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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -28,17 +28,23 @@ | |
| # ===================================================================================================================== | ||
| # METADATA | ||
| # ===================================================================================================================== | ||
| __author__ = "Harry Baker" | ||
| __author__ = ["Harry Baker", "Jonathon Hare"] | ||
| __contact__ = "[email protected]" | ||
| __license__ = "MIT License" | ||
| __copyright__ = "Copyright (C) 2024 Harry Baker" | ||
| __all__ = [ | ||
| "PairedDataset", | ||
| "MinervaNonGeoDataset", | ||
| "MinervaConcatDataset", | ||
| "PairedGeoDataset", | ||
| "PairedNonGeoDataset", | ||
| "PairedUnionDataset", | ||
| "SSL4EOS12Sentinel2", | ||
| "PairedConcatDataset", | ||
| "GeoSSL4EOS12Sentinel2", | ||
| "NonGeoSSL4EOS12Sentinel2", | ||
| "NAIPChesapeakeCVPR", | ||
| "DFC2020", | ||
| "SEN12MS", | ||
| "MultiSpectralDataset", | ||
| "construct_dataloader", | ||
| "get_collator", | ||
| "get_manifest", | ||
|
|
@@ -64,10 +70,18 @@ | |
| make_loaders, | ||
| make_manifest, | ||
| ) | ||
| from .multispectral import MultiSpectralDataset | ||
| from .naip import NAIPChesapeakeCVPR | ||
| from .paired import PairedDataset, PairedUnionDataset | ||
| from .ssl4eos12 import SSL4EOS12Sentinel2 | ||
| from .paired import ( | ||
| PairedConcatDataset, | ||
| PairedGeoDataset, | ||
| PairedNonGeoDataset, | ||
| PairedUnionDataset, | ||
| ) | ||
| from .ssl4eos12 import GeoSSL4EOS12Sentinel2, NonGeoSSL4EOS12Sentinel2 | ||
| from .utils import ( | ||
| MinervaConcatDataset, | ||
| MinervaNonGeoDataset, | ||
| get_random_sample, | ||
| intersect_datasets, | ||
| load_all_samples, | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # MIT License | ||
|
|
||
| # Copyright (c) 2024 Harry Baker | ||
|
|
||
| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
|
|
||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
|
|
||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
| # | ||
| # @org: University of Southampton | ||
| # Created under a project funded by the Ordnance Survey Ltd. | ||
| r"""Adaption of :class:`~torchvision.datasets.VisionDataset` for use with :class:`~torchgeo.datasets.NonGeoDataset`. | ||
| """ | ||
| # ===================================================================================================================== | ||
| # METADATA | ||
| # ===================================================================================================================== | ||
| __author__ = ["Jonathon Hare", "Harry Baker"] | ||
| __contact__ = "[email protected]" | ||
| __license__ = "MIT License" | ||
| __copyright__ = "Copyright (C) 2024 Harry Baker" | ||
| __all__ = ["MultiSpectralDataset"] | ||
|
|
||
| # ===================================================================================================================== | ||
| # IMPORTS | ||
| # ===================================================================================================================== | ||
| import os | ||
| from functools import partial | ||
| from typing import Any, Callable, Dict, List, Optional | ||
|
|
||
| import numpy as np | ||
| import tifffile | ||
| import torch | ||
| from torchvision.datasets import VisionDataset | ||
| from torchvision.transforms.functional import resize | ||
|
|
||
| from .utils import MinervaNonGeoDataset | ||
|
|
||
|
|
||
| # ===================================================================================================================== | ||
| # CLASSES | ||
| # ===================================================================================================================== | ||
| class MultiSpectralDataset(VisionDataset, MinervaNonGeoDataset): | ||
| """Generic dataset class for multi-spectral images that works within :mod:`torchgeo`""" | ||
|
|
||
| all_bands: List[str] = [] | ||
| rgb_bands: List[str] = [] | ||
|
|
||
| def __init__( | ||
| self, | ||
| root: str, | ||
| transforms: Optional[Callable[..., Any]] = None, | ||
| bands: Optional[List[str]] = None, | ||
| ) -> None: | ||
| super().__init__(root, transform=transforms, target_transform=None) | ||
|
|
||
| if bands is None: | ||
| bands = self.all_bands | ||
|
|
||
| self.loader = partial(tifffile.imread, key=0) | ||
| self.bands = bands | ||
| self.samples = self.make_dataset() | ||
|
|
||
| def make_dataset(self) -> List[str]: | ||
| directory = os.path.expanduser(self.root) | ||
|
|
||
| dirs = set() | ||
| for root, _, fnames in sorted(os.walk(directory, followlinks=True)): | ||
| for fname in sorted(fnames): | ||
| if fname == f"{self.bands[0]}.tif": | ||
| dirs.add(root) | ||
| return sorted(list(dirs)) | ||
|
|
||
| def __getitem__(self, index: int) -> Dict[str, Any]: | ||
| path = self.samples[index] | ||
|
|
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| images = [] | ||
| h, w = 0, 0 | ||
| for b in self.bands: | ||
| img = torch.from_numpy(self.loader(f"{path}/{b}.tif").astype(np.float32)) | ||
| h = max(img.shape[0], h) | ||
| w = max(img.shape[1], w) | ||
| images.append(img.unsqueeze(0)) | ||
|
|
||
| for i in range(len(images)): | ||
| images[i] = resize(images[i], [h, w], antialias=True) | ||
|
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| bands = torch.cat(images, dim=0) | ||
|
|
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| if self.transform is not None: | ||
| bands = self.transform(bands) | ||
|
|
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| sample = {"image": bands} | ||
|
|
||
| return sample | ||
|
|
||
| def __len__(self) -> int: | ||
| return len(self.samples) |
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