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"""Open Cortex FX v3 Dataset.""" |
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import csv |
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import io |
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import zipfile |
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from pathlib import Path |
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from typing import Iterator |
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import datasets |
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_DESCRIPTION = """\ |
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Open Cortex FX v3 is a video dataset focusing on human manual labor and physical work activities. |
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Each video has been carefully annotated to identify work-related content and categorized into specific labor types. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/Standout/open-cortex-fx-v3" |
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_LICENSE = "Apache 2.0" |
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_CITATION = """\ |
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@dataset{open_cortex_fx_v3, |
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title={Open Cortex FX v3: A Classified Dataset of Human Manual Labor}, |
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author={Standout}, |
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year={2024}, |
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url={https://huggingface.co/datasets/Standout/open-cortex-fx-v3} |
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} |
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""" |
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class OpenCortexFXv3(datasets.GeneratorBasedBuilder): |
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"""Open Cortex FX v3 Dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="default", |
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version=VERSION, |
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description="Open Cortex FX v3 dataset", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "default" |
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def _info(self) -> datasets.DatasetInfo: |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"name": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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"split": datasets.Value("string"), |
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"video": datasets.Value("binary"), |
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} |
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), |
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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: datasets.DownloadManager): |
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"""Returns SplitGenerators.""" |
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splits = [] |
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for i in range(6): |
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split_name = f"final_{i}" |
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zip_url = f"https://huggingface.co/datasets/Standout/open-cortex-fx-v3/resolve/main/final_{i}.zip" |
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try: |
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zip_path = dl_manager.download(zip_url) |
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if zip_path and Path(zip_path).exists(): |
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splits.append( |
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datasets.SplitGenerator( |
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name=datasets.Split(split_name), |
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gen_kwargs={"zip_path": Path(zip_path)}, |
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) |
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) |
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except Exception: |
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continue |
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if not splits: |
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data_dir = Path(dl_manager.download_and_extract("https://huggingface.co/datasets/Standout/open-cortex-fx-v3/tree/main")) |
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zip_files = sorted(data_dir.glob("final_*.zip")) |
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for zip_file in zip_files: |
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split_name = zip_file.stem |
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splits.append( |
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datasets.SplitGenerator( |
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name=datasets.Split(split_name), |
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gen_kwargs={"zip_path": zip_file}, |
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) |
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) |
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return splits |
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def _generate_examples(self, zip_path: Path) -> Iterator[tuple[int, dict]]: |
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"""Yields examples.""" |
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with zipfile.ZipFile(zip_path, "r") as z: |
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try: |
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with z.open("metadata.csv") as f: |
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content = f.read().decode("utf-8") |
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reader = csv.DictReader(io.StringIO(content)) |
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metadata = list(reader) |
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except KeyError: |
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return |
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split_name = zip_path.stem |
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for idx, row in enumerate(metadata): |
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video_name = row["name"] |
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category = row["category"] |
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video_paths = [ |
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f"{category}/{video_name}", |
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f"{category.replace(' ', '_')}/{video_name}", |
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video_name, |
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] |
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video_data = None |
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for video_path in video_paths: |
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try: |
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video_data = z.read(video_path) |
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break |
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except KeyError: |
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continue |
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if video_data: |
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yield idx, { |
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"name": video_name, |
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"category": category, |
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"split": split_name, |
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"video": video_data, |
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} |
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