[Run Macro on OMERO dataset] The script "Dataset_Macro-ROIs_Summary.groovy" runs a macro browsed on the local computer (e.g "Workflow_segmentation_macro_nosplit_countposcells.ijm") on all images from an OMERO dataset. It saves the ROIs and the summary table (as a csv file and as an OMERO.table) back to OMERO #BIOP #OMERO

Dataset_Macro-ROIs_Summary.groovy

/*
 * This Groovy script shows how to analyse an OMERO dataset
 * i.e. a collection of OMERO images.
 * For that example, we use the analyse particles plugin.
 * The generated ROIs are then saved back to OMERO.
 * We create a summary CSV and a summary table of the measurement and attach
 * them to the dataset.
 * Use this script in the Scripting Dialog of Fiji (File > New > Script).
 * Select Groovy as language in the Scripting Dialog.
 * Error handling is omitted to ease the reading of the script but this
 * should be added if used in production to make sure the services are closed
 * Information can be found at
 * https://docs.openmicroscopy.org/latest/omero5/developers/Java.html
 */

#@ String(label="Username") USERNAME
#@ String(label="Password", style='password') PASSWORD
#@ String(label="Host", value='omero-server.epfl.ch') HOST
#@ Integer(label="Dataset ID", value=2331) dataset_id
#@File macropath 
#@RoiManager rm


import java.util.ArrayList
import java.lang.StringBuffer
import java.nio.ByteBuffer
import java.io.File
import java.io.FileInputStream
import java.io.PrintWriter

import java.nio.file.Files

// OMERO Dependencies
import omero.gateway.Gateway
import omero.gateway.LoginCredentials
import omero.gateway.SecurityContext
import omero.gateway.facility.BrowseFacility
import omero.gateway.facility.DataManagerFacility
import omero.gateway.facility.ROIFacility
import omero.gateway.facility.TablesFacility
import omero.log.SimpleLogger
import omero.model.ChecksumAlgorithmI
import omero.model.FileAnnotationI
import omero.model.OriginalFileI
import omero.model.enums.ChecksumAlgorithmSHA1160

import static omero.rtypes.rlong
import static omero.rtypes.rstring

import omero.gateway.model.DatasetData
import omero.gateway.model.FileAnnotationData
import omero.gateway.model.ImageData
import omero.gateway.model.TableData
import omero.gateway.model.TableDataColumn
import omero.model.DatasetI

import org.openmicroscopy.shoola.util.roi.io.ROIReader

import loci.formats.FormatTools
import loci.formats.ImageTools
import loci.common.DataTools

import ij.IJ
import ij.ImagePlus
import ij.ImageStack
import ij.process.ByteProcessor
import ij.process.ShortProcessor
import ij.plugin.frame.RoiManager
import ij.measure.ResultsTable



// Prototype analysis example
gateway = connect_to_omero()
exp = gateway.getLoggedInUser()
group_id = exp.getGroupId()
ctx = new SecurityContext(group_id)
exp_id = exp.getId()


// get all images in an omero dataset
images = get_images(gateway, ctx, dataset_id)

table_rows = new ArrayList()
table_columns = null

count = 0
//Close all windows before starting
IJ.run("Close All")
RoiManager.getRoiManager()
images.each() { image ->
    // Open the image
    id = image.getId()
    open_image_plus(HOST, USERNAME, PASSWORD, group_id, String.valueOf(id))
    imp = IJ.getImage()


    // Analyse the images. This section calls a macro
    IJ.run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding feret's summarize stack display redirect=None decimal=3")
	IJ.runMacroFile(macropath.getAbsolutePath())
	if (rm.getCount() != null) {
	    rt = ResultsTable.getResultsTable()
	    
	    // Save the ROIs back to OMERO
	    roivec = save_rois_to_omero(ctx, id, imp)
	    println "creating summary results for image ID " + id
	    headings = save_row(rt, table_rows, image)
	    if (table_columns == null) {
	        table_columns = create_table_columns(headings)
	    }
	}
    
 
    imp.changes = false     // Prevent "Save Changes?" dialog
    imp.close()
    
}

//Create the result file
tmp_dir = Files.createTempDirectory("Fiji_csv")
path = tmp_dir.resolve("my_csv_filename.csv")
file_path = Files.createFile(path)
file = new File(file_path.toString())

// create a CSV file and upload it
save_summary_as_csv(file, table_rows, table_columns)
upload_csv_to_omero(ctx, file, dataset_id)

//delete the local copy of the temporary file and directory
dir = new File(tmp_dir.toString())
entries = dir.listFiles()
for (i = 0; i < entries.length; i++) {
    entries[i].delete()
}
dir.delete()


save_summary_as_omero_table(ctx, table_rows, table_columns, dataset_id)

// Close the connection
gateway.disconnect()
println "processing done"
//close everything
IJ.run("Close All")
IJ.selectWindow("Summary")
IJ.run("Close")
IJ.selectWindow("Results")
IJ.run("Close")
IJ.selectWindow("Log")
IJ.run("Close")
IJ.selectWindow("ROI Manager")
IJ.run("Close")
imp.changes = false     // Prevent "Save Changes?" dialog
imp.close()


def connect_to_omero() {
    "Connect to OMERO"

    credentials = new LoginCredentials()
    credentials.getServer().setHostname(HOST)
    credentials.getUser().setUsername(USERNAME.trim())
    credentials.getUser().setPassword(PASSWORD.trim())
    simpleLogger = new SimpleLogger()
    gateway = new Gateway(simpleLogger)
    gateway.connect(credentials)
    return gateway
}

def get_images(gateway, ctx, dataset_id) {
    "List all images contained in a Dataset"

    browse = gateway.getFacility(BrowseFacility)
    ids = new ArrayList(1)
    ids.add(new Long(dataset_id))
    return browse.getImagesForDatasets(ctx, ids)
}


def get_port(HOST) {
    port = 4064
    // check if websockets is used
    if (HOST.startsWith("ws")) {
        port = 443
    }
    return port
}

def open_image_plus(HOST, USERNAME, PASSWORD, group_id, image_id) {
    "Open the image using the Bio-Formats Importer"

    StringBuilder options = new StringBuilder()
    options.append("location=[OMERO] open=[omero:server=")
    options.append(HOST)
    options.append("\nuser=")
    options.append(USERNAME.trim())
    options.append("\nport=")
    options.append(get_port(HOST))
    options.append("\npass=")
    options.append(PASSWORD.trim())
    options.append("\ngroupID=")
    options.append(group_id)
    options.append("\niid=")
    options.append(image_id)
    options.append("] ")
    options.append("windowless=true view=Hyperstack color=default")
    IJ.runPlugIn("loci.plugins.LociImporter", options.toString())
}


def save_rois_to_omero(ctx, image_id, imp) {
    " Save ROI's back to OMERO"
    reader = new ROIReader()
    roi_list = reader.readImageJROIFromSources(image_id, imp)
    roi_facility = gateway.getFacility(ROIFacility)
    result = roi_facility.saveROIs(ctx, image_id, exp_id, roi_list)

    roivec = new ArrayList()
    j = result.iterator()
    while (j.hasNext()) {
        roidata = j.next()
        roi_id = roidata.getId()

        i = roidata.getIterator()
        while (i.hasNext()) {
            roi = i.next()
            shape = roi[0]
            t = shape.getZ()
            z = shape.getT()
            c = shape.getC()
            shape_id = shape.getId()
            roivec.add([roi_id, shape_id, z, c, t])
        }
    }
    return roivec
}


def save_row(rt, table_rows, image) {
    "Create a summary table of the measurements"
    // Remove the rows not corresponding to the specified channel
    to_delete = new ArrayList()
    
    // We only keep the first channel. Index starts at 1 in ImageJ
    ref = "c:" + 2
    max_bounding_box = 0.0f
    for (i = 0; i < rt.size(); i++) {
        label = rt.getStringValue("Label", i)
        if (label.contains(ref)) {
            w = rt.getStringValue("Width", i)
            h = rt.getStringValue("Height", i)
            area = Float.parseFloat(w) * Float.parseFloat(h)
            //max_bounding_box = Math.max(area, max_bounding_box)
        }
    }
    // Rename the table so we can read the summary table
    //IJ.renameResults("Results")
    
    rt = ResultsTable.getResultsTable("Summary")
  /*  for (i = 0; i < rt.size(); i++) {
        value = rt.getStringValue("Slice", i)
        //println(value)
        if (!value.startsWith(ref)) {
            to_delete.add(i)
        }
    }*/
    // Delete the rows we do not need
    /*for (i = 0; i < rt.size(); i++) {
        value = to_delete.get(i)
        v = value-i
        rt.deleteRow(v)
    }
    rt.updateResults()*/
    // Insert values in summary table
    /*for (i = 0; i < rt.size(); i++) {
        rt.setValue("Bounding_Box", i, max_bounding_box)
    }*/
    headings = rt.getHeadings()
    
    for (i = 0; i < headings.length; i++) {
        row = new ArrayList()
        // println(rt.size())
        for (j = 0; j < rt.size(); j++) {
            for (i = 0; i < headings.length; i++) {
                heading = rt.getColumnHeading(i)
                //println("heading "+heading)
                if (heading.equals("Slice") || heading.equals("Dataset")) {
                    row.add(rt.getStringValue(i, j))
                } else {
                    row.add(new Double(rt.getValue(i, j)))
                    //println("table value i j " + rt.getValue(i, j))
                }
            }
        }
        row.add(image)
        table_rows.add(row)
    }
    return headings
}


def save_summary_as_omero_table(ctx, rows, columns, dataset_id) {
    "Create an OMERO table with the summary result and attach it to the specified dataset"
    data = new Object[columns.length][rows.size()]
    for (r = 0; r < rows.size(); r++) {
        row = rows.get(r)
        for (i = 0; i < columns.length; i++) {
            data[i][r] = row.get(i)
        }
    }
    // Create the table
    table_data = new TableData(columns, data)
    table_facility = gateway.getFacility(TablesFacility)
    data_object = new DatasetData(new DatasetI(dataset_id, false))
    table_facility.addTable(ctx, data_object, "Summary_from_Fiji", table_data)
}


def create_table_columns(headings) {
    "Create the table headings from the ImageJ results table"
    size = headings.size()
    table_columns = new TableDataColumn[size+1]
    //populate the headings
    for (h = 0; h < headings.size(); h++) {
        heading = headings[h]
        // OMERO.tables queries don't handle whitespace well
        heading = heading.replace(" ", "_")
        if (heading.equals("Slice") || heading.equals("Label")) {
            table_columns[h] = new TableDataColumn(heading, h, String)
        } else {
            table_columns[h] = new TableDataColumn(heading, h, Double)
        }
    }
    table_columns[size] = new TableDataColumn("Image", size, ImageData)
    return table_columns
}


def save_summary_as_csv(file, rows, columns) {
    "Save the summary locally as a CSV"
    stream = null
    sb = new StringBuilder()
    try {
        stream = new PrintWriter(file)
        l = columns.length
        for (i = 0; i < l; i++) {
            sb.append(columns[i].getName())
            if (i != (l-1)) {
                sb.append(", ")
            }
        }
        sb.append("\n")
        rows.each() { row ->
            size = row.size()
            for (i = 0; i < size; i++) {
                value = row.get(i)
                if (value instanceof ImageData) {
                    sb.append(value.getId())
                } else {
                    sb.append(value)
                }
                if (i != (size-1)) {
                    sb.append(", ")
                }
            }
            sb.append("\n")
        }
        stream.write(sb.toString())
    } finally {
        stream.close()
    }
}

def upload_csv_to_omero(ctx, file, dataset_id) {
    "Upload the CSV file and attach it to the specified dataset"
    svc = gateway.getFacility(DataManagerFacility)
    data = new DatasetData(new DatasetI(dataset_id, false))
    namespace = "training.demo"
    mimetype = "text/csv"
    future = svc.attachFile(ctx, file, mimetype, "", namespace, data)
    future.get()
}

Workflow_segmentation_macro_nosplit_countposcells.ijm

imageName = getTitle();
imageID = getImageID();
close("\\Others");
close("Summary");
run("Clear Results");
roiManager("reset");
setOption("BlackBackground", true);
//run("Set Measurements...", "area mean standard modal min centroid center perimeter bounding feret's summarize stack display redirect=None decimal=3")

// USER PARAMETERS
//threshold for channels
thresh_channel_A = 25;
thresh_channel_B = 25;
//channels of interest
channelA_index = 2;
channelB_index = 3;


run("Duplicate...", "title="+imageName);

// "cleaning" using filtering
run("Median...", "radius=2");
// Threshold
run("Auto Threshold", "method=MaxEntropy white");
//run("Convert to Mask");

//get ROIs 
run("Analyze Particles...", "size=10-Infinity clear add stack summarize");
//run("Analyze Particles...", "size=5-Infinity clear add stack");
// Measure channel 2 and  3 
selectImage(imageID);

selectWindow("Summary");
Table.set("PosThresh_C2", Table.size-1, thresh_channel_A);
Table.set("PosThresh_C3", Table.size-1, thresh_channel_B);
countPositives(channelA_index,thresh_channel_A);
countPositives(channelB_index,thresh_channel_B);

//selectWindow("Summary");


//Save Output
//selectWindow("C1-"+imageName);
//run("Flatten");
function countPositives(channelNumber,posThreshold){
	run("Clear Results");
	Stack.setChannel(channelNumber);
	roiManager("Measure");
	rowNumber = nResults;
	counter = 0;
	print(counter);
	for (i = 0; i < rowNumber; i++) {
		//getResult("Mean",i);
		//print(getResult("Mean",i));
		if(getResult("Mean",i)>posThreshold){
			counter++;
		}
	}
	print(counter);
	
	selectWindow("Summary");
	//print(Table.title);

	Table.set("PosCellsCount"+channelNumber, Table.size-1, counter);
	
}