Source code for torchgeo.datasets.cowc

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

"""COWC datasets."""

import abc
import csv
import os
from typing import Callable, Dict, List, Optional, cast

import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from torch import Tensor

from .geo import NonGeoDataset
from .utils import check_integrity, download_and_extract_archive

class COWC(NonGeoDataset, abc.ABC):
    """Abstract base class for the COWC dataset.

    The `Cars Overhead With Context (COWC) <>`_ data set
    is a large set of annotated cars from overhead. It is useful for training a device
    such as a deep neural network to learn to detect and/or count cars.

    The dataset has the following attributes:

    1. Data from overhead at 15 cm per pixel resolution at ground (all data is EO).
    2. Data from six distinct locations: Toronto, Canada; Selwyn, New Zealand;
       Potsdam and Vaihingen, Germany; Columbus, Ohio and Utah, United States.
    3. 32,716 unique annotated cars. 58,247 unique negative examples.
    4. Intentional selection of hard negative examples.
    5. Established baseline for detection and counting tasks.
    6. Extra testing scenes for use after validation.

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


    def base_url(self) -> str:
        """Base URL to download dataset from."""

    def filenames(self) -> List[str]:
        """List of files to download."""

    def md5s(self) -> List[str]:
        """List of MD5 checksums of files to download."""

    def filename(self) -> str:
        """Filename containing train/test split and target labels."""

[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new COWC 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 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: AssertionError: if ``split`` argument is invalid RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ assert split in ["train", "test"] self.root = root self.split = split self.transforms = transforms self.checksum = checksum if download: self._download() if not self._check_integrity(): raise RuntimeError( "Dataset not found or corrupted. " + "You can use download=True to download it" ) self.images = [] self.targets = [] with open( os.path.join(self.root, self.filename.format(split)), encoding="utf-8-sig", newline="", ) as f: reader = csv.reader(f, delimiter=" ") for row in reader: self.images.append(row[0]) self.targets.append(row[1])
[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 """ sample = {"image": self._load_image(index), "label": self._load_target(index)} 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.targets)
def _load_image(self, index: int) -> Tensor: """Load a single image. Args: index: index to return Returns: the image """ filename = os.path.join(self.root, self.images[index]) with as img: 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)) return tensor def _load_target(self, index: int) -> Tensor: """Load a single target. Args: index: index to return Returns: the target """ target = int(self.targets[index]) tensor = torch.tensor(target) return tensor def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ for filename, md5 in zip(self.filenames, self.md5s): filepath = os.path.join(self.root, filename) 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 for filename, md5 in zip(self.filenames, self.md5s): download_and_extract_archive( self.base_url + filename, self.root, filename=filename, md5=md5 if self.checksum else None, )
[docs] def plot( self, sample: Dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> plt.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 .. versionadded:: 0.2 """ image = sample["image"] label = cast(str, sample["label"].item()) showing_predictions = "prediction" in sample if showing_predictions: prediction = cast(str, sample["prediction"].item()) else: prediction = None fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.imshow(image.permute(1, 2, 0)) ax.axis("off") if show_titles: title = f"Label: {label}" if prediction is not None: title += f"\nPrediction: {prediction}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig
class COWCCounting(COWC): """COWC Dataset for car counting.""" base_url = ( "" ) filenames = [ "COWC_train_list_64_class.txt.bz2", "COWC_test_list_64_class.txt.bz2", "COWC_Counting_Toronto_ISPRS.tbz", "COWC_Counting_Selwyn_LINZ.tbz", "COWC_Counting_Potsdam_ISPRS.tbz", "COWC_Counting_Vaihingen_ISPRS.tbz", "COWC_Counting_Columbus_CSUAV_AFRL.tbz", "COWC_Counting_Utah_AGRC.tbz", ] md5s = [ "187543d20fa6d591b8da51136e8ef8fb", "930cfd6e160a7b36db03146282178807", "bc2613196dfa93e66d324ae43e7c1fdb", "ea842ae055f5c74d0d933d2194764545", "19a77ab9932b722ef52b197d70e68ce7", "4009c1e420566390746f5b4db02afdb9", "daf8033c4e8ceebbf2c3cac3fabb8b10", "777ec107ed2a3d54597a739ce74f95ad", ] filename = "COWC_{}_list_64_class.txt" class COWCDetection(COWC): """COWC Dataset for car detection.""" base_url = ( "" ) filenames = [ "COWC_train_list_detection.txt.bz2", "COWC_test_list_detection.txt.bz2", "COWC_Detection_Toronto_ISPRS.tbz", "COWC_Detection_Selwyn_LINZ.tbz", "COWC_Detection_Potsdam_ISPRS.tbz", "COWC_Detection_Vaihingen_ISPRS.tbz", "COWC_Detection_Columbus_CSUAV_AFRL.tbz", "COWC_Detection_Utah_AGRC.tbz", ] md5s = [ "c954a5a3dac08c220b10cfbeec83893c", "c6c2d0a78f12a2ad88b286b724a57c1a", "11af24f43b198b0f13c8e94814008a48", "22fd37a86961010f5d519a7da0e1fc72", "bf053545cc1915d8b6597415b746fe48", "23945d5b22455450a938382ccc2a8b27", "f40522dc97bea41b10117d4a5b946a6f", "195da7c9443a939a468c9f232fd86ee3", ] filename = "COWC_{}_list_detection.txt" # TODO: add COCW-M datasets: # # * # * # # Same as COCW datasets, but instead of binary classification there are 4 car classes: # # 1. Sedan # 2. Pickup # 3. Other # 4. Unknown # # May need new abstract base class. Will need subclasses for different patch sizes.

© Copyright 2021, Microsoft Corporation. Revision 44fa4132.

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