public class TrackMate extends Object implements Benchmark, MultiThreaded, Algorithm, Named, Cancelable
The TrackMate_ class runs on the currently active time-lapse image (2D or 3D) and both identifies and tracks bright spots over time.
Required input: A 2D or 3D time-lapse image with bright blobs.
Modifier and Type | Field and Description |
---|---|
protected String |
errorMessage |
protected Model |
model
The model this trackmate will shape.
|
protected int |
numThreads |
static String |
PLUGIN_NAME_STR |
static String |
PLUGIN_NAME_VERSION |
protected long |
processingTime |
protected Settings |
settings |
Constructor and Description |
---|
TrackMate() |
TrackMate(Model model,
Settings settings) |
TrackMate(Settings settings) |
Modifier and Type | Method and Description |
---|---|
void |
cancel(String reason) |
boolean |
checkInput() |
boolean |
computeEdgeFeatures(boolean doLogIt)
Calculate all features for all detected spots.
|
boolean |
computeSpotFeatures(boolean doLogIt)
Calculate all features for all detected spots.
|
boolean |
computeTrackFeatures(boolean doLogIt)
Calculate all features for all tracks.
|
boolean |
execDetection()
Execute the detection part.
|
boolean |
execInitialSpotFiltering()
Execute the initial spot filtering part.
|
boolean |
execSpotFiltering(boolean doLogIt)
Execute the spot feature filtering part.
|
boolean |
execTrackFiltering(boolean doLogIt) |
boolean |
execTracking()
Execute the tracking part.
|
String |
getCancelReason() |
String |
getErrorMessage() |
Model |
getModel() |
String |
getName() |
int |
getNumThreads() |
long |
getProcessingTime() |
Settings |
getSettings() |
boolean |
isCanceled() |
boolean |
process() |
void |
setName(String name) |
void |
setNumThreads() |
void |
setNumThreads(int numThreads) |
String |
toString() |
public static final String PLUGIN_NAME_STR
public static final String PLUGIN_NAME_VERSION
protected final Model model
protected final Settings settings
protected long processingTime
protected String errorMessage
protected int numThreads
public TrackMate(Settings settings)
public TrackMate()
public Model getModel()
public Settings getSettings()
public boolean computeSpotFeatures(boolean doLogIt)
Features are calculated for each spot, using their location, and the raw image. Features to be calculated and analyzers are taken from the settings field of this object.
doLogIt
- if true
, the Logger
of the model will be
notified.true
if the calculation was performed successfully,
false
otherwise.public boolean computeEdgeFeatures(boolean doLogIt)
Features are calculated for each spot, using their location, and the raw image. Features to be calculated and analyzers are taken from the settings field of this object.
doLogIt
- if true
, the Logger
of the model will be
notified.true
if the calculation was performed successfuly,
false
otherwise.public boolean computeTrackFeatures(boolean doLogIt)
doLogIt
- if true
, messages will be sent to the logger.true
if the computation completed without errors.public boolean execTracking()
This method links all the selected spots from the thresholding part using
the selected tracking algorithm. This tracking process will generate a
graph (more precisely a SimpleWeightedGraph
)
made of the spot election for its vertices, and edges representing the
links.
The ModelChangeListener
s of the model will be notified when the
successful process is over.
true
if the computation completed without errors.public boolean execDetection()
This method configure the chosen Settings.detectorFactory
with
the source image and the detector settings and execute the detection
process for all the frames set in the Settings
object of the
target model.
public boolean execInitialSpotFiltering()
Because of the presence of noise, it is possible that some of the regional maxima found in the detection step have identified noise, rather than objects of interest. This can generates a very high number of spots, which is inconvenient to deal with when it comes to computing their features, or displaying them.
Any SpotDetector
is expected to at least compute the
Spot.QUALITY
value for each spot it creates, so it is possible to
set up an initial filtering on this feature, prior to any other
operation.
This method simply takes all the detected spots, and discard those whose
quality value is below the threshold set by
Settings.initialSpotFilterValue
. The spot field is overwritten,
and discarded spots can't be recalled.
The ModelChangeListener
s of this model will be notified with a
ModelChangeEvent.SPOTS_COMPUTED
event.
true
if the computation completed without errors.public boolean execSpotFiltering(boolean doLogIt)
Because of the presence of noise, it is possible that some of the regional maxima found in the detection step have identified noise, rather than objects of interest. A filtering operation based on the calculated features in this step should allow to rule them out.
This method simply takes all the detected spots, and mark as visible the
spots whose features satisfy all of the filters in the Settings
object.
The ModelChangeListener
s of this model will be notified with a
ModelChangeEvent.SPOTS_FILTERED
event.
doLogIt
- if true
, will send a message to the model logger.true
if the computation completed without errors.public boolean execTrackFiltering(boolean doLogIt)
public boolean checkInput()
checkInput
in interface Algorithm
public String getErrorMessage()
getErrorMessage
in interface Algorithm
public int getNumThreads()
getNumThreads
in interface MultiThreaded
public void setNumThreads()
setNumThreads
in interface MultiThreaded
public void setNumThreads(int numThreads)
setNumThreads
in interface MultiThreaded
public long getProcessingTime()
getProcessingTime
in interface Benchmark
public boolean isCanceled()
isCanceled
in interface Cancelable
public void cancel(String reason)
cancel
in interface Cancelable
public String getCancelReason()
getCancelReason
in interface Cancelable
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