Datasets:
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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")), # Keep as raw list
"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
# Parse bbox without scaling
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, # ✅ unchanged
"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", "")
}
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