def trackmate(self):
calibration = self.imp.getCalibration()
model = Model()
model.setLogger(Logger.IJ_LOGGER)
settings = Settings()
settings.setFrom(self.imp)
# Configure detector - We use the Strings for the keys
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': True,
'RADIUS': calibration.getX(self.trackmateSize),
'TARGET_CHANNEL': 1,
'THRESHOLD': self.trackmateThreashold,
'DO_MEDIAN_FILTERING': True,
}
# Configure spot filters - Classical filter on quality
filter1 = FeatureFilter('QUALITY', 0.01, True)
settings.addSpotFilter(filter1)
settings.addSpotAnalyzerFactory(SpotIntensityMultiCAnalyzerFactory())
settings.initialSpotFilterValue = 1
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
trackmate = TrackMate(model, settings)
#--------
# Process
#--------
ok = trackmate.checkInput()
if not ok:
print("NOT OK")
ok = trackmate.process()
if not ok:
print("NOT OK")
#----------------
# Display results
#----------------
#selectionModel = SelectionModel(model)
#displayer = HyperStackDisplayer(model, selectionModel, self.imp)
#displayer.render()
#displayer.refresh()
# Echo results with the logger we set at start:
spots = model.getSpots()
return spots.iterable(True)
def detection(imp, c):
cal = imp.getCalibration()
model = Model()
settings = Settings()
settings.setFrom(imp)
# Configure detector - Manually determined as best
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': True,
'RADIUS': 2.0,
'TARGET_CHANNEL': c,
'THRESHOLD': 20.0,
'DO_MEDIAN_FILTERING': False,
}
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
try:
ok = trackmate.process()
except:
IJ.log("Nothing detected")
IJ.selectWindow('test')
IJ.run('Close')
else:
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
# Get spots information
spots = model.getSpots()
spotIt = spots.iterator(0, False)
# Loop through spots and save into files
# Fetch spot features directly from spot
sid = []
x = []
y = []
q = []
r = []
spotID = 0
for spot in spotIt:
spotID = spotID + 1
sid.append(spotID)
x.append(spot.getFeature('POSITION_X'))
y.append(spot.getFeature('POSITION_Y'))
q.append(spot.getFeature('QUALITY'))
r.append(spot.getFeature('RADIUS'))
data = zip(sid, x, y, q, r)
return data
def runTrackMate(imp, targetChannel, dt, radius, threshold, frameGap,
linkingMax, closingMax):
# Get the number of channels
nChannels = imp.getNChannels()
IJ.log("->Detection threshold used: " + str(threshold))
IJ.log("->Number of frames is: " + str(imp.getStackSize()))
IJ.log("->Target channel is: " + str(targetChannel))
IJ.log('->Number of channels to measure %d' % nChannels)
# Setup settings for TrackMate
settings = Settings()
settings.setFrom(imp)
settings.dt = dt
# Spot analyzer: we want the multi-C intensity analyzer.
settings.addSpotAnalyzerFactory(SpotMultiChannelIntensityAnalyzerFactory())
# Spot detector.
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = settings.detectorFactory.getDefaultSettings()
settings.detectorSettings['RADIUS'] = radius
settings.detectorSettings['THRESHOLD'] = threshold
settings.detectorSettings['TARGET_CHANNEL'] = targetChannel
# Spot tracker.
#settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerFactory = LAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
settings.trackerSettings['MAX_FRAME_GAP'] = frameGap
settings.trackerSettings['LINKING_MAX_DISTANCE'] = linkingMax
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = closingMax
settings.trackerSettings['ALLOW_TRACK_MERGING'] = False
settings.trackerSettings['ALLOW_GAP_CLOSING'] = False
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = False
settings.trackerSettings['ALLOW_TRACK_MERGING'] = False
settings.trackerSettings['ALTERNATIVE_LINKING_COST_FACTOR'] = 0.5
settings.trackerSettings['BLOCKING_VALUE'] = 1.0
settings.trackerSettings['CUTOFF_PERCENTILE'] = 1.0
#settings.trackerSettings['SPLITTING_MAX_DISTANCE'] = 16.0
# Run TrackMate and store data into Model.
model = Model()
trackmate = TrackMate(model, settings)
if not trackmate.checkInput() or not trackmate.process():
IJ.log('Could not execute TrackMate: ' +
str(trackmate.getErrorMessage()))
else:
return model, nChannels
linkingFeaturePenalties['POSITION_X'] = 9.9999E20
linkingFeaturePenalties['QUALITY'] = 1.0
gapClosingFeaturePenalties = settings.trackerSettings['GAP_CLOSING_FEATURE_PENALTIES']
gapClosingFeaturePenalties['POSITION_X'] = 9.9999E20
# Add filters
settings.initialSpotFilterValue = 0
# settings.addSpotFilter(FeatureFilter('QUALITY', 50.0, True))
settings.addTrackFilter(FeatureFilter('TRACK_DISPLACEMENT', .75, True))
settings.addTrackFilter(FeatureFilter('NUMBER_SPOTS', 5, True))
# Add the analyzers for some spot features.
# You need to configure TrackMate with analyzers that will generate
# the data you need.
img = TMUtils.rawWraps(settings.imp)
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotRadiusEstimatorFactory())
settings.addSpotAnalyzerFactory(SpotMorphologyAnalyzerFactory())
# Add analyhzers for edges
settings.addEdgeAnalyzer(EdgeTargetAnalyzer())
settings.addEdgeAnalyzer(EdgeTimeLocationAnalyzer())
settings.addEdgeAnalyzer(EdgeVelocityAnalyzer())
# Add an analyzer for some track features, such as the track mean speed.
settings.addTrackAnalyzer(TrackSpeedStatisticsAnalyzer())
settings.addTrackAnalyzer(TrackDurationAnalyzer())
settings.addTrackAnalyzer(TrackIndexAnalyzer())
settings.addTrackAnalyzer(TrackLocationAnalyzer())
settings.addTrackAnalyzer(TrackSpotFeatureAnalyzer())
settings.trackerSettings[
'LINKING_MAX_DISTANCE'] = linking_max_distance
# settings.trackerSettings['MERGING_MAX_DISTANCE'] = linking_max_distance
# settings.trackerSettings['SPLITTING_MAX_DISTANCE'] = linking_max_distance
settings.trackerSettings[
'GAP_CLOSING_MAX_DISTANCE'] = gap_closing_max_distance
# Add ALL the feature analyzers known to TrackMate, via
# providers.
# They offer automatic analyzer detection, so all the
# available feature analyzers will be added.
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))
# Configure track filters - track should start before frame 5
track_filter = FeatureFilter('TRACK_START', track_start, False)
# Swap Z and T dimensions if T=1
dims = imp.getDimensions() # default order: XYCZT
if (dims[4] == 1):
imp.setDimensions(dims[2, 4, 3])
# Get the number of channels
nChannels = imp.getNChannels()
# Setup settings for TrackMate
settings = Settings()
settings.setFrom(imp)
settings.dt = 0.05
# Spot analyzer: we want the multi-C intensity analyzer.
settings.addSpotAnalyzerFactory(SpotMultiChannelIntensityAnalyzerFactory())
# Spot detector.
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = settings.detectorFactory.getDefaultSettings()
settings.detectorSettings['RADIUS'] = radius
settings.detectorSettings['THRESHOLD'] = threshold
# Spot tracker.
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
settings.trackerSettings['MAX_FRAME_GAP'] = frameGap
settings.trackerSettings['LINKING_MAX_DISTANCE'] = linkingMax
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = closingMax
# Run TrackMate and store data into Model.
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
results_table.
"""
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'] = 20.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 20.0
settings.trackerSettings['MAX_FRAME_GAP'] = 3
settings.initialSpotFilterValue = -1.
return trackmate
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 nucleus_detection(infile, nucleus_channel, stacksize, animation):
# Detect nucleus with 3d log filters
fullpath = infile
infile = filename(infile)
IJ.log("Start Segmentation " + str(infile))
# First get Nb Stacks
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(fullpath)
default_options = "stack_order=XYCZT color_mode=Composite view=Hyperstack specify_range c_begin=" + \
str(nucleus_channel) + " c_end=" + str(nucleus_channel) + \
" c_step=1 open=[" + fullpath + "]"
NbStack = reader.getSizeZ()
reader.close()
output = re.sub('.ids', '.csv', infile)
with open(os.path.join(folder5, output), 'wb') as outfile:
DETECTwriter = csv.writer(outfile, delimiter=',')
DETECTwriter.writerow(
['spotID', 'roundID', 'X', 'Y', 'Z', 'QUALITY', 'SNR', 'INTENSITY'])
rounds = NbStack // stacksize
spotID = 1
for roundid in xrange(1, rounds + 2):
# Process stacksize by stacksize otherwise crash because too many spots
Zstart = (stacksize * roundid - stacksize + 1)
Zend = (stacksize * roundid)
if(Zend > NbStack):
Zend = NbStack % stacksize + (roundid - 1) * stacksize
IJ.log("Round:" + str(roundid) + ' Zstart=' + str(Zstart) +
' Zend=' + str(Zend) + ' out of ' + str(NbStack))
IJ.run("Bio-Formats Importer", default_options + " z_begin=" +
str(Zstart) + " z_end=" + str(Zend) + " z_step=1")
imp = IJ.getImage()
imp.show()
cal = imp.getCalibration()
model = Model()
settings = Settings()
settings.setFrom(imp)
# Configure detector - Manually determined as best
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': True,
'RADIUS': 5.5,
'TARGET_CHANNEL': 1,
'THRESHOLD': 50.0,
'DO_MEDIAN_FILTERING': False,
}
filter1 = FeatureFilter('QUALITY', 1, True)
settings.addSpotFilter(filter1)
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
settings.addSpotAnalyzerFactory(SpotContrastAndSNRAnalyzerFactory())
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
try:
ok = trackmate.process()
except:
IJ.log("Nothing detected, Round:" + str(roundid) + ' Zstart=' +
str(Zstart) + ' Zend=' + str(Zend) + ' out of ' + str(NbStack))
IJ.selectWindow(infile)
IJ.run('Close')
continue
else:
if animation:
# For plotting purpose only
imp.setPosition(1, 1, imp.getNFrames())
imp.getProcessor().setMinAndMax(0, 4000)
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
for i in xrange(1, imp.getNSlices() + 1):
imp.setSlice(i)
time.sleep(0.05)
IJ.selectWindow(infile)
IJ.run('Close')
spots = model.getSpots()
spotIt = spots.iterator(0, False)
sid = []
sroundid = []
x = []
y = []
z = []
q = []
snr = []
intensity = []
for spot in spotIt:
sid.append(spotID)
spotID = spotID + 1
sroundid.append(roundid)
x.append(spot.getFeature('POSITION_X'))
y.append(spot.getFeature('POSITION_Y'))
q.append(spot.getFeature('QUALITY'))
snr.append(spot.getFeature('SNR'))
intensity.append(spot.getFeature('MEAN_INTENSITY'))
# Correct Z position
correct_z = spot.getFeature(
'POSITION_Z') + (roundid - 1) * float(stacksize) * cal.pixelDepth
z.append(correct_z)
with open(os.path.join(folder5, output), 'ab') as outfile:
DETECTwriter = csv.writer(outfile, delimiter=',')
Sdata = zip(sid, sroundid, x, y, z, q, snr, intensity)
for Srow in Sdata:
DETECTwriter.writerow(Srow)
# 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)
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 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 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))
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
results_table.
"""
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' ] = 15.0
settings.trackerSettings[ 'MAX_FRAME_GAP' ] = 3
settings.trackerSettings[ 'ALLOW_TRACK_SPLITTING' ] = True
settings.trackerSettings[ 'SPLITTING_MAX_DISTANCE' ] = 7.0
settings.trackerSettings
settings.initialSpotFilterValue = -1.
### print(model.getFeatureModel().getTrackFeatureNames())
# TRACK_START: Track start,
# TRACK_INDEX: Track index,
# NUMBER_MERGES: Number of merge events,
# TRACK_STD_SPEED: Velocity standard deviation,
# TRACK_ID: Track ID,
# TRACK_MEDIAN_QUALITY: Median quality,
# TRACK_STD_QUALITY: Quality standard deviation,
# TRACK_X_LOCATION: X Location (mean),
# TRACK_MEDIAN_SPEED: Median velocity,
# NUMBER_SPOTS: Number of spots in track,
# TRACK_MIN_SPEED: Minimal velocity,
# NUMBER_GAPS: Number of gaps,
# TRACK_Z_LOCATION: Z Location (mean),
# TRACK_STOP: Track stop,
# TRACK_MEAN_SPEED: Mean velocity,
# NUMBER_SPLITS: Number of split events,
# TRACK_MAX_SPEED: Maximal velocity,
# TRACK_Y_LOCATION: Y Location (mean),
# TRACK_DISPLACEMENT: Track displacement,
# NUMBER_COMPLEX: Complex points,
# TRACK_MEAN_QUALITY: Mean quality,
# TRACK_DURATION: Duration of track,
# TRACK_MAX_QUALITY: Maximal quality,
# LONGEST_GAP: Longest gap,
# TRACK_MIN_QUALITY: Minimal quality
settings.addTrackFilter(FeatureFilter('NUMBER_SPLITS', 0.9, True))
return trackmate
def track_single_batch(path, filename):
# Get currently selected image
imp = WindowManager.getCurrentImage()
# imp = IJ.openImage('https://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.detectorFactory = BlockLogDetectorFactory()
print
settings.detectorSettings = {
'DO_SUBPIXEL_LOCALIZATION': True,
'RADIUS': 7.5,
'TARGET_CHANNEL': 1,
'THRESHOLD': 0.25,
'DO_MEDIAN_FILTERING': False,
'NSPLIT': 3,
}
# Configure spot filters - Classical filter on quality
filter1 = FeatureFilter('QUALITY', 0.1, True) # in higher SNR;
settings.addSpotFilter(filter1)
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
#settings.trackerFactory = LAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap(
) # almost good enough
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = True
settings.trackerSettings['ALLOW_TRACK_MERGING'] = True
settings.trackerSettings['LINKING_MAX_DISTANCE'] = 15.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 15.0
settings.trackerSettings['MAX_FRAME_GAP'] = 5
# feature
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))
#filter2 = FeatureFilter('TRACK_DISPLACEMENT', 3, True)
#settings.addTrackFilter(filter2)
# processing
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
try:
ok = trackmate.process()
except:
IJ.log("Nothing detected")
else:
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))
save_path = os.path.join(path, 'result')
if not os.path.exists(save_path):
os.mkdir(save_path, 0755)
outFile = File(save_path, filename)
ExportTracksToXML.export(model, settings, outFile)
imp.close()
def track():
imp = IJ.getImage()
nChannels = imp.getNChannels() # Get the number of channels
orgtitle = imp.getTitle()
IJ.run("Subtract Background...", "rolling=50 sliding stack")
IJ.run("Enhance Contrast...", "saturated=0.3")
IJ.run("Multiply...", "value=10 stack")
IJ.run("Subtract Background...", "rolling=50 sliding stack")
IJ.run("Set Scale...", "distance=0")
channels = ChannelSplitter.split(imp)
imp_GFP = channels[0]
imp_RFP = channels[1]
IJ.selectWindow(orgtitle)
IJ.run("Close")
ic = ImageCalculator()
imp_merge = ic.run("Add create stack", imp_GFP, imp_RFP)
imp_merge.setTitle("add_channels")
imp_merge.show()
imp_RFP.show()
imp_GFP.show()
imp5 = ImagePlus()
IJ.run(imp5, "Merge Channels...", "c1=[" + imp_merge.title + "] c2=" + imp_GFP.title + ' c3=' + imp_RFP.title + " create")
print("c1=[" + imp_merge.title + "] c2=" + imp_GFP.title + ' c3=' + imp_RFP.title + " create")
imp5.show()
imp5 = IJ.getImage()
nChannels = imp5.getNChannels()
# Setup settings for TrackMate
settings = Settings()
settings.setFrom(imp5)
# Spot analyzer: we want the multi-C intensity analyzer.
settings.addSpotAnalyzerFactory(SpotMultiChannelIntensityAnalyzerFactory())
# Spot detector.
settings.detectorFactory = LogDetectorFactory()
settings.detectorSettings = settings.detectorFactory.getDefaultSettings()
settings.detectorSettings['TARGET_CHANNEL'] = 1
settings.detectorSettings['RADIUS'] = 24.0
settings.detectorSettings['THRESHOLD'] = 0.0
# Spot tracker.
# Configure tracker - We don't want to allow merges or splits
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap() # almost good enough
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = False
settings.trackerSettings['ALLOW_TRACK_MERGING'] = False
settings.trackerSettings['LINKING_MAX_DISTANCE'] = 8.0
settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = 8.0
settings.trackerSettings['MAX_FRAME_GAP'] = 1
# Configure track filters
settings.addTrackAnalyzer(TrackDurationAnalyzer())
settings.addTrackAnalyzer(TrackSpotQualityFeatureAnalyzer())
filter1 = FeatureFilter('TRACK_DURATION', 20, True)
settings.addTrackFilter(filter1)
# Run TrackMate and store data into Model.
model = Model()
trackmate = TrackMate(model, settings)
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp5)
displayer.render()
displayer.refresh()
IJ.log('TrackMate completed successfully.')
IJ.log('Found %d spots in %d tracks.' % (model.getSpots().getNSpots(True) , model.getTrackModel().nTracks(True)))
# Print results in the console.
headerStr = '%10s %10s %10s %10s %10s %10s' % ('Spot_ID', 'Track_ID', 'Frame', 'X', 'Y', 'Z')
rowStr = '%10d %10d %10d %10.1f %10.1f %10.1f'
for i in range( nChannels ):
headerStr += (' %10s' % ( 'C' + str(i+1) ) )
rowStr += ( ' %10.1f' )
#open a file to save results
myfile = open('/home/rickettsia/Desktop/data/Clamydial_Image_Analysis/EMS_BMECBMELVA_20X_01122019/data/'+orgtitle.split('.')[0]+'.csv', 'wb')
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(['Spot_ID', 'Track_ID', 'Frame', 'X', 'Y', 'Z', 'Channel_1', 'Channel_2'])
IJ.log('\n')
IJ.log(headerStr)
tm = model.getTrackModel()
trackIDs = tm.trackIDs(True)
for trackID in trackIDs:
spots = tm.trackSpots(trackID)
# Let's sort them by frame.
ls = ArrayList(spots)
for spot in ls:
values = [spot.ID(), trackID, spot.getFeature('FRAME'), \
spot.getFeature('POSITION_X'), spot.getFeature('POSITION_Y'), spot.getFeature('POSITION_Z')]
for i in range(nChannels):
values.append(spot.getFeature('MEAN_INTENSITY%02d' % (i+1)))
IJ.log(rowStr % tuple(values))
l1 = (values[0], values[1], values[2], values[3], values[4], values[5], values[7], values[8])
wr.writerow(l1)
myfile.close()
IJ.selectWindow("Merged")
IJ.run("Close")
# Swap Z and T dimensions if T=1 dims = imp.getDimensions() # default order: XYCZT if (dims[4] == 1): imp.setDimensions( dims[2,4,3] ) # Get the number of channels nChannels = imp.getNChannels() # Setup settings for TrackMate settings = Settings() settings.setFrom( imp ) settings.dt = 0.05 # Spot analyzer: we want the multi-C intensity analyzer. settings.addSpotAnalyzerFactory( SpotMultiChannelIntensityAnalyzerFactory() ) # Spot detector. settings.detectorFactory = LogDetectorFactory() settings.detectorSettings = settings.detectorFactory.getDefaultSettings() settings.detectorSettings['RADIUS'] = radius settings.detectorSettings['THRESHOLD'] = threshold # Spot tracker. settings.trackerFactory = SparseLAPTrackerFactory() settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap() settings.trackerSettings['MAX_FRAME_GAP'] = frameGap settings.trackerSettings['LINKING_MAX_DISTANCE'] = linkingMax settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = closingMax # Run TrackMate and store data into Model.
# 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)
settings.detectorSettings['RADIUS'] = 1.
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
# format for json conversion
tracker_settings = tracker_settings.replace('{', '{"').replace(':',
'":').replace(
', ', ', "')
tracker_settings = json.loads(tracker_settings)
for key, val in tracker_settings.items():
settings.trackerSettings[key] = val
settings.addSpotAnalyzerFactory(SpotIntensityAnalyzerFactory())
#-------------------
# Instantiate plugin
#-------------------
trackmate = TrackMate(model, settings)
trackmate.setNumThreads(threads)
#--------
# Process
#--------
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
