towhee
/
transform-image
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63 lines
2.3 KiB
63 lines
2.3 KiB
# Copyright 2021 Zilliz. All rights reserved.
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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 NamedTuple, Union
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import torch
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import numpy as np
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from PIL import Image
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from torchvision import transforms
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from towhee.operator import Operator
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class TransformImage(Operator):
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"""
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Use PyTorch to transform an image (resize, crop, normalize, etc...)
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Args:
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size (`int`):
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Image size to use. A resize to `size x size` followed by center crop and
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image normalization will be done.
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"""
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def __init__(self, size: int) -> None:
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super().__init__()
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# user defined transform
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self.tfms = transforms.Compose(
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[
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transforms.Resize(size),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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]
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)
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def __call__(self, img_tensor: Union[np.ndarray, Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
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"""
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Call it when use this class.
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Args:
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img_tensor (`Union[np.ndarray, Image.Image, torch.Tensor, str]`):
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The image data to be normalized, you can try one of the
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four formats: np.ndarray, Image.Image, torch.Tensor and str.
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Returns:
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(`torch.Tensor`)
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The normalized image tensor.
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"""
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if isinstance(img_tensor, str):
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img_tensor = Image.open(img_tensor)
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if isinstance(img_tensor, Image.Image):
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img_tensor = img_tensor.convert('RGB')
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Outputs = NamedTuple('Outputs', [('img_transformed', torch.Tensor)])
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return Outputs(self.tfms(img_tensor).unsqueeze(0))
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