pyclesperanto 0.6.2

GPU-accelerated image processing in python using OpenCL

py-clesperanto

pyclesperanto is the python package of clEsperanto - a multi-language framework for GPU-accelerated image processing. clEsperanto uses OpenCL kernels from CLIJ. This package relies on the CLIc back-end for processing.

Installation

Get a conda/python environment, e.b. via mini-conda. If you never used python/conda environments before, please follow the instructions here first.

conda create --name my_env python=3.10
conda activate my_env

Install pyclesperanto package using conda:

conda install -c conda-forge pyclesperanto

OR using pip:

pip install pyclesperanto

Code Example

Note: This project is under heavy development. General API, functions, and parameters are subject to change.

from pyclesperanto import cle
from skimage.io import imread, imsave

# initialize GPU
device = cle.select_device("GTX")
print("Used GPU: ", device)

image = imread("https://imagej.nih.gov/ij/images/blobs.gif")

# push and create buffer
gpu_output = cle.create(image.shape)
gpu_input = cle.push(image)

# process the image
inverted = cle.subtract_image_from_scalar(image, scalar=255)
blurred = cle.gaussian_blur(inverted, sigma_x=1, sigma_y=1)
binary = cle.threshold_otsu(blurred)
labeled = cle.connected_components_labeling_box(binary)

# The maxmium intensity in a label image corresponds to the number of objects
num_labels = cle.maximum_of_all_pixels(labeled)

# print out result
print("Num objects in the image: " + str(num_labels))

# save image to disc
imsave("result.tif", labeled)

Example gallery

	Segment + analyze blobs
	Cell segmentation + Voronoi labeling
	3D image filtering
	Find local maxima
	3D Tribolium nuclei segmentation
	Explore application programming interface (API)

More usage and example can be found as notebooks in the user documentation folder

Contributing

Contributions are very welcome. Before spending effort on coding and filing a pull-request, please get in touch, [file an issue], and let's discuss your potential contribution.

Feedback welcome!

clesperanto is developed in the open because we believe in the open source community. See our community guidelines. Feel free to drop feedback as github issue or via image.sc.