| Package | Description |
|---|---|
| trainableSegmentation |
| Modifier and Type | Method and Description |
|---|---|
FeatureStack |
FeatureStackArray.get(int n)
Get n-th stack in the array (remember n>=0)
|
FeatureStack |
WekaSegmentation.getFeatureStack(int i)
Get current feature stack
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
WekaSegmentation.addBinaryData(ij.ImagePlus labelImage,
FeatureStack featureStack,
String className)
Add instances to a specific class from a label (binary) image.
|
boolean |
WekaSegmentation.addBinaryData(ij.ImagePlus labelImage,
FeatureStack featureStack,
String className1,
String className2)
Add instances to two classes from a label (binary) image.
|
boolean |
WekaSegmentation.addLabeledData(ij.ImagePlus labelImage,
FeatureStack featureStack)
Add instances from a labeled image.
|
boolean |
WekaSegmentation.addLabeledData(ij.process.ImageProcessor labelImage,
FeatureStack featureStack,
int[] classIndexToLabel,
int numSamples)
Add instances reading the pixel classes from a label image.
|
boolean |
WekaSegmentation.addLabeledData(ij.process.ImageProcessor labelImage,
FeatureStack featureStack,
String[] classNames,
int numSamples)
Add instances reading the pixel classes from a label image.
|
boolean |
WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage,
FeatureStack featureStack,
String whiteClassName,
String blackClassName,
int numSamples)
Add instances to two classes from a label (binary) image in a random
and balanced way.
|
boolean |
WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage,
ij.process.ImageProcessor mask,
FeatureStack featureStack,
String whiteClassName,
String blackClassName,
int numSamples)
Add instances to two classes from a label (binary) image in a random
and balanced way (with repetition).
|
boolean |
WekaSegmentation.addRandomBalancedBinaryData(ij.process.ImageProcessor labelImage,
ij.process.ImageProcessor mask,
ij.process.ImageProcessor weights,
FeatureStack featureStack,
String whiteClassName,
String blackClassName,
int numSamples)
Add instances to two classes from a label (binary) image in a random
and balanced way (with repetition).
|
boolean |
WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage,
FeatureStack featureStack,
int numSamples)
Add instances from a labeled image in a random and balanced way.
|
boolean |
WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage,
FeatureStack featureStack,
String[] classNames,
int numSamples)
Add instances reading the pixel classes from a label image in a random
and balanced way.
|
boolean |
WekaSegmentation.addRandomBalancedLabeledData(ij.process.ImageProcessor labelImage,
int[] classIndexToLabel,
FeatureStack featureStack,
int numSamples)
Add instances from a labeled image in a random and balanced way.
|
boolean |
WekaSegmentation.addRandomData(ij.ImagePlus labelImage,
FeatureStack featureStack,
String whiteClassName,
int numSamples)
Add instances defined by a label (binary) image in a random
way.
|
Callable<Instances> |
WekaSegmentation.createInstances(ArrayList<String> classNames,
FeatureStack featureStack)
Create instances of a feature stack (to be submitted to an Executor Service)
|
static void |
WekaSegmentation.filterFeatureStackByList(ArrayList<String> featureNames,
FeatureStack featureStack)
Filter feature stack based on the list of feature names to use
|
void |
FeatureStackArray.set(FeatureStack fs,
int index)
Set a member of the list
|
Callable<Boolean> |
FeatureStackArray.updateFeatures(FeatureStack fs)
Update features of a feature stack (to be submitted to an Executor Service)
|
Copyright © 2015–2021 Fiji. All rights reserved.