pylibjpeg-rle 2.2.0

pylibjpeg-rle

A fast DICOM (PackBits) RLE plugin for pylibjpeg, written in Rust with a Python wrapper.

Linux, MacOS and Windows are all supported.

Installation

Installing the current release

pip install pylibjpeg-rle

Installing the development version

Make sure Python, Git and Rust are installed. For Windows, you also need to install Microsoft's C++ Build Tools.

git clone https://github.com/pydicom/pylibjpeg-rle
cd pylibjpeg-rle
python -m pip install .

Supported Transfer Syntaxes

Usage

Decoding

With pylibjpeg

from pydicom import dcmread
from pydicom.data import get_testdata_file

ds = dcmread(get_testdata_file("OBXXXX1A_rle.dcm"))
arr = ds.pixel_array

Standalone with pydicom

Alternatively you can use the included functions to decode a given dataset:

from rle import pixel_array, generate_frames

# Return the entire Pixel Data as an ndarray
arr = pixel_array(ds)

# Generator function that only processes 1 frame at a time,
# may help reduce memory usage when dealing with large Pixel Data
for arr in generate_frames(ds):
    print(arr.shape)

Encoding

Standalone with pydicom

Convert uncompressed pixel data to RLE encoding and save:

from pydicom import dcmread
from pydicom.data import get_testdata_file
from pydicom.uid import RLELossless

from rle import pixel_data

# Get the uncompressed pixel data
ds = dcmread(get_testdata_file("OBXXXX1A.dcm"))
arr = ds.pixel_array

# RLE encode and encapsulate `arr`
ds.PixelData = pixel_data(arr, ds)
# Set the correct *Transfer Syntax UID*
ds.file_meta.TransferSyntaxUID = RLELossless
ds.save_as('as_rle.dcm')

Benchmarks

Decoding

Time per 1000 decodes, pydicom's default RLE decoder vs. pylibjpeg-rle:

Encoding

Time per 1000 encodes, pydicom's default RLE encoder vs. pylibjpeg-rle and python-gdcm: