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# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
from typing import List
import datasets
import logging
import xml.etree.ElementTree as ET
import os
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={Shixuan An
},
year={2024}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"dataset": "https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/5ty2wb6gvg-1.zip"
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
_URLS = _URLS
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"image_id": datasets.Value("string"),
"country": datasets.Value("string"),
"type": datasets.Value("string"),
"image_resolution": datasets.Features({
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"depth": datasets.Value("int32"),
}),
"image_path": datasets.Value("string"),
"pics_array": datasets.Array3D(shape=(None, None, 3), dtype="uint8"),
"crack_type": datasets.Sequence(datasets.Value("string")),
"crack_coordinates": datasets.Sequence(datasets.Features({
"x_min": datasets.Value("int32"),
"x_max": datasets.Value("int32"),
"y_min": datasets.Value("int32"),
"y_max": datasets.Value("int32"),
})),
}),
homepage='https://data.mendeley.com/datasets/5ty2wb6gvg/1',
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""This method downloads/extracts the data and defines the splits."""
data_dir = dl_manager.download_and_extract(_URLS["dataset"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images_dir": os.path.join(data_dir, "train"),
"annotations_dir": os.path.join(data_dir, "train", "annotations"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images_dir": os.path.join(data_dir, "test1"),
"annotations_dir": os.path.join(data_dir, "test1", "annotations"),
"split": "test1",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"images_dir": os.path.join(data_dir, "test2"),
"annotations_dir": os.path.join(data_dir, "test2", "annotations"),
"split": "test2",
},
),
]
def _generate_examples(self, images_dir, annotations_dir, split):
"""Yields examples as (key, example) tuples."""
for image_file in os.listdir(images_dir):
if not image_file.endswith('.jpg'):
continue
image_id = image_file.split('.')[0]
annotation_file = image_id + '.xml'
annotation_path = os.path.join(annotations_dir, annotation_file)
if not os.path.exists(annotation_path):
continue
tree = ET.parse(annotation_path)
root = tree.getroot()
country = split.capitalize()
image_path = os.path.join(images_dir, image_file)
crack_type = []
crack_coordinates = []
for obj in root.findall('object'):
crack_type.append(obj.find('name').text)
bndbox = obj.find('bndbox')
coordinates = {
"x_min": int(bndbox.find('xmin').text),
"x_max": int(bndbox.find('xmax').text),
"y_min": int(bndbox.find('ymin').text),
"y_max": int(bndbox.find('ymax').text),
}
crack_coordinates.append(coordinates)
# Assuming images are of uniform size, you might want to adjust this or extract from image directly
image_resolution = {"width": 600, "height": 600, "depth": 3} if country != "India" else {"width": 720,
"height": 720,
"depth": 3}
yield image_id, {
"image_id": image_id,
"country": country,
"type": split,
"image_resolution": image_resolution,
"image_path": image_path,
"crack_type": crack_type,
"crack_coordinates": crack_coordinates,
}
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