applyWekaModel

Applies a Weka model using functionality of Fijis Trainable Weka Segmentation plugin.

It takes a 3D feature stack (e.g. first plane original image, second plane blurred, third plane edge image)and applies a pre-trained a Weka model. Take care that the feature stack has been generated in the sameway as for training the model!

Categories: Segmentation, Machine Learning

Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx-weka_-0.32.0.1.jar.

Usage in ImageJ macro

Ext.CLIJx_applyWekaModel(Image featureStack3D, Image prediction2D_destination, String loadModelFilename);

Usage in object oriented programming languages

Java

// init CLIJ and GPU
import net.haesleinhuepf.clijx.CLIJx;
import net.haesleinhuepf.clij.clearcl.ClearCLBuffer;
CLIJx clijx = CLIJx.getInstance();

// get input parameters
ClearCLBuffer featureStack3D = clijx.push(featureStack3DImagePlus);
prediction2D_destination = clijx.create(featureStack3D);

// Execute operation on GPU
CLIJxWeka2 resultApplyWekaModel = clijx.applyWekaModel(featureStack3D, prediction2D_destination, loadModelFilename);

// show result
System.out.println(resultApplyWekaModel);
prediction2D_destinationImagePlus = clijx.pull(prediction2D_destination);
prediction2D_destinationImagePlus.show();

// cleanup memory on GPU
clijx.release(featureStack3D);
clijx.release(prediction2D_destination);

Matlab

% init CLIJ and GPU
clijx = init_clatlabx();

% get input parameters
featureStack3D = clijx.pushMat(featureStack3D_matrix);
prediction2D_destination = clijx.create(featureStack3D);

% Execute operation on GPU
CLIJxWeka2 resultApplyWekaModel = clijx.applyWekaModel(featureStack3D, prediction2D_destination, loadModelFilename);

% show result
System.out.println(resultApplyWekaModel);
prediction2D_destination = clijx.pullMat(prediction2D_destination)

% cleanup memory on GPU
clijx.release(featureStack3D);
clijx.release(prediction2D_destination);

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