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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Please describe the motivation of this PR and the goal you want to achieve through this PR. ## Modification Please briefly describe what modification is made in this PR. 1. add `NYUDataset`class 2. add script to process NYU dataset 3. add transforms for loading depth map 4. add docs & unittest ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 5. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 6. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 7. The documentation has been modified accordingly, like docstring or example tutorials.
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import gzip
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import io
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import pickle
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import cv2
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import numpy as np
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def datafrombytes(content: bytes, backend: str = 'numpy') -> np.ndarray:
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"""Data decoding from bytes.
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Args:
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content (bytes): The data bytes got from files or other streams.
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backend (str): The data decoding backend type. Options are 'numpy',
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'nifti', 'cv2' and 'pickle'. Defaults to 'numpy'.
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Returns:
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numpy.ndarray: Loaded data array.
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"""
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if backend == 'pickle':
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data = pickle.loads(content)
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else:
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with io.BytesIO(content) as f:
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if backend == 'nifti':
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f = gzip.open(f)
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try:
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from nibabel import FileHolder, Nifti1Image
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except ImportError:
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print('nifti files io depends on nibabel, please run'
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'`pip install nibabel` to install it')
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fh = FileHolder(fileobj=f)
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data = Nifti1Image.from_file_map({'header': fh, 'image': fh})
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data = Nifti1Image.from_bytes(data.to_bytes()).get_fdata()
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elif backend == 'numpy':
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data = np.load(f)
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elif backend == 'cv2':
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data = np.frombuffer(f.read(), dtype=np.uint16)
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data = cv2.imdecode(data, 2)
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else:
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raise ValueError
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return data
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