wehub-resource-sync
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from typing import List, Union
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import torch
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from PIL import Image
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from torchvision.transforms import functional as TVF
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from torchvision.transforms.functional import InterpolationMode, to_tensor
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class AreaResize:
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def __init__(
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self,
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max_area: float,
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downsample_only: bool = False,
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interpolation: InterpolationMode = InterpolationMode.BICUBIC,
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):
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self.max_area = max_area
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self.downsample_only = downsample_only
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self.interpolation = interpolation
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def __call__(self, image: Union[torch.Tensor, Image.Image, List[Image.Image]]):
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if isinstance(image, torch.Tensor):
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height, width = image.shape[-2:]
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elif isinstance(image, Image.Image):
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width, height = image.size
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elif isinstance(image, list) and isinstance(image[0], Image.Image):
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width, height = image[0].size
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else:
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raise NotImplementedError
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scale = math.sqrt(self.max_area / (height * width))
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# keep original height and width for small pictures.
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scale = 1 if scale >= 1 and self.downsample_only else scale
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resized_height, resized_width = round(height * scale), round(width * scale)
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if isinstance(image, list) and isinstance(image[0], Image.Image):
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image = torch.stack(
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[
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to_tensor(
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TVF.resize(
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_image,
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size=(resized_height, resized_width),
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interpolation=self.interpolation,
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)
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)
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for _image in image
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]
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)
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else:
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image = TVF.resize(
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image,
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size=(resized_height, resized_width),
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interpolation=self.interpolation,
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)
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if isinstance(image, Image.Image):
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image = to_tensor(image)
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return image
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