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 standard deviation 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_localStandardDeviationTouchingNeighborCountMap(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.localStandardDeviationTouchingNeighborCountMap(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.localStandardDeviationTouchingNeighborCountMap(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_standard_deviation_touching_neighbor_count_map(input, destination)