Update info.

anime-tagging-dataset.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = "Anime tagging."
+
+tags = ['long_hair', 'original', 'headphones', 'open_mouth', 'hoshino_katsura', 'brown_hair', 'shoes', 'elbow_gloves', 'flower', 'glasses', 'bow', 'sitting', 'scan', 'yellow_eyes', 'hatsune_miku', 'uniform', 'suzumiya_haruhi_no_yuuutsu', 'purple_hair', 'flat_chest', 'thigh-highs', 'ponytail', 'vector', 'ribbon', 'blonde_hair', 'cat_ears', 'red_eyes', '1girl', 'hair_ornament', 'hat', 'sky', 'seifuku', 'water', 'smile', 'brown_eyes', 'bad_id', 'twintails', 'black_hair', 'pink_hair', 'vocaloid', 'ahoge', 'red_hair', 'tagme', 'monochrome', 'cleavage', 'nail_polish', 'hair_ribbon', 'weapon', 'wings', 'legs', 'necktie', 'touhou', 'zettai_ryouiki', 'dress', 'purple_eyes', 'solo', 'closed_eyes', 'catgirl', 'male', 'blue_hair', 'midriff', 'navel', 'white_hair', 'skirt', 'aqua_hair', 'scarf', 'jewelry', 'trap', 'kagamine_len', 'animal_ears', 'very_long_hair', 'wink', 'pantyhose', 'socks', 'green_eyes', 'nagato_yuki', 'sword', 'thigh_highs', 'boots', 'swimsuit', 'd.gray-man', 'wallpaper', 'blue_eyes', 'blush', 'detached_sleeves', 'bikini', 'short_hair', 'japanese_clothes', 'kimono', 'thighhighs', 'green_hair', 'hair_bow', 'multiple_girls', 'tail', 'highres', 'breasts', 'white', 'school_uniform', 'gloves', 'megurine_luka', 'itou_noiji']
+
+
+class AnimeTagging(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "labels": datasets.features.ClassLabel(names=tags),
+                }
+            ),
+            supervised_keys=("image_file_path", "labels"),
+            task_templates=[
+                ImageClassification(
+                    image_file_path_column="image_file_path", label_column="labels", labels=tags
+                )
+            ],
+        )
+
+    # def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    #     images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImages"
+    #     return [
+    #         datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images_path": images_path}),
+    #     ]
+
+    def _generate_examples(self, images_path):
+        logger.info("generating examples from = %s", images_path)
+        for i, filepath in enumerate(images_path.glob("**/*.jpg")):
+            with filepath.open("rb") as f:
+                if b"JFIF" in f.peek(10):
+                    yield str(i), {
+                        "image_file_path": str(filepath),
+                        "labels": filepath.parent.name.lower(),
+                    }
+                    continue