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import numpy as np |
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import datasets |
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from PIL import Image |
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_DESCRIPTION = """ |
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This dataset contains photos of rivers on which there may be waste. The waste items are annotated |
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through bounding boxes, and are assigned to one of the 4 following categories: plastic bottle, plastic bag, |
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another plastic waste, or non-plastic waste. Note that some photos may not contain any waste. |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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_URL = "https://storage.googleapis.com/kili-datasets-public/plastic-in-river/<VERSION>/" |
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_URLS = { |
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"train_images": f"{_URL}train/images.tar.gz", |
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"train_annotations": f"{_URL}train/annotations.tar.gz", |
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"validation_images": f"{_URL}validation/images.tar.gz", |
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"validation_annotations": f"{_URL}validation/annotations.tar.gz", |
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"test_images": f"{_URL}test/images.tar.gz", |
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"test_annotations": f"{_URL}test/annotations.tar.gz" |
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} |
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class PlasticInRiver(datasets.GeneratorBasedBuilder): |
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"""Download script for the Plastic In River dataset""" |
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VERSION = datasets.Version("1.3.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"litter": datasets.Sequence( |
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{ |
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"label": datasets.ClassLabel(num_classes=4, names=["PLASTIC_BAG", "PLASTIC_BOTTLE", "OTHER_PLASTIC_WASTE", "NOT_PLASTIC_WASTE"]), |
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"bbox": datasets.Sequence(datasets.Value("float32"), length=4), |
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} |
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) |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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urls = {k: v.replace("<VERSION>", f"v{str(self.VERSION)}") for k, v in _URLS.items()} |
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downloaded_files = dl_manager.download(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=split, |
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gen_kwargs={ |
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"image_files": dl_manager.iter_archive(downloaded_files[f"{split_name}_images"]), |
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"annotations_files": dl_manager.iter_archive(downloaded_files[f"{split_name}_annotations"]), |
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"split": split_name, |
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}, |
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) for split, split_name in [ |
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(datasets.Split.TRAIN, "train"), |
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(datasets.Split.TEST, "test"), |
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(datasets.Split.VALIDATION, "validation"), |
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] |
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] |
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def _generate_examples(self, image_files, annotations_files, split): |
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for idx, (image_file, annotations_file) in enumerate(zip(image_files, annotations_files)): |
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image_array = np.array(Image.open(image_file[1])) |
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data = { |
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"image": image_array, |
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"litter": [] |
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} |
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for l in annotations_file[1].readlines(): |
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numbers = l.decode("utf-8").split(" ") |
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data["litter"].append({ |
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"label": int(numbers[0]), |
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"bbox": [float(n) for n in numbers[1:]] |
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}) |
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yield idx, data |
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