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"""Residential Floorplans and City Scapes dataset for Urban planning"""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
"""
_DESCRIPTION = """\
Text-to-image model to build an AI architect
"""
_URL = "https://huggingface.co/datasets/wheres-my-python/image-trial/resolve/main/images.tar.gz"
# descriptions = [] #optional text data
class ImagesTrial(datasets.GeneratorBasedBuilder):
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
# Option to use any Apache arrow feature other than "string"
{
"image": datasets.Image(),
"text": datasets.Value("string"),
# "prompt": datasets.Value("string"), (optional)
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://huggingface.co/datasets/wheres-my-python/floorplans-cityscapes",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# download manager - hf utility
path = dl_manager.download_and_extract(_URL)
image_iters = dl_manager.iter_archive(path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN
, gen_kwargs={
"images": image_iters
}
),
]
def _generate_examples(self, images):
"""This function returns the examples in the raw (text) form."""
idx = 0
#iteratre through images
for filepath, image in images:
yield idx, {
"image": {"path":filepath, "bytes":image.read()},
#Option to map text
# "text": descriptions[idx],
}
idx +=1 |