Source code for torchgeo.datasets.millionaid
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Million-AID dataset."""
import glob
import os
from collections.abc import Callable
from typing import Any, cast
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from PIL import Image
from torch import Tensor
from torchgeo.datasets import NonGeoDataset
from .utils import DatasetNotFoundError, check_integrity, extract_archive
[docs]class MillionAID(NonGeoDataset):
"""Million-AID Dataset.
The `MillionAID <https://captain-whu.github.io/DiRS/>`_ dataset consists
of one million aerial images from Google Earth Engine that offers
either `a multi-class learning task
<https://competitions.codalab.org/competitions/35945#learn_the_details-dataset>`_
with 51 classes or a `multi-label learning task
<https://competitions.codalab.org/competitions/35974#learn_the_details-dataset>`_
with 73 different possible labels. For more details please consult
the accompanying `paper <https://ieeexplore.ieee.org/document/9393553>`_.
Dataset features:
* RGB aerial images with varying resolutions from 0.5 m to 153 m per pixel
* images within classes can have different pixel dimension
Dataset format:
* images are three-channel jpg
If you use this dataset in your research, please cite the following paper:
* https://ieeexplore.ieee.org/document/9393553
.. versionadded:: 0.3
"""
multi_label_categories = [
"agriculture_land",
"airport_area",
"apartment",
"apron",
"arable_land",
"bare_land",
"baseball_field",
"basketball_court",
"beach",
"bridge",
"cemetery",
"church",
"commercial_area",
"commercial_land",
"dam",
"desert",
"detached_house",
"dry_field",
"factory_area",
"forest",
"golf_course",
"grassland",
"greenhouse",
"ground_track_field",
"helipad",
"highway_area",
"ice_land",
"industrial_land",
"intersection",
"island",
"lake",
"leisure_land",
"meadow",
"mine",
"mining_area",
"mobile_home_park",
"oil_field",
"orchard",
"paddy_field",
"parking_lot",
"pier",
"port_area",
"power_station",
"public_service_land",
"quarry",
"railway",
"railway_area",
"religious_land",
"residential_land",
"river",
"road",
"rock_land",
"roundabout",
"runway",
"solar_power_plant",
"sparse_shrub_land",
"special_land",
"sports_land",
"stadium",
"storage_tank",
"substation",
"swimming_pool",
"tennis_court",
"terraced_field",
"train_station",
"transportation_land",
"unutilized_land",
"viaduct",
"wastewater_plant",
"water_area",
"wind_turbine",
"woodland",
"works",
]
multi_class_categories = [
"apartment",
"apron",
"bare_land",
"baseball_field",
"bapsketball_court",
"beach",
"bridge",
"cemetery",
"church",
"commercial_area",
"dam",
"desert",
"detached_house",
"dry_field",
"forest",
"golf_course",
"greenhouse",
"ground_track_field",
"helipad",
"ice_land",
"intersection",
"island",
"lake",
"meadow",
"mine",
"mobile_home_park",
"oil_field",
"orchard",
"paddy_field",
"parking_lot",
"pier",
"quarry",
"railway",
"river",
"road",
"rock_land",
"roundabout",
"runway",
"solar_power_plant",
"sparse_shrub_land",
"stadium",
"storage_tank",
"substation",
"swimming_pool",
"tennis_court",
"terraced_field",
"train_station",
"viaduct",
"wastewater_plant",
"wind_turbine",
"works",
]
md5s = {
"train": "1b40503cafa9b0601653ca36cd788852",
"test": "51a63ee3eeb1351889eacff349a983d8",
}
filenames = {"train": "train.zip", "test": "test.zip"}
tasks = ["multi-class", "multi-label"]
splits = ["train", "test"]
[docs] def __init__(
self,
root: str = "data",
task: str = "multi-class",
split: str = "train",
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
checksum: bool = False,
) -> None:
"""Initialize a new MillionAID dataset instance.
Args:
root: root directory where dataset can be found
task: type of task, either "multi-class" or "multi-label"
split: train or 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:
DatasetNotFoundError: If dataset is not found.
"""
self.root = root
self.transforms = transforms
self.checksum = checksum
assert task in self.tasks
assert split in self.splits
self.task = task
self.split = split
self._verify()
self.files = self._load_files(self.root)
self.classes = sorted({cls for f in self.files for cls in f["label"]})
self.class_to_idx: dict[str, int] = {c: i for i, c in enumerate(self.classes)}
[docs] def __len__(self) -> int:
"""Return the number of data points 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]
image = self._load_image(files["image"])
cls_label = [self.class_to_idx[label] for label in files["label"]]
label = torch.tensor(cls_label, dtype=torch.long)
sample = {"image": image, "label": label}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_files(self, root: str) -> list[dict[str, Any]]:
"""Return the paths of the files in the dataset.
Args:
root: root directory of dataset
Returns:
list of dicts containing paths for each pair of image, and list of labels
"""
imgs_no_subcat = list(
glob.glob(os.path.join(root, self.split, "*", "*", "*.jpg"))
)
imgs_subcat = list(
glob.glob(os.path.join(root, self.split, "*", "*", "*", "*.jpg"))
)
scenes = [p.split(os.sep)[-3] for p in imgs_no_subcat] + [
p.split(os.sep)[-4] for p in imgs_subcat
]
subcategories = ["Missing" for p in imgs_no_subcat] + [
p.split(os.sep)[-3] for p in imgs_subcat
]
classes = [p.split(os.sep)[-2] for p in imgs_no_subcat] + [
p.split(os.sep)[-2] for p in imgs_subcat
]
if self.task == "multi-label":
labels = [
[sc, sub, c] if sub != "Missing" else [sc, c]
for sc, sub, c in zip(scenes, subcategories, classes)
]
else:
labels = [[c] for c in classes]
images = imgs_no_subcat + imgs_subcat
files = [dict(image=image, label=label) for image, label in zip(images, labels)]
return files
def _load_image(self, path: str) -> Tensor:
"""Load a single image.
Args:
path: path to the image
Returns:
the image
"""
with Image.open(path) as img:
array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
tensor: Tensor = torch.from_numpy(array)
# Convert from HxWxC to CxHxW
tensor = tensor.permute((2, 0, 1))
return tensor
def _verify(self) -> None:
"""Checks the integrity of the dataset structure.
Returns:
True if the dataset directories are found, else False
"""
filepath = os.path.join(self.root, self.split)
if os.path.isdir(filepath):
return
filepath = os.path.join(self.root, self.split + ".zip")
if os.path.isfile(filepath):
if self.checksum and not check_integrity(filepath, self.md5s[self.split]):
raise RuntimeError("Dataset found, but corrupted.")
extract_archive(filepath)
return
raise DatasetNotFoundError(self)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: str | None = 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"].numpy(), 0, 3)
labels = [self.classes[cast(int, label)] for label in sample["label"]]
showing_predictions = "prediction" in sample
if showing_predictions:
prediction_labels = [
self.classes[cast(int, label)] for label in sample["prediction"]
]
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(image)
ax.axis("off")
if show_titles:
title = f"Label: {labels}"
if showing_predictions:
title += f"\nPrediction: {prediction_labels}"
ax.set_title(title)
if suptitle is not None:
plt.suptitle(suptitle)
return fig
