Yummy Optimizer for Gorgeous Assets
Project description
YOGA is a command-line tool and a library that can:
convert and optimize images from various format to JPEG, PNG and WEBP,
convert and optimize 3D models from various formats to glTF and GLB.
Images are opened using Pillow and optimized using Guetzli (for JPEGs), Zopflipng (for PNGs) and libwebp (for WEBPs).
3D Models are converted and optimized using assimp. If models contain or reference images, they are processed by YOGA’s image optimizer.
EXAMPLE: Converting and optimizing an image from CLI:
yoga image input.png output.png yoga image --output-format=jpeg --jpeg-quality=84 input.png output.jpg yoga image --help
EXAMPLE: Converting and optimizing a 3D model from CLI:
yoga model input.fbx output.glb yoga model --no-graph-optimization --no-meshes-optimization --image-output-format=jpeg --image-jpeg-quality=84 input.fbx output.glb yoga model --help
Install
Documentation
Changelog
[NEXT] (changes on master that have not been released yet):
Nothing yet…
1.1.2:
Add flag to CFFI builder to fix MacOS build
1.1.1 (not published):
JPEG: ignore invalid values for the orientation tag (#38)
Python 3.10 support and wheels
1.1.0:
JPEG Optimization:
Honor the JPEG orientation EXIF tag
JPEG optimization has been improved by using some optimizations from MozJPEG after the Guetzli encoding (from 2.4 % to 7.3 % of additional size reduction)
PNG Optimization:
YOGA can no more output a PNG larger than the input one when performing a PNG to PNG optimization
CLI:
Allow to cancel an optimization using Ctrl+C (NOTE: may not work on Windows)
Add a --version option to get YOGA’s version
Improve yoga --help usage
Python versions:
Python 2.7 support dropped
NOTE for packagers:
new dependency to mozjpeg-lossless-optimization
1.0.0:
WEBP (lossy and lossless) images supported as output format
PNG default optimization preset changed to a 10× faster preset (old preset stil available with --png-slow-optimization flag)
New model flag --no-fix-infacing-normals to disable Assimp’s “fix infacing normals” postprocess (#32, #33)
Show CLI usage when no parameter given
Developer documentation improved (#31)
ASSIMP library updated
WARNING: This is the last version to actively support Python 2.7!
0.11.1:
Automated workflow for deploying the PyPI packages
Wheel are now distributed on PyPI
0.11.0:
Allows to build YOGA on Windows
Scripts and workflow to build Windows standalone versions
0.10.2:
Updates assimp and python libraries
0.10.1:
Fixes an issue that occures when output file does not already exist
0.10.0:
Prevent overwriting of the output file when an error occurs (#17)
Unicode path support (#16)
0.10.0b1:
Verbose and quiet modes,
Allows to pass textures from memory instead of looking on the filesystem,
Allows to pass a fallback texture instead of raising an error.
0.9.1b1:
Automatic selection of the output format (png or jpeg),
Prevent duplication of textures that are shared between materials,
Fixes Windows paths of textures.
0.9.0b1: First release (only GLB output for models, no image auto output format)
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
Built Distributions
Hashes for yoga-1.1.2-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ba98ecf08977322227758316823b1e866475a3e675e0b05dbb4af237f351e5fa |
|
MD5 | d0fd1bb303b2c8fe3cb3702b7914a88d |
|
BLAKE2b-256 | e4866356e5ca65a83649d7d1a240d55fdc31dcc893b33cd911d4cf20a20155d1 |
Hashes for yoga-1.1.2-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0d2a512b26d6dc7614dedc68847e302c6ea7977403917c13de44a302f1104824 |
|
MD5 | 39803d6052bf953d8b42e0406bffa2c6 |
|
BLAKE2b-256 | 28938912eeaba0291764787c1dd61a185753199bc6189dd77e92dfdd8557ef4a |
Hashes for yoga-1.1.2-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | cd6ccfaa6f926c30fe565ae75aa2b738ebd40d74dcb003735a72ea787f4d957d |
|
MD5 | d16ca00f5f4fa89fea39d07c563e730b |
|
BLAKE2b-256 | 2ba8940f0695e9b0295bde7e5205f3d8b9b89b3fa6e5b3102b70827136bfaad1 |
Hashes for yoga-1.1.2-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a1960ae6f8d702ec090ec921cf4430f1fd2c280274138a67e5eebc03f612339 |
|
MD5 | a96bf3b54df6ea4a1bfb1917e3495b1a |
|
BLAKE2b-256 | 8f67ae291b68a6c3afa1ff648365d149fe2809cd5714b2dec1beff15559c3fad |
Hashes for yoga-1.1.2-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fb15aa63db94fc8109194f50cb11458884a7bb31639a558af58d8d1299f7ae08 |
|
MD5 | bdaa073d5ab7ae5e0570ccd7757971ec |
|
BLAKE2b-256 | 3e3f2bf90bca51d6afd4c3ec9b7cbe45d4837144ea8e8bbea64636f54d4b35bc |
Hashes for yoga-1.1.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5594ad6eaf52c5ea674db19c32b1704c033e587a3a09e69a04309c3c6bf9e624 |
|
MD5 | e178b9d95694e2119ef5f7b31d5ab7a3 |
|
BLAKE2b-256 | aca1b5a87584a3f861b2d911d30b79ecd38bf5387a16c5ca78d4e6a20f88bca9 |
Hashes for yoga-1.1.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5f6383411ed19cdda1bd8d834e2dd9931fc85d3895f5761a6ce33b54cf6f6439 |
|
MD5 | 81ca200418a9ef473c86427b52d7b1f9 |
|
BLAKE2b-256 | 39ce1671285522be593199fef8b9d4a2a5ae5b1a5f771cee714433aa08b6d781 |
Hashes for yoga-1.1.2-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1df416eb173aa7c72c2e4b76fece70e5832f54efbe30588b0549df0969497bc0 |
|
MD5 | b213027438e0100fa653c7ebe1ab2116 |
|
BLAKE2b-256 | 5a7ec62695dafabdf2c0c9c62b276b767e1ea1211cfd5fbe64fbcb19f86f2eb4 |
Hashes for yoga-1.1.2-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 518d29deb744f3d1e0db5a419af99aed4e02bfdf80da890994b598ffd797e6fb |
|
MD5 | 8257f35aa7a36bbad45a1634b58f077a |
|
BLAKE2b-256 | 57ea2f29bc6c2b8f303c0fc3145581d870aaf94e85aea70c4ab54b8f91c1d8ef |
Hashes for yoga-1.1.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 892ca8c687c56bf65821c0a095862813b72725decbdd3bcb2e15edbe2ba5e56c |
|
MD5 | 857dcc6e52009ae9a8ae4141a212421b |
|
BLAKE2b-256 | 028d7a193bd8d6f047fac71daafad9a4b4a1d20e9fd3940a28f05256967afefb |
Hashes for yoga-1.1.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9bca7f2051838170a324c74d41e38453e0fdf2cb1e65f033c9aff6a77ab481cf |
|
MD5 | ab00485cd64a2d4c7764182b64bed36a |
|
BLAKE2b-256 | 40fa3275b323cfba420e1cbc83c25f96ebea24baf16712305705c8bba45d7ea1 |
Hashes for yoga-1.1.2-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 52587ff57eeada99b7dbc32936aa1b7aaee96a9eb0608df5409a5d452e7be9ba |
|
MD5 | ef654b4fa3d683a9de3d5ffa5eec97d3 |
|
BLAKE2b-256 | 61c2c8976d7463984554f43c5e70d4d6fe9a1490d1385255c95b6bb52341c8a1 |
Hashes for yoga-1.1.2-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2cf0e5df40b5d1995cd6d0f4aa75d2f4e784a576e383270ed809b38f13b93060 |
|
MD5 | 471a95f011198e446ecd18cdba754c6d |
|
BLAKE2b-256 | 0240d84fcef2a272e57ecbfc4407b454d864185c3680f98dd43b3efc1854958c |
Hashes for yoga-1.1.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1c2ea7759c0ed0cdd767fa8a0a8f4423fc5a2031079f2ae16f132fb80142b1c1 |
|
MD5 | 396028e612068c086b3c923869628ed9 |
|
BLAKE2b-256 | 6bb097f4fea7b4fedbca7ab9d15a8d38423d8883cf93ecffcb2801b5f502f2be |
Hashes for yoga-1.1.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 117ac94584f3698bf14f1bb4626d6770b6967b5c830b09d37e965190c93e855f |
|
MD5 | 28a1cea8348133f78118cca35d4a0329 |
|
BLAKE2b-256 | 7bca49152a8a69111320b7f970d4354497dd24b86a4bfaf41b3403976155e982 |
Hashes for yoga-1.1.2-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d5a6c86909753507efda75c3eccdad8e7b6d12a070300f29dbd68b5fd32d3a06 |
|
MD5 | 4c7a5254611b2a3a766b51767689f797 |
|
BLAKE2b-256 | c6f3fe22d42b704e6f4d253685aa6be3656e8448092fdd0d15ab97811dbc7231 |