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Source code for torchgeo.datasets.inria

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

"""Inria Aerial Image Labeling Dataset."""

import glob
import os
from typing import Any, Callable, Dict, List, Optional

import matplotlib.pyplot as plt
import numpy as np
import rasterio as rio
import torch
from matplotlib.figure import Figure
from torch import Tensor

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


class InriaAerialImageLabeling(NonGeoDataset):
    r"""Inria Aerial Image Labeling Dataset.

    The `Inria Aerial Image Labeling <https://project.inria.fr/aerialimagelabeling/>`__
    dataset is a building detection dataset over dissimilar settlements ranging from
    densely populated areas to alpine towns. Refer to the dataset homepage to download
    the dataset.

    Dataset features:

    * Coverage of 810 km\ :sup:`2`\  (405 km\ :sup:`2`\  for training and 405
      km\ :sup:`2`\  for testing)
    * Aerial orthorectified color imagery with a spatial resolution of 0.3 m
    * Number of images: 360 (train: 180, test: 180)
    * Train cities: Austin, Chicago, Kitsap, West Tyrol, Vienna
    * Test cities: Bellingham, Bloomington, Innsbruck, San Francisco, East Tyrol

    Dataset format:

    * Imagery - RGB aerial GeoTIFFs of shape 5000 x 5000
    * Labels - RGB aerial GeoTIFFs of shape 5000 x 5000

    If you use this dataset in your research, please cite the following paper:

    * https://doi.org/10.1109/IGARSS.2017.8127684

    .. versionadded:: 0.3
    """

    directory = "AerialImageDataset"
    filename = "NEW2-AerialImageDataset.zip"
    md5 = "4b1acfe84ae9961edc1a6049f940380f"

[docs] def __init__( self, root: str, split: str = "train", transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, checksum: bool = False, ) -> None: """Initialize a new InriaAerialImageLabeling Dataset instance. Args: root: root directory where dataset can be found split: train/test split 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 RuntimeError: if dataset is missing """ self.root = root assert split in {"train", "test"} self.split = split self.transforms = transforms self.checksum = checksum self._verify() self.files = self._load_files(root)
def _load_files(self, root: str) -> List[Dict[str, str]]: """Return the paths of the files in the dataset. Args: root: root dir of dataset Returns: list of dicts containing paths for each pair of image and label """ files = [] root_dir = os.path.join(root, self.directory, self.split) images = glob.glob(os.path.join(root_dir, "images", "*.tif")) images = sorted(images) if self.split == "train": labels = glob.glob(os.path.join(root_dir, "gt", "*.tif")) labels = sorted(labels) for img, lbl in zip(images, labels): files.append({"image": img, "label": lbl}) else: for img in images: files.append({"image": img}) return files def _load_image(self, path: str) -> Tensor: """Load a single image. Args: path: path to the image Returns: the image """ with rio.open(path) as img: array = img.read().astype(np.int32) tensor = torch.from_numpy(array) return tensor def _load_target(self, path: str) -> Tensor: """Loads the target mask. Args: path: path to the mask Returns: the target mask """ with rio.open(path) as img: array = img.read().astype(np.int32) array = np.clip(array, 0, 1) mask = torch.from_numpy(array[0]) return mask
[docs] def __len__(self) -> int: """Return the number of samples in the dataset. Returns: length of the dataset """ return len(self.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] img = self._load_image(files["image"]) sample = {"image": img} if files.get("label"): mask = self._load_target(files["label"]) sample["mask"] = mask if self.transforms is not None: sample = self.transforms(sample) return sample
def _verify(self) -> None: """Checks the integrity of the dataset structure.""" if os.path.isdir(os.path.join(self.root, self.directory)): return archive_path = os.path.join(self.root, self.filename) md5_hash = self.md5 if self.checksum else None if not os.path.isfile(archive_path): raise RuntimeError( f"Dataset not found in `root={self.root}` " "either specify a different `root` directory " "or download the dataset to this directory" ) if not check_integrity(archive_path, md5_hash): raise RuntimeError("Dataset corrupted") print("Extracting...") extract_archive(archive_path)
[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 """ image = np.rollaxis(sample["image"][:3].numpy(), 0, 3) image = percentile_normalization(image, axis=(0, 1)) ncols = 1 show_mask = "mask" in sample show_predictions = "prediction" in sample if show_mask: mask = sample["mask"].numpy() ncols += 1 if show_predictions: prediction = sample["prediction"].numpy() ncols += 1 fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 8, 8)) if not isinstance(axs, np.ndarray): axs = [axs] axs[0].imshow(image) axs[0].axis("off") if show_titles: axs[0].set_title("Image") if show_mask: axs[1].imshow(mask, interpolation="none") axs[1].axis("off") if show_titles: axs[1].set_title("Label") if show_predictions: axs[2].imshow(prediction, interpolation="none") axs[2].axis("off") if show_titles: axs[2].set_title("Prediction") if suptitle is not None: plt.suptitle(suptitle) return fig

© Copyright 2021, Microsoft Corporation. Revision 44fa4132.

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