Shortcuts

Source code for torchgeo.datasets.geo

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""Base classes for all :mod:`torchgeo` datasets."""

import abc
import functools
import glob
import os
import re
import sys
import warnings
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, cast

import fiona
import fiona.transform
import numpy as np
import pyproj
import rasterio
import rasterio.merge
import shapely
import torch
from rasterio.crs import CRS
from rasterio.io import DatasetReader
from rasterio.vrt import WarpedVRT
from rasterio.windows import from_bounds
from rtree.index import Index, Property
from torch import Tensor
from torch.utils.data import Dataset
from torchvision.datasets import ImageFolder
from torchvision.datasets.folder import default_loader as pil_loader

from .utils import BoundingBox, concat_samples, disambiguate_timestamp, merge_samples

# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
Dataset.__module__ = "torch.utils.data"
ImageFolder.__module__ = "torchvision.datasets"


class GeoDataset(Dataset[Dict[str, Any]], abc.ABC):
    """Abstract base class for datasets containing geospatial information.

    Geospatial information includes things like:

    * coordinates (latitude, longitude)
    * :term:`coordinate reference system (CRS)`
    * resolution

    :class:`GeoDataset` is a special class of datasets. Unlike :class:`NonGeoDataset`,
    the presence of geospatial information allows two or more datasets to be combined
    based on latitude/longitude. This allows users to do things like:

    * Combine image and target labels and sample from both simultaneously
      (e.g. Landsat and CDL)
    * Combine datasets for multiple image sources for multimodal learning or data fusion
      (e.g. Landsat and Sentinel)

    These combinations require that all queries are present in *both* datasets,
    and can be combined using an :class:`IntersectionDataset`:

    .. code-block:: python

       dataset = landsat & cdl

    Users may also want to:

    * Combine datasets for multiple image sources and treat them as equivalent
      (e.g. Landsat 7 and Landsat 8)
    * Combine datasets for disparate geospatial locations
      (e.g. Chesapeake NY and PA)

    These combinations require that all queries are present in *at least one* dataset,
    and can be combined using a :class:`UnionDataset`:

    .. code-block:: python

       dataset = landsat7 | landsat8
    """

    #: Resolution of the dataset in units of CRS.
    res: float
    _crs: CRS

    # NOTE: according to the Python docs:
    #
    # * https://docs.python.org/3/library/exceptions.html#NotImplementedError
    #
    # the correct way to handle __add__ not being supported is to set it to None,
    # not to return NotImplemented or raise NotImplementedError. The downside of
    # this is that we have no way to explain to a user why they get an error and
    # what they should do instead (use __and__ or __or__).

    #: :class:`GeoDataset` addition can be ambiguous and is no longer supported.
    #: Users should instead use the intersection or union operator.
    __add__ = None  # type: ignore[assignment]

[docs] def __init__( self, transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None ) -> None: """Initialize a new Dataset instance. Args: transforms: a function/transform that takes an input sample and returns a transformed version """ self.transforms = transforms # Create an R-tree to index the dataset self.index = Index(interleaved=False, properties=Property(dimension=3))
[docs] @abc.abstractmethod def __getitem__(self, query: BoundingBox) -> Dict[str, Any]: """Retrieve image/mask and metadata indexed by query. Args: query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: sample of image/mask and metadata at that index Raises: IndexError: if query is not found in the index """
[docs] def __and__(self, other: "GeoDataset") -> "IntersectionDataset": """Take the intersection of two :class:`GeoDataset`. Args: other: another dataset Returns: a single dataset Raises: ValueError: if other is not a :class:`GeoDataset` .. versionadded:: 0.2 """ return IntersectionDataset(self, other)
[docs] def __or__(self, other: "GeoDataset") -> "UnionDataset": """Take the union of two GeoDatasets. Args: other: another dataset Returns: a single dataset Raises: ValueError: if other is not a :class:`GeoDataset` .. versionadded:: 0.2 """ return UnionDataset(self, other)
[docs] def __len__(self) -> int: """Return the number of files in the dataset. Returns: length of the dataset """ return len(self.index)
[docs] def __str__(self) -> str: """Return the informal string representation of the object. Returns: informal string representation """ return f"""\ {self.__class__.__name__} Dataset type: GeoDataset bbox: {self.bounds} size: {len(self)}"""
# NOTE: This hack should be removed once the following issue is fixed: # https://github.com/Toblerity/rtree/issues/87
[docs] def __getstate__( self, ) -> Tuple[ Dict[Any, Any], List[Tuple[int, Tuple[float, float, float, float, float, float], str]], ]: """Define how instances are pickled. Returns: the state necessary to unpickle the instance """ objects = self.index.intersection(self.index.bounds, objects=True) tuples = [(item.id, item.bounds, item.object) for item in objects] return self.__dict__, tuples
[docs] def __setstate__( self, state: Tuple[ Dict[Any, Any], List[Tuple[int, Tuple[float, float, float, float, float, float], str]], ], ) -> None: """Define how to unpickle an instance. Args: state: the state of the instance when it was pickled """ attrs, tuples = state self.__dict__.update(attrs) for item in tuples: self.index.insert(*item)
@property def bounds(self) -> BoundingBox: """Bounds of the index. Returns: (minx, maxx, miny, maxy, mint, maxt) of the dataset """ return BoundingBox(*self.index.bounds) @property def crs(self) -> CRS: """:term:`coordinate reference system (CRS)` for the dataset. Returns: the :term:`coordinate reference system (CRS)` .. versionadded:: 0.2 """ return self._crs @crs.setter def crs(self, new_crs: CRS) -> None: """Change the :term:`coordinate reference system (CRS)` of a GeoDataset. If ``new_crs == self.crs``, does nothing, otherwise updates the R-tree index. Args: new_crs: new :term:`coordinate reference system (CRS)` .. versionadded:: 0.2 """ if new_crs == self._crs: return new_index = Index(interleaved=False, properties=Property(dimension=3)) project = pyproj.Transformer.from_crs( pyproj.CRS(str(self._crs)), pyproj.CRS(str(new_crs)), always_xy=True ).transform for hit in self.index.intersection(self.index.bounds, objects=True): old_minx, old_maxx, old_miny, old_maxy, mint, maxt = hit.bounds old_box = shapely.geometry.box(old_minx, old_miny, old_maxx, old_maxy) new_box = shapely.ops.transform(project, old_box) new_minx, new_miny, new_maxx, new_maxy = new_box.bounds new_bounds = (new_minx, new_maxx, new_miny, new_maxy, mint, maxt) new_index.insert(hit.id, new_bounds, hit.object) self._crs = new_crs self.index = new_index class RasterDataset(GeoDataset): """Abstract base class for :class:`GeoDataset` stored as raster files.""" #: Glob expression used to search for files. #: #: This expression should be specific enough that it will not pick up files from #: other datasets. It should not include a file extension, as the dataset may be in #: a different file format than what it was originally downloaded as. filename_glob = "*" #: Regular expression used to extract date from filename. #: #: The expression should use named groups. The expression may contain any number of #: groups. The following groups are specifically searched for by the base class: #: #: * ``date``: used to calculate ``mint`` and ``maxt`` for ``index`` insertion #: #: When :attr:`separate_files`` is True, the following additional groups are #: searched for to find other files: #: #: * ``band``: replaced with requested band name filename_regex = ".*" #: Date format string used to parse date from filename. #: #: Not used if :attr:`filename_regex` does not contain a ``date`` group. date_format = "%Y%m%d" #: True if dataset contains imagery, False if dataset contains mask is_image = True #: True if data is stored in a separate file for each band, else False. separate_files = False #: Names of all available bands in the dataset all_bands: List[str] = [] #: Names of RGB bands in the dataset, used for plotting rgb_bands: List[str] = [] #: Color map for the dataset, used for plotting cmap: Dict[int, Tuple[int, int, int, int]] = {}
[docs] def __init__( self, root: str, crs: Optional[CRS] = None, res: Optional[float] = None, transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, cache: bool = True, ) -> None: """Initialize a new Dataset instance. Args: root: root directory where dataset can be found crs: :term:`coordinate reference system (CRS)` to warp to (defaults to the CRS of the first file found) res: resolution of the dataset in units of CRS (defaults to the resolution of the first file found) transforms: a function/transform that takes an input sample and returns a transformed version cache: if True, cache file handle to speed up repeated sampling Raises: FileNotFoundError: if no files are found in ``root`` """ super().__init__(transforms) self.root = root self.cache = cache # Populate the dataset index i = 0 pathname = os.path.join(root, "**", self.filename_glob) filename_regex = re.compile(self.filename_regex, re.VERBOSE) for filepath in glob.iglob(pathname, recursive=True): match = re.match(filename_regex, os.path.basename(filepath)) if match is not None: try: with rasterio.open(filepath) as src: # See if file has a color map if len(self.cmap) == 0: try: self.cmap = src.colormap(1) except ValueError: pass if crs is None: crs = src.crs if res is None: res = src.res[0] with WarpedVRT(src, crs=crs) as vrt: minx, miny, maxx, maxy = vrt.bounds except rasterio.errors.RasterioIOError: # Skip files that rasterio is unable to read continue else: mint: float = 0 maxt: float = sys.maxsize if "date" in match.groupdict(): date = match.group("date") mint, maxt = disambiguate_timestamp(date, self.date_format) coords = (minx, maxx, miny, maxy, mint, maxt) self.index.insert(i, coords, filepath) i += 1 if i == 0: raise FileNotFoundError( f"No {self.__class__.__name__} data was found in '{root}'" ) self._crs = cast(CRS, crs) self.res = cast(float, res)
[docs] def __getitem__(self, query: BoundingBox) -> Dict[str, Any]: """Retrieve image/mask and metadata indexed by query. Args: query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: sample of image/mask and metadata at that index Raises: IndexError: if query is not found in the index """ hits = self.index.intersection(tuple(query), objects=True) filepaths = [hit.object for hit in hits] if not filepaths: raise IndexError( f"query: {query} not found in index with bounds: {self.bounds}" ) if self.separate_files: data_list: List[Tensor] = [] filename_regex = re.compile(self.filename_regex, re.VERBOSE) for band in getattr(self, "bands", self.all_bands): band_filepaths = [] for filepath in filepaths: filename = os.path.basename(filepath) directory = os.path.dirname(filepath) match = re.match(filename_regex, filename) if match: if "date" in match.groupdict(): start = match.start("band") end = match.end("band") filename = filename[:start] + band + filename[end:] filepath = glob.glob(os.path.join(directory, filename))[0] band_filepaths.append(filepath) data_list.append(self._merge_files(band_filepaths, query)) data = torch.cat(data_list) else: data = self._merge_files(filepaths, query) key = "image" if self.is_image else "mask" sample = {key: data, "crs": self.crs, "bbox": query} if self.transforms is not None: sample = self.transforms(sample) return sample
def _merge_files(self, filepaths: Sequence[str], query: BoundingBox) -> Tensor: """Load and merge one or more files. Args: filepaths: one or more files to load and merge query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: image/mask at that index """ if self.cache: vrt_fhs = [self._cached_load_warp_file(fp) for fp in filepaths] else: vrt_fhs = [self._load_warp_file(fp) for fp in filepaths] bounds = (query.minx, query.miny, query.maxx, query.maxy) if len(vrt_fhs) == 1: src = vrt_fhs[0] out_width = round((query.maxx - query.minx) / self.res) out_height = round((query.maxy - query.miny) / self.res) out_shape = (src.count, out_height, out_width) dest = src.read( out_shape=out_shape, window=from_bounds(*bounds, src.transform) ) else: dest, _ = rasterio.merge.merge(vrt_fhs, bounds, self.res) # fix numpy dtypes which are not supported by pytorch tensors if dest.dtype == np.uint16: dest = dest.astype(np.int32) elif dest.dtype == np.uint32: dest = dest.astype(np.int64) tensor = torch.tensor(dest) return tensor @functools.lru_cache(maxsize=128) def _cached_load_warp_file(self, filepath: str) -> DatasetReader: """Cached version of :meth:`_load_warp_file`. Args: filepath: file to load and warp Returns: file handle of warped VRT """ return self._load_warp_file(filepath) def _load_warp_file(self, filepath: str) -> DatasetReader: """Load and warp a file to the correct CRS and resolution. Args: filepath: file to load and warp Returns: file handle of warped VRT """ src = rasterio.open(filepath) # Only warp if necessary if src.crs != self.crs: vrt = WarpedVRT(src, crs=self.crs) src.close() return vrt else: return src class VectorDataset(GeoDataset): """Abstract base class for :class:`GeoDataset` stored as vector files.""" #: Glob expression used to search for files. #: #: This expression should be specific enough that it will not pick up files from #: other datasets. It should not include a file extension, as the dataset may be in #: a different file format than what it was originally downloaded as. filename_glob = "*"
[docs] def __init__( self, root: str = "data", crs: Optional[CRS] = None, res: float = 0.0001, transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, ) -> None: """Initialize a new Dataset instance. Args: root: root directory where dataset can be found crs: :term:`coordinate reference system (CRS)` to warp to (defaults to the CRS of the first file found) res: resolution of the dataset in units of CRS transforms: a function/transform that takes input sample and its target as entry and returns a transformed version Raises: FileNotFoundError: if no files are found in ``root`` """ super().__init__(transforms) self.root = root self.res = res # Populate the dataset index i = 0 pathname = os.path.join(root, "**", self.filename_glob) for filepath in glob.iglob(pathname, recursive=True): try: with fiona.open(filepath) as src: if crs is None: crs = CRS.from_dict(src.crs) minx, miny, maxx, maxy = src.bounds (minx, maxx), (miny, maxy) = fiona.transform.transform( src.crs, crs.to_dict(), [minx, maxx], [miny, maxy] ) except fiona.errors.FionaValueError: # Skip files that fiona is unable to read continue else: mint = 0 maxt = sys.maxsize coords = (minx, maxx, miny, maxy, mint, maxt) self.index.insert(i, coords, filepath) i += 1 if i == 0: raise FileNotFoundError( f"No {self.__class__.__name__} data was found in '{root}'" ) self._crs = crs
[docs] def __getitem__(self, query: BoundingBox) -> Dict[str, Any]: """Retrieve image/mask and metadata indexed by query. Args: query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: sample of image/mask and metadata at that index Raises: IndexError: if query is not found in the index """ hits = self.index.intersection(tuple(query), objects=True) filepaths = [hit.object for hit in hits] if not filepaths: raise IndexError( f"query: {query} not found in index with bounds: {self.bounds}" ) shapes = [] for filepath in filepaths: with fiona.open(filepath) as src: # We need to know the bounding box of the query in the source CRS (minx, maxx), (miny, maxy) = fiona.transform.transform( self.crs.to_dict(), src.crs, [query.minx, query.maxx], [query.miny, query.maxy], ) # Filter geometries to those that intersect with the bounding box for feature in src.filter(bbox=(minx, miny, maxx, maxy)): # Warp geometries to requested CRS shape = fiona.transform.transform_geom( src.crs, self.crs.to_dict(), feature["geometry"] ) shapes.append(shape) # Rasterize geometries width = (query.maxx - query.minx) / self.res height = (query.maxy - query.miny) / self.res transform = rasterio.transform.from_bounds( query.minx, query.miny, query.maxx, query.maxy, width, height ) if shapes: masks = rasterio.features.rasterize( shapes, out_shape=(round(height), round(width)), transform=transform ) else: # If no features are found in this query, return an empty mask # with the default fill value and dtype used by rasterize masks = np.zeros((round(height), round(width)), dtype=np.uint8) sample = {"mask": torch.tensor(masks), "crs": self.crs, "bbox": query} if self.transforms is not None: sample = self.transforms(sample) return sample
class NonGeoDataset(Dataset[Dict[str, Any]], abc.ABC): """Abstract base class for datasets lacking geospatial information. This base class is designed for datasets with pre-defined image chips. """
[docs] @abc.abstractmethod def __getitem__(self, index: int) -> Dict[str, Any]: """Return an index within the dataset. Args: index: index to return Returns: data and labels at that index Raises: IndexError: if index is out of range of the dataset """
[docs] @abc.abstractmethod def __len__(self) -> int: """Return the length of the dataset. Returns: length of the dataset """
[docs] def __str__(self) -> str: """Return the informal string representation of the object. Returns: informal string representation """ return f"""\ {self.__class__.__name__} Dataset type: NonGeoDataset size: {len(self)}"""
class VisionDataset(NonGeoDataset): """Abstract base class for datasets lacking geospatial information. .. deprecated:: 0.3 Use :class:`NonGeoDataset` instead. """ def __new__(cls, *args: Any, **kwargs: Any) -> "VisionDataset": """Create a new instance of VisionDataset.""" msg = "VisionDataset is deprecated, use NonGeoDataset instead." warnings.warn(msg, DeprecationWarning) return super().__new__(cls, *args, **kwargs) class NonGeoClassificationDataset(NonGeoDataset, ImageFolder): # type: ignore[misc] """Abstract base class for classification datasets lacking geospatial information. This base class is designed for datasets with pre-defined image chips which are separated into separate folders per class. """
[docs] def __init__( self, root: str, transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, loader: Optional[Callable[[str], Any]] = pil_loader, is_valid_file: Optional[Callable[[str], bool]] = None, ) -> None: """Initialize a new NonGeoClassificationDataset instance. Args: root: root directory where dataset can be found transforms: a function/transform that takes input sample and its target as entry and returns a transformed version loader: a callable function which takes as input a path to an image and returns a PIL Image or numpy array is_valid_file: A function that takes the path of an Image file and checks if the file is a valid file """ # When transform & target_transform are None, ImageFolder.__getitem__(index) # returns a PIL.Image and int for image and label, respectively super().__init__( root=root, transform=None, target_transform=None, loader=loader, is_valid_file=is_valid_file, ) # Must be set after calling super().__init__() self.transforms = transforms
[docs] def __getitem__(self, index: int) -> Dict[str, Tensor]: """Return an index within the dataset. Args: index: index to return Returns: data and label at that index """ image, label = self._load_image(index) sample = {"image": image, "label": label} if self.transforms is not None: sample = self.transforms(sample) return sample
[docs] def __len__(self) -> int: """Return the number of data points in the dataset. Returns: length of the dataset """ return len(self.imgs)
def _load_image(self, index: int) -> Tuple[Tensor, Tensor]: """Load a single image and it's class label. Args: index: index to return Returns: the image the image class label """ img, label = ImageFolder.__getitem__(self, index) array: "np.typing.NDArray[np.int_]" = np.array(img) tensor = torch.from_numpy(array) # Convert from HxWxC to CxHxW tensor = tensor.permute((2, 0, 1)) label = torch.tensor(label) return tensor, label class VisionClassificationDataset(NonGeoClassificationDataset): """Abstract base class for classification datasets lacking geospatial information. .. deprecated:: 0.3 Use :class:`NonGeoClassificationDataset` instead. """ def __new__(cls, *args: Any, **kwargs: Any) -> "VisionClassificationDataset": """Create a new instance of VisionClassificationDataset.""" msg = "VisionClassificationDataset is deprecated, " msg += "use NonGeoClassificationDataset instead." warnings.warn(msg, DeprecationWarning) return cast(VisionClassificationDataset, super().__new__(cls)) class IntersectionDataset(GeoDataset): """Dataset representing the intersection of two GeoDatasets. This allows users to do things like: * Combine image and target labels and sample from both simultaneously (e.g. Landsat and CDL) * Combine datasets for multiple image sources for multimodal learning or data fusion (e.g. Landsat and Sentinel) These combinations require that all queries are present in *both* datasets, and can be combined using an :class:`IntersectionDataset`: .. code-block:: python dataset = landsat & cdl .. versionadded:: 0.2 """
[docs] def __init__( self, dataset1: GeoDataset, dataset2: GeoDataset, collate_fn: Callable[ [Sequence[Dict[str, Any]]], Dict[str, Any] ] = concat_samples, ) -> None: """Initialize a new Dataset instance. Args: dataset1: the first dataset dataset2: the second dataset collate_fn: function used to collate samples Raises: ValueError: if either dataset is not a :class:`GeoDataset` """ super().__init__() self.datasets = [dataset1, dataset2] self.collate_fn = collate_fn for ds in self.datasets: if not isinstance(ds, GeoDataset): raise ValueError("IntersectionDataset only supports GeoDatasets") self._crs = dataset1.crs self.res = dataset1.res # Force dataset2 to have the same CRS/res as dataset1 if dataset1.crs != dataset2.crs: print( f"Converting {dataset2.__class__.__name__} CRS from " f"{dataset2.crs} to {dataset1.crs}" ) dataset2.crs = dataset1.crs if dataset1.res != dataset2.res: print( f"Converting {dataset2.__class__.__name__} resolution from " f"{dataset2.res} to {dataset1.res}" ) dataset2.res = dataset1.res # Merge dataset indices into a single index self._merge_dataset_indices()
def _merge_dataset_indices(self) -> None: """Create a new R-tree out of the individual indices from two datasets.""" i = 0 ds1, ds2 = self.datasets for hit1 in ds1.index.intersection(ds1.index.bounds, objects=True): for hit2 in ds2.index.intersection(hit1.bounds, objects=True): box1 = BoundingBox(*hit1.bounds) box2 = BoundingBox(*hit2.bounds) self.index.insert(i, tuple(box1 & box2)) i += 1
[docs] def __getitem__(self, query: BoundingBox) -> Dict[str, Any]: """Retrieve image and metadata indexed by query. Args: query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: sample of data/labels and metadata at that index Raises: IndexError: if query is not within bounds of the index """ if not query.intersects(self.bounds): raise IndexError( f"query: {query} not found in index with bounds: {self.bounds}" ) # All datasets are guaranteed to have a valid query samples = [ds[query] for ds in self.datasets] return self.collate_fn(samples)
[docs] def __str__(self) -> str: """Return the informal string representation of the object. Returns: informal string representation """ return f"""\ {self.__class__.__name__} Dataset type: IntersectionDataset bbox: {self.bounds} size: {len(self)}"""
class UnionDataset(GeoDataset): """Dataset representing the union of two GeoDatasets. This allows users to do things like: * Combine datasets for multiple image sources and treat them as equivalent (e.g. Landsat 7 and Landsat 8) * Combine datasets for disparate geospatial locations (e.g. Chesapeake NY and PA) These combinations require that all queries are present in *at least one* dataset, and can be combined using a :class:`UnionDataset`: .. code-block:: python dataset = landsat7 | landsat8 .. versionadded:: 0.2 """
[docs] def __init__( self, dataset1: GeoDataset, dataset2: GeoDataset, collate_fn: Callable[ [Sequence[Dict[str, Any]]], Dict[str, Any] ] = merge_samples, ) -> None: """Initialize a new Dataset instance. Args: dataset1: the first dataset dataset2: the second dataset collate_fn: function used to collate samples Raises: ValueError: if either dataset is not a :class:`GeoDataset` """ super().__init__() self.datasets = [dataset1, dataset2] self.collate_fn = collate_fn for ds in self.datasets: if not isinstance(ds, GeoDataset): raise ValueError("UnionDataset only supports GeoDatasets") self._crs = dataset1.crs self.res = dataset1.res # Force dataset2 to have the same CRS/res as dataset1 if dataset1.crs != dataset2.crs: print( f"Converting {dataset2.__class__.__name__} CRS from " f"{dataset2.crs} to {dataset1.crs}" ) dataset2.crs = dataset1.crs if dataset1.res != dataset2.res: print( f"Converting {dataset2.__class__.__name__} resolution from " f"{dataset2.res} to {dataset1.res}" ) dataset2.res = dataset1.res # Merge dataset indices into a single index self._merge_dataset_indices()
def _merge_dataset_indices(self) -> None: """Create a new R-tree out of the individual indices from two datasets.""" i = 0 for ds in self.datasets: hits = ds.index.intersection(ds.index.bounds, objects=True) for hit in hits: self.index.insert(i, hit.bounds) i += 1
[docs] def __getitem__(self, query: BoundingBox) -> Dict[str, Any]: """Retrieve image and metadata indexed by query. Args: query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index Returns: sample of data/labels and metadata at that index Raises: IndexError: if query is not within bounds of the index """ if not query.intersects(self.bounds): raise IndexError( f"query: {query} not found in index with bounds: {self.bounds}" ) # Not all datasets are guaranteed to have a valid query samples = [] for ds in self.datasets: if ds.index.intersection(tuple(query)): samples.append(ds[query]) return self.collate_fn(samples)
[docs] def __str__(self) -> str: """Return the informal string representation of the object. Returns: informal string representation """ return f"""\ {self.__class__.__name__} Dataset type: UnionDataset bbox: {self.bounds} size: {len(self)}"""

© Copyright 2021, Microsoft Corporation. Revision 44fa4132.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: stable
Versions
latest
stable
v0.3.1
v0.3.0
v0.2.1
v0.2.0
v0.1.1
v0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources