GPU accelerated image processing for everyone
Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local maximum of this count.
Categories: Graphs, Labels, Measurements, Visualisation
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx_-0.30.1.21.jar.
Ext.CLIJx_localMaximumTouchingNeighborCountMap(Image input, Image destination);
// init CLIJ and GPU import net.haesleinhuepf.clijx.CLIJx; import net.haesleinhuepf.clij.clearcl.ClearCLBuffer; CLIJx clijx = CLIJx.getInstance(); // get input parameters ClearCLBuffer input = clijx.push(inputImagePlus); destination = clijx.create(input);
// Execute operation on GPU clijx.localMaximumTouchingNeighborCountMap(input, destination);
// show result destinationImagePlus = clijx.pull(destination); destinationImagePlus.show(); // cleanup memory on GPU clijx.release(input); clijx.release(destination);
% init CLIJ and GPU clijx = init_clatlabx(); % get input parameters input = clijx.pushMat(input_matrix); destination = clijx.create(input);
% Execute operation on GPU clijx.localMaximumTouchingNeighborCountMap(input, destination);
% show result destination = clijx.pullMat(destination) % cleanup memory on GPU clijx.release(input); clijx.release(destination);
import pyclesperanto_prototype as cle cle.local_maximum_touching_neighbor_count_map(input, destination)