GPU accelerated image processing for everyone
Starting from a label map, draw lines between n closest labels for each label resulting in a mesh.
The end points of the lines correspond to the centroids of the labels.
Categories: Graphs, Labels, Measurements
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clijx_-0.30.1.22.jar.
Ext.CLIJx_drawMeshBetweenNClosestLabels(Image input, Image destination, Number number_of_closest_labels);
// 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); int number_of_closest_labels = 10;
// Execute operation on GPU clijx.drawMeshBetweenNClosestLabels(input, destination, number_of_closest_labels);
// 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); number_of_closest_labels = 10;
% Execute operation on GPU clijx.drawMeshBetweenNClosestLabels(input, destination, number_of_closest_labels);
% show result destination = clijx.pullMat(destination) % cleanup memory on GPU clijx.release(input); clijx.release(destination);
import pyclesperanto_prototype as cle cle.draw_mesh_between_n_closest_labels(input, destination, number_of_closest_labels)
mesh_nearest_neighbors.ipynb
mesh_with_distances.ipynb