| import pathlib
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|
|
| import cv2
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| import numpy as np
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| from PIL import Image
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|
|
|
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| def img_write_to_bytes(array, fmt: str = '.png') -> bytes:
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| return cv2.imencode(fmt, array)[1].tobytes()
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|
|
|
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| def img_read(input_file: pathlib.Path, flags: int = cv2.IMREAD_COLOR):
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| return cv2.imdecode(np.fromfile(input_file, np.uint8), flags)
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|
|
|
|
| def downscale_image(img, max_width):
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|
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| width, height = img.size
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|
|
|
|
| if width <= max_width:
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| return img
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|
|
|
|
| new_height = int((max_width / width) * height)
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|
|
|
|
| img_resized = img.resize((max_width, new_height), Image.Resampling.LANCZOS)
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|
|
| return img_resized
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|
|
|
|
| def downscale_and_compress_from_path(
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| old_path: pathlib.Path,
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| new_path: pathlib.Path,
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| max_width: int = 500,
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| quality: int = 80,
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| ):
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|
|
| img = Image.open(old_path)
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| img = downscale_image(img, max_width=max_width)
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| img.save(new_path, format="JPEG", quality=quality)
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|
|
|
|
| def downscale_and_compress(
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| img: Image.Image,
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| save_to_path: pathlib.Path,
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| max_width: int = 500,
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| quality: int = 80,
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| ):
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| img = downscale_image(img, max_width=max_width)
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| img.save(save_to_path, format="JPEG", quality=quality)
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|
|
