Skip to main content

Utilities for JPEG data access and manipulation in pytorch

Project description

TorchJPEG

pipeline status coverage report PyPI License

This package contains a C++ extension for pytorch that interfaces with libjpeg to allow for manipulation of low-level JPEG data. By using libjpeg, quantization results are guaranteed to be consistent with other applications, like image viewers or MATLAB, which use libjpeg to compress and decompress images. This is useful because JPEG images can be effected by round-off errors or slight differences in the decompression procedure. Besides this, this library can be used to read and write DCT coefficients, functionality which is not available from other python interfaces.

Besides this, the library includes many utilities related to JPEG compression, many of which are written using native pytorch code meaning they can be differentiated or GPU accelerated. The library currently includes packages related to the DCT, quantization, metrics, and dataset transformations.

LIBJPEG

Currently builds against: libjpeg-9d. libjpeg is statically linked during the build process. See http://www.ijg.org/files/ for libjpeg source. The full libjpeg source is included with the torchjpeg source code and will be built during the install process (for a source or sdist install).

Install

Packages are hosted on pypi. Install using pip, note that only Linux builds are supported at the moment.

pip install torchjpeg

If there is demand for builds on other platforms it may happen in the future. Also note that the wheel is intended to be compatible with manylinux2014 which means it should work on modern Linux systems, however, because of they way pytorch works, we can't actually build it using all of the manylinux2014 tools. So compliance is not guaranteed and YMMV.

torchjpeg is currently in pre-beta development and consists mostly of converted research code. The public facing API, including any and all names of
parameters and functions, is subject to change at any time. We follow semver for versioning and will adhere to that before making and breaking
changes.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchjpeg-0.9.7.tar.gz (1.0 MB view details)

Uploaded Source

Built Distributions

torchjpeg-0.9.7-cp38-cp38-manylinux2014_x86_64.whl (4.0 MB view details)

Uploaded CPython 3.8

torchjpeg-0.9.7-cp37-cp37m-manylinux2014_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.7m

torchjpeg-0.9.7-cp36-cp36m-manylinux2014_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.6m

File details

Details for the file torchjpeg-0.9.7.tar.gz.

File metadata

  • Download URL: torchjpeg-0.9.7.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.0.10 CPython/3.8.5 Linux/4.19.78-coreos

File hashes

Hashes for torchjpeg-0.9.7.tar.gz
Algorithm Hash digest
SHA256 20ee2bd5cb24aea5e4fd6a99766373f801f8ea6d3a426f69c148ab11e1629748
MD5 d284f2e34606d7353f8ba11afb0c85a8
BLAKE2b-256 0e13f64ab239ca56f4da53ae56e5129e15ed17d3b801a1e64660e854fa42485b

See more details on using hashes here.

File details

Details for the file torchjpeg-0.9.7-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for torchjpeg-0.9.7-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 45bb64d4bb08eca4d761a6be4442708b31fa7933ede450676f06ab0d71ead1bd
MD5 7d0eb50bf0387c9f1976b64eeaaa26ef
BLAKE2b-256 cc01d4d2355590c8c5050246f92af42def7a1bee86dcf7f8ef75dee9eaab5f54

See more details on using hashes here.

File details

Details for the file torchjpeg-0.9.7-cp37-cp37m-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for torchjpeg-0.9.7-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 75c6259993c740130d0f9175321fd0c824a6fd4efe033fbadbd9a0755b0dabb7
MD5 30fc8d7631d6cd3d5be731765e8e13ae
BLAKE2b-256 031612614de01e5927ddb9ae576a698d677584e55325f2b1dec284952111a0b7

See more details on using hashes here.

File details

Details for the file torchjpeg-0.9.7-cp36-cp36m-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for torchjpeg-0.9.7-cp36-cp36m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f57f294fc8daccb7f8d743e8b39a21bdee0668466a8ade4262549169279b42e5
MD5 bad4d96497ea34abc7af9748493a0649
BLAKE2b-256 e40ccaccc4dd1eb17e212780e033094793ad8d8331b6e5e6e5a849bbc2f6c6db

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page