зеркало из https://github.com/microsoft/torchgeo.git
L8 Biome: convert to IntersectionDataset (#2058)
This commit is contained in:
Adam J. Stewart
GitHub
Родитель
171cb919e7
Коммит
5976bd15bf
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@ -5,6 +5,7 @@ import glob
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import os
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import shutil
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from pathlib import Path
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from typing import cast
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import matplotlib.pyplot as plt
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import pytest
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@ -65,7 +66,9 @@ class TestL8Biome:
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plt.close()
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def test_already_extracted(self, dataset: L8Biome) -> None:
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L8Biome(dataset.paths, download=True)
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paths = cast(str, dataset.paths)
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L8Biome(paths, download=True)
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L8Biome([paths], download=True)
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def test_already_downloaded(self, tmp_path: Path) -> None:
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pathname = os.path.join('tests', 'data', 'l8biome', '*.tar.gz')
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@ -6,19 +6,79 @@
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import glob
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import os
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from collections.abc import Callable, Iterable, Sequence
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from typing import Any, cast
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from typing import Any
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import matplotlib.pyplot as plt
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import torch
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from matplotlib.figure import Figure
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from rasterio.crs import CRS
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from torch import Tensor
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from .errors import DatasetNotFoundError, RGBBandsMissingError
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from .geo import RasterDataset
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from .geo import IntersectionDataset, RasterDataset
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from .utils import BoundingBox, download_url, extract_archive
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class L8Biome(RasterDataset):
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class L8BiomeImage(RasterDataset):
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"""Images from the L8 Biome dataset."""
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# https://gisgeography.com/landsat-file-naming-convention/
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filename_glob = 'LC8*.TIF'
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filename_regex = r"""
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^LC8
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(?P<wrs_path>\d{3})
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(?P<wrs_row>\d{3})
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(?P<date>\d{7})
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(?P<gsi>[A-Z]{3})
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(?P<version>\d{2})
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\.TIF$
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"""
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date_format = '%Y%j'
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is_image = True
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rgb_bands = ['B4', 'B3', 'B2']
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all_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11']
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class L8BiomeMask(RasterDataset):
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"""Masks from the L8 Biome dataset."""
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# https://gisgeography.com/landsat-file-naming-convention/
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filename_glob = 'LC8*_fixedmask.TIF'
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filename_regex = r"""
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^LC8
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(?P<wrs_path>\d{3})
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(?P<wrs_row>\d{3})
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(?P<date>\d{7})
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(?P<gsi>[A-Z]{3})
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(?P<version>\d{2})
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_fixedmask
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\.TIF$
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"""
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date_format = '%Y%j'
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is_image = False
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classes = ['Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud']
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ordinal_map = torch.zeros(256, dtype=torch.long)
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ordinal_map[64] = 1
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ordinal_map[128] = 2
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ordinal_map[192] = 3
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ordinal_map[255] = 4
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def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
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"""Retrieve image/mask and metadata indexed by query.
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Args:
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query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index
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Returns:
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sample of image, mask and metadata at that index
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Raises:
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IndexError: if query is not found in the index
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"""
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sample = super().__getitem__(query)
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sample['mask'] = self.ordinal_map[sample['mask']]
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return sample
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class L8Biome(IntersectionDataset):
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"""L8 Biome dataset.
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The `L8 Biome <https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data>`__
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@ -70,31 +130,12 @@ class L8Biome(RasterDataset):
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'wetlands': '1f86cc354631ca9a50ce54b7cab3f557',
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}
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classes = ['Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud']
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# https://gisgeography.com/landsat-file-naming-convention/
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filename_glob = 'LC8*.TIF'
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filename_regex = r"""
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^LC8
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(?P<wrs_path>\d{3})
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(?P<wrs_row>\d{3})
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(?P<date>\d{7})
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(?P<gsi>[A-Z]{3})
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(?P<version>\d{2})
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\.TIF$
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"""
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date_format = '%Y%j'
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separate_files = False
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rgb_bands = ['B4', 'B3', 'B2']
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all_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11']
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def __init__(
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self,
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paths: str | Iterable[str],
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crs: CRS | None = CRS.from_epsg(3857),
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res: float | None = None,
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bands: Sequence[str] = all_bands,
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bands: Sequence[str] = L8BiomeImage.all_bands,
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transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
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cache: bool = True,
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download: bool = False,
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@ -124,18 +165,25 @@ class L8Biome(RasterDataset):
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self._verify()
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super().__init__(
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paths, crs=crs, res=res, bands=bands, transforms=transforms, cache=cache
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)
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self.image = L8BiomeImage(paths, crs, res, bands, transforms, cache)
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self.mask = L8BiomeMask(paths, crs, res, None, transforms, cache)
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super().__init__(self.image, self.mask)
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def _verify(self) -> None:
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"""Verify the integrity of the dataset."""
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# Check if the extracted files already exist
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if self.files:
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if not isinstance(self.paths, str):
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return
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for classname in [L8BiomeImage, L8BiomeMask]:
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pathname = os.path.join(self.paths, '**', classname.filename_glob)
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if not glob.glob(pathname, recursive=True):
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break
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else:
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return
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# Check if the tar.gz files have already been downloaded
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assert isinstance(self.paths, str)
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pathname = os.path.join(self.paths, '*.tar.gz')
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if glob.glob(pathname):
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self._extract()
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@ -163,51 +211,6 @@ class L8Biome(RasterDataset):
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for tarfile in glob.iglob(pathname):
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extract_archive(tarfile)
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def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
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"""Retrieve image/mask and metadata indexed by query.
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Args:
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query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index
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Returns:
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sample of image, mask and metadata at that index
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Raises:
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IndexError: if query is not found in the index
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"""
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hits = self.index.intersection(tuple(query), objects=True)
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filepaths = cast(list[str], [hit.object for hit in hits])
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if not filepaths:
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raise IndexError(
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f'query: {query} not found in index with bounds: {self.bounds}'
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)
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image = self._merge_files(filepaths, query, self.band_indexes)
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mask_filepaths = []
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for filepath in filepaths:
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mask_filepath = filepath.replace('.TIF', '_fixedmask.TIF')
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mask_filepaths.append(mask_filepath)
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mask = self._merge_files(mask_filepaths, query)
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mask_mapping = {64: 1, 128: 2, 192: 3, 255: 4}
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for k, v in mask_mapping.items():
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mask[mask == k] = v
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sample = {
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'crs': self.crs,
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'bbox': query,
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'image': image.float(),
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'mask': mask.long(),
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}
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if self.transforms is not None:
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sample = self.transforms(sample)
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return sample
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def plot(
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self,
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sample: dict[str, Tensor],
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@ -217,7 +220,7 @@ class L8Biome(RasterDataset):
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"""Plot a sample from the dataset.
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Args:
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sample: a sample returned by :meth:`__getitem__`
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sample: a sample returned by :meth:`RasterDataset.__getitem__`
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show_titles: flag indicating whether to show titles above each panel
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suptitle: optional string to use as a suptitle
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@ -228,9 +231,9 @@ class L8Biome(RasterDataset):
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RGBBandsMissingError: If *bands* does not include all RGB bands.
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"""
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rgb_indices = []
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for band in self.rgb_bands:
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if band in self.bands:
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rgb_indices.append(self.bands.index(band))
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for band in self.image.rgb_bands:
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if band in self.image.bands:
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rgb_indices.append(self.image.bands.index(band))
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else:
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raise RGBBandsMissingError()
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