diff --git a/2d_spots_in_fibers.py b/2d_spots_in_fibers.py
new file mode 100644
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+++ b/2d_spots_in_fibers.py
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+#@ OpService ops
+#@ CommandService command
+#@ RoiManager rm
+#@ File (label="select image", description="select your input image", style=file) path_to_image
+#@ Integer (label="select image file series", description="leave 1 if not needed", value=1, min=1, max=20, stepSize=1, persist=false, style=slider) series_number
+#@ File (label="Fiber Segmentation", description="select the Fiber Segmentation RoiSet .zip file", style=file) fiber_segmentation_roiset
+#@ Integer (label="DAPI channel", description="select the DAPI channel", min=1, max=20, stepSize=1, persist=true, style=slider) dapi_channel_number
+#@ Integer (label="DAPI threshold", description="0 = Auto", min=0) threshold
+#@ String (label="processing channels", description="comma separated list of channels, e.g. 2,3,6,7", value="1,2,3,4") processing_channels_string
+#@ String (label="Spot detection quality threshold", description="comma separated list of values, one per channel", value="80,80,80,80" ) quality_thresholds_string
+#@ Double (label="Minimum spot diameter", description="smaller spots will be discarded", value=0.3, min=1, max=10, stepSize=0.1) spot_diameter
+
+# trackmate imports
+from fiji.plugin.trackmate import Settings
+from fiji.plugin.trackmate import TrackMate
+from fiji.plugin.trackmate.detection import DogDetectorFactory
+from fiji.plugin.trackmate.action import LabelImgExporter
+# ij imports
+from ij import IJ
+from ij.plugin import ImageCalculator
+from ij.measure import ResultsTable
+from ij.measure import Measurements as M
+from ij.plugin.filter import Analyzer
+# Bio-Formats imports
+from loci.plugins import BF
+from loci.common import Region
+from loci.plugins.in import ImporterOptions
+from loci.formats import ImageReader, MetadataTools, Memoizer
+# MorpholibJ imports
+from inra.ijpb.binary import BinaryImages
+# BIOP imports
+from ch.epfl.biop.ij2command import Labels2Rois
+# python imports
+import os
+
+
+def get_parentdir_filename_ext_from_path(path):
+ """ returns parent directory, filename, and file extension from a given path
+
+ Parameters
+ ----------
+ path : String
+ the full path to a file
+
+ Returns
+ -------
+ String
+ the parent directory, the filename without extension and the extension split after the dot
+ """
+ parent_dir = os.path.dirname(path)
+ base = os.path.basename(path)
+ (filename, ext) = os.path.splitext(base)
+
+ return (parent_dir, filename, ext)
+
+
+def write_bf_memoryfile(path_to_file):
+ """Write a BF memo-file so subsequent access to the same file is faster.
+
+ The Bio-Formats memo-file is written next to the image file (i.e. in the
+ same folder as the given file).
+
+ Parameters
+ ----------
+ string
+ The full path to the image file.
+ """
+ reader = Memoizer(ImageReader())
+ reader.setId(path_to_file)
+ reader.close()
+
+
+def get_ome_metadata(path_to_image, series_number):
+ """Get some metadata fields using BF from an image on disk from a given series.
+ Gets number of channels, the image width, image height and bit depth.
+
+ Parameters
+ ----------
+ path_to_image : str
+ full path to the image
+ series_number : int
+ the Bio-Formats series number
+
+ Returns
+ -------
+ int
+ n_channels : the number of channels
+ int
+ image_width : the image width in px
+ int
+ image_height : the image height in px
+ int
+ bit_depth : the image bit depth
+ """
+ series_number -= 1 # python starts counting from 0
+ reader = ImageReader()
+ ome_meta = MetadataTools.createOMEXMLMetadata()
+ reader.setMetadataStore(ome_meta)
+ reader.setId(path_to_image)
+ reader.setSeries(series_number)
+ n_channels = reader.getSizeC()
+ image_width = reader.getSizeX()
+ image_height = reader.getSizeY()
+ bit_depth = reader.getBitsPerPixel()
+
+ reader.close()
+
+ return n_channels, image_width, image_height, bit_depth
+
+
+def BFopen_image(path_to_image, channel_number, series_number, region=None, z_slice_number=None):
+ """Use Bio-Formats to open a given image or subset.
+
+ Parameters
+ ----------
+ path_to_image : str
+ full path to the image file on disk
+ channel_number : int
+ the number of the channel to open
+ series_number : int
+ the Bio-Formats series number to open
+ region : list, optional
+ Bio-Formats crop region, by default None. format = [start_x, start_y, width in px, height in px]
+ z_slice_number : int, optional
+ the number of the z-slice to open, by default None
+
+ Returns
+ -------
+ ImagePlus
+ the requested image
+ """
+ series_number -= 1 # python starts counting from 0
+ channel_number -= 1 # python starts counting from 0
+ options = ImporterOptions()
+ options.setId(path_to_image)
+ options.setColorMode(ImporterOptions.COLOR_MODE_GRAYSCALE)
+ options.setSplitChannels(False)
+ options.setSplitFocalPlanes(False)
+ options.setSplitTimepoints(False)
+ options.setSeriesOn(series_number, True)
+
+ if region is not None:
+ options.setCrop(True)
+ options.setCropRegion(series_number, Region(region[0], region[1], region[2], region[3])) # x,y,w,h
+
+ options.setSpecifyRanges(True)
+ options.setCBegin(series_number, channel_number)
+ options.setCEnd(series_number, channel_number)
+ options.setCStep(series_number, 1)
+
+ if z_slice_number is not None:
+ z_slice_number -= 1 # python starts counting from 0
+ options.setZBegin(series_number, z_slice_number)
+ options.setZEnd(series_number, z_slice_number)
+ options.setZStep(series_number, 1)
+
+ imps = BF.openImagePlus(options)
+
+ return imps[0]
+
+
+def run_trackmate_dog_spot_detector(imp, spot_diameter, quality_threshold):
+ """Run TrackMates DoG detector with a given quality threshold
+ on a target image
+
+ Parameters
+ ----------
+ imp : ImagePlus
+ the input image
+ quality_threshold : float or double
+ the spot detection quality threshold
+
+ Returns
+ -------
+ ImagePlus
+ a label image of the detected spots.
+ """
+
+ cal = imp.getCalibration()
+ settings = Settings(imp)
+
+ settings.detectorFactory = DogDetectorFactory()
+ settings.detectorSettings['DO_SUBPIXEL_LOCALIZATION'] = True
+ settings.detectorSettings['RADIUS'] = spot_diameter / 2 # type = double
+ settings.detectorSettings['TARGET_CHANNEL'] = 0
+ settings.detectorSettings['THRESHOLD'] = quality_threshold # type = double
+ settings.detectorSettings['DO_MEDIAN_FILTERING'] = False
+
+ trackmate = TrackMate(settings)
+ trackmate.computeSpotFeatures(False)
+ trackmate.computeTrackFeatures(False)
+
+ if not trackmate.checkInput():
+ print( str( trackmate.getErrorMessage() ) )
+
+ if not trackmate.process():
+ print( str( trackmate.getErrorMessage() ) )
+
+ exportSpotsAsDots = True
+ exportTracksOnly = False
+ labelIdPainting = LabelImgExporter.LabelIdPainting.LABEL_IS_INDEX_MOVIE_UNIQUE
+ label_imp = LabelImgExporter.createLabelImagePlus(trackmate, exportSpotsAsDots, exportTracksOnly, labelIdPainting)
+ label_imp.setCalibration(cal)
+ imp.close()
+
+ return label_imp
+
+
+def get_threshold_from_method(imp, method):
+ """returns the threshold value of chosen IJ AutoThreshold method from a stack (hyperstack?)
+
+ Parameters
+ ----------
+ imp : ImagePlus (maybe also accepts ij2 dataset, img?)
+ the image from which to get the threshold value
+ method : string
+ the AutoThreshold method to use, e.g. "otsu", "yen", ...
+
+ Returns
+ -------
+ int
+ """
+ histogram = ops.run("image.histogram", imp)
+ threshold_value = ops.run("threshold.%s" % method, histogram)
+ threshold_value = int(round(threshold_value.get()))
+
+ return threshold_value
+
+
+def convert_to_binary(imp, threshold=1, scale_binary=True):
+ """Convert grayscale image to a mask / binary image
+
+ Parameters
+ ----------
+ imp : ImagePlus
+ the input image
+ threshold : int, optional
+ the threshold above which pixels are considered foreground, by default 1
+ scale_binary : bool, optional
+ scale the mask image from 0/255 to 0/1, by default True
+
+ Returns
+ -------
+ ImagePlus
+ the mask / binary image
+ """
+ mask_imp = imp.duplicate()
+ bitdepth = mask_imp.getBitDepth()
+ if bitdepth > 16.0:
+ IJ.run("Conversions...", " ")
+ IJ.run(mask_imp, "16-bit", "")
+ IJ.run("Conversions...", "scale")
+
+ IJ.setRawThreshold(mask_imp, threshold, (2**bitdepth)-1, None)
+ IJ.run(mask_imp, "Convert to Mask", "")
+ if scale_binary == True:
+ IJ.run(mask_imp, "Divide...", "value=255")
+ IJ.run(mask_imp, "Enhance Contrast", "saturated=0.35")
+ mask_imp.changes = False
+
+ return mask_imp
+
+
+def save_image_as_IJtif(imp, filename, suffix, target):
+ """Save the image as ImageJ-Tiff
+
+ Parameters
+ ----------
+ imp : ImagePlus
+ the input Image to be saved
+ filename : str
+ the image filename without extension
+ suffix : int, float or str
+ a suffix that will be added to the filename as a string
+ target : str
+ the target directory in with to save the image
+ """
+
+ savename = filename + "_" + str(suffix) + ".tif"
+ savepath = os.path.join(target, savename)
+ IJ.log("now saving: " + str(savepath))
+ IJ.saveAs(imp, "Tiff", savepath)
+
+
+def convert_labelimage_to_binary(label_imp, scale_binary=True):
+ """Convert a label image to a mask / binary image
+
+ Parameters
+ ----------
+ label_imp : ImagePlus
+ the input label image
+ scale_binary : bool, optional
+ scale the mask image from 0/255 to 0/1, by default True
+
+ Returns
+ -------
+ ImagePlus
+ the mask / binary image
+ """
+ binary_imp = BinaryImages.binarize(label_imp)
+ if scale_binary == True:
+ IJ.run(binary_imp, "Divide...", "value=255")
+
+ return binary_imp
+
+
+def measure_intensity_sum(imp, rm):
+ """
+ Measure theraw integrated intensity for all rois in target imp.
+
+ Parameters
+ ----------
+ imp : ImagePlus
+ The image from which the intensity will be measured.
+ rm : RoiManager
+ The ROI Manager containing the regions of interest to be analyzed.
+
+ Returns
+ -------
+ label_id : list of str
+ A list of labels corresponding to each ROI.
+ sum_intensity : list of int
+ A list of summed integrated intensities for each ROI.
+
+ Notes
+ -----
+ The results are stored in a `ij.ResultsTable`,
+ from which the raw integrated density values are extracted.
+
+ Example
+ -------
+ >>> labels, intensities = measure_intensity_sum(imp, roi_manager)
+ """
+
+ rt_ = ResultsTable()
+ options = M.INTEGRATED_DENSITY
+ an = Analyzer (imp, options, rt_)
+ an.setPrecision(0)
+
+ label_id = []
+ sum_intensity = []
+
+ for index, roi in enumerate(rm.getRoisAsArray()):
+ imp.setRoi(roi)
+ an.measure()
+ label_id.append(roi.getName())
+ sum_intensity.append(int(rt_.getColumn("RawIntDen")[index]))
+
+ return label_id, sum_intensity
+
+
+def add_results_to_resultstable(results_table, column, values):
+ """Add values to the ResultsTable starting from row 0 of a given column.
+
+ Parameters
+ ----------
+ results_table : ij.measure.ResultsTable
+ a reference of the IJ-ResultsTable
+ column : string
+ the column in which to add the values
+ values : list(int, double or float)
+ array with values to be added
+ """
+ for index, value in enumerate(values):
+ results_table.setValue(column, index, value)
+
+
+def save_results_table(results_table, filename, suffix, target):
+ """Save an IJ-ResultsTable as txt to a given location
+
+ Parameters
+ ----------
+ results_table : IJ-ResultsTable
+ an IJ-ResultsTable
+ filename : str
+ filename to include in the tables name
+ suffix : str, int or float
+ a suffix to be included in the tables name
+ target : str
+ the target directory in which to save the table
+ """
+
+ savename = filename + "_" + str(suffix) + ".txt"
+ savepath = os.path.join(target, savename)
+ results_table.save(savepath)
+
+
+def save_labelimage_as_ijroiset(label_imp, rm, filename, suffix, target):
+ """Save all labels i a label Images as IJ-ROIset to target location.
+ Only 2D, requires PTBIOP update site.
+
+ Parameters
+ ----------
+ label_imp : ImagePlus
+ the input label image
+ filename : str
+ the filename to include in the ROIset name
+ suffix : str, int or float
+ a suffix to be included in the ROIset name
+ target : str
+ the target directory in which to save the table
+ """
+ command.run( Labels2Rois , False , 'imp' , label_imp , 'rm', rm).get()
+ savename = filename + "_" + str(suffix) + ".zip"
+ savepath = os.path.join(target, savename)
+ rm.runCommand("Save", savepath)
+ rm.reset()
+
+
+def load_rois_from_zip(path, rm):
+ """Load ROIs from the given zip file and add them to the RoiManager.
+
+ Parameters
+ ----------
+ path : string
+ Path to the ROI zip file.
+ rm : RoiManager
+ The ROI Manager.
+ """
+ rm.runCommand("Open", path)
+
+
+def close_images(list_of_imps):
+ """Close given ImagePlus images
+
+ Parameters
+ ----------
+ list_of_imps : list of ImagePlus
+ list of ImagePlus images to be closed
+ """
+ for imp in list_of_imps:
+ imp.changes = False
+ imp.close()
+
+
+path_to_image = str(path_to_image).replace("\\","/")
+fiber_segmentation_roiset = str(fiber_segmentation_roiset).replace("\\","/")
+parent_dir, filename, ext = get_parentdir_filename_ext_from_path(path_to_image)
+write_bf_memoryfile(path_to_image)
+n_channels, image_width, image_height, bit_depth = get_ome_metadata(path_to_image, series_number)
+processing_channels_string = processing_channels_string.replace(" ", "")
+processing_channels = processing_channels_string.split(",")
+quality_thresholds_string = quality_thresholds_string.replace(" ", "")
+quality_thresholds = quality_thresholds_string.split(",")
+
+# threshold DAPI channel and convert to binary
+dapi_channel = BFopen_image(path_to_image, dapi_channel_number, series_number)
+if threshold <= 0:
+ threshold = get_threshold_from_method(dapi_channel, "otsu")
+dapi_binary = convert_to_binary(dapi_channel, threshold)
+
+# detect spots and count them per fiber
+results_table = ResultsTable()
+for index, channel in enumerate(processing_channels):
+ channel = int(channel)
+ quality_threshold = float(quality_thresholds[index])
+ spots_channel = BFopen_image(path_to_image, channel, series_number)
+ spots_label_imp = run_trackmate_dog_spot_detector(spots_channel, spot_diameter, quality_threshold)
+ save_labelimage_as_ijroiset(spots_label_imp, rm, filename, "spots_ch" + str(channel), parent_dir)
+ spots_binary_imp = convert_labelimage_to_binary(spots_label_imp)
+ dapi_positive_spots_binary = ImageCalculator.run(spots_binary_imp, dapi_binary, "Multiply create")
+ dapi_negative_spots_binary = ImageCalculator.run(spots_binary_imp, dapi_positive_spots_binary, "Subtract create")
+
+ load_rois_from_zip(fiber_segmentation_roiset, rm)
+ fiber_label_IDs, n_spots_per_fiber = measure_intensity_sum(spots_binary_imp, rm)
+ _, n_dapi_positive_spots_per_fiber = measure_intensity_sum(dapi_positive_spots_binary, rm)
+ _, n_dapi_negative_spots_per_fiber = measure_intensity_sum(dapi_negative_spots_binary, rm)
+
+ # TODO: the column fiber label ID needs to be added only one time. Maybe check before if column exists.
+ add_results_to_resultstable(results_table, "fiber label ID", fiber_label_IDs)
+ add_results_to_resultstable(results_table, "n spots channel " + str(channel), n_spots_per_fiber)
+ add_results_to_resultstable(results_table, "n spots dapi positive channel " + str(channel), n_dapi_positive_spots_per_fiber)
+ add_results_to_resultstable(results_table, "n spots dapi negative channel " + str(channel), n_dapi_negative_spots_per_fiber)
+ close_images([spots_channel, spots_label_imp, spots_binary_imp, spots_binary_imp, dapi_binary, dapi_positive_spots_binary, dapi_negative_spots_binary])
+
+results_table.show("Spots")
+save_results_table(results_table, filename, "Spots", parent_dir)
+IJ.run("Collect Garbage", "")
+IJ.log("DONE")
\ No newline at end of file