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Scripting TrackMate

9,392 bytes added, 07:20, 5 September 2019
Add the script to track spots entered via the ROI manager.
print "The detector could not process the data."
== Tracking spots that are taken from the ROI manager. ==
You have to start from a 2D+T image (nothing else) and a results table that contains at least the center of mass XM, YM, the slice and the Area for the cells in the movie. The results table is typically generated from the ROI manager, that would contain the results of the particle analyzer.
So an ideal starting situation would like this:
this script will generate the following tracks:
Cool no? The output can be controlled via a TrackMate GUI that will be shown upon running the script. Showing the GUI might not be desirable in batch mode, but from the GUI you can save your data, export to IJ tables and save to CSV, export a movie etc.
The script also offers to color the ROIs by track ID, if you have the ROI manager that was used to create the results table. It looks like this:
<source lang="python">
import sys
from math import pi
from math import sqrt
from random import shuffle
from java.awt import Color
from ij import WindowManager
from ij.measure import ResultsTable
from ij.plugin.frame import RoiManager
from fiji.plugin.trackmate import Logger
from fiji.plugin.trackmate import Model
from fiji.plugin.trackmate import SelectionModel
from fiji.plugin.trackmate import Settings
from fiji.plugin.trackmate import Spot
from fiji.plugin.trackmate import SpotCollection
from fiji.plugin.trackmate import TrackMate
from fiji.plugin.trackmate.detection import ManualDetectorFactory
from fiji.plugin.trackmate.tracking import LAPUtils
from fiji.plugin.trackmate.providers import SpotAnalyzerProvider
from fiji.plugin.trackmate.providers import EdgeAnalyzerProvider
from fiji.plugin.trackmate.providers import TrackAnalyzerProvider
from fiji.plugin.trackmate.tracking.sparselap import SparseLAPTrackerFactory
from fiji.plugin.trackmate.visualization.hyperstack import HyperStackDisplayer
from fiji.plugin.trackmate.gui import TrackMateGUIController
from org.jfree.chart.renderer.InterpolatePaintScale import Jet
def spots_from_results_table( results_table, frame_interval ):
Creates a spot collection from a results table in ImageJ.
Requires the current results table, in which the results from
particle analysis should be. We need at least the center
of mass, the area and the slice to be specified there.
We also query the frame interval to properly generate the
POSITION_T spot feature.
frames = results_table.getColumnAsDoubles( results_table.getColumnIndex( 'Slice' ) )
xs = results_table.getColumnAsDoubles( results_table.getColumnIndex( 'XM' ) )
ys = results_table.getColumnAsDoubles( results_table.getColumnIndex( 'YM' ) )
z = 0.
# Get radiuses from area.
areas = results_table.getColumnAsDoubles( results_table.getColumnIndex( 'Area' ) )
spots = SpotCollection()
for i in range( len( xs ) ):
x = xs[ i ]
y = ys[ i ]
frame = frames[ i ]
area = areas[ i ]
t = ( frame - 1 ) * frame_interval
radius = sqrt( area / pi )
quality = i # Store the line index, to later retrieve the ROI.
spot = Spot( x, y, z, radius, quality )
spot.putFeature( 'POSITION_T', t )
spots.add( spot, int( frame ) )
return spots
def create_trackmate( imp, results_table ):
Creates a TrackMate instance configured to operated on the specified
ImagePlus imp with cell analysis stored in the specified ResultsTable
cal = imp.getCalibration()
# TrackMate.
# Model.
model = Model()
model.setLogger( Logger.IJ_LOGGER )
model.setPhysicalUnits( cal.getUnit(), cal.getTimeUnit() )
# Settings.
settings = Settings()
settings.setFrom( imp )
# Create the TrackMate instance.
trackmate = TrackMate( model, settings )
# Add ALL the feature analyzers known to TrackMate, via
# providers.
# They offer automatic analyzer detection, so all the
# available feature analyzers will be added.
# Some won't make sense on the binary image (e.g. contrast)
# but nevermind.
spotAnalyzerProvider = SpotAnalyzerProvider()
for key in spotAnalyzerProvider.getKeys():
print( key )
settings.addSpotAnalyzerFactory( spotAnalyzerProvider.getFactory( key ) )
edgeAnalyzerProvider = EdgeAnalyzerProvider()
for key in edgeAnalyzerProvider.getKeys():
print( key )
settings.addEdgeAnalyzer( edgeAnalyzerProvider.getFactory( key ) )
trackAnalyzerProvider = TrackAnalyzerProvider()
for key in trackAnalyzerProvider.getKeys():
print( key )
settings.addTrackAnalyzer( trackAnalyzerProvider.getFactory( key ) )
trackmate.getModel().getLogger().log( settings.toStringFeatureAnalyzersInfo() )
trackmate.computeSpotFeatures( True )
trackmate.computeEdgeFeatures( True )
trackmate.computeTrackFeatures( True )
# Skip detection and get spots from results table.
spots = spots_from_results_table( results_table, cal.frameInterval )
model.setSpots( spots, False )
# Configure detector. We put nothing here, since we already have the spots
# from previous step.
settings.detectorFactory = ManualDetectorFactory()
settings.detectorSettings = {}
settings.detectorSettings[ 'RADIUS' ] = 1.
# Configure tracker
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
settings.trackerSettings[ 'LINKING_MAX_DISTANCE' ] = 10.0
settings.trackerSettings[ 'GAP_CLOSING_MAX_DISTANCE' ] = 10.0
settings.trackerSettings[ 'MAX_FRAME_GAP' ] = 3
settings.initialSpotFilterValue = -1.
return trackmate
def process( trackmate ):
Execute the full process BUT for the detection step.
# Check settings.
ok = trackmate.checkInput()
# Initial filtering
print( 'Spot initial filtering' )
ok = ok and trackmate.execInitialSpotFiltering()
# Compute spot features.
print( 'Computing spot features' )
ok = ok and trackmate.computeSpotFeatures( True )
# Filter spots.
print( 'Filtering spots' )
ok = ok and trackmate.execSpotFiltering( True )
# Track spots.
print( 'Tracking' )
ok = ok and trackmate.execTracking()
# Compute track features.
print( 'Computing track features' )
ok = ok and trackmate.computeTrackFeatures( True )
# Filter tracks.
print( 'Filtering tracks' )
ok = ok and trackmate.execTrackFiltering( True )
# Compute edge features.
print( 'Computing link features' )
ok = ok and trackmate.computeEdgeFeatures( True )
return ok
def display_results_in_GUI( trackmate ):
Creates and show a TrackMate GUI to configure the display
of the results.
This might not always be desriable in e.g. batch mode, but
this allows to save the data, export statistics in IJ tables then
save them to CSV, export results to AVI etc...
gui = TrackMateGUIController( trackmate )
# Link displayer and GUI.
model = trackmate.getModel()
selectionModel = SelectionModel( model)
displayer = HyperStackDisplayer( model, selectionModel, imp )
gui.getGuimodel().addView( displayer )
displaySettings = gui.getGuimodel().getDisplaySettings()
for key in displaySettings.keySet():
displayer.setDisplaySettings( key, displaySettings.get( key ) )
gui.setGUIStateString( 'ConfigureViews' )
def color_rois_by_track( trackmate, rm ):
Colors the ROIs stored in the specified ROIManager rm using a color
determined by the track ID they have.
We retrieve the IJ ROI that matches the TrackMate Spot because in the
latter we stored the index of the spot in the quality feature. This
is a hack of course. On top of that, it supposes that the index of the
ROI in the ROIManager corresponds to the line in the ResultsTable it
generated. So any changes to the ROIManager or the ResultsTable is
likely to break things.
model = trackmate.getModel()
track_colors = {}
track_indices = []
for i in model.getTrackModel().trackIDs( True ):
track_indices.append( i )
shuffle( track_indices )
index = 0
for track_id in track_indices:
color = Jet.getPaint( float(index) / ( len( track_indices) - 1 ) )
track_colors[ track_id ] = color
index = index + 1
spots = model.getSpots()
for spot in spots.iterable( True ):
q = spot.getFeature( 'QUALITY' ) # Stored the ROI id.
roi_id = int( q )
roi = rm.getRoi( roi_id )
# Get track id.
track_id = model.getTrackModel().trackIDOf( spot )
if track_id is None:
color = Color.GRAY
color = track_colors[ track_id ]
roi.setFillColor( color )
# Get current image.
imp = WindowManager.getCurrentImage()
# Remove overlay if any.
imp.setOverlay( None )
# Get results table.
results_table = ResultsTable.getResultsTable()
# Create TrackMate instance.
trackmate = create_trackmate( imp, results_table )
# Process.
ok = process( trackmate )
if not ok:
# Display results.
# Create the GUI and let it control display of results.
display_results_in_GUI( trackmate )
# Color ROI by track ID!
rm = RoiManager.getInstance()
color_rois_by_track( trackmate, rm )
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