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GPU-accelerated image processing in python using OpenCL

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. We mostly use it in the life sciences for analysing 3- and 4-dimensional microsopy data, e.g. as we face it developmental biology when segmenting cells and studying their individual properties as well as properties of compounds of cells forming tissues.

Image data source: Daniela Vorkel, Myers lab, MPI-CBG, rendered using napari

clesperanto uses OpenCL kernels from CLIJ.

For users convenience, there are code generators available for napari and Fiji. Also check out the napari workflow optimizer for semi-automatic parameter tuning of clesperanto-functions.

Reference

The preliminary API reference is available here. Furthermore, parts of the reference are also available within the CLIJ2 documentation.

Installation

  • Get a conda/python environment, e.g. via mamba-forge.
  • If you never used python/conda environments before, please follow these instructions first.
conda create --name cle_39 python=3.9
conda activate cle_39
mamba install -c conda-forge pyclesperanto-prototype

OR using pip:

pip install pyclesperanto-prototype

Troubleshooting: Graphics cards drivers

In case error messages contains "ImportError: DLL load failed while importing cl: The specified procedure could not be found" see also or ""clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR", please install recent drivers for your graphics card and/or OpenCL device. Select the right driver source depending on your hardware from this list:

Sometimes, mac-users need to install this:

mamba install -c conda-forge ocl_icd_wrapper_apple

Sometimes, linux users need to install this:

mamba install -c conda-forge ocl-icd-system

Computing on Central Processing units (CPUs)

If no OpenCL-compatible GPU is available, pyclesperanto-prototype can make use of CPUs instead. Just install oclgrind or pocl, e.g. using mamba / conda. Oclgrind is recommended for Windows systems, PoCL for Linux. MacOS typically comes with OpenCL support for CPUs.

mamba install  oclgrind -c conda-forge

OR

mamba install  pocl -c conda-forge

Owners of compatible Intel Xeon CPUs can also install a driver to use them for computing:

Example code

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

import pyclesperanto_prototype as cle

from skimage.io import imread, imsave

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

# load data
image = imread('https://imagej.nih.gov/ij/images/blobs.gif')

# 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("Number of objects in the image: " + str(num_labels))

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

Example gallery

Select GPU

Image processing in Jupyter Notebooks

Counting blobs

Voronoi-Otsu labeling

3D Image segmentation

Cell segmentation based on membranes

Counting nuclei according to expression in multiple channels

Differentiating nuclei according to signal intensity

Detecting beads and measuring their size

Label statistics

Parametric maps

Measure intensity along lines

Crop and paste images

Inspecting 3D image data

Rotation, scaling, translation, affine transforms

Deskewing

Multiply vectors and matrices

Matrix multiplication

Mesh between centroids

Mesh between touching neighbors

Mesh with distances

Mesh nearest_neighbors

Export to igraph and networkx

Neighborhood definitions

Tissue neighborhood quantification

Neighbors of neighbors

Voronoi diagrams

Shape descriptors based on neighborhood graphs

Measuring distances between labels in two label images

Tribolium morphometry + Napari

Tribolium morphometry (archived version)

napari+dask timelapse processing

Technical insights

Browsing operations

Interactive widgets

Automatic workflow optimization

Tracing memory consumtion on NVidia GPUs

Exploring and switching between GPUs

Interoperability with cupy

Using the cupy backend

Related projects

napari-pyclesperanto-assistant: A graphical user interface for general purpose GPU-accelerated image processing and analysis in napari.

napari-accelerated-pixel-and-object-classification: GPU-accelerated Random Forest Classifiers for pixel and labeled object classification

napari-clusters-plotter: Clustering of objects according to their quantitative properties

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!

See also

There are other libraries for code acceleration and GPU-acceleration for image processing.

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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