Source code for torchgeo.datasets.cbf

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

"""Canadian Building Footprints dataset."""

import os
from import Iterable
from typing import Any, Callable, Optional, Union

import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from import CRS

from .geo import VectorDataset
from .utils import DatasetNotFoundError, check_integrity, download_and_extract_archive

[docs]class CanadianBuildingFootprints(VectorDataset): """Canadian Building Footprints dataset. The `Canadian Building Footprints <>`__ dataset contains 11,842,186 computer generated building footprints in all Canadian provinces and territories in GeoJSON format. This data is freely available for download and use. """ # TODO: how does one cite this dataset? # url = "" provinces_territories = [ "Alberta", "BritishColumbia", "Manitoba", "NewBrunswick", "NewfoundlandAndLabrador", "NorthwestTerritories", "NovaScotia", "Nunavut", "Ontario", "PrinceEdwardIsland", "Quebec", "Saskatchewan", "YukonTerritory", ] md5s = [ "8b4190424e57bb0902bd8ecb95a9235b", "fea05d6eb0006710729c675de63db839", "adf11187362624d68f9c69aaa693c46f", "44269d4ec89521735389ef9752ee8642", "65dd92b1f3f5f7222ae5edfad616d266", "346d70a682b95b451b81b47f660fd0e2", "bd57cb1a7822d72610215fca20a12602", "c1f29b73cdff9a6a9dd7d086b31ef2cf", "76ba4b7059c5717989ce34977cad42b2", "2e4a3fa47b3558503e61572c59ac5963", "9ff4417ae00354d39a0cf193c8df592c", "a51078d8e60082c7d3a3818240da6dd5", "c11f3bd914ecabd7cac2cb2871ec0261", ]
[docs] def __init__( self, paths: Union[str, Iterable[str]] = "data", crs: Optional[CRS] = None, res: float = 0.00001, transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new Dataset instance. Args: paths: one or more root directories to search or files to load 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 an input sample and returns a transformed version download: if True, download dataset and store it in the root directory checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: DatasetNotFoundError: If dataset is not found and *download* is False. .. versionchanged:: 0.5 *root* was renamed to *paths*. """ self.paths = paths self.checksum = checksum if download: self._download() if not self._check_integrity(): raise DatasetNotFoundError(self) super().__init__(paths, crs, res, transforms)
def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ assert isinstance(self.paths, str) for prov_terr, md5 in zip(self.provinces_territories, self.md5s): filepath = os.path.join(self.paths, prov_terr + ".zip") if not check_integrity(filepath, md5 if self.checksum else None): return False return True def _download(self) -> None: """Download the dataset and extract it.""" if self._check_integrity(): print("Files already downloaded and verified") return assert isinstance(self.paths, str) for prov_terr, md5 in zip(self.provinces_territories, self.md5s): download_and_extract_archive( self.url + prov_terr + ".zip", self.paths, md5=md5 if self.checksum else None, )
[docs] def plot( self, sample: dict[str, Any], show_titles: bool = True, suptitle: Optional[str] = None, ) -> Figure: """Plot a sample from the dataset. Args: sample: a sample returned by :meth:`VectorDataset.__getitem__` show_titles: flag indicating whether to show titles above each panel suptitle: optional string to use as a suptitle Returns: a matplotlib Figure with the rendered sample .. versionchanged:: 0.3 Method now takes a sample dict, not a Tensor. Additionally, it is possible to show subplot titles and/or use a custom suptitle. """ image = sample["mask"].squeeze(0) ncols = 1 showing_prediction = "prediction" in sample if showing_prediction: pred = sample["prediction"].squeeze(0) ncols = 2 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(4, 4)) if showing_prediction: axs[0].imshow(image) axs[0].axis("off") axs[1].imshow(pred) axs[1].axis("off") if show_titles: axs[0].set_title("Mask") axs[1].set_title("Prediction") else: axs.imshow(image) axs.axis("off") if show_titles: axs.set_title("Mask") if suptitle is not None: plt.suptitle(suptitle) return fig

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