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from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value, ClassLabel, Image, Sequence |
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import csv |
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import datasets |
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import ast |
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class CAFOSatConfig(datasets.BuilderConfig): |
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def __init__(self, split_column="cafosat_set1_training_train", **kwargs): |
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super().__init__(**kwargs) |
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self.split_column = split_column |
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class CAFOSat(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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CAFOSatConfig(name="set1_train", split_column="cafosat_set1_training_train", description="Set 1 training split"), |
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CAFOSatConfig(name="set1_val", split_column="cafosat_set1_training_val", description="Set 1 validation split"), |
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CAFOSatConfig(name="verified_train", split_column="cafosat_verified_training_train", description="Verified training split"), |
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CAFOSatConfig(name="all_train", split_column="cafosat_all_training_train", description="Verified training split"), |
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] |
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DEFAULT_CONFIG_NAME = "all_train" |
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def _info(self): |
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return DatasetInfo( |
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description="CAFOSat: Remote sensing CAFO dataset with bounding boxes and infrastructure tags.", |
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features=Features({ |
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"patch_file": Image(), |
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"label": ClassLabel( |
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names=["Negative", "Swine", "Dairy", "Beef", "Poultry", "Horses", "Sheep/Goats"] |
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), |
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"barn": Value("float32"), |
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"manure_pond": Value("float32"), |
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"grazing_area": Value("float32"), |
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"others": Value("float32"), |
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"geom_bbox": Sequence(Value("float32")), |
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"category": Value("string"), |
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"state": Value("string"), |
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"image_type": Value("string"), |
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"CAFO_UNIQUE_ID": Value("string"), |
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"verified_label": Value("string"), |
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"patch_res": Value("string") |
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}), |
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supervised_keys=None, |
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homepage="https://huggingface.co/datasets/oishee3003/CAFOSat/", |
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license="cc-by-4.0" |
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) |
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def _split_generators(self, dl_manager): |
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csv_path = dl_manager.download_and_extract("cafosat.csv") |
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return [ |
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SplitGenerator(name=Split.TRAIN, gen_kwargs={"csv_path": csv_path, "split_flag": self.config.split_column}) |
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] |
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def _generate_examples(self, csv_path, split_flag): |
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with open(csv_path, encoding="utf-8") as f: |
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reader = csv.DictReader(f) |
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for idx, row in enumerate(reader): |
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if row.get(split_flag, "0") != "1": |
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continue |
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try: |
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bbox = ast.literal_eval(row.get("geom_bbox", "[5.0, 5.0, 700.0, 700.0]")) |
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except: |
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bbox = [5.0, 5.0, 700.0, 700.0] |
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yield idx, { |
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"patch_file": row["patch_file"], |
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"label": int(row["label"]), |
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"barn": float(row.get("barn", 0)), |
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"manure_pond": float(row.get("manure_pond", 0)), |
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"grazing_area": float(row.get("grazing_area", 0)), |
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"others": float(row.get("others", 0)), |
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"geom_bbox": bbox, |
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"category": row.get("category", ""), |
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"state": row.get("state", ""), |
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"image_type": row.get("image_type", ""), |
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"CAFO_UNIQUE_ID": row.get("CAFO_UNIQUE_ID", ""), |
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"verified_label": row.get("verified_label", ""), |
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"patch_res": row.get("patch_res", "") |
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"refine_x": row.get("refine_x", "") |
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"refine_y": row.get("refine_y", "") |
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} |
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