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