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