GPU accelerated image processing for everyone
Computes the Gaussian blurred image of an image given two sigma values in X and Y.
Thus, the filterkernel can have non-isotropic shape.
The implementation is done separable. In case a sigma equals zero, the direction is not blurred.
Categories: Noise, Filter
Availability: Available in Fiji by activating the update sites clij and clij2. This function is part of clij2_-2.5.0.1.jar.
Ext.CLIJ2_gaussianBlur2D(Image source, Image destination, Number sigma_x, Number sigma_y);
// init CLIJ and GPU import net.haesleinhuepf.clij2.CLIJ2; import net.haesleinhuepf.clij.clearcl.ClearCLBuffer; CLIJ2 clij2 = CLIJ2.getInstance(); // get input parameters ClearCLBuffer source = clij2.push(sourceImagePlus); destination = clij2.create(source); float sigma_x = 1.0; float sigma_y = 2.0;
// Execute operation on GPU clij2.gaussianBlur2D(source, destination, sigma_x, sigma_y);
// show result destinationImagePlus = clij2.pull(destination); destinationImagePlus.show(); // cleanup memory on GPU clij2.release(source); clij2.release(destination);
% init CLIJ and GPU clij2 = init_clatlab(); % get input parameters source = clij2.pushMat(source_matrix); destination = clij2.create(source); sigma_x = 1.0; sigma_y = 2.0;
% Execute operation on GPU clij2.gaussianBlur2D(source, destination, sigma_x, sigma_y);
% show result destination = clij2.pullMat(destination) % cleanup memory on GPU clij2.release(source); clij2.release(destination);
// init CLIJ and GPU importClass(net.haesleinhuepf.clicy.CLICY); importClass(Packages.icy.main.Icy); clij2 = CLICY.getInstance(); // get input parameters source_sequence = getSequence(); source = clij2.pushSequence(source_sequence); destination = clij2.create(source); sigma_x = 1.0; sigma_y = 2.0;
// Execute operation on GPU clij2.gaussianBlur2D(source, destination, sigma_x, sigma_y);
// show result destination_sequence = clij2.pullSequence(destination) Icy.addSequence(destination_sequence); // cleanup memory on GPU clij2.release(source); clij2.release(destination);
import pyclesperanto_prototype as cle cle.gaussian_blur(source, destination, sigma_x, sigma_y)
basics
basic_image_processing
custom_clij_macro_functions
morpholibj_classic_watershed
outlines_numbers_overlay
parametric_images
voronoi_otsu_labeling
count_blobs.ipynb
napari_dask.ipynb
bead_segmentation.ipynb
voronoi_otsu_labeling.ipynb
tribolium_morphometry.ipynb
benchmarking_some_operations.ipynb
gaussian_blur.ipynb
basics.ijm
basic_image_processing.ijm
custom_clij_macro_functions.ijm
mean_squared_error.ijm
morpholibj_classic_watershed.ijm
outlines_numbers_overlay.ijm
parametric_images.ijm
push_pull_selections.ijm
push_pull_slices.ijm
spot_distance_measurement.ijm
voronoi_otsu_labeling.ijm
simplePipeline.js
count_blobs.py
napari_.py
napari_magicgui.py
tribolium.py