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OpenCL-based GPU-accelerated image processing

Project description

py-clEsperanto

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

For users convenience, there are code generators available for napari and Fiji.

Reference

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

Installation

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

If installation of pyopencl for Windows fails, consider downloading a precompiled wheel (e.g. from here ) and installing it manually. Note that "cl12" and "cp38" in the filename matter: They allow you using OpenCL 1.2 compatible GPU devices from Python 3.8.

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

Alternatively, installing via conda also works:

conda install -c conda-forge pyopencl=2020.3.1

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

Voronoi-Otsu labeling

3D Image segmentation

Cell segmentation based on membranes

Crop and paste images

Inspecting 3D image data

Rotation, scaling, translation, affine transforms

Multiply vectors and matrices

Matrix multiplication

Working with spots, pointlist and matrices

Mesh between centroids

Mesh between touching neighbors

Mesh with distances

Mesh nearest_neighbors

Neighborhood definitions

Tissue neighborhood quantification

Neighbors of neighbors

Voronoi diagrams

Shape descriptors based on neighborhood graphs

Tribolium morphometry + Napari

Tribolium morphometry

napari+dask timelapse processing

pyclesperanto assistant

Benchmarking

We implemented some basic benchmarking notebooks allowing to see performance differences between pyclesperanto and some other image processing libraries, typically using the CPU. Such benchmarking results vary heavily depending on image size, kernel size, used operations, parameters and used hardware. Feel free to use those notebooks, adapt them to your use-case scenario and benchmark on your target hardware. If you have different scenarios or use-cases, you are very welcome to submit your notebook as pull-request!

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

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