n_channels = 1
n_slices = 1 # Z slices
n_frames = 1 # time frames
# Get current image
image = IJ.getImage()
# Check that we have correct dimensions
stack_size = image.getImageStackSize() # raw number of images in the stack
if n_channels * n_slices * n_frames == stack_size:
image.setDimensions(n_channels, n_slices, n_frames)
else:
IJ.log('The product of channels (' + str(n_channels) + '), slices (' +
str(n_slices) + ')')
IJ.log('and frames (' + str(n_frames) + ') must equal the stack size (' +
str(stack_size) + ').')
# Set calibration
pixel_width = 1
pixel_height = 1
pixel_depth = 1
space_unit = 'µm'
frame_interval = 1
time_unit = 's'
calibration = Calibration() # new empty calibration
calibration.pixelWidth = pixel_width
calibration.pixelHeight = pixel_height
calibration.pixelDepth = pixel_depth
calibration.frameInterval = frame_interval
calibration.setUnit(space_unit)
calibration.setTimeUnit(time_unit)
image.setCalibration(calibration)
image.repaintWindow()
imp.setDisplayMode(IJ.COLOR)
imp.setC(color)
# imp.setDisplayRange(0.0, 3.0)
# imp.show()
log_info += 'frames: ' + str(imp.getNFrames()) + '\n'
log_info += 'width: ' + str(imp.getWidth()) + '\n'
log_info += 'height: ' + str(imp.getHeight()) + '\n'
cal = Calibration()
cal.setUnit('micron')
cal.pixelHeight = resolution
cal.pixelWidth = resolution
cal.pixelDepth = 0.
cal.fps = 1
cal.frameInterval = 1
imp.setCalibration(cal)
#-------------------------
# Instantiate model object
#-------------------------
model = Model()
model.setPhysicalUnits('micron', 'frames')
# Set logger
model.setLogger(Logger.IJ_LOGGER)
#------------------------
# Prepare settings object
#------------------------
# Set dimensions
n_channels = 1
n_slices = 1 # Z slices
n_frames = 1 # time frames
# Get current image
image = IJ.getImage()
# Check that we have correct dimensions
stack_size = image.getImageStackSize() # raw number of images in the stack
if n_channels * n_slices * n_frames == stack_size:
image.setDimensions(n_channels, n_slices, n_frames)
else:
IJ.log('The product of channels ('+str(n_channels)+'), slices ('+str(n_slices)+')')
IJ.log('and frames ('+str(n_frames)+') must equal the stack size ('+str(stack_size)+').')
# Set calibration
pixel_width = 1
pixel_height = 1
pixel_depth = 1
space_unit = 'µm'
frame_interval = 1
time_unit = 's'
calibration = Calibration() # new empty calibration
calibration.pixelWidth = pixel_width
calibration.pixelHeight = pixel_height
calibration.pixelDepth = pixel_depth
calibration.frameInterval = frame_interval
calibration.setUnit(space_unit)
calibration.setTimeUnit(time_unit)
image.setCalibration(calibration)
image.repaintWindow()
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)])
