Create gen_script.py

gen_script.py

@@ -0,0 +1,93 @@
+from functools import cached_property
+from pathlib import Path
+
+import datasets
+
+_VERSION = "0.1.0"
+
+_CITATION = """
+@inproceedings{5539970,
+	title     = {SUN database: Large-scale scene recognition from abbey to zoo},
+	author    = {Xiao, Jianxiong and Hays, James and Ehinger, Krista A. and Oliva, Aude and Torralba, Antonio},
+	year      = 2010,
+	booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
+	volume    = {},
+	number    = {},
+	pages     = {3485--3492},
+	doi       = {10.1109/CVPR.2010.5539970},
+	keywords  = {Sun;Large-scale systems;Layout;Humans;Image databases;Computer vision;Anthropometry;Bridges;Legged locomotion;Spatial databases}
+}
+@article{Xiao2014SUNDE,
+	title     = {SUN Database: Exploring a Large Collection of Scene Categories},
+	author    = {Jianxiong Xiao and Krista A. Ehinger and James Hays and Antonio Torralba and Aude Oliva},
+	year      = 2014,
+	journal   = {International Journal of Computer Vision},
+	volume    = 119,
+	pages     = {3--22},
+	url       = {https://api.semanticscholar.org/CorpusID:10224573}
+}
+"""
+
+_DESCRIPTION = """
+Scene categorization is a fundamental problem in computer vision.
+However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories.
+Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes.
+In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images.
+We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance.
+We measure human scene classification performance on the SUN database and compare this with computational methods. 
+"""
+
+_HOMEPAGE = "https://vision.princeton.edu/projects/2010/SUN/"
+
+_LICENSE = ""
+
+_URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz"
+
+
+class SUN397(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    @cached_property
+    def archive_path(self):
+        dl_manager = datasets.DownloadManager()
+        return Path(dl_manager.download_and_extract(_URL)) / "SUN397"
+
+    @property
+    def features(self):
+        return datasets.Features(
+            {
+                "image": datasets.Image(mode="RGB"),
+                "label": datasets.ClassLabel(names_file=self.archive_path / "ClassName.txt"),
+            }
+        )
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=self.features,
+            supervised_keys=None,
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            version=_VERSION,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        images = sorted(list(self.archive_path.rglob("*.jpg")))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"images": images},
+            ),
+        ]
+
+    def _generate_examples(self, images: list[Path]):
+        for i, image in enumerate(images):
+            yield (
+                i,
+                {
+                    "image": str(image),
+                    "label": f"/{image.relative_to(self.archive_path).parent}",
+                },
+            )