| import tarfile |
| from io import BytesIO |
| from PIL import Image |
| from tqdm import tqdm |
| import datasets |
|
|
|
|
| class DTD(datasets.GeneratorBasedBuilder): |
| """Describable Textures Dataset (DTD) |
| |
| DTD is a texture database, consisting of 5640 images, organized according to a list of 47 terms (categories) |
| inspired from human perception. There are 120 images for each category. Image sizes range between 300x300 and |
| 640x640, and the images contain at least 90% of the surface representing the category attribute. The images were |
| collected from Google and Flickr by entering our proposed attributes and related terms as search queries. The images |
| were annotated using Amazon Mechanical Turk in several iterations. For each image we provide key attribute (main |
| category) and a list of joint attributes. |
| |
| The data is split in three equal parts, in train, validation and test, 40 images per class, for each split. We |
| provide the ground truth annotation for both key and joint attributes, as well as the 10 splits of the data we used |
| for evaluation. |
| """ |
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description="""Describing Textures in the Wild (DTD) is a dataset for texture classification. |
| It contains 5640 images organized into 47 categories.""", |
| features=datasets.Features( |
| { |
| "image": datasets.Image(), |
| "label": datasets.ClassLabel(names=[ |
| "banded", "blotchy", "braided", "bubbly", "bumpy", "chequered", "cobwebbed", |
| "cracked", "crosshatched", "crystalline", "dotted", "fibrous", "flecked", |
| "freckled", "frilly", "gauzy", "grid", "grooved", "honeycombed", "interlaced", |
| "knitted", "lacelike", "lined", "marbled", "matted", "meshed", "paisley", |
| "perforated", "pitted", "pleated", "polka-dotted", "porous", "potholed", "scaly", |
| "smeared", "spiralled", "sprinkled", "stained", "stratified", "striped", |
| "studded", "swirly", "veined", "waffled", "woven", "wrinkled", "zigzagged" |
| ]) |
| } |
| ), |
| supervised_keys=("image", "label"), |
| homepage="https://www.robots.ox.ac.uk/~vgg/data/dtd/", |
| citation="""@InProceedings{cimpoi14describing, |
| Author = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi}, |
| Title = {Describing Textures in the Wild}, |
| Booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})}, |
| Year = {2014}}""", |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| archive_path = dl_manager.download( |
| "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz" |
| ) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"archive_path": archive_path, "split": "train"}, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={"archive_path": archive_path, "split": "val"}, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={"archive_path": archive_path, "split": "test"}, |
| ), |
| ] |
|
|
| def _generate_examples(self, archive_path, split): |
| with tarfile.open(archive_path, "r:gz") as tar: |
| split_file = f"dtd/labels/{split}1.txt" |
| file_names = self._read_split_file(tar, split_file) |
|
|
| for idx, file_name in enumerate(tqdm(file_names, desc=f"Processing {split} split")): |
| member = tar.getmember(f"dtd/images/{file_name}") |
| file = tar.extractfile(member) |
| image = Image.open(BytesIO(file.read())).convert("RGB") |
|
|
| yield idx, { |
| "image": image, |
| "label": file_name.split("/")[0], |
| } |
|
|
| def _read_split_file(self, tar, split_file): |
| """Helper function to read split file from the tar archive.""" |
| split_content = tar.extractfile(split_file).read().decode("utf-8") |
| return split_content.splitlines() |
|
|
