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87a0ba3db3
* export clip models * export to hf refactored export code * export mclip, general refactoring cleanup * updated conda deps * do transforms with pillow and numpy, add tokenization config to export, general refactoring * moved conda dockerfile, re-added poetry * minor fixes * updated link * updated tests * removed `requirements.txt` from workflow * fixed mimalloc path * removed torchvision * cleaner np typing * review suggestions * update default model name * update test
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import numpy as np
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from PIL import Image
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from app.schemas import ndarray_f32
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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_f32:
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return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0
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def normalize(img: ndarray_f32, mean: float | ndarray_f32, std: float | ndarray_f32) -> ndarray_f32:
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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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