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import os |
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from glob import glob |
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from PIL import Image |
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import datasets |
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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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_REPO = "data" |
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_URLS = {"train": f"{_REPO}/train.zip", "valid": f"{_REPO}/valid.zip"} |
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_CATEGORIES = ["watermark"] |
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class WatermarkPita(datasets.GeneratorBasedBuilder): |
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"""Watermark Dataset""" |
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VERSION = datasets.Version("1.0.0") |
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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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"label": datasets.ClassLabel(names=_CATEGORIES), |
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"bbox": datasets.Sequence(datasets.Value("int32"), length=4) |
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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(_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 + "/*/*.png")) |
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label_paths = sorted(glob(label_dir + "/*/*.txt")) |
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for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)): |
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im = Image.open(image_path) |
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width, height = im.size |
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with open(label_path, "r") as f: |
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lines = f.readlines() |
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objects = [] |
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for line in lines: |
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line = line.strip().split() |
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bbox_class = int(line[0]) |
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bbox_top_left = int(float(line[1]) * width) |
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bbox_top_right = int(float(line[2]) * height) |
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bbox_bottom_left = int(float(line[3]) * width) |
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bbox_bottom_right = int(float(line[4]) * height) |
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objects.append({ |
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"label": bbox_class, |
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"bbox": [bbox_top_left, bbox_top_right, bbox_bottom_left, bbox_bottom_right] |
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}) |
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yield idx, {"image": image_path, "objects": objects} |