def assisted_SNTtrace(context, imp):
'''
Calls SNT in a given img for user assisted tracing.
Uses WaitForUserDialog to confirm the end of tracing operations.
Returns list of SNT Paths.
'''
snt = prepare_SNT(context, imp)
pafm = snt.getPathAndFillManager()
dialog = WaitForUserDialog("Go to next step...", "Press ok when tracing is done.")
dialog.show()
if dialog.escPressed():
raise RuntimeError("Canceled by the user.")
SNTpaths = pafm.getPaths()
return SNTpaths
def runTrackMate(imp):
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
import fiji.plugin.trackmate.visualization.trackscheme.TrackScheme as TrackScheme
import fiji.plugin.trackmate.visualization.PerTrackFeatureColorGenerator as PerTrackFeatureColorGenerator
#-------------------------
# Instantiate model object
#-------------------------
nFrames = imp.getNFrames()
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: 12.30,
DetectorKeys.KEY_TARGET_CHANNEL: 1,
DetectorKeys.KEY_THRESHOLD: 100.,
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
#----------------
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
#---------------------
# Select correct spots
#---------------------
# Prepare display.
sm = SelectionModel(model)
color = PerTrackFeatureColorGenerator(model, 'TRACK_INDEX')
# launch TrackScheme to select spots and tracks
trackscheme = TrackScheme(model, sm)
trackscheme.setDisplaySettings('TrackColoring', color)
trackscheme.render()
# Update image with TrackScheme commands
view = HyperStackDisplayer(model, sm, imp)
view.setDisplaySettings('TrackColoring', color)
view.render()
# Wait for the user to select correct spots and tracks before collecting data
dialog = WaitForUserDialog(
"Spots",
"Delete incorrect spots and edit tracks if necessary. (Press ESC to cancel analysis)"
)
dialog.show()
if dialog.escPressed():
IJ.run("Remove Overlay", "")
imp.close()
return ([], nFrames)
# The feature model, that stores edge and track features.
#model.getLogger().log('Found ' + str(model.getTrackModel().nTracks(True)) + ' tracks.')
fm = model.getFeatureModel()
crds_perSpot = []
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.(remove """ to enable)
"""v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) + ': mean velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits())"""
trackID = str(id)
track = model.getTrackModel().trackSpots(id)
spot_track = {}
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))
spot_track[t] = (x, y)
crds_perSpot.append(spot_track)
#print ("Spot", crds_perSpot.index(spot_track),"has the following coordinates:", crds_perSpot[crds_perSpot.index(spot_track)])
return (crds_perSpot, nFrames)
