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"""Bosch Small Traffic Lights Dataset""" |
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import os |
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import yaml |
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import datasets |
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_CITATION = """\ |
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@inproceedings{BehrendtNovak2017ICRA, |
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title={A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification}, |
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author={Behrendt, Karsten and Novak, Libor}, |
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booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on}, |
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organization={IEEE} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains about |
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24000 annotated traffic lights. The annotations include bounding boxes of traffic lights as well |
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as the current state (active light) of each traffic light. The camera images are provided as raw |
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12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit RGB color |
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images. The RGB images are provided for debugging and can also be used for training. However, the |
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RGB conversion process has some drawbacks. Some of the converted images may contain artifacts and |
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the color distribution may seem unusual. |
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""" |
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_HOMEPAGE = "https://hci.iwr.uni-heidelberg.de/content/bosch-small-traffic-lights-dataset" |
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_LICENSE = "non-commercial use only" |
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_URL = { |
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"train": "train.tar.gz", |
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"test": "test.tar.gz", |
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} |
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class BoschSmallTrafficLights(datasets.GeneratorBasedBuilder): |
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"""Bosch Small Traffic Lights Dataset, an accurate dataset for vision-based traffic light detection.""" |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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features = datasets.Features({ |
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"img": datasets.Image(), |
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"boxes": datasets.features.Sequence({ |
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"label": datasets.Value("string"), |
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"occluded": datasets.Value("bool"), |
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"x_max": datasets.Value("float"), |
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"x_min": datasets.Value("float"), |
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"y_max": datasets.Value("float"), |
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"y_min": datasets.Value("float"), |
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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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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls = _URL |
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data_dir = dl_manager.download_and_extract(urls) |
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print(data_dir) |
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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={ |
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"root_dir": data_dir["train"], |
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"filepath": "train.yaml", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"root_dir": data_dir["test"], |
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"filepath": "test.yaml", |
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}, |
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), |
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] |
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def _generate_examples(self, root_dir, filepath): |
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filepath = os.path.join(root_dir, filepath) |
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with open(filepath, encoding="utf-8") as f: |
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data = yaml.load(f, Loader=yaml.FullLoader) |
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for key, row in enumerate(data): |
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yield key, { |
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"img": os.path.join(root_dir, row["path"]), |
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"boxes": row["boxes"] |
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} |
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