A Jython script to run the detection with cellpose.

Author: tinevez

scripts/TrackMate-Cellpose-script.py

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+from fiji.plugin.trackmate import Model
+from fiji.plugin.trackmate import Settings
+from fiji.plugin.trackmate import TrackMate
+from fiji.plugin.trackmate import Logger
+from fiji.plugin.trackmate.io import TmXmlWriter
+from fiji.plugin.trackmate.util import LogRecorder;
+from fiji.plugin.trackmate.tracking.sparselap import SparseLAPTrackerFactory
+from fiji.plugin.trackmate.tracking import LAPUtils
+from fiji.plugin.trackmate.util import TMUtils
+from fiji.plugin.trackmate.visualization.hyperstack import HyperStackDisplayer
+from fiji.plugin.trackmate import SelectionModel
+from fiji.plugin.trackmate.cellpose import CellposeDetectorFactory
+import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter
+from fiji.plugin.trackmate.gui.displaysettings import DisplaySettings
+from fiji.plugin.trackmate.gui.displaysettings import DisplaySettings
+from fiji.plugin.trackmate.gui.displaysettings import DisplaySettingsIO
+from fiji.plugin.trackmate.action import CaptureOverlayAction
+from fiji.plugin.trackmate.cellpose.CellposeSettings import PretrainedModel
+
+from ij import IJ
+
+
+from datetime import datetime as dt
+import sys 
+
+reload(sys)
+sys.setdefaultencoding('utf-8')
+
+# ------------------------------------------------------
+# 	EDIT FILE PATHS BELOW.
+# ------------------------------------------------------
+
+# Shall we display the results each time?
+show_output = True
+
+# Channel to process? 
+channel_to_process = 1
+
+# Image files to analyse.
+file_paths = []
+file_paths.append( '/Users/tinevez/Desktop/BreastCancerCells_multiC.tif' )
+
+
+
+# ------------------------------------------------------
+# 	ACTUAL CODE.
+# ------------------------------------------------------
+def run( image_file ):
+
+	# Open image.
+	imp  = IJ.openImage( image_file )
+	cal = imp.getCalibration()
+
+	# Logger -> content will be saved in the XML file.
+	logger = LogRecorder( Logger.VOID_LOGGER )
+
+	logger.log( 'TrackMate-Cellpose analysis script\n' )
+
+	dt_string = dt.now().strftime("%d/%m/%Y %H:%M:%S")
+	logger.log( dt_string + '\n\n' )
+
+	#------------------------
+	# Prepare settings object
+	#------------------------
+
+	settings = Settings(imp)
+	setup = settings.toStringImageInfo() 
+
+	# Configure Cellpose default detector.
+	
+	settings.detectorFactory = CellposeDetectorFactory()
+	
+	settings.detectorSettings['TARGET_CHANNEL'] = 1
+	settings.detectorSettings['OPTIONAL_CHANNEL_2'] = 0
+	settings.detectorSettings['CELLPOSE_PYTHON_FILEPATH'] = '/opt/anaconda3/envs/cellpose/bin/python'
+	settings.detectorSettings['CELLPOSE_MODEL'] = PretrainedModel.CYTO2
+	settings.detectorSettings['CELL_DIAMETER'] = 40.0
+	settings.detectorSettings['USE_GPU'] = True
+	settings.detectorSettings['SIMPLIFY_CONTOURS'] = False	
+
+
+	# Configure tracker
+	settings.trackerFactory = SparseLAPTrackerFactory()
+	settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
+	settings.trackerSettings[ 'LINKING_MAX_DISTANCE' ] 		= 5.0
+	settings.trackerSettings[ 'GAP_CLOSING_MAX_DISTANCE' ]	= 5.0
+	settings.trackerSettings[ 'MAX_FRAME_GAP' ]				= 2
+	settings.initialSpotFilterValue = -1.
+
+	# Analyzers 
+	settings.addAllAnalyzers()
+
+	# Add some filters for tracks/spots 
+
+	# filter on track duration = keep tracks > 75% of total duration 
+	duration_threshold = 75
+	maxduration = (duration_threshold/100.0) * (imp.getNFrames() * cal.frameInterval)                
+	filter1_track = FeatureFilter('TRACK_DURATION', maxduration, True)
+	settings.addTrackFilter(filter1_track)
+
+	# filter on spot = keep spots having radius > 1.6 um, and circularity > 0.7
+	#filter1_spot = FeatureFilter('RADIUS', 1.6, True)
+	#filter2_spot = FeatureFilter('CIRCULARITY', 0.7, True)
+	#settings.addSpotFilter(filter1_spot)
+	#settings.addSpotFilter(filter2_spot)
+
+	print "Spot filters added = ", settings.getSpotFilters()
+	print "Track filters added = ", settings.getTrackFilters(), "\n"
+
+	#-------------------
+	# Instantiate plugin
+	#-------------------
+
+	trackmate = TrackMate( settings )
+	trackmate.computeSpotFeatures( True )
+	trackmate.computeTrackFeatures( True )
+	trackmate.getModel().setLogger( logger )
+
+	#--------
+	# Process
+	#--------
+
+	ok = trackmate.checkInput()
+	if not ok:
+	    print( str( trackmate.getErrorMessage() ) )
+	    return
+
+	ok = trackmate.process()
+	if not ok:
+	    print( str( trackmate.getErrorMessage() ) )
+	    return
+
+	#----------------
+	# Save results
+	#----------------
+
+	saveFile = TMUtils.proposeTrackMateSaveFile( settings, logger )
+
+	writer = TmXmlWriter( saveFile, logger )
+	writer.appendLog( logger.toString() )
+	writer.appendModel( trackmate.getModel() )
+	writer.appendSettings( trackmate.getSettings() )
+	writer.writeToFile();
+	print( "Results saved to: " + saveFile.toString() + '\n' );
+
+	#----------------
+	# Display results
+	#----------------
+
+	if show_output:
+		model = trackmate.getModel()
+		selectionModel = SelectionModel( model )
+		ds = DisplaySettings()
+		ds = DisplaySettingsIO.readUserDefault()
+		ds.spotDisplayedAsRoi = True
+		displayer =  HyperStackDisplayer( model, selectionModel, imp, ds )
+		displayer.render()
+		displayer.refresh()
+
+	# capture overlay - RGB file
+	image = trackmate.getSettings().imp
+	capture = CaptureOverlayAction.capture(image, -1, imp.getNFrames(), logger)
+	capture.setTitle("TracksOverlay")
+	capture.show()
+
+# ------------------------------------------------------
+
+for file_path in file_paths:
+
+	dt_string = dt.now().strftime("%d/%m/%Y %H:%M:%S")
+	print( '\nRunning analysis on %s - %s' % ( file_path, dt_string ) )
+
+	run( file_path )
+
+print( 'Finished!' )