java time

pytrackmate/trackmate_mpt_script.java

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+import sys
+
+from ij import IJ
+from ij import WindowManager
+
+from fiji.plugin.trackmate import Model
+from fiji.plugin.trackmate import Settings
+from fiji.plugin.trackmate import TrackMate
+from fiji.plugin.trackmate import SelectionModel
+from fiji.plugin.trackmate import Logger
+from fiji.plugin.trackmate.detection import LogDetectorFactory
+from fiji.plugin.trackmate.tracking.jaqaman import SparseLAPTrackerFactory
+from fiji.plugin.trackmate.gui.displaysettings import DisplaySettingsIO
+from fiji.plugin.trackmate.gui.displaysettings.DisplaySettings import TrackMateObject
+from fiji.plugin.trackmate.features.track import TrackIndexAnalyzer
+
+import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer as HyperStackDisplayer
+import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter
+
+# We have to do the following to avoid errors with UTF8 chars generated in 
+# TrackMate that will mess with our Fiji Jython.
+reload(sys)
+sys.setdefaultencoding('utf-8')
+
+# Get currently selected image
+# imp = WindowManager.getCurrentImage()
+imp = IJ.openImage('https://fiji.sc/samples/FakeTracks.tif')
+imp.show()
+
+
+#----------------------------
+# Create the model object now
+#----------------------------
+
+# Some of the parameters we configure below need to have
+# a reference to the model at creation. So we create an
+# empty model now.
+
+model = Model()
+
+# Send all messages to ImageJ log window.
+model.setLogger(Logger.IJ_LOGGER)
+
+
+
+#------------------------
+# Prepare settings object
+#------------------------
+
+settings = Settings(imp)
+
+# Configure detector - We use the Strings for the keys
+settings.detectorFactory = LogDetectorFactory()
+settings.detectorSettings = {
+    'DO_SUBPIXEL_LOCALIZATION' : True,
+    'RADIUS' : 2.5,
+    'TARGET_CHANNEL' : 1,
+    'THRESHOLD' : 0.,
+    'DO_MEDIAN_FILTERING' : False,
+}  
+
+# Configure spot filters - Classical filter on quality
+filter1 = FeatureFilter('QUALITY', 30, True)
+settings.addSpotFilter(filter1)
+
+# Configure tracker - We want to allow merges and fusions
+settings.trackerFactory = SparseLAPTrackerFactory()
+settings.trackerSettings = settings.trackerFactory.getDefaultSettings() # almost good enough
+settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = True
+settings.trackerSettings['ALLOW_TRACK_MERGING'] = True
+
+# Add ALL the feature analyzers known to TrackMate. They will 
+# yield numerical features for the results, such as speed, mean intensity etc.
+settings.addAllAnalyzers()
+
+# Configure track filters - We want to get rid of the two immobile spots at
+# the bottom right of the image. Track displacement must be above 10 pixels.
+
+filter2 = FeatureFilter('TRACK_DISPLACEMENT', 10, True)
+settings.addTrackFilter(filter2)
+
+
+#-------------------
+# 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
+#----------------
+
+# A selection.
+selectionModel = SelectionModel( model )
+
+# Read the default display settings.
+ds = DisplaySettingsIO.readUserDefault()
+# Color by tracks.
+ds.setTrackColorBy( TrackMateObject.TRACKS, TrackIndexAnalyzer.TRACK_INDEX )
+ds.setSpotColorBy( TrackMateObject.TRACKS, TrackIndexAnalyzer.TRACK_INDEX )
+
+displayer =  HyperStackDisplayer( model, selectionModel, imp, ds )
+displayer.render()
+displayer.refresh()
+
+# Echo results with the logger we set at start:
+model.getLogger().log( str( model ) )