mirror of
https://github.com/vladmandic/automatic
synced 2026-09-15 02:58:44 +02:00
Vladimir Mandic
41 lines
1.5 KiB
Python
41 lines
1.5 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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from typing import 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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class DivisibleCrop:
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def __init__(self, factor):
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if not isinstance(factor, tuple):
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factor = (factor, factor)
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self.height_factor, self.width_factor = factor[0], factor[1]
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def __call__(self, image: Union[torch.Tensor, 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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else:
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raise NotImplementedError
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cropped_height = height - (height % self.height_factor)
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cropped_width = width - (width % self.width_factor)
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image = TVF.center_crop(img=image, output_size=(cropped_height, cropped_width))
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return image
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