# 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())
filter2 = FeatureFilter('QUALITY', 10, True)
settings.addSpotFilter(filter2)
filter3 = FeatureFilter('MEDIAN_INTENSITY', 10, True)
settings.addSpotFilter(filter3)
filter4 = FeatureFilter('SNR', 0.5, True)
settings.addSpotFilter(filter4)
# Add an analyzer for some track features, such as the track mean speed.
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
settings.addTrackAnalyzer(TrackDurationAnalyzer())
filter5 = FeatureFilter('TRACK_DISPLACEMENT', 5, True)
settings.addTrackFilter(filter5)
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)
def track_cells(folder_w, filename, imp, correction):
#imp = IJ.openImage(os.path.join(folder,filename))
#imp.show()
#get image dimensions, set ROI remove part of flouresncent ring
x_size = ImagePlus.getDimensions(imp)[0]
y_size = ImagePlus.getDimensions(imp)[1]
x_start = 0
y_start = 0
#calculate alternative ROI
if crop_ring:
x_start = 170 / 2
y_start = 170 / 2
x_size = x_size - 170
y_size = y_size - 170
print(
str(x_start) + ", " + str(y_start) + ", " + str(x_size) + ", " +
str(y_size))
imp.setRoi(OvalRoi(x_start, y_start, x_size, y_size))
#imp_dup = imp.duplicate()
#imp_dup.show()
#red_corrected_img.show()
IJ.run(imp, "Make Inverse", "")
IJ.setForegroundColor(0, 0, 0)
IJ.run(imp, "Fill", "stack")
imp.killRoi()
#imp.show()
#sys.exit()
#img_filename = filename+"_corrected_red_stack.tif"
#folder_filename= os.path.join(well_folder,img_filename)
#IJ.save(imp, folder_filename)
#----------------------------
# 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': SUBPIXEL_LOCALIZATION,
'RADIUS': RADIUS,
'TARGET_CHANNEL': TARGET_CHANNEL,
'THRESHOLD': THRESHOLD,
'DO_MEDIAN_FILTERING': MEDIAN_FILTERING,
}
# Configure spot filters - Classical filter on quality
settings.initialSpotFilterValue = SPOT_FILTER
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotMorphologyAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotRadiusEstimatorFactory())
filter1 = FeatureFilter('QUALITY', QUALITY, True)
filter2 = FeatureFilter('CONTRAST', CONTRAST, True)
filter2a = FeatureFilter('ESTIMATED_DIAMETER', MAX_ESTIMATED_DIAMETER,
False)
filter2b = FeatureFilter('MEDIAN_INTENSITY', MAX_MEDIAN_INTENSITY, False)
settings.addSpotFilter(filter1)
settings.addSpotFilter(filter2)
settings.addSpotFilter(filter2a)
settings.addSpotFilter(filter2b)
print(settings.spotFilters)
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap(
) # almost good enough
##adapted from https://forum.image.sc/t/trackmate-scripting-automatically-exporting-spots-in-tracks-links-in-tracks-tracks-statistics-and-branching-analysis-to-csv/6256
#linking settings
settings.trackerSettings['LINKING_MAX_DISTANCE'] = LINKING_MAX_DISTANCE
if LINKING_FEATURE_PENALTIES == True:
settings.trackerSettings['LINKING_FEATURE_PENALTIES'] = {
LINKING_FEATURE_PENALTIES_TYPE: LINKING_FEATURE_PENALTIES_VALUE
}
else:
settings.trackerSettings['LINKING_FEATURE_PENALTIES'] = {}
#gap closing settings
settings.trackerSettings['ALLOW_GAP_CLOSING'] = ALLOW_GAP_CLOSING
if ALLOW_GAP_CLOSING == True:
settings.trackerSettings[
'GAP_CLOSING_MAX_DISTANCE'] = GAP_CLOSING_MAX_DISTANCE
settings.trackerSettings['MAX_FRAME_GAP'] = MAX_FRAME_GAP
if GAP_CLOSING_FEATURE_PENALTIES == True:
settings.trackerSettings['GAP_CLOSING_FEATURE_PENALTIES'] = {
GAP_CLOSING_FEATURE_PENALTIES_TYPE:
GAP_CLOSING_FEATURE_PENALTIES_VALUE
}
else:
settings.trackerSettings['GAP_CLOSING_FEATURE_PENALTIES'] = {}
#splitting settings
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = ALLOW_TRACK_SPLITTING
if ALLOW_TRACK_SPLITTING == True:
settings.trackerSettings[
'SPLITTING_MAX_DISTANCE'] = SPLITTING_MAX_DISTANCE
if SPLITTING_FEATURE_PENALTIES == True:
settings.trackerSettings['SPLITTING_FEATURE_PENALTIES'] = {
SPLITTING_FEATURE_PENALTIES_TYPE:
SPLITTING_FEATURE_PENALTIES_VALUE
}
else:
settings.trackerSettings['SPLITTING_FEATURE_PENALTIES'] = {}
#merging settings
settings.trackerSettings['ALLOW_TRACK_MERGING'] = ALLOW_TRACK_MERGING
if ALLOW_TRACK_MERGING == True:
settings.trackerSettings['MERGING_MAX_DISTANCE'] = MERGING_MAX_DISTANCE
if MERGING_FEATURE_PENALTIES == True:
settings.trackerSettings['MERGING_FEATURE_PENALTIES'] = {
MERGING_FEATURE_PENALTIES_TYPE: MERGING_FEATURE_PENALTIES_VALUE
}
else:
settings.trackerSettings['MERGING_FEATURE_PENALTIES'] = {}
print(settings.trackerSettings)
# 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())
settings.addTrackAnalyzer(TrackSpotQualityFeatureAnalyzer())
# 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.
filter3 = FeatureFilter('TRACK_DISPLACEMENT', TRACK_DISPLACEMENT, True)
filter4 = FeatureFilter('TRACK_START', TRACK_START, False)
#filter5 = FeatureFilter('TRACK_STOP', float(imp.getStack().getSize())-1.1, True)
settings.addTrackFilter(filter3)
settings.addTrackFilter(filter4)
#settings.addTrackFilter(filter5)
#-------------------
# 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
#----------------
#Set output folder and filename and create output folder
well_folder = os.path.join(folder_w, filename)
output_folder = os.path.join(well_folder, "Tracking")
create_folder(output_folder)
xml_file_name = filename + "_" + correction + "_trackmate_analysis.xml"
folder_filename_xml = os.path.join(output_folder, xml_file_name)
#ExportTracksToXML.export(model, settings, File(folder_filename_xml))
outfile = TmXmlWriter(File(folder_filename_xml))
outfile.appendSettings(settings)
outfile.appendModel(model)
outfile.writeToFile()
# Echo results with the logger we set at start:
#model.getLogger().log(str(model))
#create araray of timepoint length with filled 0
cell_counts = zerolistmaker(imp.getStack().getSize())
if ok:
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
track = model.getTrackModel().trackSpots(id)
for spot in track:
# Fetch spot features directly from spot.
t = spot.getFeature('FRAME')
print(t)
cell_counts[int(t)] = cell_counts[int(t)] + 1
else:
print("No spots detected!")
if HEADLESS == False:
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
del imp
return (cell_counts + [len(model.getTrackModel().trackIDs(True))])
def processImages(cfg, wellName, wellPath, images):
firstImage = IJ.openImage(images[0][0][0][0])
imgWidth = firstImage.getWidth()
imgHeight = firstImage.getHeight()
for c in range(0, cfg.getValue(ELMConfig.numChannels)):
chanName = cfg.getValue(ELMConfig.chanLabel)[c]
if cfg.getValue(ELMConfig.chanLabel)[c] in cfg.getValue(
ELMConfig.chansToSkip):
continue
imColorSeq = ImageStack(imgWidth, imgHeight)
imSeq = ImageStack(imgWidth, imgHeight)
totalHist = []
for z in range(0, cfg.getValue(ELMConfig.numZ)):
for t in range(0, cfg.getValue(ELMConfig.numT)):
currIP = IJ.openImage(images[c][z][t][0])
imColorSeq.addSlice(currIP.duplicate().getProcessor())
currIP = ELMImageUtils.getGrayScaleImage(
currIP, c, chanName, cfg)
imSeq.addSlice(currIP.getProcessor())
imgStats = currIP.getStatistics()
currHist = imgStats.getHistogram()
if not totalHist:
for i in range(len(currHist)):
totalHist.append(currHist[i])
else:
for i in range(len(currHist)):
totalHist[i] += currHist[i]
if cfg.hasValue(ELMConfig.thresholdFromWholeRange) and cfg.getValue(
ELMConfig.thresholdFromWholeRange) == True:
threshMethod = "Otsu" # Default works very poorly for this data
if cfg.hasValue(ELMConfig.thresholdMethod):
threshMethod = cfg.getValue(ELMConfig.thresholdMethod)
thresholder = AutoThresholder()
computedThresh = thresholder.getThreshold(threshMethod, totalHist)
cfg.setValue(ELMConfig.imageThreshold, computedThresh)
print("\tComputed threshold from total hist (" + threshMethod +
"): " + str(computedThresh))
print()
else:
print("\tUsing threshold computed on individual images!")
print()
computedThresh = 0
chanName = cfg.getValue(ELMConfig.chanLabel)[c]
imp = ImagePlus()
imp.setStack(imSeq)
imp.setDimensions(1, 1, cfg.getValue(ELMConfig.numT))
imp.setTitle(wellName + ", channel " + str(c))
impColor = ImagePlus()
impColor.setStack(imColorSeq)
impColor.setDimensions(1, 1, cfg.getValue(ELMConfig.numT))
impColor.setTitle(wellName + ", channel " + str(c) + " (Color)")
#----------------------------
# 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)
pa_features = [
"Area", "PercentArea", "Mean", "StdDev", "Mode", "Min", "Max", "X",
"Y", "XM", "YM", "Perim.", "BX", "BY", "Width", "Height", "Major",
"Minor", "Angle", "Circ.", "Feret", "IntDen", "Median", "Skew",
"Kurt", "RawIntDen", "FeretX", "FeretY", "FeretAngle", "MinFeret",
"AR", "Round", "Solidity"
]
featureNames = {}
featureShortNames = {}
featureDimensions = {}
isInt = {}
for feature in pa_features:
featureNames[feature] = feature
featureShortNames[feature] = feature
featureDimensions[feature] = Dimension.STRING
isInt[feature] = False
model.getFeatureModel().declareSpotFeatures(pa_features, featureNames,
featureShortNames,
featureDimensions, isInt)
#------------------------
# Prepare settings object
#------------------------
settings = Settings()
settings.setFrom(imp)
dbgPath = os.path.join(wellPath, 'debugImages_' + chanName)
if not os.path.exists(dbgPath):
os.makedirs(dbgPath)
if cfg.hasValue(ELMConfig.thresholdMethod):
threshMethod = cfg.getValue(ELMConfig.thresholdMethod)
else:
threshMethod = "Default"
# Configure detector - We use the Strings for the keys
settings.detectorFactory = ThresholdDetectorFactory()
settings.detectorSettings = {
'THRESHOLD': computedThresh,
'ABOVE': True,
'DEBUG_MODE': True,
'DEBUG_OUTPATH': dbgPath,
'THRESHOLD_METHOD': threshMethod
}
#settings.detectorFactory = LocalThresholdDetectorFactory()
#settings.detectorSettings = {
# 'THRESHOLD' : computedThresh,
# 'DEBUG_MODE' : True,
# 'DEBUG_OUTPATH' : dbgPath
#}
# Configure spot filters - Classical filter on quality
filter1 = FeatureFilter('QUALITY', 150, True)
settings.addSpotFilter(filter1)
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap(
) # almost good enough
# Linking
settings.trackerSettings[TrackerKeys.KEY_LINKING_MAX_DISTANCE] = 220.0
# in pixels
linkFeaturePenalties = HashMap()
linkFeaturePenalties['Area'] = 1.0
linkFeaturePenalties['POSITION_X'] = 1.0
linkFeaturePenalties['POSITION_Y'] = 1.0
#linkFeaturePenalties['Circ.'] = 1.0
#linkFeaturePenalties['Mean'] = 1.0
settings.trackerSettings[
TrackerKeys.KEY_LINKING_FEATURE_PENALTIES] = linkFeaturePenalties
# Gap closing
settings.trackerSettings[TrackerKeys.KEY_ALLOW_GAP_CLOSING] = True
settings.trackerSettings[TrackerKeys.KEY_GAP_CLOSING_MAX_FRAME_GAP] = 8
settings.trackerSettings[
TrackerKeys.KEY_GAP_CLOSING_MAX_DISTANCE] = 120.0
# in pixels
#settings.trackerSettings[TrackerKeys.KEY_GAP_CLOSING_FEATURE_PENALTIES] = new HashMap<>(DEFAULT_GAP_CLOSING_FEATURE_PENALTIES));
# Track splitting
settings.trackerSettings[TrackerKeys.KEY_ALLOW_TRACK_SPLITTING] = False
settings.trackerSettings[TrackerKeys.KEY_SPLITTING_MAX_DISTANCE] = 45.0
# in pixels
#settings.trackerSettings[TrackerKeys.KEY_SPLITTING_FEATURE_PENALTIES] = new HashMap<>(DEFAULT_SPLITTING_FEATURE_PENALTIES));
# Track merging
settings.trackerSettings[TrackerKeys.KEY_ALLOW_TRACK_MERGING] = True
settings.trackerSettings[TrackerKeys.KEY_MERGING_MAX_DISTANCE] = 45.0
# in pixels
#settings.trackerSettings[TrackerKeys.KEY_MERGING_FEATURE_PENALTIES] = new HashMap<>(DEFAULT_MERGING_FEATURE_PENALTIES));
# Others
settings.trackerSettings[TrackerKeys.KEY_BLOCKING_VALUE] = float("inf")
settings.trackerSettings[
TrackerKeys.KEY_ALTERNATIVE_LINKING_COST_FACTOR] = 1.05
settings.trackerSettings[TrackerKeys.KEY_CUTOFF_PERCENTILE] = 0.9
# 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,
# no features are calculated.
# The displacement feature is provided by the TrackDurationAnalyzer.
settings.addTrackAnalyzer(TrackDurationAnalyzer())
settings.addTrackAnalyzer(TrackBranchingAnalyzer())
settings.addTrackAnalyzer(TrackIndexAnalyzer())
settings.addTrackAnalyzer(TrackLocationAnalyzer())
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
# 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', 1, True)
#settings.addTrackFilter(filter2)
#filter2 = FeatureFilter('TRACK_DISPLACEMENT', 1, True)
#settings.addTrackFilter(filter2)
#print("Spot feature analyzers: " + settings.toStringFeatureAnalyzersInfo())
#-------------------
# Instantiate plugin
#-------------------
trackmate = TrackMate(model, settings)
trackmate.setNumThreads(1)
#--------
# Process
#--------
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
print("Processing " + chanName + "...")
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
#----------------
# Display results
#----------------
print("Rendering...")
# Set spot names based on track IDs
# This allows track IDs to be displayed in the rendered video
for tId in model.getTrackModel().trackIDs(True):
trackSpots = model.getTrackModel().trackSpots(tId)
for spot in trackSpots:
spot.setName(str(tId))
# Determine sub-tracks within a track
# Since tracks can merge, we want to keep track of which track a spot is
# in prior to the merge
spotToSubTrackMap = {}
spotIt = model.getSpots().iterator(False)
trackModel = model.getTrackModel()
subTrackCount = {}
while spotIt.hasNext():
spot = spotIt.next()
spotEdges = trackModel.edgesOf(spot)
# Find merge points within a track: ignore spots with fewer than 2 edges
if (len(spotEdges) < 2):
continue
# We have a merge if we have multiple incoming edges
incomingEdges = 0
edgeIt = spotEdges.iterator()
ancestorSpots = []
while edgeIt.hasNext():
edge = edgeIt.next()
src = trackModel.getEdgeSource(edge)
dst = trackModel.getEdgeTarget(edge)
if dst.ID() == spot.ID():
ancestorSpots.append(src)
incomingEdges += 1
# Ignore non-merges
if incomingEdges < 2:
continue
trackId = trackModel.trackIDOf(spot)
if trackId in subTrackCount:
subTrackId = subTrackCount[trackId]
else:
subTrackId = 1
for ancestorSpot in ancestorSpots:
labelSubTrackAncestors(trackModel, spotToSubTrackMap,
ancestorSpot, subTrackId, trackId,
False)
subTrackId += 1
subTrackCount[trackId] = subTrackId
# Spots after the last merge still need to be labeled
for tId in trackModel.trackIDs(True):
trackSpots = trackModel.trackSpots(tId)
spotIt = trackSpots.iterator()
lastSpot = None
while spotIt.hasNext():
spot = spotIt.next()
outgoingEdges = 0
spotEdges = trackModel.edgesOf(spot)
edgeIt = spotEdges.iterator()
while edgeIt.hasNext():
edge = edgeIt.next()
src = trackModel.getEdgeSource(edge)
dst = trackModel.getEdgeTarget(edge)
if src.ID() == spot.ID():
outgoingEdges += 1
if outgoingEdges == 0 and len(spotEdges) > 0:
lastSpot = spot
if tId in subTrackCount:
subTrackId = subTrackCount[tId]
else:
subTrackId = 1
if not lastSpot == None:
labelSubTrackAncestors(trackModel, spotToSubTrackMap, lastSpot,
subTrackId, tId, True)
# Create output file
trackOut = os.path.join(wellPath, chanName + "_spotToTrackMap.csv")
trackFile = open(trackOut, 'w')
# Fetch the track feature from the feature model.
trackFile.write('Spot Id, Track Sub Id, Track Id, Frame \n')
for spotId in spotToSubTrackMap:
trackFile.write(
str(spotId) + ', ' + ','.join(spotToSubTrackMap[spotId]) +
'\n')
trackFile.close()
# Write Edge Set
trackOut = os.path.join(wellPath, chanName + "_mergeEdgeSet.csv")
trackFile = open(trackOut, 'w')
trackFile.write('Track Id, Spot Id, Spot Id \n')
edgeIt = trackModel.edgeSet().iterator()
while edgeIt.hasNext():
edge = edgeIt.next()
src = trackModel.getEdgeSource(edge)
dst = trackModel.getEdgeTarget(edge)
trackId = trackModel.trackIDOf(edge)
srcSubTrack = spotToSubTrackMap[src.ID()][0]
dstSubTrack = spotToSubTrackMap[dst.ID()][0]
if not srcSubTrack == dstSubTrack:
trackFile.write(
str(trackId) + ', ' + str(src.ID()) + ', ' +
str(dst.ID()) + '\n')
trackFile.close()
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, impColor)
displayer.setDisplaySettings(
TrackMateModelView.KEY_TRACK_COLORING,
PerTrackFeatureColorGenerator(model,
TrackIndexAnalyzer.TRACK_INDEX))
displayer.setDisplaySettings(
TrackMateModelView.KEY_SPOT_COLORING,
SpotColorGeneratorPerTrackFeature(model,
TrackIndexAnalyzer.TRACK_INDEX))
displayer.setDisplaySettings(TrackMateModelView.KEY_DISPLAY_SPOT_NAMES,
True)
displayer.setDisplaySettings(
TrackMateModelView.KEY_TRACK_DISPLAY_MODE,
TrackMateModelView.TRACK_DISPLAY_MODE_LOCAL_BACKWARD_QUICK)
displayer.setDisplaySettings(
TrackMateModelView.KEY_TRACK_DISPLAY_DEPTH, 2)
displayer.render()
displayer.refresh()
trackmate.getSettings().imp = impColor
coa = CaptureOverlayAction(None)
coa.execute(trackmate)
WindowManager.setTempCurrentImage(coa.getCapture())
IJ.saveAs('avi', os.path.join(wellPath, chanName + "_out.avi"))
imp.close()
impColor.close()
displayer.clear()
displayer.getImp().hide()
displayer.getImp().close()
coa.getCapture().hide()
coa.getCapture().close()
# Echo results with the logger we set at start:
model.getLogger().log(str(model))
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
# Write output for tracks
numTracks = model.getTrackModel().trackIDs(True).size()
print "Writing track data for " + str(numTracks) + " tracks."
trackDat = {}
for tId in model.getTrackModel().trackIDs(True):
track = model.getTrackModel().trackSpots(tId)
# Ensure track spots dir exists
trackOut = os.path.join(wellPath, chanName + "_track_spots")
if not os.path.exists(trackOut):
os.makedirs(trackOut)
# Create output file
trackOut = os.path.join(trackOut, "track_" + str(tId) + ".csv")
trackFile = open(trackOut, 'w')
# Write Header
header = 'Name, ID, Frame, '
for feature in track.toArray()[0].getFeatures().keySet():
if feature == 'Frame':
continue
header += feature + ", "
header = header[0:len(header) - 2]
header += '\n'
trackFile.write(header)
# Write spot data
avgTotalIntensity = 0
for spot in track:
#print spot.echo()
data = [
spot.getName(),
str(spot.ID()),
str(spot.getFeature('FRAME'))
]
for feature in spot.getFeatures():
if feature == 'Frame':
continue
elif feature == 'TOTAL_INTENSITY':
avgTotalIntensity += spot.getFeature(feature)
data.append(str(spot.getFeature(feature)))
trackFile.write(','.join(data) + '\n')
trackFile.close()
avgTotalIntensity /= len(track)
# Write out track stats
# Make sure dir exists
trackOut = os.path.join(wellPath, chanName + "_tracks")
if not os.path.exists(trackOut):
os.makedirs(trackOut)
# Create output file
trackOut = os.path.join(trackOut, "track_" + str(tId) + ".csv")
trackFile = open(trackOut, 'w')
# Fetch the track feature from the feature model.
header = ''
for featName in fm.getTrackFeatureNames():
header += featName + ", "
header = header[0:len(header) - 2]
header += '\n'
trackFile.write(header)
features = ''
for featName in fm.getTrackFeatureNames():
features += str(fm.getTrackFeature(tId, featName)) + ', '
features = features[0:len(features) - 2]
features += '\n'
trackFile.write(features)
trackFile.write('\n')
trackFile.close()
trackDat[tId] = [
str(tId),
str(fm.getTrackFeature(tId, 'TRACK_DURATION')),
str(avgTotalIntensity),
str(fm.getTrackFeature(tId, 'TRACK_START')),
str(fm.getTrackFeature(tId, 'TRACK_STOP'))
]
# Create output file
trackOut = os.path.join(wellPath, chanName + "_trackSummary.csv")
trackFile = open(trackOut, 'w')
# Fetch the track feature from the feature model.
trackFile.write(
'Track Id, Duration, Avg Total Intensity, Start Frame, Stop Frame \n'
)
for track in trackDat:
trackFile.write(','.join(trackDat[track]) + '\n')
trackFile.close()
trackOut = os.path.join(wellPath, chanName + "_trackModel.xml")
trackFile = File(trackOut)
writer = TmXmlWriter(trackFile, model.getLogger())
#writer.appendLog( logPanel.getTextContent() );
writer.appendModel(trackmate.getModel())
writer.appendSettings(trackmate.getSettings())
#writer.appendGUIState( controller.getGuimodel() );
writer.writeToFile()
model.clearSpots(True)
model.clearTracks(True)
return trackDat
def run_trackmate(imp, path, filename, params, batch_mode=False):
# initialize trackmate model
model = Model()
# Set logger - use to see outputs, not needed in batch mode
model.setLogger(Logger.IJ_LOGGER)
# Create setting object from image
settings = Settings()
settings.setFrom(imp)
cal = imp.getCalibration()
model.setPhysicalUnits("micron", "sec")
# Configure detector
settings.detectorFactory = LogDetectorFactory()
# settings.detectorFactory = DogDetectorFactory()
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': params.do_subpixel_localization,
'RADIUS': params.radius,
'TARGET_CHANNEL': 0,
'THRESHOLD': params.threshold,
'DO_MEDIAN_FILTERING': params.do_median_filtering,
}
# print(params)
# Add spot filters
filter_quality = FeatureFilter('QUALITY', params.quality, True)
settings.addSpotFilter(filter_quality)
filter_snr = FeatureFilter('SNR', params.snr, True)
settings.addSpotFilter(filter_snr)
# Compute spot features
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
# Compute track features
settings.addTrackAnalyzer(TrackBranchingAnalyzer())
settings.addTrackAnalyzer(TrackDurationAnalyzer())
settings.addTrackAnalyzer(TrackIndexAnalyzer())
settings.addTrackAnalyzer(TrackLocationAnalyzer())
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
settings.addTrackAnalyzer(TrackSpotQualityFeatureAnalyzer())
# Update model
ModelFeatureUpdater(model, settings)
# Configure tracker
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
settings.trackerSettings[
'LINKING_MAX_DISTANCE'] = params.linking_max_distance
settings.trackerSettings[
'GAP_CLOSING_MAX_DISTANCE'] = params.gap_closing_max_distance
settings.trackerSettings['MAX_FRAME_GAP'] = params.max_frame_gap
# Add track filters
filter_T1 = FeatureFilter('TRACK_DURATION', params.track_duration, True)
filter_MTD = FeatureFilter('TRACK_DISPLACEMENT', params.track_displacement,
True)
settings.addTrackFilter(filter_T1)
settings.addTrackFilter(filter_MTD)
# Instantiate trackmate
trackmate = TrackMate(model, settings)
# Execute all
ok = trackmate.checkInput()
if not ok:
IJ.showMessage("No spots found... Adjust detection parameter.\n" +
str(trackmate.getErrorMessage()))
sys.exit(str(trackmate.getErrorMessage()))
ok = trackmate.process()
if not ok:
IJ.showMessage("No spots found... Adjust detection parameter.\n" +
str(trackmate.getErrorMessage()))
sys.exit(str(trackmate.getErrorMessage()))
filename = os.path.splitext(filename)[0] #filename without extension
outFile = File(os.path.join(path, filename + "_Tracks.xml"))
ExportTracksToXML.export(model, settings, outFile)
#imp.close()
tm_writer = TmXmlWriter(File(os.path.join(path, filename + "_TM.xml")))
tm_writer.appendModel(model)
tm_writer.appendSettings(settings)
tm_writer.writeToFile()
if not batch_mode:
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))