Source code for torchgeo.datasets.dfc2022

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

"""2022 IEEE GRSS Data Fusion Contest (DFC2022) dataset."""

import glob
import os
from import Sequence
from typing import Callable, Optional

import matplotlib.pyplot as plt
import numpy as np
import rasterio
import torch
from matplotlib import colors
from matplotlib.figure import Figure
from rasterio.enums import Resampling
from torch import Tensor

from .geo import NonGeoDataset
from .utils import check_integrity, extract_archive, percentile_normalization

[docs]class DFC2022(NonGeoDataset): """DFC2022 dataset. The `DFC2022 <>`__ dataset is used as a benchmark dataset for the 2022 IEEE GRSS Data Fusion Contest and extends the MiniFrance dataset for semi-supervised semantic segmentation. The dataset consists of a train set containing labeled and unlabeled imagery and an unlabeled validation set. The dataset can be downloaded from the `IEEEDataPort DFC2022 website <>`_. Dataset features: * RGB aerial images at 0.5 m per pixel spatial resolution (~2,000x2,0000 px) * DEMs at 1 m per pixel spatial resolution (~1,000x1,0000 px) * Masks at 0.5 m per pixel spatial resolution (~2,000x2,0000 px) * 16 land use/land cover categories * Images collected from the `IGN BD ORTHO database <>`_ * DEMs collected from the `IGN RGE ALTI database <>`_ * Labels collected from the `UrbanAtlas 2012 database <>`_ * Data collected from 19 regions in France Dataset format: * images are three-channel geotiffs * DEMS are single-channel geotiffs * masks are single-channel geotiffs with the pixel values represent the class Dataset classes: 0. No information 1. Urban fabric 2. Industrial, commercial, public, military, private and transport units 3. Mine, dump and construction sites 4. Artificial non-agricultural vegetated areas 5. Arable land (annual crops) 6. Permanent crops 7. Pastures 8. Complex and mixed cultivation patterns 9. Orchards at the fringe of urban classes 10. Forests 11. Herbaceous vegetation associations 12. Open spaces with little or no vegetation 13. Wetlands 14. Water 15. Clouds and Shadows If you use this dataset in your research, please cite the following paper: * .. versionadded:: 0.3 """ # noqa: E501 classes = [ "No information", "Urban fabric", "Industrial, commercial, public, military, private and transport units", "Mine, dump and construction sites", "Artificial non-agricultural vegetated areas", "Arable land (annual crops)", "Permanent crops", "Pastures", "Complex and mixed cultivation patterns", "Orchards at the fringe of urban classes", "Forests", "Herbaceous vegetation associations", "Open spaces with little or no vegetation", "Wetlands", "Water", "Clouds and Shadows", ] colormap = [ "#231F20", "#DB5F57", "#DB9757", "#DBD057", "#ADDB57", "#75DB57", "#7BC47B", "#58B158", "#D4F6D4", "#B0E2B0", "#008000", "#58B0A7", "#995D13", "#579BDB", "#0062FF", "#231F20", ] metadata = { "train": { "filename": "", "md5": "2e87d6a218e466dd0566797d7298c7a9", "directory": "labeled_train", }, "train-unlabeled": { "filename": "", "md5": "1016d724bc494b8c50760ae56bb0585e", "directory": "unlabeled_train", }, "val": { "filename": "", "md5": "6ddd9c0f89d8e74b94ea352d4002073f", "directory": "val", }, } image_root = "BDORTHO" dem_root = "RGEALTI" target_root = "UrbanAtlas"
[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None, checksum: bool = False, ) -> None: """Initialize a new DFC2022 dataset instance. Args: root: root directory where dataset can be found split: one of "train" or "test" transforms: a function/transform that takes input sample and its target as entry and returns a transformed version checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: AssertionError: if ``split`` is invalid """ assert split in self.metadata self.root = root self.split = split self.transforms = transforms self.checksum = checksum self._verify() self.class2idx = {c: i for i, c in enumerate(self.classes)} self.files = self._load_files()
[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 """ files = self.files[index] image = self._load_image(files["image"]) dem = self._load_image(files["dem"], shape=image.shape[1:]) image =[image, dem], dim=0) sample = {"image": image} if self.split == "train": mask = self._load_target(files["target"]) sample["mask"] = mask 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.files)
def _load_files(self) -> list[dict[str, str]]: """Return the paths of the files in the dataset. Returns: list of dicts containing paths for each pair of image/dem/mask """ directory = os.path.join(self.root, self.metadata[self.split]["directory"]) images = glob.glob( os.path.join(directory, "**", self.image_root, "*.tif"), recursive=True ) files = [] for image in sorted(images): dem = image.replace(self.image_root, self.dem_root) dem = f"{os.path.splitext(dem)[0]}_RGEALTI.tif" if self.split == "train": target = image.replace(self.image_root, self.target_root) target = f"{os.path.splitext(target)[0]}_UA2012.tif" files.append(dict(image=image, dem=dem, target=target)) else: files.append(dict(image=image, dem=dem)) return files def _load_image(self, path: str, shape: Optional[Sequence[int]] = None) -> Tensor: """Load a single image. Args: path: path to the image shape: the (h, w) to resample the image to Returns: the image """ with as f: array: "np.typing.NDArray[np.float_]" = out_shape=shape, out_dtype="float32", resampling=Resampling.bilinear ) tensor = torch.from_numpy(array) return tensor def _load_target(self, path: str) -> Tensor: """Load the target mask for a single image. Args: path: path to the image Returns: the target mask """ with as f: array: "np.typing.NDArray[np.int_]" = indexes=1, out_dtype="int32", resampling=Resampling.bilinear ) tensor = torch.from_numpy(array) tensor = return tensor def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if checksum fails or the dataset is not downloaded """ # Check if the files already exist exists = [] for split_info in self.metadata.values(): exists.append( os.path.exists(os.path.join(self.root, split_info["directory"])) ) if all(exists): return # Check if .zip files already exists (if so then extract) exists = [] for split_info in self.metadata.values(): filepath = os.path.join(self.root, split_info["filename"]) if os.path.isfile(filepath): if self.checksum and not check_integrity(filepath, split_info["md5"]): raise RuntimeError("Dataset found, but corrupted.") exists.append(True) extract_archive(filepath) else: exists.append(False) if all(exists): return # Check if the user requested to download the dataset raise RuntimeError( "Dataset not found in `root` directory, either specify a different" + " `root` directory or manually download the dataset to this directory." )
[docs] def plot( self, sample: dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> Figure: """Plot a sample from the dataset. Args: sample: a sample returned by :meth:`__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 """ ncols = 2 image = sample["image"][:3] image = image = image.permute(1, 2, 0).numpy() dem = sample["image"][-1].numpy() dem = percentile_normalization(dem, lower=0, upper=100, axis=(0, 1)) showing_mask = "mask" in sample showing_prediction = "prediction" in sample cmap = colors.ListedColormap(self.colormap) if showing_mask: mask = sample["mask"].numpy() ncols += 1 if showing_prediction: pred = sample["prediction"].numpy() ncols += 1 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(10, ncols * 10)) axs[0].imshow(image) axs[0].axis("off") axs[1].imshow(dem) axs[1].axis("off") if showing_mask: axs[2].imshow(mask, cmap=cmap, interpolation="none") axs[2].axis("off") if showing_prediction: axs[3].imshow(pred, cmap=cmap, interpolation="none") axs[3].axis("off") elif showing_prediction: axs[2].imshow(pred, cmap=cmap, interpolation="none") axs[2].axis("off") if show_titles: axs[0].set_title("Image") axs[1].set_title("DEM") if showing_mask: axs[2].set_title("Ground Truth") if showing_prediction: axs[3].set_title("Predictions") elif showing_prediction: axs[2].set_title("Predictions") if suptitle is not None: plt.suptitle(suptitle) return fig

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