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import os |
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from glob import glob |
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import datasets |
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import json |
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from PIL import Image |
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_DESCRIPTION = """\ |
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Watermark Dataset |
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""" |
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_VERSION = datasets.Version("1.0.0") |
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class WatermarkPitaConfig(datasets.BuilderConfig): |
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"""Builder Config for Food-101""" |
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def __init__(self, repository, urls, categories, **kwargs): |
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"""BuilderConfig for Food-101. |
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Args: |
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repository: `string`, the name of the repository. |
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urls: `dict<string, string>`, the urls to the data. |
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categories: `list<string>`, the categories of the data. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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_VERSION = datasets.Version("1.0.0") |
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super(WatermarkPitaConfig, self).__init__(version=_VERSION, **kwargs) |
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self.repository = repository |
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self.urls = categories |
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self.categories = categories |
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class WatermarkPita(datasets.GeneratorBasedBuilder): |
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"""Watermark Dataset""" |
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BUILDER_CONFIGS = [ |
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WatermarkPitaConfig( |
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name="text", |
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repository="data", |
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urls={"train": "data/train.zip", "valid": "data/valid.zip"}, |
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categories=["text"], |
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) |
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] |
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_DEFAULT_CONFIG_NAME = "text" |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"objects": datasets.Sequence( |
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{ |
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"label": datasets.ClassLabel(names=self.config.categories), |
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"bbox": datasets.Sequence( |
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datasets.Value("int32"), length=4 |
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), |
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} |
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), |
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} |
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), |
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description=_DESCRIPTION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(self.config.urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"split": "train", "data_dir": data_dir["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"split": "valid", "data_dir": data_dir["valid"]}, |
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), |
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] |
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def _generate_examples(self, split, data_dir): |
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image_dir = os.path.join(data_dir, "images") |
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label_dir = os.path.join(data_dir, "labels") |
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image_paths = sorted(glob(image_dir + "/*.jpg")) |
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label_paths = sorted(glob(label_dir + "/*.json")) |
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for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)): |
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with open(label_path, "r") as f: |
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bbox = json.load(f) |
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objects = [] |
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objects.append( |
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{ |
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"label": bbox["label"], |
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"bbox": [ |
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bbox["x"], |
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bbox["y"], |
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bbox["width"], |
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bbox["height"], |
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], |
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} |
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) |
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yield idx, {"image": image_path, "objects": objects} |
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