I’m quite new to scripting and I’ve attempted to create a TrackMate macro using the tutorials online. My images are dark spots on a light background so I have to invert the image and I also resize the image otherwise I start to get memory errors. It seems to be successfully detecting my targets as spots, and shows multiple spots lining up on the tracks left by targets, but won’t generate the tracks themselves. There are no errors when I run the code and I’ve manually tracked everything with the GUI and used the same settings that worked there.

I have tried to pull a file from my WD, invert and resize the file, detect spots, and generate tracks. It seems to do everything except for generating tracks. I’m not even sure TrackMate is properly initialised, the output looks completely different to what I get when I generate the tracks using the GUI. When I open the saved track file it says 0 tracks detected which can’t be right, there are definitely tracks in this video! The script is here:

from ij import IJ
import os
from os.path import join
import sys

from fiji.plugin.trackmate import Model, Settings, TrackMate, SelectionModel, Logger
from fiji.plugin.trackmate.detection import LogDetectorFactory
from fiji.plugin.trackmate.tracking.jaqaman import SimpleSparseLAPTrackerFactory
from fiji.plugin.trackmate.gui.displaysettings import DisplaySettingsIO
from fiji.plugin.trackmate.features.track import TrackIndexAnalyzer
from fiji.plugin.trackmate.visualization.hyperstack import HyperStackDisplayer
import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter

from fiji.plugin.trackmate.io import TmXmlWriter
import fiji.plugin.trackmate.action.ExportTracksToXML as ExportTracksToXML
from java.io import File

# Ensure the script uses UTF-8 encoding
if sys.version_info[0] < 3:
    reload(sys)
    sys.setdefaultencoding('utf-8')

# Directory for saving results
directory = "/Users/SHARMAN1/Library/CloudStorage/OneDrive-TheUniversityofMelbourne/tm_vid/PRE 2024-04-02"

# Get currently selected image
file_path = "/Users/SHARMAN1/Library/CloudStorage/OneDrive-TheUniversityofMelbourne/tm_vid/PRE 2024-04-02/Well_A1.avi"
imp = IJ.openImage(file_path)
IJ.run(imp, "8-bit", "")
imp = imp.resize(1000, 563, 1, "bilinear")
IJ.run(imp, "Invert", "")
imp.show()

#----------------------------
# Create the model object now
#----------------------------

model = Model()

# Send all messages to ImageJ log window.
model.setLogger(Logger.IJ_LOGGER)

#------------------------
# Prepare settings object
#------------------------

settings = Settings(imp)

# Configure detector
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = {
    'DO_SUBPIXEL_LOCALIZATION': True,
    'RADIUS': 5.0,
    'TARGET_CHANNEL': 1,
    'THRESHOLD': 2.0,
    'DO_MEDIAN_FILTERING': False,
}

# Configure tracker
settings.trackerFactory = SimpleSparseLAPTrackerFactory()
settings.trackerSettings = settings.trackerFactory.getDefaultSettings()
settings.trackerSettings['LINKING_MAX_DISTANCE'] = 60.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 60.0
settings.trackerSettings['MAX_FRAME_GAP'] = 2
settings.trackerSettings['SPLITTING_MAX_DISTANCE'] = 15.0
settings.trackerSettings['ALLOW_GAP_CLOSING'] = True
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = False
settings.trackerSettings['ALLOW_TRACK_MERGING'] = False

# Add the analyzers for some spot features.
settings.addAllAnalyzers()

# We configure the initial filtering to discard spots
# with a quality lower than 1.
settings.initialSpotFilterValue = 5.0

#-------------------
# Instantiate plugin
#-------------------

trackmate = TrackMate(model, settings)

#--------
# Process
#--------

ok = trackmate.checkInput()
if not ok:
    sys.exit(str(trackmate.getErrorMessage()))

ok = trackmate.process()
if not ok:
    sys.exit(str(trackmate.getErrorMessage()))

#----------------
# Display results
#----------------
 
model.getLogger().log('Found ' + str(model.getTrackModel().nTracks(True)) + ' tracks.')
 
# A selection.
sm = SelectionModel( model )
 
# Read the default display settings.
ds = DisplaySettingsIO.readUserDefault()
 
# The viewer.
displayer =  HyperStackDisplayer( model, sm, imp, ds ) 
displayer.render()
 
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
 
# Iterate over all the tracks that are visible.
for id in model.getTrackModel().trackIDs(True):
 
    # Fetch the track feature from the feature model.
    v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
    model.getLogger().log('')
    model.getLogger().log('Track ' + str(id) + ': mean velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits())

#------------------------
# Save results as XML
#------------------------

# Save the tracks only XML
tracks_xml_path = join(directory, "exportTracks.xml")
tracks_out_file = File(tracks_xml_path)
ExportTracksToXML.export(model, settings, tracks_out_file)

# Save the full TrackMate model XML
model_xml_path = join(directory, "exportModel.xml")
model_out_file = File(model_xml_path)
writer = TmXmlWriter(model_out_file)
writer.appendModel(model)
writer.appendSettings(settings)
writer.writeToFile()

print("Tracks XML saved to: {}".format(tracks_xml_path))
print("Model XML saved to: {}".format(model_xml_path))