pyclesperanto-prototype 0.5.0

OpenCL based GPU-accelerated image processing (an early prototype)

pyclesperanto

pyclesperanto is a prototype for clEsperanto - a multi-platform multi-language framework for GPU-accelerated image processing. It uses OpenCL kernels from CLIJ

Right now, this is very preliminary.

Reference

The full reference is available as part of the CLIJ2 documentation.

Installation

• Get a python environment, e.g. via mini-conda
• Install pyopencl.

If installation of pyopencl for Windows fails, consider downloading a precompiled wheel (e.g. from here ) and installing it manually:

pip install pyopencl-2019.1.1+cl12-cp37-cp37m-win_amd64.whl

Afterwards, install pyclesperanto:

pip install pyclesperanto-prototype

Troubleshooting installation

If you receive an error like

DLL load failed: The specified procedure could not be found.

Try downloading and installing a pyopencl with a lower cl version, e.g. cl12 : pyopencl-2020.1+cl12-cp37-cp37m-win_amd64

Example code

A basic image procressing workflow loads blobs.gif and counts the number of gold particles:

import pyclesperanto_prototype as cle

from skimage.io import imread, imsave

# initialize GPU
cle.select_device("GTX")
print("Used GPU: " + cle.get_device().name)

# load data
image = imread('https://imagej.nih.gov/ij/images/blobs.gif')
print("Loaded image size: " + str(image.shape))

# push image to GPU memory
input = cle.push(image)
print("Image size in GPU: " + str(input.shape))

# 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))

# for debugging: print out image
print(labeled)

# for debugging: save image to disc
imsave("result.tif", cle.pull(labeled))

Example gallery

	Select GPU
	Counting blobs
	Crop and paste images
	Inspecting 3D image data
	Multiply vectors and matrices
	Matrix multiplication
	Working with spots, pointlist and matrices
	Voronoi diagrams
	Tribolium morphometry + Napari
	Tribolium morphometry
	Napari+Dask Timelapse processing
	Napari Particle Analyser

Feedback welcome!

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