GitHub - pydicom/pylibjpeg-openjpeg: A J2K, JP2 and HTJ2K plugin for pylibjpeg (original) (raw)
pylibjpeg-openjpeg
A Python 3.8+ wrapper foropenjpeg, with a focus on use as a plugin for pylibjpeg.
Linux, OSX and Windows are all supported.
Installation
Dependencies
Installing the current release
python -m pip install -U pylibjpeg-openjpeg
Installing the development version
Make sure Python, Git and CMake are installed. For Windows, you also need to installMicrosoft's C++ Build Tools.
git clone --recurse-submodules https://github.com/pydicom/pylibjpeg-openjpeg python -m pip install pylibjpeg-openjpeg
Supported JPEG Formats
Decoding
| ISO/IEC Standard | ITU Equivalent | JPEG Format |
|---|---|---|
| 15444-1 | T.800 | JPEG 2000 |
Encoding
Encoding of NumPy ndarrays is supported for the following:
- Array dtype: bool, uint8, int8, uint16, int16, uint32 and int32 (1-24 bit-depth only)
- Array shape: (rows, columns) and (rows, columns, planes)
- Number of rows/columns: up to 65535
- Number of planes: 1, 3 or 4
Transfer Syntaxes
| UID | Description |
|---|---|
| 1.2.840.10008.1.2.4.90 | JPEG 2000 Image Compression (Lossless Only) |
| 1.2.840.10008.1.2.4.91 | JPEG 2000 Image Compression |
| 1.2.840.10008.1.2.4.201 | High-Throughput JPEG 2000 Image Compression (Lossless Only) |
| 1.2.840.10008.1.2.4.202 | High-Throughput JPEG 2000 with RPCL Options Image Compression (Lossless Only) |
| 1.2.840.10008.1.2.4.203 | High-Throughput JPEG 2000 Image Compression |
Usage
With pylibjpeg and pydicom
from pydicom import dcmread from pydicom.data import get_testdata_file
ds = dcmread(get_testdata_file('JPEG2000.dcm')) arr = ds.pixel_array
Standalone JPEG decoding
You can also decode JPEG 2000 images to a numpy ndarray:
from openjpeg import decode
with open('filename.j2k', 'rb') as f: # Returns a numpy array arr = decode(f)
Or simply...
arr = decode('filename.j2k')
Standalone JPEG encoding
Lossless encoding of RGB with multiple-component transformation:
import numpy as np from openjpeg import encode_array
arr = np.random.randint(low=0, high=65536, size=(100, 100, 3), dtype="uint8") encode_array(arr, photometric_interpretation=1) # 1: sRGB
Lossy encoding of a monochrome image using compression ratios:
import numpy as np from openjpeg import encode_array
arr = np.random.randint(low=-215, high=215, size=(100, 100), dtype="int8")
You must determine your own values for compression_ratios
as these are for illustration purposes only
encode_array(arr, compression_ratios=[5, 2])
Lossy encoding of a monochrome image using peak signal-to-noise ratios:
import numpy as np from openjpeg import encode_array
arr = np.random.randint(low=-215, high=215, size=(100, 100), dtype="int8")
You must determine your own values for signal_noise_ratios
as these are for illustration purposes only
encode_array(arr, signal_noise_ratios=[50, 80, 100])
See the docstring for the encode_array() function for full details.