Python wrapper for CUDA-accelerated 3D deconvolution
This package provides a python wrapper and convenience functions for cudaDecon, which is a CUDA/C++ implementation of an accelerated Richardson Lucy Deconvolution algorithm1.
- CUDA accelerated deconvolution with a handful of artifact-reducing features.
- radially averaged OTF generation with interpolation for voxel size independence between PSF and data volumes
- 3D deskew, rotation, general affine transformations
- CUDA-based camera-correction for sCMOS artifact correction
The conda package includes the required pre-compiled libraries for Windows and Linux. See GPU driver requirements below
conda install -c conda-forge pycudadecon
macOS is not supported
This software requires a CUDA-compatible NVIDIA GPU. The underlying cudadecon libraries have been compiled against different versions of the CUDA toolkit. The required CUDA libraries are bundled in the conda distributions so you don't need to install the CUDA toolkit separately. If desired, you can pick which version of CUDA you'd like based on your needs, but please note that different versions of the CUDA toolkit have different GPU driver requirements:
To specify a specific cudatoolkit version, install as follows (for instance, to
conda install -c conda-forge pycudadecon cudatoolkit=10.2
|CUDA||Linux driver||Win driver|
|10.2||≥ 440.33||≥ 441.22|
|11.0||≥ 450.36.06||≥ 451.22|
|11.1||≥ 455.23||≥ 456.38|
|11.2||≥ 460.27.03||≥ 460.82|
If you run into trouble, feel free to open an issue and describe your setup.
pycudadecon.decon() function is designed be able to handle most basic applications:
from pycudadecon import decon # pass filenames of an image and a PSF result = decon('/path/to/3D_image.tif', '/path/to/3D_psf.tif') # decon also accepts numpy arrays result = decon(img_array, psf_array) # the image source can also be a sequence of arrays or paths result = decon([img_array, '/path/to/3D_image.tif'], psf_array) # see docstrings for additional parameter options
For finer-tuned control, you may wish to make an OTF file from your PSF using
pycudadecon.make_otf(), and then use the
pycudadecon.RLContext context manager to setup the GPU for use with the
pycudadecon.rl_decon() function. (Note all images processed in the same context must have the same input shape).
from pycudadecon import RLContext, rl_decon from glob import glob import tifffile image_folder = '/path/to/some_images/' imlist = glob(image_folder + '*488*.tif') otf_path = '/path/to/pregenerated_otf.tif' with tifffile.TiffFile(imlist) as tf: imshape = tf.series.shape with RLContext(imshape, otf_path, dz) as ctx: for impath in imlist: image = tifffile.imread(impath) result = rl_decon(image, ctx.out_shape) # do something with result...
If you have a 3D PSF volume, the
pycudadecon.TemporaryOTF context manager facilitates temporary OTF generation...
# continuing with the variables from the previous example... psf_path = "/path/to/psf_3D.tif" with TemporaryOTF(psf) as otf: with RLContext(imshape, otf.path, dz) as ctx: for impath in imlist: image = tifffile.imread(impath) result = rl_decon(image, ctx.out_shape) # do something with result...
... and that bit of code is essentially what the
pycudadecon.decon() function is doing, with a little bit of additional conveniences added in.
Each of these functions has many options and accepts multiple keyword arguments. See the documentation for further information on the respective functions.
For examples and information on affine transforms, volume rotations, and deskewing (typical of light sheet volumes acquired with stage-scanning), see the documentation on Affine Transformations
1 D.S.C. Biggs and M. Andrews, Acceleration of iterative image restoration algorithms, Applied Optics, Vol. 36, No. 8, 1997. https://doi.org/10.1364/AO.36.001766
- 07 November 2022
- 10 August 2022
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