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20be42cec0
* 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>
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
import numpy as np
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from numpy.typing import NDArray
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from PIL import Image
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_PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling}
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def resize(img: Image.Image, size: int) -> Image.Image:
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if img.width < img.height:
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return img.resize((size, int((img.height / img.width) * size)), resample=Image.BICUBIC)
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else:
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return img.resize((int((img.width / img.height) * size), size), resample=Image.BICUBIC)
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# https://stackoverflow.com/a/60883103
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def crop(img: Image.Image, size: int) -> Image.Image:
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left = int((img.size[0] / 2) - (size / 2))
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upper = int((img.size[1] / 2) - (size / 2))
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right = left + size
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lower = upper + size
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return img.crop((left, upper, right, lower))
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def to_numpy(img: Image.Image) -> NDArray[np.float32]:
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return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0
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def normalize(
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img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
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) -> NDArray[np.float32]:
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return (img - mean) / std
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def get_pil_resampling(resample: str) -> Image.Resampling:
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return _PIL_RESAMPLING_METHODS[resample.lower()]
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