A Python framework for decoding JPEG and decoding/encoding DICOM RLE data, with a focus on supporting pydicom
Project description
pylibjpeg
A Python 3.8+ framework for decoding JPEG images and decoding/encoding RLE datasets, 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 | 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")
Project details
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