| import collections | |
| import json | |
| import os | |
| import datasets | |
| ANNOTATION_FILENAME = "_annotations.coco.json" | |
| class AHv3Config(datasets.BuilderConfig): | |
| def __init__(self, data_urls, **kwargs): | |
| super(AHv3Config, self).__init__(**kwargs) | |
| self.data_urls = data_urls | |
| class AHv3(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| AHv3Config(name="With-augmentation", data_urls={"train": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/train.zip", "validation": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/valid.zip", "test": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/test.zip"}), | |
| AHv3Config(name="Without-augmentation", data_urls={"train": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/train.zip", "validation": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/valid.zip", "test": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/test.zip"}), | |
| ] | |
| def _info(self): | |
| features = datasets.Features({"image_id": datasets.Value("int64"), "image": datasets.Image(), "width": datasets.Value("int32"), "height": datasets.Value("int32"), "objects": datasets.Sequence({"id": datasets.Value("int64"), "area": datasets.Value("int64"), "bbox": datasets.Sequence(datasets.Value("float32"), length=4), "category": datasets.Value("string")})}) | |
| return datasets.DatasetInfo(features=features) | |
| def _split_generators(self, dl_manager): | |
| data_files = dl_manager.download_and_extract(self.config.data_urls) | |
| return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"folder_dir": data_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"folder_dir": data_files["validation"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"folder_dir": data_files["test"]})] | |
| def _generate_examples(self, folder_dir): | |
| def process_annot(annot, category_id_to_category): | |
| return {"id": annot["id"], "area": annot["area"], "bbox": annot["bbox"], "category": category_id_to_category[annot["category_id"]]} | |
| image_id_to_image = {} | |
| idx = 0 | |
| annotation_filepath = os.path.join(folder_dir, ANNOTATION_FILENAME) | |
| with open(annotation_filepath, "r") as f: | |
| annotations = json.load(f) | |
| category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]} | |
| image_id_to_annotations = collections.defaultdict(list) | |
| for annot in annotations["annotations"]: | |
| image_id_to_annotations[annot["image_id"]].append(annot) | |
| filename_to_image = {image["file_name"]: image for image in annotations["images"]} | |
| for filename in os.listdir(folder_dir): | |
| filepath = os.path.join(folder_dir, filename) | |
| if filename in filename_to_image: | |
| image = filename_to_image[filename] | |
| with open(filepath, "rb") as f: | |
| image_bytes = f.read() | |
| objects = [process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]] | |
| yield idx, {"image_id": image["id"], "image": {"path": filepath, "bytes": image_bytes}, "height": image["height"], "width": image["width"], "objects": objects} | |
| idx += 1 | |