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adb55f3726
* fix(deps): update machine-learning * typing fixes --------- Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
import string
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from io import BytesIO
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from typing import IO
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import cv2
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import numpy as np
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import orjson
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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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_PUNCTUATION_TRANS = str.maketrans("", "", string.punctuation)
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def resize_pil(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.Resampling.BICUBIC)
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else:
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return img.resize((int((img.width / img.height) * size), size), resample=Image.Resampling.BICUBIC)
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# https://stackoverflow.com/a/60883103
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def crop_pil(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 if img.mode == "RGB" else img.convert("RGB"), dtype=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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def pil_to_cv2(image: Image.Image) -> NDArray[np.uint8]:
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return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) # type: ignore
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def decode_pil(image_bytes: bytes | IO[bytes] | Image.Image) -> Image.Image:
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if isinstance(image_bytes, Image.Image):
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return image_bytes
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image: Image.Image = Image.open(BytesIO(image_bytes) if isinstance(image_bytes, bytes) else image_bytes)
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image.load()
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if not image.mode == "RGB":
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image = image.convert("RGB")
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return image
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def decode_cv2(image_bytes: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
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match image_bytes:
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case bytes() | memoryview() | bytearray():
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return pil_to_cv2(decode_pil(image_bytes)) # pillow is much faster than cv2
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case Image.Image():
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return pil_to_cv2(image_bytes)
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case _:
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return image_bytes
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def clean_text(text: str, canonicalize: bool = False) -> str:
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text = " ".join(text.split())
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if canonicalize:
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text = text.translate(_PUNCTUATION_TRANS).lower()
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return text
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# this allows the client to use the array as a string without deserializing only to serialize back to a string
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# TODO: use this in a less invasive way
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def serialize_np_array(arr: NDArray[np.float32]) -> str:
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return orjson.dumps(arr, option=orjson.OPT_SERIALIZE_NUMPY).decode()
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