pylibjpeg 2.1.0

A Python framework for decoding JPEG and decoding/encoding DICOM RLE data, with a focus on supporting pydicom

pylibjpeg

A Python framework for decoding JPEG and JPEG-LS and decoding/encoding JPEG 2000 and RLE, with a focus on providing support for pydicom.

Installation

Installing the current release

pip install pylibjpeg

Installing extra requirements

The package can be installed with extra requirements to enable support for JPEG (with libjpeg), JPEG 2000 (with openjpeg) and Run-Length Encoding (RLE) (with rle), respectively:

pip install pylibjpeg[libjpeg,openjpeg,rle]

Or alternatively with just all:

pip install pylibjpeg[all]

Installing the development version

Make sure Git is installed, then

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

Plugins

One or more plugins are required before pylibjpeg is able to handle JPEG images or RLE datasets. To handle a given format or DICOM Transfer Syntax you first have to install the corresponding package:

Supported Image Formats

Format	Decode?	Encode?	Plugin	License	Based on
JPEG, JPEG-LS and JPEG XT	Yes	No	pylibjpeg-libjpeg	GPLv3	libjpeg
JPEG 2000	Yes	Yes	pylibjpeg-openjpeg	MIT/BSD	openjpeg
High-throughput JPEG 2000	Yes	No	pylibjpeg-openjpeg	MIT/BSD	openjpeg
RLE Lossless (PackBits)	Yes	Yes	pylibjpeg-rle	MIT	-

Supported DICOM Transfer Syntaxes

UID	Description	Plugin
1.2.840.10008.1.2.4.50	JPEG Baseline (Process 1)	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.51	JPEG Extended (Process 2 and 4)	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.57	JPEG Lossless, Non-Hierarchical (Process 14)	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.70	JPEG Lossless, Non-Hierarchical, First-Order Prediction (Process 14, Selection Value 1)	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.80	JPEG-LS Lossless	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.81	JPEG-LS Lossy (Near-Lossless) Image Compression	pylibjpeg-libjpeg
1.2.840.10008.1.2.4.90	JPEG 2000 Image Compression (Lossless Only)	pylibjpeg-openjpeg
1.2.840.10008.1.2.4.91	JPEG 2000 Image Compression	pylibjpeg-openjpeg
1.2.840.10008.1.2.4.201	High-Throughput JPEG 2000 Image Compression (Lossless Only)	pylibjpeg-openjpeg
1.2.840.10008.1.2.4.202	High-Throughput JPEG 2000 with RPCL Options Image Compression (Lossless Only)	pylibjpeg-openjpeg
1.2.840.10008.1.2.4.203	High-Throughput JPEG 2000 Image Compression	pylibjpeg-openjpeg
1.2.840.10008.1.2.5	RLE Lossless	pylibjpeg-rle

If you're not sure what the dataset's Transfer Syntax UID is, it can be determined with:

>>> from pydicom import dcmread
>>> ds = dcmread('path/to/dicom_file')
>>> ds.file_meta.TransferSyntaxUID.name

Usage

Decoding

With pydicom

Assuming you have pydicom v2.1+ and suitable plugins installed:

from pydicom import dcmread
from pydicom.data import get_testdata_file

# With the pylibjpeg-libjpeg plugin
ds = dcmread(get_testdata_file('JPEG-LL.dcm'))
jpg_arr = ds.pixel_array

# With the pylibjpeg-openjpeg plugin
ds = dcmread(get_testdata_file('JPEG2000.dcm'))
j2k_arr = ds.pixel_array

# With the pylibjpeg-rle plugin and pydicom v2.2+
ds = dcmread(get_testdata_file('OBXXXX1A_rle.dcm'))
# pydicom defaults to the numpy handler for RLE so need
# to explicitly specify the use of pylibjpeg
ds.decompress("pylibjpeg")
rle_arr = ds.pixel_array

Standalone JPEG decoding

You can also just use pylibjpeg to decode JPEG images to a numpy ndarray, provided you have a suitable plugin installed:

from pylibjpeg import decode

# Can decode using the path to a JPG file as str or path-like
arr = decode('filename.jpg')

# Or a file-like...
with open('filename.jpg', 'rb') as f:
    arr = decode(f)

# Or bytes...
with open('filename.jpg', 'rb') as f:
    arr  = decode(f.read())

Encoding

With pydicom

Assuming you have pydicom v2.2+ and suitable plugins installed:

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

ds = dcmread(get_testdata_file("CT_small.dcm"))

# Encode in-place using RLE Lossless and update the dataset
# Updates the Pixel Data, Transfer Syntax UID and Planar Configuration
ds.compress(RLELossless)

# Save compressed
ds.save_as("CT_small_rle.dcm")