Revision as of 02:48, 5 September 2017

TrackMate scripting principle

TrackMate_ can also be used without the GUI, using a scripting language that allows making calls to Java. The most simple way to get started is to use the Script Editor, which takes care of the difficult & boring part for you (such as path). The examples proposed on this page all use Jython, but can be adapted to anything.

Since we are calling the internals of TrackMate, we must get to know a bit of its guts. I have tried to come up with a rational design; though not always successfully. There is 3 main classes to interact with in a script:

• Model (fiji.plugin.trackmate.Model) is the class in charge of storing the data. It cannot do anything to create it. It can help you follow manual modifications you would made in the manual editing mode, interrogate it, ... but it is conceptually just a data recipient.

• Settings (fiji.plugin.trackmate.Settings) is the class storing the fields that will configure TrackMate and pilot how the data is created. This is where you specify what is the source image, what are the detector and tracking algorithms to use, what are the filters to use, etc...

• TrackMate (fiji.plugin.trackmate.TrackMate) is the guy that does the actual work. In scripts, we use it to actually perform the analysis tasks, such as generating spots from images, linking them into track, etc... It reads configuration information in the Settings object mentioned above and put the resulting data in the model.

So getting a working script is all about configuring a proper Settings object and calling exec* methods on a TrackMate object. Then we read the results in the Model object.

A full example

Here is an example of full tracking process, using the easy image found in the first tutorial. The following (Jython) script works as following:

• It fetches the image from the web
• It configures settings for segmentation and tracking
• The model is instantiated, with the settings and imp objects
• The TrackMate class is instantiated with the model object
• Then the TrackMate object performs all the steps needed.
• The final results is displayed as an overlay.

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.sparselap import SparseLAPTrackerFactory
from fiji.plugin.trackmate.tracking import LAPUtils
from ij import IJ
import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer as HyperStackDisplayer
import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter
import sys
import fiji.plugin.trackmate.features.track.TrackDurationAnalyzer as TrackDurationAnalyzer
   
# Get currently selected image
#imp = WindowManager.getCurrentImage()
imp = IJ.openImage('http://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()
settings.setFrom(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 = LAPUtils.getDefaultLAPSettingsMap() # almost good enough
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = True
settings.trackerSettings['ALLOW_TRACK_MERGING'] = True
   
# Configure track analyzers - Later on we want to filter out tracks 
# based on their displacement, so we need to state that we want 
# track displacement to be calculated. By default, out of the GUI, 
# not features are calculated. 
   
# The displacement feature is provided by the TrackDurationAnalyzer.
   
settings.addTrackAnalyzer(TrackDurationAnalyzer())
   
# 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
#----------------
    
selectionModel = SelectionModel(model)
displayer =  HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
   
# Echo results with the logger we set at start:
model.getLogger().log(str(model))

Loading and reading from a saved TrackMate XML file

Scripting is a good way to interrogate and play non-interactively with tracking results. The example below shows how to load a XML TrackMate file and rebuild a full working model from it.

That way you could for instance redo a full tracking process by only changing one parameter with respect to the saved one. You might also want to check results without relying on the GUI. Etc...

For the example below to work for you, you will have to edit line 20 and put the actual path to your TrackMate file.

from fiji.plugin.trackmate.visualization.hyperstack import HyperStackDisplayer
from fiji.plugin.trackmate.io import TmXmlReader
from fiji.plugin.trackmate import Logger
from fiji.plugin.trackmate import Settings
from fiji.plugin.trackmate import SelectionModel
from fiji.plugin.trackmate.providers import DetectorProvider
from fiji.plugin.trackmate.providers import TrackerProvider
from fiji.plugin.trackmate.providers import SpotAnalyzerProvider
from fiji.plugin.trackmate.providers import EdgeAnalyzerProvider
from fiji.plugin.trackmate.providers import TrackAnalyzerProvider
from java.io import File
import sys
 
 
#----------------
# Setup variables
#----------------
 
# Put here the path to the TrackMate file you want to load
file = File('/Users/tinevez/Desktop/Data/FakeTracks.xml')
 
# We have to feed a logger to the reader.
logger = Logger.IJ_LOGGER
 
#-------------------
# Instantiate reader
#-------------------
 
reader = TmXmlReader(file)
if not reader.isReadingOk():
    sys.exit(reader.getErrorMessage())
#-----------------
# Get a full model
#-----------------
 
# This will return a fully working model, with everything
# stored in the file. Missing fields (e.g. tracks) will be 
# null or None in python
model = reader.getModel()
# model is a fiji.plugin.trackmate.Model
 
#----------------
# Display results
#----------------
 
# We can now plainly display the model. It will be shown on an
# empty image with default magnification.
sm = SelectionModel(model)
displayer =  HyperStackDisplayer(model, sm)
displayer.render()
 
#---------------------------------------------
# Get only part of the data stored in the file
#---------------------------------------------
 
# You might want to access only separate parts of the 
# model. 
 
spots = model.getSpots()
# spots is a fiji.plugin.trackmate.SpotCollection
 
logger.log(str(spots))
 
# If you want to get the tracks, it is a bit trickier. 
# Internally, the tracks are stored as a huge mathematical
# simple graph, which is what you retrieve from the file. 
# There are methods to rebuild the actual tracks, taking
# into account for everything, but frankly, if you want to 
# do that it is simpler to go through the model:
 
trackIDs = model.getTrackModel().trackIDs(True) # only filtered out ones
for id in trackIDs:
    logger.log(str(id) + ' - ' + str(model.getTrackModel().trackEdges(id)))
 
 
#---------------------------------------
# Building a settings object from a file
#---------------------------------------
 
# Reading the Settings object is actually currently complicated. The 
# reader wants to initialize properly everything you saved in the file,
# including the spot, edge, track analyzers, the filters, the detector,
# the tracker, etc...
# It can do that, but you must provide the reader with providers, that
# are able to instantiate the correct TrackMate Java classes from
# the XML data.
 
# We start by creating an empty settings object
settings = Settings()
 
# Then we create all the providers, and point them to the target model:
detectorProvider        = DetectorProvider()
trackerProvider         = TrackerProvider()
spotAnalyzerProvider    = SpotAnalyzerProvider()
edgeAnalyzerProvider    = EdgeAnalyzerProvider()
trackAnalyzerProvider   = TrackAnalyzerProvider()
 
# Ouf! now we can flesh out our settings object:
reader.readSettings(settings, detectorProvider, trackerProvider, spotAnalyzerProvider, edgeAnalyzerProvider, trackAnalyzerProvider)
 
logger.log(str('\n\nSETTINGS:'))
logger.log(str(settings))
 
# The settings object is also instantiated with the target image.
# Note that the XML file only stores a link to the image.
# If the link is not valid, the image will not be found.
imp = settings.imp
imp.show()
 
# With this, we can overlay the model and the source image:
displayer =  HyperStackDisplayer(model, sm, imp)
displayer.render()

Export spot, edge and track numerical features after tracking

This example shows how to extract numerical features from tracking results.

TrackMate computes and stores three kind of numerical features:

• Spot features, such as a spot location (X, Y, Z), its mean intensity, radius etc...
• Edge or link features: An edge is a link between two spots. Its feature typically stores the velocity and displacement, which are defined only for two consecutive spots in the same track.
• Track features: numerical features that apply to a whole track, such as the number of spots it contains.

By default, TrackMate only computes a very limited number of features. The GUI forces TrackMate to compute them all, but if you do scripting, you will have to explicitly configures TrackMate to compute the features you desire. This is done by adding feature analyzers to the settings object.

There are some gotchas: some feature analyzers require other numerical features to be already calculated. If something does not work, it is a good idea to directly check the preamble in the source code of the analyzers (TrackMate feature logic).

Finally, depending on their type, numerical features are not stored at the same place:

• Spot features are simply conveyed by the spot object, and you can access them through spot.getFeature('FEATURE_NAME')
• Edge and track features are stored in a sub-component of the model object called the FeatureModel (FeatureModel.java).

Check the script below to see a working example.

from ij import IJ, ImagePlus, ImageStack
import fiji.plugin.trackmate.Settings as Settings
import fiji.plugin.trackmate.Model as Model
import fiji.plugin.trackmate.SelectionModel as SelectionModel
import fiji.plugin.trackmate.TrackMate as TrackMate
import fiji.plugin.trackmate.Logger as Logger
import fiji.plugin.trackmate.detection.DetectorKeys as DetectorKeys
import fiji.plugin.trackmate.detection.DogDetectorFactory as DogDetectorFactory
import fiji.plugin.trackmate.tracking.sparselap.SparseLAPTrackerFactory as SparseLAPTrackerFactory
import fiji.plugin.trackmate.tracking.LAPUtils as LAPUtils
import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer as HyperStackDisplayer
import fiji.plugin.trackmate.features.FeatureFilter as FeatureFilter
import fiji.plugin.trackmate.features.FeatureAnalyzer as FeatureAnalyzer
import fiji.plugin.trackmate.features.spot.SpotContrastAndSNRAnalyzerFactory as SpotContrastAndSNRAnalyzerFactory
import fiji.plugin.trackmate.action.ExportStatsToIJAction as ExportStatsToIJAction
import fiji.plugin.trackmate.io.TmXmlReader as TmXmlReader
import fiji.plugin.trackmate.action.ExportTracksToXML as ExportTracksToXML
import fiji.plugin.trackmate.io.TmXmlWriter as TmXmlWriter
import fiji.plugin.trackmate.features.ModelFeatureUpdater as ModelFeatureUpdater
import fiji.plugin.trackmate.features.SpotFeatureCalculator as SpotFeatureCalculator
import fiji.plugin.trackmate.features.spot.SpotContrastAndSNRAnalyzer as SpotContrastAndSNRAnalyzer
import fiji.plugin.trackmate.features.spot.SpotIntensityAnalyzerFactory as SpotIntensityAnalyzerFactory
import fiji.plugin.trackmate.features.track.TrackSpeedStatisticsAnalyzer as TrackSpeedStatisticsAnalyzer
import fiji.plugin.trackmate.util.TMUtils as TMUtils
  
  
# Get currently selected image
#imp = WindowManager.getCurrentImage()
imp = IJ.openImage('http://fiji.sc/samples/FakeTracks.tif')
#imp.show()
  
  
#-------------------------
# Instantiate model object
#-------------------------
  
model = Model()
  
# Set logger
model.setLogger(Logger.IJ_LOGGER)
  
#------------------------
# Prepare settings object
#------------------------
     
settings = Settings()
settings.setFrom(imp)
     
# Configure detector
settings.detectorFactory = DogDetectorFactory()
settings.detectorSettings = {
    DetectorKeys.KEY_DO_SUBPIXEL_LOCALIZATION : True,
    DetectorKeys.KEY_RADIUS : 2.5,
    DetectorKeys.KEY_TARGET_CHANNEL : 1,
    DetectorKeys.KEY_THRESHOLD : 5.,
    DetectorKeys.KEY_DO_MEDIAN_FILTERING : False,
} 
   
# 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
  
# Add the analyzers for some spot features.
# You need to configure TrackMate with analyzers that will generate 
# the data you need. 
# Here we just add two analyzers for spot, one that computes generic
# pixel intensity statistics (mean, max, etc...) and one that computes
# an estimate of each spot's SNR. 
# The trick here is that the second one requires the first one to be in
# place. Be aware of this kind of gotchas, and read the docs. 
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
  
# Add an analyzer for some track features, such as the track mean speed.
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
  
settings.initialSpotFilterValue = 1
  
print(str(settings))
     
#----------------------
# Instantiate trackmate
#----------------------
  
trackmate = TrackMate(model, settings)
     
#------------
# Execute all
#------------
  
    
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.')
   
selectionModel = SelectionModel(model)
displayer =  HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
  
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
  
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())
      
    track = model.getTrackModel().trackSpots(id)
    for spot in track:
        sid = spot.ID()
        # Fetch spot features directly from spot. 
        x=spot.getFeature('POSITION_X')
        y=spot.getFeature('POSITION_Y')
        t=spot.getFeature('FRAME')
        q=spot.getFeature('QUALITY')
        snr=spot.getFeature('SNR') 
        mean=spot.getFeature('MEAN_INTENSITY')
        model.getLogger().log('\tspot ID = ' + str(sid) + ': x='+str(x)+', y='+str(y)+', t='+str(t)+', q='+str(q) + ', snr='+str(snr) + ', mean = ' + str(mean))

Manually creating a model

TrackMate aims at combining automatic and manual tracking facilities. This is also the case when scripting: a part of the API offers to a edit a model extensively. A few code patterns must be followed.

First, every edit must happen between a call to model.beginUpdate() and model.endUpdate():

model.beginUpdate()
# ... do whatever you want to the model here.
model.endUpdate()

The reason for this is that TrackMate caches each modification made to its model. This is required because we can deal with a rather complex content. For instance: imagine you have a single track that splits in two branches at some point. If you decide to remove the spot at the fork, a complex series of events will happen:

• First, three edges will be removed: the ones that were connected to the spot you just removed.
• Then the spot will actually be removed from the model.
• But then you need to recompute the tracks, because now, you have 3 tracks instead of 1.
• But also: all the numerical features of the tracks are now invalid, and you need to recompute them.
• And what happens to the track name? What track, amongst the 3 new ones, will receive the old name?

Well, TrackMate does that for you automatically, but for the chain of events to happen timely, you must make your edits within this model.beginUpdate() / model.endUpdate() code block.

This script just shows you how to use this construct to build and populate a model from scratch. Appending content to a model is done by, sequentially:

• Creating spot objects. You have to provide their x, y, z location, as well as a radius and a quality value for each. At this stage, you don't provide at what frame (or time) they belong.
• This is done by adding the spot to the model, using model.addSpotTo(Spot, frame), frame being a positive integer number.
• Then you create a link, or an edge as it is called in TrackMate, between two spots. You have to provide the link cost: model.addEdge(Spot1, Spot2, cost).

Spot quality and link cost are typically useful to quantify automatic spot detection and linking. We typically use negative values for these two numbers when doing manual edits.

The script below does this:

import ij.gui.NewImage as NewImage
import fiji.plugin.trackmate.Settings as Settings
import fiji.plugin.trackmate.Model as Model
import fiji.plugin.trackmate.Logger as Logger
import fiji.plugin.trackmate.Spot as Spot
import fiji.plugin.trackmate.SelectionModel as SelectionModel
import fiji.plugin.trackmate.TrackMate as TrackMate
import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer as HyperStackDisplayer
import fiji.plugin.trackmate.visualization.trackscheme.TrackScheme as TrackScheme
import fiji.plugin.trackmate.visualization.PerTrackFeatureColorGenerator as PerTrackFeatureColorGenerator
import fiji.plugin.trackmate.features.ModelFeatureUpdater as ModelFeatureUpdater
import fiji.plugin.trackmate.features.track.TrackIndexAnalyzer as TrackIndexAnalyzer
import ij.plugin.Animator as Animator
import math
 
# We just need a model for this script. Nothing else, since 
# we will do everything manually.
model = Model()
model.setLogger(Logger.IJ_LOGGER)

# Well actually, we still need a bit:
# We want to color-code the tracks by their feature, for instance 
# with the track index. But for this, we need to compute the 
# features themselves. 
#
# Manuall, this is done by declaring what features interest you
# in a settings object, and creating a ModelFeatureUpdater that 
# will listen to changes in the model, and compute the feautures
# on the fly.
settings = Settings()
settings.addTrackAnalyzer( TrackIndexAnalyzer() )
# If you want more, add more analyzers.

# The object in charge of keeping the numerical features
# up to date:
ModelFeatureUpdater( model, settings )
# Nothing more to do. When the model changes, this guy will be notified and
# recalculate all the features you declared in the settings object.


 
# Every manual edit to the model must be made 
# between a model.beginUpdate() and a model.endUpdate()
# call, otherwise you will mess with the event signalling
# and feature calculation.
model.beginUpdate()
 
# 1.
 
s1 = None
for t in range(0, 5):
    x = 10 + t * 10
    if s1 is None:
 
        # When you create a spot, you always have to specify its x, y, z 
        # coordinates (even if z=0 in 2D images), AND its radius, AND its
        # quality. We enforce these 5 values so as to avoid any bad surprise
        # in other TrackMate component.
        # Typically, we use negative quality values to tag spot created 
        # manually. 
        s1 = Spot(x, 10, 0, 1, -1)
        model.addSpotTo(s1, t)
        continue
 
         
    s2 = Spot(x, 10, 0, 1, -1)
    model.addSpotTo(s2, t)
    # You need to specify an edge cost for the link you create between two spots.
    # Again, we use negative costs to tag edges created manually.
    model.addEdge(s1, s2, -1)
    s1 = s2
 
# So that's how you manually build a model from scratch. 
# The next lines just do more of this, to build something enjoyable.
 
middle = s2
s1 = s2
for t in range(0, 4):
    x = 60 + t * 10
    s2 = Spot(x, 10, 0, 1, -1)
    model.addSpotTo(s2, t + 5)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
s1 = middle
for t in range(0, 16):
    y = 20 + t * 6
    s2 = Spot(50, y, 0, 1, -1)
    model.addSpotTo(s2, t + 5)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
 
# 2.
 
s1 = None
for t in range(0, 21):
    if s1 is None:
        s1 = Spot(110, 10, 0, 1, -1)
        model.addSpotTo(s1, t)
        start = s1
        continue
     
    y = 10 + t * 5
    s2 = Spot(110, y, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
     
    if t == 10:
        middle = s2
     
    s1 = s2
 
s1 = start
for t in range(1, 20):
    theta = math.pi - t * math.pi / 20
    x = 110 + 40 * math.sin(theta) 
    y = 35 + 25 * math.cos(theta)
    s2 = Spot(x, y, 0, 1, -1)   
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
s1 = middle
for t in range(1, 11):
    x = 110 + t * 5
    y = 60 + t * 5
    s2 = Spot(x, y, 0, 1, -1)   
    model.addSpotTo(s2, t+10)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
 
# 3.
 
s1 = None
for t in range(0, 21):
    if s1 is None:
        s1 = Spot(170, 110, 0, 1, -1)
        model.addSpotTo(s1, t)
        continue
 
    x = 170 + t * 4
    if t < 10:
        y = 110 - t * 10
    else:
        y = 10 + (t-10) * 10
    s2 = Spot(x, y, 0, 1, -1)   
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
    if t == 5:
        start = s2
    if t == 15:
        end = s2
 
s1 = start
for t in range(6, 15):
    x = 194 + (t-5) * 3.5
    s2 = Spot(x, 60, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
model.addEdge(s2, end, -1)
 
 
# 4.
# A nice partial squircle
 
n = 4
a = 30
b = 50
s1 = None
for t in range(0, 21):
    theta = math.pi/10.0 + t/20.0 * (2 * math.pi - math.pi/5.0)
 
    ct = math.cos(theta)
    if  ct > 0:
        sgn = +1 # copysign is not available to us :(
    else:
        sgn = -1
    x = 290 + math.pow(math.fabs( ct ) , (2.0/n)) * a * sgn
 
    st = math.sin(theta)
    if  st > 0:
        sgn = +1
    else:
        sgn = -1
    y = 60 + math.pow(math.fabs( st ) , (2.0/n)) * b * sgn
 
    s2 = Spot(x, y, 0, 1, -1)
    model.addSpotTo(s2, t)
 
    if s1 is None:
        s1 = s2
        continue
     
    model.addEdge(s1, s2, -1)
    s1 = s2
 
 
#5.
 
s1 = None
s3 = None
for t in range(0, 21):
    x1 = 340
    y1 = 10 + t * 5
    y2 = y1
 
    s2 = Spot(x1, y1, 0, 1, -1)
    model.addSpotTo(s2, t)
     
    if t == 10:
        s4 = s2
    else:
        if t < 10:
            x2 = 400 - t *  6
        else:
            x2 = 340 + (t-10) * 6
        s4 = Spot(x2, y1, 0, 1, -1)
         
    model.addSpotTo(s4, t)
 
    if s1 is None:
        s1 = s2
        s3 = s4
        continue
 
    model.addEdge(s1, s2, -1)
    model.addEdge(s3, s4, -1)
    s1 = s2
    s3 = s4
 
 
# 6.
s1 = None
s3 = None
for t in range(0, 6):
    y = 40 - t * 6
    x1 = 450 - t * 8
    x2 = 450 + t * 8
 
    s2 = Spot(x1, y, 0, 1, -1)
    model.addSpotTo(s2, t)
    if s1 is None:
        s1 = s2
        s3 = s2
        continue
 
    s4 = Spot(x2, y, 0, 1, -1)
    model.addSpotTo(s4, t)
    model.addEdge(s1, s2, -1)
    model.addEdge(s3, s4, -1)
    s1 = s2
    s3 = s4
 
# If, at this stage, you start thinking I have too much free time, 
# then I would welcome your application as a babysitter to watch
# over the kids while I go out and spend a nice evening with my 
# wife.
 
for t in range(6, 21):
    x1 = 410
    x2 = 490
    y = 10 + (t-6) * 7
    s2 = Spot(x1, y, 0, 1, -1)
    s4 = Spot(x2, y, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addSpotTo(s4, t)
    model.addEdge(s1, s2, -1)
    model.addEdge(s3, s4, -1)
    s1 = s2
    s3 = s4
 
 
# 7.
# The power of copy-paste
 
s1 = None
for t in range(0, 21):
    if s1 is None:
        s1 = Spot(510, 110, 0, 1, -1)
        model.addSpotTo(s1, t)
        continue
 
    x = 510+ t * 4
    if t < 10:
        y = 110 - t * 10
    else:
        y = 10 + (t-10) * 10
    s2 = Spot(x, y, 0, 1, -1)   
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
    if t == 5:
        start = s2
    if t == 15:
        end = s2
 
s1 = start
for t in range(6, 15):
    x = 534 + (t-5) * 3.5
    s2 = Spot(x, 60, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
model.addEdge(s2, end, -1)
 
 
# 8.
# The power of copy-paste
# At this stage I wish Nick named this plugin Tatata.
 
s1 = None
for t in range(0, 5):
    x = 590 + t * 10
    if s1 is None:
        s1 = Spot(x, 10, 0, 1, -1)
        model.addSpotTo(s1, t)
        continue
 
         
    s2 = Spot(x, 10, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
 
middle = s2
 
s1 = s2
for t in range(0, 4):
    x = 640 + t * 10
    s2 = Spot(x, 10, 0, 1, -1)
    model.addSpotTo(s2, t + 5)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
s1 = middle
for t in range(0, 16):
    y = 20 + t * 6
    s2 = Spot(630, y, 0, 1, -1)
    model.addSpotTo(s2, t + 5)
    model.addEdge(s1, s2, -1)
    s1 = s2
 
 
# 9.
s1 = None
for t in range(0, 6):
    y = 60
    x = 720 - t * 8
    s2 = Spot(x, y, 0, 1, -1)
    model.addSpotTo(s2, t)
 
    if s1 is None:
        s1 = s2
        continue
 
    model.addEdge(s1, s2, -1)
    s1 = s2
 
s3 = s1
for t in range(6, 11):
    x = 680
    y1 = 60 + (t-5) * 10
    y2 = 60 - (t-5) * 10
    s2 = Spot(x, y1, 0, 1, -1)
    s4 = Spot(x, y2, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addSpotTo(s4, t)
    model.addEdge(s1, s2, -1)
    model.addEdge(s3, s4, -1)
    s1 = s2
    s3 = s4
 
for t in range(11, 21):
    y1 = 110
    y2 = 10
    x = 680 + (t-10) * 5
    s2 = Spot(x, y1, 0, 1, -1)
    s4 = Spot(x, y2, 0, 1, -1)
    model.addSpotTo(s2, t)
    model.addSpotTo(s4, t)
    model.addEdge(s1, s2, -1)
    model.addEdge(s3, s4, -1)
    s1 = s2
    s3 = s4
 
# Change the radius of the last one, so that we create some blank
# space around the model for display.
s2.putFeature('RADIUS', 20)
 
 
# Commit all of this.
model.endUpdate()
# This actually triggers the features to be recalculated.

 
# Prepare display.
sm = SelectionModel(model)
color = PerTrackFeatureColorGenerator(model, 'TRACK_INDEX')
# The last line does not work if you did not compute the 'TRACK_INDEX'
# feature earlier.

 
# The TrackScheme view is a bit hard to interpret.
trackscheme = TrackScheme(model, sm)
trackscheme.setDisplaySettings('TrackColoring', color)
trackscheme.render()
 
# You can create an hyperstack viewer without specifying any ImagePlus.
# It will then create a dummy one tuned to display the model content.
view = HyperStackDisplayer(model, sm)
# Display tracks as comets
view.setDisplaySettings('TrackDisplaymode', 1)
view.setDisplaySettings('TrackDisplayDepth', 20)
view.setDisplaySettings('TrackColoring', color)
view.render()
 
# Animate it a bit
imp = view.getImp()
imp.getCalibration().fps = 30
Animator().run('start')

Jean-Yves Tinevez (talk) 09:20, 11 March 2015 (CDT)