# Display results
#----------------
model.getLogger().log('Found ' + str(model.getTrackModel().nTracks(True)) + ' tracks.')
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) + ': mean velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits())
track = model.getTrackModel().trackSpots(id)
for spot in track:
sid = spot.ID()
# Fetch spot features directly from spot.
x=spot.getFeature('POSITION_X')
y=spot.getFeature('POSITION_Y')
t=spot.getFeature('FRAME')
q=spot.getFeature('QUALITY')
snr=spot.getFeature('SNR')
mean=spot.getFeature('MEAN_INTENSITY')
model.getLogger().log('\tspot ID = ' + str(sid) + ': x='+str(x)+', y='+str(y)+', t='+str(t)+', q='+str(q) + ', snr='+str(snr) + ', mean = ' + str(mean))
displayer.setDisplaySettings(TrackMateModelView.KEY_TRACK_DISPLAY_MODE, TrackMateModelView.TRACK_DISPLAY_MODE_LOCAL)
displayer.setDisplaySettings(TrackMateModelView.KEY_TRACK_DISPLAY_DEPTH, int(round(10/deltaT)))
# Display the displayer
displayer.render()
displayer.refresh()
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
v = fm.getTrackFeature(id, 'TRACK_MEDIAN_SPEED')
model.getLogger().log('')
trackinfo = 'Track ' + str(id) + ': median velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits()
model.getLogger().log(trackinfo)
loglist.append('\n' + trackinfo)
track = model.getTrackModel().trackSpots(id)
for spot in track:
sid = spot.ID()
# Fetch spot features directly from spot.
x=spot.getFeature('POSITION_X')
y=spot.getFeature('POSITION_Y')
t=spot.getFeature('FRAME')
q=spot.getFeature('QUALITY')
snr=spot.getFeature('SNR')
mean=spot.getFeature('MEAN_INTENSITY')
spotinfo = '\tspot ID = ' + str(sid) + ': x='+str(x)+', y='+str(y)+', t='+str(t)+', q='+str(q) + ', snr='+str(snr) + ', mean = ' + str(mean)
model.getLogger().log(spotinfo)
# Display results
#----------------
model.getLogger().log('Found ' + str(model.getTrackModel().nTracks(True)) + ' tracks.')
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) + ': mean velocity = ' + str(v) + ' ' + model.getSpaceUnits() + '/' + model.getTimeUnits())
track = model.getTrackModel().trackSpots(id)
for spot in track:
sid = spot.ID()
# Fetch spot features directly from spot.
x=spot.getFeature('POSITION_X')
y=spot.getFeature('POSITION_Y')
t=spot.getFeature('FRAME')
q=spot.getFeature('QUALITY')
snr=spot.getFeature('SNR')
mean=spot.getFeature('MEAN_INTENSITY')
model.getLogger().log('\tspot ID = ' + str(sid) + ': x='+str(x)+', y='+str(y)+', t='+str(t)+', q='+str(q) + ', snr='+str(snr) + ', mean = ' + str(mean))
# Echo results with the logger we set at start:
model.getLogger().log(str(model))
print "Done"
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
X = []
Y = []
T = []
Track = []
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
v = fm.getTrackFeature(id, 'TRACK_MEAN_SPEED')
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) + ': mean velocity = ' +
str(v) + ' ' + model.getSpaceUnits() + '/' +
model.getTimeUnits())
track = model.getTrackModel().trackSpots(id)
for spot in track:
sid = spot.ID()
# Fetch spot features directly from spot.
x = spot.getFeature('POSITION_X')
y = spot.getFeature('POSITION_Y')
t = spot.getFeature('FRAME')
q = spot.getFeature('QUALITY')
snr = spot.getFeature('SNR')
mean = spot.getFeature('MEAN_INTENSITY')
X.append(x)
Y.append(y)
T.append(t)
def process(srcDir, dstDir, currentDir, fileName, keepDirectories):
print "Processing:"
# Opening the image
print "Open image file", fileName
imp = IJ.openImage(os.path.join(currentDir, fileName))
#Here we make sure the calibration are correct
units = "pixel"
TimeUnit = "unit"
newCal = Calibration()
newCal.pixelWidth = 1
newCal.pixelHeight = 1
newCal.frameInterval = 1
newCal.setXUnit(units)
newCal.setYUnit(units)
newCal.setTimeUnit(TimeUnit)
imp.setCalibration(newCal)
cal = imp.getCalibration()
dims = imp.getDimensions() # default order: XYCZT
if (dims[4] == 1):
imp.setDimensions(1, 1, dims[3])
# Start the tracking
model = Model()
#Read the image calibration
model.setPhysicalUnits(cal.getUnit(), cal.getTimeUnit())
# Send all messages to ImageJ log window.
model.setLogger(Logger.IJ_LOGGER)
settings = Settings()
settings.setFrom(imp)
# Configure detector - We use the Strings for the keys
# Configure detector - We use the Strings for the keys
settings.detectorFactory = DownsampleLogDetectorFactory()
settings.detectorSettings = {
DetectorKeys.KEY_RADIUS: 2.,
DetectorKeys.KEY_DOWNSAMPLE_FACTOR: 2,
DetectorKeys.KEY_THRESHOLD: 1.,
}
print(settings.detectorSettings)
# Configure spot filters - Classical filter on quality
filter1 = FeatureFilter('QUALITY', 0, True)
settings.addSpotFilter(filter1)
# Configure tracker - We want to allow merges and fusions
settings.trackerFactory = SparseLAPTrackerFactory()
settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap(
) # almost good enough
settings.trackerSettings['LINKING_MAX_DISTANCE'] = LINKING_MAX_DISTANCE
settings.trackerSettings['ALLOW_TRACK_SPLITTING'] = ALLOW_TRACK_SPLITTING
settings.trackerSettings['SPLITTING_MAX_DISTANCE'] = SPLITTING_MAX_DISTANCE
settings.trackerSettings['ALLOW_TRACK_MERGING'] = ALLOW_TRACK_MERGING
settings.trackerSettings['MERGING_MAX_DISTANCE'] = MERGING_MAX_DISTANCE
settings.trackerSettings[
'GAP_CLOSING_MAX_DISTANCE'] = GAP_CLOSING_MAX_DISTANCE
settings.trackerSettings['MAX_FRAME_GAP'] = MAX_FRAME_GAP
# 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(TrackSpeedStatisticsAnalyzer())
filter2 = FeatureFilter('TRACK_DISPLACEMENT', 10, True)
settings.addTrackFilter(filter2)
#-------------------
# Instantiate plugin
#-------------------
trackmate = TrackMate(model, settings)
#--------
# Process
#--------
ok = trackmate.checkInput()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
ok = trackmate.process()
if not ok:
sys.exit(str(trackmate.getErrorMessage()))
#----------------
# Display results
#----------------
if showtracks:
model.getLogger().log('Found ' +
str(model.getTrackModel().nTracks(True)) +
' tracks.')
selectionModel = SelectionModel(model)
displayer = HyperStackDisplayer(model, selectionModel, imp)
displayer.render()
displayer.refresh()
# The feature model, that stores edge and track features.
fm = model.getFeatureModel()
with open(dstDir + fileName + 'tracks_properties.csv', "w") as file:
writer1 = csv.writer(file)
writer1.writerow([
"track #", "TRACK_MEAN_SPEED (micrometer.secs)",
"TRACK_MAX_SPEED (micrometer.secs)", "NUMBER_SPLITS",
"TRACK_DURATION (secs)", "TRACK_DISPLACEMENT (micrometer)"
])
with open(dstDir + fileName + 'spots_properties.csv',
"w") as trackfile:
writer2 = csv.writer(trackfile)
#writer2.writerow(["spot ID","POSITION_X","POSITION_Y","Track ID", "FRAME"])
writer2.writerow(
["Tracking ID", "Timepoint", "Time (secs)", "X pos", "Y pos"])
for id in model.getTrackModel().trackIDs(True):
# Fetch the track feature from the feature model.
v = (fm.getTrackFeature(id, 'TRACK_MEAN_SPEED') *
Pixel_calibration) / Time_interval
ms = (fm.getTrackFeature(id, 'TRACK_MAX_SPEED') *
Pixel_calibration) / Time_interval
s = fm.getTrackFeature(id, 'NUMBER_SPLITS')
d = fm.getTrackFeature(id, 'TRACK_DURATION') * Time_interval
e = fm.getTrackFeature(
id, 'TRACK_DISPLACEMENT') * Pixel_calibration
model.getLogger().log('')
model.getLogger().log('Track ' + str(id) +
': mean velocity = ' + str(v) + ' ' +
model.getSpaceUnits() + '/' +
model.getTimeUnits())
track = model.getTrackModel().trackSpots(id)
writer1.writerow(
[str(id), str(v),
str(ms), str(s),
str(d), str(e)])
for spot in track:
sid = spot.ID()
x = spot.getFeature('POSITION_X')
y = spot.getFeature('POSITION_Y')
z = spot.getFeature('TRACK_ID')
t = spot.getFeature('FRAME')
time = int(t) * int(Time_interval)
writer2.writerow(
[str(id), str(t),
str(time), str(x),
str(y)])
