import glob
import pandas as pd
from .multilabel_classification import MultiLabelClassificationDataset
LABELS = [
"airplane",
"airport",
"bare soil",
"baseball diamond",
"basketball court",
"beach",
"bridge",
"buildings",
"cars",
"cloud",
"containers",
"crosswalk",
"dense residential area",
"desert",
"dock",
"factory",
"field",
"football field",
"forest",
"freeway",
"golf course",
"grass",
"greenhouse",
"gully",
"habor",
"intersection",
"island",
"lake",
"mobile home",
"mountain",
"overpass",
"park",
"parking lot",
"parkway",
"pavement",
"railway",
"railway station",
"river",
"road",
"roundabout",
"runway",
"sand",
"sea",
"ships",
"snow",
"snowberg",
"sparse residential area",
"stadium",
"swimming pool",
"tanks",
"tennis court",
"terrace",
"track",
"trail",
"transmission tower",
"trees",
"water",
"chaparral",
"wetland",
"wind turbine",
]
[docs]class MLRSNetMultiLabelDataset(MultiLabelClassificationDataset):
url = "https://data.mendeley.com/datasets/7j9bv9vwsx/2"
labels = LABELS
name = "MLRSNet dataset"
def __init__(self, config):
# now call the constructor to validate the schema and load the data
super().__init__(config)
# Function to convert the dataset in PASCAL VOC data format
# First unrar all the images in the images folder using this command for linux: for file in *.rar; do unrar e "$file"; done
[docs]def prepare(root_folder):
all_csv_filenames = [
i for i in glob.glob("{}{}/*.{}".format(root_folder, "labels", "csv"))
]
combined_csv = pd.concat([pd.read_csv(f) for f in all_csv_filenames])
combined_csv["image"] = combined_csv["image"].str.replace(".jpg", "", regex=False)
combined_csv.to_csv(
"{}/multilabels.txt".format(root_folder),
index=False,
sep="\t",
encoding="utf-8",
)
