imageIds);

Shortcut functions are available for the main object and annotation types, e.g. to retrieve the tag annotations linked to images:

tagAnnotations = getImageTagAnnotations(session, imageIds);

Annotations can be filtered by namespace. To include only annotations with a given namespace ns, use the include parameter/key value:

tagAnnotations = getImageTagAnnotations(session, imageIds, 'include', ns);

To exclude all annotations with a given namespace ns, use the exclude parameter/key value:

tagAnnotations = getImageTagAnnotations(session, imageIds, 'exclude', ns);

By default, only the annotations owned by the session owner are returned. To specify the owner of the annotations, use the owner paramter/key value pair. For instance to return all tag annotations owned by user with an identifier equals to 5:

tagAnnotations = getImageTagAnnotations(session, imageIds, 'owner', 5);

To retrieve all annotations independently of their owner, use -1 as the owner identifier:

tagAnnotations = getImageTagAnnotations(session, imageIds, 'owner', -1);

Reading file annotations

The content of a file annotation can be downloaded to local disk using the getFileAnnotationContent function. If the file annotation has been retrieved from the server as fileAnnotation, then the content of its OriginalFile can be downloaded under target_file using:

getFileAnnotationContent(session, fileAnnotation, target_file);

Alternatively, if only the identifier of the file annotation faId is known:

getFileAnnotationContent(session, faId, target_file);

Writing and linking annotations

New annotations can be created using the corresponding write*Annotation function:

% Create a comment annotation
commentAnnotation = writeCommentAnnotation(session, 'comment');
% Create a double annotation
doubleAnnotation = writeDoubleAnnotation(session, .5);
% Create a map annotation
mapAnnotation = writeMapAnnotation(session, 'key', value);
% Create a tag annotation
tagAnnotation = writeTagAnnotation(session, 'tag name');
% Create a timestamp annotation
timestampAnnotation = writeTimestampAnnotation(session, now);
% Create an XML annotation
xmlAnnotation = writeXmlAnnotation(session, xmlString);

File annotations can also be created from the content of a local_file_path:

fileAnnotation = writeFileAnnotation(session, local_file_path);

Each annotation creation function uses the context of the session group by default. To create the annotation in a different group, use the group key/value pair:

commentAnnotation = writeCommentAnnotation(session, 'comment', 'group', groupId);
doubleAnnotation = writeDoubleAnnotation(session, .5, 'group', groupId);
mapAnnotation = writeMapAnnotation(session, 'key', value, 'group', groupId);
tagAnnotation = writeTagAnnotation(session, 'tag name', 'group', groupId);
timestampAnnotation = writeTimestampAnnotation(session, now, 'group', groupId);
xmlAnnotation = writeXmlAnnotation(session, xmlString, 'group', groupId);
fileAnnotation = writeFileAnnotation(session, local_file_path, 'group', groupId);

Existing annotations can be linked to existing objects on the server using the linkAnnotation function. For example, to link a tag annotation and a file annotation to the image image_id:

link1 = linkAnnotation(session, tagAnnotation, 'image', imageId);
link2 = linkAnnotation(session, fileAnnotation, 'image', imageId);

For existing file annotations, it is possible to replace the content of the original file without having to recreate a new file annotation using the updateFileAnnotation function. If the file annotation has been retrieved from the server as fileAnnotation, then the content of its OriginalFile can be replaced by the content of local_file_path using:

updateFileAnnotation(session, fileAnnotation, local_file_path);

See also

WriteData.m: Example script showing methods to write, link and retrieve annotations.

Writing data¶

Projects/Datasets

Projects and datasets can be created in the context of the session group using the createProject and createDataset functions:

% Create a new project in the context of the session group
newproject = createProject(session, 'project name');
% Create a new dataset in the context of the session group
newdataset = createDataset(session, 'dataset name');

Writing projects/datasets in a different context than the session group can be achieved by passing the group identifier using the group parameter:

% Create a new project in the specified group
newproject = createProject(session, 'project name', 'group', groupId);
% Create a new dataset in the specified group
newdataset = createDataset(session, 'dataset name', 'group', groupId);

When creating a dataset, it is possible to link it to an existing project using either the project object or its identifier. In this case, the group context is determined by the parent project:

% Create two new projects in different groups
project1 = createProject(session, 'project name');
project2 = createProject(session, 'project name', 'group', groupId);
% Create new datasets linked to each project
dataset1 = createDataset(session, 'dataset name', project1);
dataset2 = createDataset(session, 'dataset name', project2.getId().getValue());

Screens/Plates

Screens and plates can be created in the context of the session group using the createScreen and createPlate functions:

% Create a new screen in the context of the session group
newscreen = createScreen(session, 'screen name');
% Create a new plate in the context of the session group
newplate = createPlate(session, 'plate name');

Writing screens/plates in a different context than the session group can be achieved by passing the group identifier using the group parameter:

% Create a new screen in the specified group
newscreen = createScreen(session, 'screen name', 'group', groupId);
% Create a new plate in the specified group
newplate = createPlate(session, 'plate name', 'group', groupId);

When creating a plate, it is possible to link it to an existing screen using either the screen object or its identifier. In this case, the group context is determined by the parent screen:

% Create two new projects in different groups
screen1 = createScreen(session, 'screen name');
screen2 = createScreen(session, 'screen name', 'group', groupId);
% Create new datasets linked to each project
plate1 = createPlate(session, 'plate name', screen1);
plate2 = createPlate(session, 'plate name', screen2.getId().getValue());

See also

WriteData.m: Example script showing methods to create projects, datasets, plates and screens.

How to use OMERO tables¶

Create a table. In the following example, a table is created with 2 columns and is linked to an Image.

name = char(java.util.UUID.randomUUID());
columns = javaArray('omero.grid.Column', 2);
columns(1) = omero.grid.LongColumn('Uid', 'testLong', []);
valuesString = javaArray('java.lang.String', 1);
columns(2) = omero.grid.StringColumn('MyStringColumn', '', 64, valuesString);

% Create a new table.
table = session.sharedResources().newTable(1, name);

% Initialize the table
table.initialize(columns);

% Create and populate omero.grid (The following java wrapping logic is compatible Matlab2014b onwards)
data = javaArray('omero.grid.Column', 2);
data(1) = omero.grid.LongColumn('Uid', 'test Long', [2]);
valuesString = javaArray('java.lang.String', 1);
valuesString(1) = java.lang.String('add');
data(2) = omero.grid.StringColumn('MyStringColumn', '', 64, valuesString);

% Add data to the table.
table.addData(data);
file = table.getOriginalFile(); % if you need to interact with the table

% link table to an Image
fa = omero.model.FileAnnotationI;
fa.setFile(file);
% Currently OMERO.tables are displayed only in OMERO.web and
% for Screen/plate/wells alone. In all cases the file annotation
% needs to contain a namespace.
fa.setNs(rstring(omero.constants.namespaces.NSBULKANNOTATIONS.value));
link = linkAnnotation(session, fa, 'image', imageId);

Read the contents of the table.

of = omero.model.OriginalFileI(file.getId(), false);
tablePrx = session.sharedResources().openTable(of);

% Read headers
headers = tablePrx.getHeaders();
for i = 1 : size(headers, 1)
    headers(i).name; % name of the header
    % Do something
end

% Depending on the size of table, you may only want to read some blocks.
cols = [0:size(headers, 1)-1]; % The number of columns you wish to read.
rows = [0:tablePrx.getNumberOfRows()-1]; % The number of rows you wish to read.
data = tablePrx.slice(cols, rows); % Read the data.
c = data.columns;
for i = 1 : size(c)
    column = c(i);
    % Do something
end
tablePrx.close(); % Important to close when done.

ROIs¶

To learn about the model, see the developers guide to the ROI model. Note that annotations can be linked to ROI.

Creating ROI

This example creates a ROI with shapes, a rectangle, an ellipse and a polygon, and attaches it to an image:

% First create a rectangular shape.
rectangle = createRectangle(0, 0, 10, 20);
% Indicate on which plane (z, c, t) to attach the shape
setShapeCoordinates(rectangle, 0, 0, 0);

% First create an ellipse shape.
ellipse = createEllipse(0, 0, 10, 20);
% Indicate on which plane (z, c, t) to attach the shape
setShapeCoordinates(ellipse, 0, 0, 0);

% First create a polygon shape.
% Specify x-coordinates, y-coordinates
polygon = createPolygon([1 5 10 8], [1 5 5 10]);
% Indicate on which plane (z, c, t) to attach the shape
setShapeCoordinates(polygon, 0, 0, 0);

% Create the roi.
roi = omero.model.RoiI;
% Attach the shapes to the roi, several shapes can be added.
roi.addShape(rectangle);
roi.addShape(ellipse);
roi.addShape(polygon);

% Link the roi and the image
roi.setImage(omero.model.ImageI(imageId, false));
% Save
iUpdate = session.getUpdateService();
roi = iUpdate.saveAndReturnObject(roi);
% Check that the shape has been added.
numShapes = roi.sizeOfShapes;
for ns = 1 : numShapes
   shape = roi.getShape(ns-1);
end

See also

ROI utility functions: OMERO.matlab functions for creating and managing Shape and ROI objects.

Retrieving ROIs linked to an image

service = session.getRoiService();
roiResult = service.findByImage(imageId, []);
rois = roiResult.rois;
n = rois.size;
shapeType = '';
for thisROI  = 1 : n
    roi = rois.get(thisROI-1);
    numShapes = roi.sizeOfShapes;
    for ns = 1 : numShapes
        shape = roi.getShape(ns-1);
        if (isa(shape, 'omero.model.Rectangle'))
           rectangle = shape;
           rectangle.getX().getValue();
        elseif (isa(shape, 'omero.model.Ellipse'))
           ellipse = shape;
           ellipse.getX().getValue();
        elseif (isa(shape, 'omero.model.Point'))
           point = shape;
           point.getX().getValue();
        elseif (isa(shape, 'omero.model.Line'))
           line = shape;
           line.getX1().getValue();
        end
    end
end

Adding Transforms to a Shape object

% Apply rotation alone to an ellipse object
% (angle of rotation set to 10 degrees)
% create ellipse (shape object)
ellipse = createEllipse(0, 0, 10, 20);
setShapeCoordinates(ellipse, 0, 0, 0);
% set angle of rotation
theta = 10;
% create transform object
newTform = omero.model.AffineTransformI;
newTform.setA00(rdouble(cos(theta)));
newTform.setA10(rdouble(-sin(theta)));
newTform.setA01(rdouble(sin(theta)));
newTform.setA11(rdouble(cos(theta)));
newTform.setA02(rdouble(0));
newTform.setA12(rdouble(0));
% apply transform
ellipse.setTransform(newTform);
% Create the ROI
roi = omero.model.RoiI;
roi.addShape(ellipse);
roi = session.getUpdateService().saveAndReturnObject(roi);

Retrieving Transforms linked to an Image

for i = 1 : nShapes
    shape = roi.getShape(i - 1);

    %http://blog.openmicroscopy.org/data-model/future-plans/2016/06/20/shape-transforms/
    transform = shape.getTransform();
    xScaling = transform.getA00().getValue();
    xShearing = transform.getA01().getValue();
    xTranslation = transform.getA02().getValue();

    yScaling = transform.getA11().getValue();
    yShearing = transform.getA10().getValue();
    yTranslation = transform.getA12().getValue();

    %tformMatrix = [A00, A10, 0; A01, A11, 0; A02, A12, 1];
    tformMatrix = [xScaling, yShearing, 0; xShearing, yScaling, 0; xTranslation, yTranslation, 1];

    fprintf(1, 'Shape Type : %s\n', char(shape.toString));
    fprintf(1, 'xScaling : %s\n', num2str(tformMatrix(1,1)));
    fprintf(1, 'yScaling : %s\n', num2str(tformMatrix(2,2)));
    fprintf(1, 'xShearing : %s\n', num2str(tformMatrix(2,1)));
    fprintf(1, 'yShearing : %s\n', num2str(tformMatrix(1,2)));
    fprintf(1, 'xTranslation: %s\n', num2str(tformMatrix(3,1)));
    fprintf(1, 'yTranslation: %s\n', num2str(tformMatrix(3,2)));
end

Removing a shape from ROI

// Retrieve the roi linked to an image
service = session.getRoiService();
roiResult = service.findByImage(imageId, []);
n = rois.size;
for thisROI  = 1 : n
    roi = rois.get(thisROI-1);
    numShapes = roi.sizeOfShapes;
    for ns = 1:numShapes
        shape = roi.getShape(ns-1);
        % Remove the shape
        roi.removeShape(shape);
    end
    % Update the roi.
    roi = iUpdate.saveAndReturnObject(roi);
end

Analyzing shapes

// Retrieve the roi linked to an image
service = session.getRoiService();
roiResult = service.findByImage(imageId, []);
n = rois.size;
toAnalyse = java.util.ArrayList;
for thisROI  = 1 : n
    roi = rois.get(thisROI-1);
    numShapes = roi.sizeOfShapes;
    for ns = 1:numShapes
        shape = roi.getShape(ns-1);
        toAnalyse.add(java.lang.Long(shape.getId().getValue()));
    end
end
//For convenience, we assume the shapes are on the first plane
z = 0;
c = 0;
t = 0;
stats = service.getShapeStatsRestricted(toAnalyse, z, t, [c]);
calculated = stats(1,1);
mean = calculated.mean(1,1);

Deleting data¶

It is possible to delete projects, datasets, images, ROIs, etc. and objects linked to them depending on the specified options (see Deleting in OMERO). For example, images of known identifiers can be deleted from the server using the deleteImages function:

deleteImages(session, imageIds);

See also

deleteProjects, deleteDatasets, deleteScreens, deletePlates: Utility functions to delete objects.

Rendering images¶

The RenderImages.m example script shows how to initialize the rendering engine and render an image.

Creating Image¶

The CreateImage.m example script shows how to create an image in OMERO. A similar approach can be applied when uploading an image. To upload individual planes onto the server, the data must be converted into a byte (int8) array first. If the Pixels object has been created, this conversion can done using the toByteArray function.

OMERO MATLAB language bindings¶

Installing the OMERO.matlab toolbox¶

Configuring the OMERO.matlab connection¶

Creating a connection¶

Keeping your session alive¶

Working in a different group¶

Creating an unencrypted session¶

Closing your connection¶

Unloading OMERO¶

Reading data¶

Raw data access¶

Annotations¶

Writing data¶

How to use OMERO tables¶

ROIs¶

Deleting data¶

Rendering images¶

Creating Image¶

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