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https://github.com/open-mmlab/mmsegmentation.git
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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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from collections import defaultdict
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from typing import Sequence, Union
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import numpy as np
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from mmengine.dataset import Compose
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from mmengine.model import BaseModel
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ImageType = Union[str, np.ndarray, Sequence[str], Sequence[np.ndarray]]
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def _preprare_data(imgs: ImageType, model: BaseModel):
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cfg = model.cfg
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for t in cfg.test_pipeline:
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if t.get('type') == 'LoadAnnotations':
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cfg.test_pipeline.remove(t)
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is_batch = True
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if not isinstance(imgs, (list, tuple)):
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imgs = [imgs]
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is_batch = False
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if isinstance(imgs[0], np.ndarray):
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cfg.test_pipeline[0]['type'] = 'LoadImageFromNDArray'
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# TODO: Consider using the singleton pattern to avoid building
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# a pipeline for each inference
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pipeline = Compose(cfg.test_pipeline)
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data = defaultdict(list)
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for img in imgs:
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if isinstance(img, np.ndarray):
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data_ = dict(img=img)
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else:
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data_ = dict(img_path=img)
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data_ = pipeline(data_)
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data['inputs'].append(data_['inputs'])
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data['data_samples'].append(data_['data_samples'])
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return data, is_batch
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