Create dtd.py

dtd.py

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+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()