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immich/machine-learning/app/models/transforms.py
renovate[bot] 20be42cec0
chore(deps): update machine-learning (#6302)
* chore(deps): update machine-learning

* fix typing, use new lifespan syntax

* wrap in try / finally

* move log

---------

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
2024-01-13 05:00:09 +00:00

37 lines
1.1 KiB
Python

import numpy as np
from numpy.typing import NDArray
from PIL import Image
_PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling}
def resize(img: Image.Image, size: int) -> Image.Image:
if img.width < img.height:
return img.resize((size, int((img.height / img.width) * size)), resample=Image.BICUBIC)
else:
return img.resize((int((img.width / img.height) * size), size), resample=Image.BICUBIC)
# https://stackoverflow.com/a/60883103
def crop(img: Image.Image, size: int) -> Image.Image:
left = int((img.size[0] / 2) - (size / 2))
upper = int((img.size[1] / 2) - (size / 2))
right = left + size
lower = upper + size
return img.crop((left, upper, right, lower))
def to_numpy(img: Image.Image) -> NDArray[np.float32]:
return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0
def normalize(
img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
) -> NDArray[np.float32]:
return (img - mean) / std
def get_pil_resampling(resample: str) -> Image.Resampling:
return _PIL_RESAMPLING_METHODS[resample.lower()]