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b53bd8c525
* fix(deps): update machine-learning * update openvino options, cuda * update openvino build * fix indentation * update minimum nvidia driver --------- Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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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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_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 np.divide(img - mean, std, dtype=np.float32)
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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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if isinstance(image_bytes, bytes):
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image_bytes = decode_pil(image_bytes) # pillow is much faster than cv2
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if isinstance(image_bytes, Image.Image):
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return pil_to_cv2(image_bytes)
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return image_bytes
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