pylibjpeg-openjpeg

A Python wrapper for openjpeg, with a focus on use as a plugin for for pylibjpeg

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Project description

pylibjpeg-openjpeg

A Python 3.8+ wrapper for openjpeg, with a focus on use as a plugin for pylibjpeg.

Linux, OSX and Windows are all supported.

Installation

Dependencies

NumPy

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 install Microsoft'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=-2**15, high=2**15, 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=-2**15, high=2**15, 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.

Project details

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Project links

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Release history Release notifications | RSS feed

2.4.0

Oct 28, 2024

2.3.0

Jun 18, 2024

2.2.2 yanked

Jun 17, 2024

Reason this release was yanked:

Use v2.3.0 for numpy v2.0

This version

2.2.1

Mar 25, 2024

2.2.0

Mar 23, 2024

2.1.1

Jan 20, 2024

2.1.0

Jan 13, 2024

2.0.0

Jan 7, 2024

1.3.2

Feb 20, 2023

1.3.1

Jan 16, 2023

1.3.0

Dec 17, 2022

1.2.1

Feb 3, 2022

1.2.0

Jan 22, 2022

1.1.1

Apr 10, 2021

1.1.0

Apr 10, 2021

1.0.1

Jun 15, 2020

1.0.0

Jun 12, 2020

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Hashes for pylibjpeg_openjpeg-2.2.1-cp39-cp39-macosx_12_0_x86_64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp39-cp39-macosx_12_0_x86_64.whl
Algorithm	Hash digest
SHA256	`f15856bfb081ed053f0548bae50eba74ea3be3b0f1dd6d5f10868f27faf07d5e`
MD5	`782294017275c0711e169b7ba817291b`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp39-cp39-macosx_12_0_arm64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp39-cp39-macosx_12_0_arm64.whl
Algorithm	Hash digest
SHA256	`de825e375fc4267de6b0bf109eb1bb0eacc0eb187d7d622fe8c0361f3e0522af`
MD5	`f1ef70332ef977753e3404ae737f9ec6`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-win_amd64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
SHA256	`d56a3e8c79e307485753291fc7c4786c161106e849a696ba058995781336636d`
MD5	`bfe55aa143d8c9e9f91abcc7dd384951`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-win32.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-win32.whl
Algorithm	Hash digest
SHA256	`9686026457437e0016d38de29d86463c5016f2ba3ec1a99eee02dd3fe98e73bd`
MD5	`8a4fa7c21fe39d566ce2b697fe2e863f`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`fb22e04cfb3461c066156bc9ee010e343e102db605f15250e4bbcbf7db38661c`
MD5	`cf0325680f43878d4f9b85da101ac3f3`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm	Hash digest
SHA256	`54e97fd7a541adb816a8fbba81062582c81efd8b424de211b7366266397537b0`
MD5	`d6e1782673d28992e5cbd2ad494eb7a7`
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Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-macosx_12_0_x86_64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-macosx_12_0_x86_64.whl
Algorithm	Hash digest
SHA256	`a0e3bf729fd715cf478d650814c1a3c3971c1e1049171a3601b150b9e976d4a2`
MD5	`e527c049e7bea7ea47942d9318dcd11e`
BLAKE2b-256	`eb439264f76441747ca3dd8cb01f2d41a69e140f942978fa7e1d5814b120e039`

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-macosx_12_0_arm64.whl

Hashes for pylibjpeg_openjpeg-2.2.1-cp38-cp38-macosx_12_0_arm64.whl
Algorithm	Hash digest
SHA256	`fbfc67656eee4ecec409e0ee40deabb4f2ffb9a107d3f267fab29ffb0ca873da`
MD5	`963aa02e3f6ee266b261215d8f1d8ee7`
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